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Noradrenergic mechanisms of arousal-enhanced memory selectivity

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Noradrenergic mechanisms of arousal-enhanced memory selectivity

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Content RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY i

NORADRENERGIC MECHANISMS OF
AROUSAL-ENHANCED MEMORY SELECTIVITY
by

David Vaughn Clewett

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)

August 2016

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY ii
Table of Contents
Dedication……………………………………………………………………………………..….…….iii
Acknowledgements………………………………………………………………………….……..…iv
Abstract………………………………………………………………………………………………....vi
Introduction……………………………………………………………………………….…………….1
Overview of Dissertation………………………………………………………………......…………2
Chapter 1: Background……………………………………………………………………………….5
The contradictory effects of arousal on cognition…………………………..……………….5
Arousal-biased competition theory……………………………………………………………8
Current models of emotion-cognition interactions in the brain…………………..……….11
Locus coeruleus neuromodulation of arousal and cognition…………………….……….18
Open questions in the LC-NE system literature………………………………………..….25
Glutamate Amplifies Noradrenergic Effects (GANE): Arousal’s core selectivity
mechanism in the brain……………………………………………………………………….27
Biomarkers of LC-NE system structure and function…………………………...…………33
Dissertation aims………………………………………………………………………………38
Chapter 2: Emotional arousal amplifies goal-relevant memory selectivity under elevated
noradrenergic activity in women but not men………………………...……..…………40
Introduction……………………………………………………………………………………..40
Methods…………………………………………………………………………………………42
Results……………………………………………………………………………………….….49
Discussion………………………………………………………………………….……...……55
Chapter 3: Noradrenergic mechanisms of arousal-biased competition in memory………61
Introduction…………………………………………………………………………………...…61
Methods………………………………………………………………………………………….65
Results………………………………………………………………………………………..…74
Discussion…………………………………………………………………………………….…84
Chapter 4: Locus coeruleus activity strengthens goal-relevant memories under threat of
punishment………………………………………………………………………………….…93
Introduction…………………………………………………………………………………...…93
Methods……………………………………………………………………………………….…97
Results…………………………………………………………………………………………113
Discussion…………………………………………………………………………………..…129
Chapter 5: General Discussion……………………………………………………………....……139
Summary of results………………………………………………………………………...…139
Updating the GANE model…………………………………………………………………..148
Implications for the aging brain……………………………………………………………...154
Concluding Remarks…………………………………………………………………..…...……….160
References………………………………………………………………………………….…………162
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY iii
Dedication
I dedicate this dissertation to my parents, Rich and Becky, who have always supported
and loved me unconditionally. Thank you for always being there and for showing up. I also
dedicate my work to my grandparents, Mel, Jane, Ken, and Marge. What an honor it is to be
your grandson and to carry on your legacy.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY iv
Acknowledgements
None of this work would have been possible without the support and dedication of my
mentors, research collaborators, close friends/labmates, and family.
Above all, I wish to thank my advisor, Mara Mather, for being such a wonderful role
model, teacher, and collaborator. I’m so grateful for your unwavering support throughout
graduate school, your kindness of spirit, and for always treating me as a peer rather than a
student. As a graduate student, it means the world to feel like your voice is not only heard but
also valued. Your work ethic and creative mind are an inspiration to me and I aspire to be more
like you as both a researcher and a person. In addition, thank you Carolyn Harley for being a
huge source of inspiration for me. Of all the people I’ve had the honor of working with, you are
one of the best. You make research so much fun! I’ve had a blast exchanging ideas with you
and getting amped with you about science!
I would also like to extend my gratitude to my committee mentors and rotation advisors:
John Monterosso, Mary Helen Immordino-Yang, Hanna Damasio, Pat Levitt, and Giselle
Petzinger. It was an honor to be a part of your labs and work with you. Thank you for your
mentorship and for your insightful feedback throughout graduate school! In particular, I’d like to
thank John for putting up with me and for helping make research feel like fun rather than a
chore.
I owe a debt of gratitude to my intelligent and hard-working research assistants, Ringo
Huang and Eshed Margalit, who served as my capable left and right hands for several years.
You are both exceptional people. Thank you for teaching me more than I ever taught you; you’re
both destined for success and I feel fortunate to have had opportunity to play a small role in
shaping your interests in neuroscience. Additionally, I wish to thank Jolie Cooperman for also
being an amazing research assistant and for contributing to data collection and analysis on the
drug study in this dissertation. You are very bright and I’m very excited to see you excel as a
medical student at USC! I’d also like to thank my honors student, Shelby Bachman, for her
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY v
assistance on other projects. You exude a positive energy and are well equipped to succeed in
any endeavor you pursue in the future.
My sincerest thank you to my awesome labmates in the Emotion and Cognition Lab –
both current and former – who have been an inexhaustible source of laughter, support, and
inspiration throughout my time here. Thanks for filling the role of both dear friends and
therapists during this journey. Working here would not have been the same without! I’d like to
particularly acknowledge my wonderful collaborators who contributed to the experiments in this
dissertation: Shawn Nielsen, Rico Velasco, and Tae-Ho Lee.
Most importantly, I wish to express my gratitude for my helpful, kind, and generous
collaborator, Michiko Sakaki. You have a rare combination of intelligence, humility, kindness,
and strong work ethic that have – and will continue to make you – very successful. It was a
privilege to learn so much from you and to have had the opportunity to work alongside you on
these projects.
My sincerest thank you to my incredible family for their love, encouragement, and
support: Rich, Becky, Jeff, Sarah, Jen, Courtnie, Jacob, and Joel (and precious nieces and
nephews). Thank you for giving meaning to my life and for always rooting for me. A very special
thank you to the Price and Dayka families, as well, who have been second families to me;
you’re all dear to my heart and I’m so lucky to have you in my life.
Last, but most certainly not least, I wish to thank my partner in life, Jonathan Dayka, for
being my best friend and center of my universe. Although I don’t deserve you, I will always strive
to make you feel as loved and supported as I do. I love you.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY vi
Abstract
Memories are not arbitrary records of past events – they are highly selective. In
particular, decades of research show that perception and memory are biased towards
emotionally arousing experiences, such as a car crash or the birth of a child. However, this
focus on the superiority of emotional memories has led to a blind spot in the emotion-cognition
literature. Beyond simply enhancing emotional stimuli, arousal exerts a selective – and often
contradictory - influence on processing nearby neutral stimuli. So what determines which
representations will be enhanced or suppressed by arousal? Identifying these factors and their
underlying brain mechanisms is foundational to our understanding of how adaptive memories
are formed when it matters most.
To reconcile arousal’s contradictory effects on cognition, the arousal-biased competition
model (ABC) proposes that arousal amplifies the effects of priority in perception and memory,
such that processing goal-relevant or perceptually salient information is enhanced, while
processing less salient information is suppressed. While increasing behavioral evidence
supports the ABC model, it is less clear how arousal impacts high and lower priority
representations differently in the brain. The aim of this dissertation was to determine whether
the noradrenergic system, which modulates cognitive processing via the stress hormone
norepinephrine, serves as the brain’s core memory selectivity mechanism under arousal.
Specifically, my goal was to test the recent Glutamate Amplifies Noradrenergic Effects (GANE)
model of emotion-cognition interactions, which posits that arousal-induced activation of the
noradrenergic, or locus coeruleus-norepinephrine (LC-NE), system enhances the gain of
prioritized inputs over less dominant ones via local, global, and receptor subtype-specific
effects.
In three experiments, I tested the GANE model in humans using a combination of a
psychophysiological, pharmacological, and neuroimaging techniques. Study 1 investigated
whether task-induced increases in salivary alpha-amylase (sAA), a biomarker of noradrenergic
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY vii
system activity, moderated emotional arousal’s influence on memory of competing goal-relevant
and distracting neutral images. Results indicated that, in women showing a larger increase in
sAA, task-irrelevant arousing sounds enhanced memory of preceding goal-relevant neutral
images at the cost of memory for their corresponding distracters. This finding demonstrated that
arousal enhances memory selectivity in the context of greater (global) noradrenergic activity in
women.
In Study 2, I tested GANE’s key hypothesis that β-adrenoreceptors regulate arousal’s
dichotomous influence on memory of high and lower priority neutral information. Results
demonstrated that β adrenergic blockade with propranolol attenuated an anterograde amnesic
effect of emotional arousal on lower priority information. Propranolol reduced selective memory
trade-offs favoring top-down prioritized over less-attended information on a trial-by-trial basis.
Additionally, task-induced sAA change was associated with enhanced memory for goal relevant
images under arousal across both groups. Together these findings demonstrate that, whereas
elevated noradrenergic activity at encoding relates to “winner-take-more” effects under arousal,
β-adrenergic receptors appear to contribute to memory selectivity by inhibiting processing of
less-attended memoranda.
In Study 3, I combined functional magnetic resonance imaging (fMRI) with measures of
pupil dilation, a proxy for phasic LC activity, to determine whether threat-induced activation of
the LC-NE system amplifies mnemonic processing in goal-relevant cortical networks. Results
indicated that, at the behavioral level, pupil dilation was associated with enhanced memory for
high priority scenes when participants were motivated to memorize those target scenes via
threat of monetary punishment. In the brain, greater pupil dilation was associated with enhanced
scene encoding activity in the parahippocampal gyrus, a region that processes scene
information, the LC and ventral tegmental area/substantia nigra (VTA/SN). Together these
findings suggest that threat-induced arousal strengthens goal-relevant memories by co-
activating the LC-NE system and dopaminergic motivational systems.
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In summary, the findings from these three studies provided initial support for the GANE
model by linking candidate biomarkers of noradrenergic activity to “winner-take-more” and
“loser-take-less” outcomes in memory. These data have broad implications for a variety of
cognitive phenomena supported by the noradrenergic system, including motivated learning,
cognitive aging, and the reliability of eyewitness testimony. Furthermore, through better
understanding the role of the noradrenergic system in mnemonic processes, the current results
may inform targeted therapeutic interventions to improve cognition in older adults and mitigate
the formation of maladaptive memories that characterize various disorders of emotion, including
post-traumatic stress disorder (PTSD).

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 1
Introduction
Take a moment to think about your past. What memories most readily come to mind? Do
they relate to day-to-day activities, such as brushing your teeth? Or do they relate to something
emotionally arousing and distressing, such as the terrorist attacks on September 11th?
When you try to remember that tragic day, odds are you are better at recalling certain
details compared to others. For example, you may recall that when you first heard the shocking
news, you were driving to work; in contrast, you might not remember the color of the shirt you
were wearing. Furthermore, even though you’ve driven to work countless times, you’ve probably
forgotten most of those experiences. So what makes pursuing this goal more memorable if it
occurred in an emotionally arousing context versus a more mundane context? Moreover, why
do you remember what you were doing at that shocking moment, but not the color of your shirt?
Years of emotion-cognition research demonstrate that arousal is a double-edged sword
that profoundly influences memory selectivity. But it is less clear how arousal’s dual effects arise
in the brain. The aim of this dissertation was to determine the brain mechanisms by which
arousal simultaneously enhances and impairs declarative memory.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 2
Overview of Dissertation
Selectivity is essential for effective cognitive processing, helping us to prioritize
motivationally significant information among competing sensory inputs. The ability to prioritize
behaviorally relevant representations is especially important under highly arousing situations,
such as hearing a sudden explosion or the pressure to perform a mentally demanding task, that
demand attention and rapid action. It is also highly adaptive to capture these events in memory,
as doing so guides how we behave and make decisions in the future.
Much research indicates that emotional experiences dominate this competition for
representation and tend to be vividly perceived and stored as enduring memories (Dolan, 2002;
LaBar & Cabeza, 2006; McGaugh, 2000, 2013). Yet the effects of arousal go beyond the simple
enhancement of emotional stimuli themselves and exert a selective – and often contradictory -
influence on processing of spatially or temporally adjacent neutral information (Anderson et al.,
2006; Bocanegra & Zeelenberg, 2009; Kensinger et al., 2007; Knight & Mather, 2009; Mather,
2007; Mather & Sutherland, 2011). So how does arousal “select” which inputs to process and
store when it matters most?
An initial answer to this question was provided by the arousal-biased competition model,
which proposes that the priority (e.g., goal relevance or perceptual salience) of a neutral
stimulus determines how it will fare under arousing conditions (Mather & Sutherland, 2011).
Specifically, ABC predicts that emotional arousal increases attention biases even further, such
that processing prioritized representations is enhanced, while processing competing, weaker
representations is suppressed. In this way, arousal brightens the spotlight of attention and
darkens the periphery, such that things that matter stand out even more and are remembered
even better, while less important things fade even more into the background and are more likely
to be forgotten.
Until recently, however, we lacked a unifying account of how arousal’s dual effects are
instantiated in the brain. The majority of brain-based emotion-cognition models fail to explain
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 3
arousal’s ability to both enhance and impair cognition, with most research either centering on: 1)
the amygdala’s role in preferentially processing emotional over neutral stimuli, or 2) only the
enhancement or impairment of neutral representations under arousal.
To fill this gap, we proposed the Glutamate Amplifies Noradrenergic Effects (GANE)
model, which posits that arousal-induced activation of the locus coeruleus-norepinephrine (LC-
NE), or noradrenergic system, yields different cognitive outcomes for high and lower priority
information (Mather et al., in press). According to GANE, the brain’s primary excitatory
neurotransmitter, glutamate, is the neurochemical substrate of priority. When arousal is induced,
strong glutamate signals transmitting prioritized information activate a positive feedback loop
with local NE release; in turn, this generates high enough levels of both transmitters to engage
synaptic plasticity processes, thereby privileging memory storage of “winning” representations.
In contrast, relatively modest increases in NE elsewhere suppress weaker glutamate signals
even further via lateral and auto-inhibitory processes. Together these dichotomous effects of NE
on local brain activity provide a contrast mechanism for amplifying salient representations at the
cost of processing mundane or goal-irrelevant inputs.
An important challenge for testing the GANE model in humans is that it largely focuses
on local synaptic processes, making it difficult to examine at the systems level. Despite this
limitation, emerging research indicates that indirect biomarkers of LC-NE system activity,
including salivary alpha-amylase and pupillometry, make it possible to examine how global or
phasic (transient) patterns of noradrenergic activity modulate cognitive processing. Furthermore,
pharmacological manipulations and neuroimaging techniques, such as functional magnetic
resonance imaging (fMRI), enable researchers to test GANE’s core predictions concerning the
local activity- and receptor-subtype-dependent effects of arousal on mnemonic processing.
The purpose of this dissertation was to test the GANE model by linking indices of human
noradrenergic system activity to “winner-take-more” and “lose-take-less” effects of emotional
and motivational arousal on memory. To this end, I used a combination of pharmacological,
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 4
neurohormonal, and functional/structural magnetic resonance imaging (MRI) techniques to
examine whether arousal-enhanced noradrenergic activity biases memory in favor of goal-
relevant stimuli over task-irrelevant distracters. In three separate studies, I aimed to advance
our understanding of how the brain optimizes neuronal processing under situations of arousal,
such as hearing a scream, promote the selection of behaviorally relevant inputs to guide
appropriate behavioral responses and store important memories.

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Chapter 1. Background
The contradictory effects of arousal on cognition
Decades of research show that emotional stimuli, such as the image of mutilated body or
the disturbing sound of a scream, are often preferentially perceived and stored as indelible
memories (Dolan, 2002; LaBar & Cabeza, 2006; McGaugh, 2000, 2013). Emotional stimuli tend
to be prioritized due to their relevance to motivational goals (e.g., increasing pleasure and
avoiding pain), their affective saliency (e.g., associations with reward/punishment) and/or
perceptual salience (e.g., a gunshot is loud as well as a threat to safety; Markovic et al., 2014).
For instance, many studies show that emotionally salient stimuli are often detected more quickly
than neutral stimuli, which may be driven by the evolutionary advantage of rapidly responding to
threat or danger (Leclerc & Kensinger, 2008; Mather & Knight, 2006; Öhman et al., 2001).
Emotional stimuli are also resistant to the attentional blink, a phenomenon by which the ability to
detect an image closely following a target image is temporarily suppressed (Anderson & Phelps,
2001).
Because of their many sources of priority, emotionally arousing stimuli often dominate
the competition for awareness at their particular spatiotemporal position (Wang et al., 2012).
Since mental resources are limited, the preferential processing of emotional material often leads
to decrements in perception and memory for surrounding information (Figure 1). Such selective
effects of arousal are exemplified by the “weapon focus effect,” in which eyewitnesses to a
crime tend to remember the perpetrator’s weapon in great detail, but often at the cost of
memory for the perpetrator’s face (Steblay, 1992). These emotion-related memory trade-offs
have also been repeatedly demonstrated in laboratory settings. For instance, emotional stimuli
impair subsequent perception of a neutral stimulus presented in the same location less than one
second later (Kennedy & Most, 2012; Most et al., 2005).
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 6

Figure 1. A schematic example of the “weapon focus effect.” In this phenomenon, when
eyewitnesses to a crime recall their experience, they often recall the perpetrator’s weapon in
great detail, but at the cost of memory for the perpetrator’s face. Presumably, this is due to the
gun having more immediate relevance to survival and being highly salient (e.g., located in the
foreground), whereas the face is less salient because it is relatively less conspicuous (e.g.,
located in the background). These memory trade-offs are less likely to occur under non-
arousing conditions, such as if the man was holding his wallet rather than a gun. In the brain,
processing in separate category-selective areas of cortex are differentially impacted by arousal,
leading to “winner-take-more” and “loser-take-less” outcomes in memory.

Viewing an emotional stimulus, however, does not always impair processing of
subsequent neutral stimuli. For instance, seeing a fearful rather than neutral face or hearing a
tone conditioned to shock enhances lower-level perception of a subsequently presented low-
contrast Gabor patch (Padmala & Pessoa, 2008; Phelps et al., 2006). Similarly, seeing an
emotionally salient word enhances rather than impairs perception of a target neutral word when
it appears 1000 ms rather than 50 or 500 ms later (Bocanegra & Zeelenberg, 2009). These
findings suggest that emotional arousal can instead enhance perception of something neutral
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 7
when the arousal-inducing stimulus does not overshadow neutral information appearing soon
after.
Inconsistencies are also apparent in research examining arousal’s influence on memory
encoding and consolidation. The most robust finding in the emotion-cognition literature is that
people show superior memory for emotionally arousing versus neutral stimuli (LaBar & Cabeza,
2006). Moreover, such emotional memory enhancements often occur at the cost of
remembering nearby neutral information. For instance, seeing an emotionally arousing object,
such as a wrecked car, can impair memory for its neutral background scene, such as a city
street (Kensinger et al., 2007; Payne et al., 2012; Waring & Kensinger, 2011). However, the
presence of emotional stimuli does not always lead to decrements in memory for competing
neutral information. For example, seeing an emotionally arousing object can diminish trade-offs
in memory for spatially or temporally adjacent neutral information when those neutral stimuli
receive more attention (Kensinger et al., 2007; Knight & Mather, 2009).
Memory trade-offs also occur when emotional and neutral memoranda compete nearby
in time. Whereas some evidence indicates that emotional stimuli enhance memory for preceding
neutral items (Anderson et al., 2006; Knight & Mather, 2009), other studies have yielded the
opposite finding, reporting an emotion-induced retrograde amnesia for neutral stimuli
(Hurlemann et al., 2005; Hurlemann et al., 2007; Strange et al., 2003).
Together these findings suggest that knowing that something neutral is central or
peripheral to an emotionally arousing event isn’t enough to predict whether it will be
remembered or forgotten later on. This raises the fundamental question of how arousal selects
which specific representations to enhance versus suppress.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 8
Arousal-biased competition theory
According to the arousal-biased competition (ABC) model, a momentary increase in
arousal amplifies the effects of priority, such that processing highly salient or important
information is enhanced, whereas processing lower priority information is impaired (Mather &
Sutherland, 2011). Thus, a surge of arousal, such as during threat or excitement, increases the
stakes of on-going cognitive selection processes, leading to “winner-take-more” and “loser-take-
less” effects in perception and memory.
The ABC framework builds upon the idea of biased competition in the brain, whereby
bottom-up perceptual salience or top-down goals help resolve competition among incoming
sensory inputs for limited mental resources (Beck & Kastner, 2009; Desimone & Duncan, 1995).
Attention can be driven in a bottom-up manner based on how much a stimulus stands out from
its surroundings, either via its perceptual properties or novelty (Itti & Koch, 2000; Parkhurst et
al., 2002; Reynolds & Desimone, 2003). For example, a bright pink highlighter on a brown desk
might “pop-out” and automatically grab attention regardless of its task-relevance, such as any
intention to locate and grab that highlighter. Similarly, hearing a sudden loud sound, such as a
gunshot, might draw attention away from the task at hand. Alternatively, people can direct their
attention in a top-down manner to prioritize processing of goal-relevant representations
(Corbetta & Shulman, 2002; Levine & Edelstein, 2009). In addition, the self-relevance or history
of reward or punishment with a stimulus can also increase its salience and determine how
effectively it attracts attention (Awh et al., 2012; Hutchinson & Turk-Browne, 2012). According to
ABC, arousal will increase any of these attention biases, irrespective of whether priority is
determined by bottom-up, top-down or emotional factors.
To test the ABC hypothesis explicitly, Sakaki et al. (2014) manipulated priority in a visual
oddball paradigm by altering the goal-relevance of neutral object images appearing just before
or after an emotional versus neutral oddball image (Sakaki et al., 2014). As predicted, emotional
arousal led to impaired memory for oddball-1 objects when the oddball image was prioritized
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(meaning the oddball-1 had low priority), whereas prioritizing the neutral oddball-1 image
instead led to an emotion-induced retrograde memory enhancement for such objects (Figure 2).
Thus, this result supports the ABC framework and helps explain why emotionally arousing
oddballs often impair memory for neutral images seen just beforehand: In those manipulations,
the preceding neutral stimuli were relatively inconspicuous and did not receive special attention
(Knight & Mather, 2009; Strange et al., 2003).

Figure 2. Schematic representations of two trials from the emotional oddball paradigm in
Sakaki et al. (2014). (A) Trial where participants had to prioritize the image appearing
before a neutral black-framed oddball image (i.e., cabbage) in attention and memory. (B)
Trial where participants had to prioritize the image appearing before an emotionally
arousing oddball image. (C) Memory performance for oddball-1 position neutral objects
differed based on their priority and the valence of oddball pictures, such that memory for
oddball-1 items was enhanced by emotion when they received more attention than the
oddball image itself; conversely, prioritizing the oddball item itself led to memory
impairment for the preceding item under emotion. This pattern represents the canonical
arousal-biased competition (ABC) effect in memory. Note that oddball pictures depicted
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 10
here were obtained from iStockPhoto for illustration purposes and different from those
used in the experiments.

Other studies also support the notion that priority determines how neutral perceptual and
memory traces will fare under arousing conditions. For instance, viewing an emotionally laden
image can enhance subsequent learning of the tilt of a perceptually salient versus less salient
(i.e., less angled) tilted line (Lee et al., 2012). Similarly, hearing an emotional sound or a tone
conditioned to shock enhances attention and memory for subsequently presented perceptually
salient or goal-relevant visual stimuli, while suppressing memory of less salient visual stimuli
(Lee et al., 2014). Furthermore, hearing an emotional sound immediately after seeing an object-
scene pair leads to memory impairments for the less salient background scene (Ponzio &
Mather, 2014). Together these findings suggest that arousal amplifies priority effects in
perception and memory regardless of emotional salience.
In summary, a growing number of behavioral studies support ABC’s predictions that
arousal modulates mental representations differently based their priority. However, it is
noteworthy that there may be timing-dependent effects of arousal on perceptual and
consolidation processes: whereas arousal induced prior to viewing neutral stimuli enhances its
bottom-up salience in perception and working memory (e.g., free recall of high-contrast letters in
an array), arousal induced immediately after seeing something neutral enhances its goal
relevance in memory consolidation (e.g. seeing an emotional image after a neutral object
image). An interesting open question is whether motivational incentives, which also increase
sympathetic arousal (Dolcos et al., 2014), proactively amplify the effects of goal relevance in
cognition. Consistent with this possibility, past work indicates that motivation narrows the scope
of attention and memory prior to goal attainment, such as receiving a reward (Kaplan et al.,
2012; Levine & Edelstein, 2009; Montagrin et al., 2013; Talmi, 2013).

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Current models of emotion-cognition interactions in the brain
Although the ABC model provides an initial answer to the question of how arousal
influences cognition, the brain mechanisms underlying such effects are less clear. Most brain-
based models of emotion-cognition interactions are inadequate, because they cannot account
for arousal’s simultaneous ability to enhance and impair information processing. To fill this gap,
we recently proposed GANE, a unifying model of how NE released under arousal biases
memory selectivity in favor of prioritized information. Prior to describing the central tenets of the
GANE model, I will first critique how previous mechanistic frameworks of emotion-cognition
interactions fail to encompass arousal’s dual effects on perception and memory consolidation.
Brain-based models addressing emotion’s effects on attention and perception
To date, the majority of emotion-cognition models have focused on how emotional
stimuli dominate attention and perceptual processes. One possibility is that the evolutionary
utility of detecting something threatening, such as a snake in the grass, spurred the
development of a specialized brain system for processing emotional events (Tamietto & de
Gelder, 2010). Consistent with this view, the Multiple Attention Gain Control (MAGiC) model
proposes that there are “emotional attention” pathways centered upon that amygdala that
operate in parallel to attention control systems transmitting bottom-up (exogenous; stimulus-
driven) and top-down (endogenous; top-down goals) information (see also Vuilleumier, 2005).
While these anatomically distinct emotion and cognition networks represent different forms of
priority, their top-down inputs converge on posterior and ventral sensory pathways to enhance
processing of emotional stimuli in an additive manner.
In a similar vein, other researchers proposed that emotional stimuli compete for
executive attention resources (Bishop, 2007; Choi et al., 2012; Eysenck et al.) via competition
between a ventral affective system and a dorsal fronto-parietal attention system (Bush et al.,
2000; Dolcos et al., 2011). For example, neuroimaging evidence shows that when emotional
distracters impair working memory for target neutral faces, activity in dorsal attention regions,
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such as the prefrontal cortex, is reduced (Dolcos et al., 2008; Dolcos & McCarthy, 2006).
However, meta-analyses indicate that emotional responses are associated with enhanced
activity in both ventral and dorsal prefrontal cortical regions (Phan et al., 2002; Shackman et al.,
2011); therefore, task-irrelevant emotional stimuli do not always recruit ventral brain regions to
suppress dorsolateral PFC (Dolcos et al., 2008).
As opposed to viewing emotion-cognition interactions as an antagonistic relationship
between distinct ventral emotional networks and dorsal executive networks (Dolcos et al.,
2011), other models posit that emotional stimuli compete at multiple levels of brain function. For
instance, the dual competition model proposes that emotional stimuli compete for shared
resources at both perceptual and executive levels of processing (Pessoa, 2009; Pessoa, 2013).
According to this framework, cortical and subcortical structures help amplify visual cortex
responses to emotional stimuli. Likewise, at the executive level, emotionally arousing stimuli,
such as tones conditioned to shock, increase – rather than attenuate – activity in dorsal fronto-
parietal regions that support top-down attention (Lim et al., 2009). As a result, the priority given
to salient emotional stimuli leaves fewer resources available to process concurrent stimuli,
thereby leading to impaired perception and attention for competing goal-relevant neutral
information.
The multiple waves model also favors an integrated-systems approach to emotion-
cognition interactions (Pessoa & Adolphs, 2010). Like many brain-based models of emotional
processing, this framework proposes that affective stimuli rapidly engage the amygdala and
other brain regions, including the orbitofrontal cortex, anterior cingulate cortex and anterior
insula, that evaluate motivational salience; in turn, the amygdala sends re-entrant inputs to the
sensory cortex to enhance processing emotional representations even further. Rather than
competing with other attention control networks, the amygdala coordinates activity in other
functional brain networks that regulate selective attention (Hermans et al., 2014). Thus, from
this perspective, emotional stimuli dominate awareness by activating general-purpose
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perceptual and attention systems rather than activating independent brain networks specialized
to enhance emotional representations.
These models, however, cannot account for evidence that lengthening the delay
between the presentation of an emotional stimulus and neutral stimulus enhances subsequent
detection of the neutral item (Bocanegra & Zeelenberg, 2009). Why would arousal enhance
subsequent perception of neutral stimuli when mental resources are no longer taxed by
something emotional? What neuromechanism supports arousal’s lingering effects on cognition?
The Biased Attention via Norepinephrine (BANE) model goes one step further by
accommodating the role of neuromodulation in large-scale patterns of brain activity under
arousal (Markovic et al., 2014). According to BANE, emotional salience is detected by an
“anterior affective system,” including the amygdala and the orbitofrontal cortex, based on the
recent history of reward and punishment; in turn, the amygdala recruits the LC-NE system to
bias sensory cortical activity in favor of affectively relevant information.
BANE shares many similarities with the previously reviewed models: it posits that an
amygdala-centric network enhances emotional representations even when emotional stimuli are
not the focus of attention. However, like other emotion-cognition frameworks, the BANE model
focuses exclusively on how affectively salient stimuli outcompete neutral stimuli by engaging the
amygdala, and does not address how arousal can also enhance perception of proximal neutral
information. More critically, this framework also fails to acknowledge that NE’s modulatory
effects are not specific to affective brain regions, such as the amygdala and orbitofrontal cortex,
but are rather widespread. As reviewed later on, the broad distribution of noradrenergic fibers
makes NE well positioned to influence any on-going functional network activity when it is
released (Alnæs et al., 2014; Corbetta et al., 2008; Coull et al., 1999; Grefkes et al., 2010;
Hermans et al., 2011; Raizada & Poldrack, 2008).

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 14
Summary of attention and perception models
Existing brain-based models that acknowledge both competitive and cooperative
interactions between various attention and sensory networks are more compatible with our
behavioral findings than segregated-systems models: If emotional stimuli are processed via a
unique pathway that disrupts activity in executive attention regions, it is not clear how arousal
can also enhance prioritized neutral representations. However, these integrative network
models are limited in that they only consider a) the enhanced processing of emotionally
arousing stimuli, and/or b) competition between arousing and neutral stimuli/tasks. Critically, our
empirical work indicates that arousal also influences competition between proximal neutral
stimuli, such that processing high priority stimuli is enhanced, while processing lower priority
stimuli is impaired. Within these emotion-neutral competition frameworks, it is not clear arousal
would impact high and lower priority neutral representations differently in the brain.
Brain-based models addressing emotion’s effects on memory
Beyond shaping initial perception and attention processes, arousal continues to
influence competition between mental traces during consolidation. The ruthless-competition
hypothesis proposes that encoding novel emotional information suppresses recently potentiated
synapses, thereby creating new emotional memories at the cost of memory for whatever was
encoded just beforehand (Diamond et al., 2005). This model, however, cannot account for
retroactive memory enhancements. For instance, post-learning arousal inductions, such as
through cold-pressor (physical) stress or exposure to emotional images, selectively enhances
memory for preceding emotional but not neutral pictures presented in mixed lists (Cahill et al.,
2003; Liu et al., 2008).
Instead, this finding that post-encoding arousal selectively enhances prior emotional
events is more compatible with the emotional-tagging hypothesis, which posits that arousal at
encoding “tags” synapses transmitting emotional information (LTP; Bergado et al., 2011;
Richter-Levin & Akirav, 2003; Segal & Cahill, 2009; Tully & Bolshakov, 2010). Rather than
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 15
suppressing potentiated synapses from prior learning, subsequent arousal inductions interact
with emotional tags to enable those particular synapses to capture plasticity-related proteins,
which trigger long-term potentiation (LTP).
A problem for emotional-tagging is that retroactive memory selectivity isn’t specific to
emotional stimuli. For instance, emotional experiences sometimes enhance memory for
preceding neutral stimuli (Anderson et al., 2006; Dunsmoor et al., 2015; Knight & Mather, 2009;
Nielson & Powless, 2007; Sakaki et al., 2014). None of the hypotheses outlined above can
explain retrograde memory enhancement for something neutral.
The canonical model of emotional memory consolidation posits that the amygdala
selectively modulates activity in medial temporal lobe (MTL) structures, particularly the
hippocampus, to favor memory consolidation of emotional stimuli over neutral stimuli (e.g.,
McGaugh, 2004). This idea is supported by a wealth of evidence showing that greater amygdala
functional connectivity with MTL regions is associated with enhanced memory for emotional
items but not neutral items, as is amygdala activity (Dolcos et al., 2004; Fastenrath et al., 2014;
Kilpatrick & Cahill, 2003; Richardson et al., 2004; Ritchey et al., 2008). Consistent with animal
research (McIntyre et al., 2012), recent causal modeling evidence in humans indicates that such
interactions are driven by the amygdala modulating memory storage processes in the
hippocampus (Fastenrath et al., 2014).
Accruing evidence also indicates that these amygdala-mediated enhancements of
emotional stimuli rely on the stress hormone NE (Cahill et al., 1994; McGaugh, 2002). For
instance, infusing noradrenergic agonists into the basolateral amygdala after fear conditioning
enhances memory for emotionally arousing events (Hatfield & McGaugh, 1999; LaLumiere et
al., 2003). Furthermore, administering the β-adrenoreceptor antagonist propranolol to humans
impairs emotional memories (Cahill et al., 1994), and this emotional memory-impairing effect of
β-adrenergic blockade corresponds with decreased amygdala activity at encoding (Strange &
Dolan, 2004). On the other hand, pharmacological challenges that elevate central NE levels
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 16
tend to enhance emotional memories (Chamberlain & Robbins, 2013).
Since propranolol administration in the study conduced by Strange and Dolan (2004)
was also associated with reduced hippocampal responses to emotional stimuli during retrieval, it
was suggested that β-adrenergic effects drive memory enhancements via amygdala-
hippocampal interactions (Strange & Dolan, 2004). This notion is consistent with evidence in
rodents showing that hippocampal expression of Arc protein, a putative marker of memory-
related plasticity, increases after post-training infusion of a β-adrenergic agonist into the
basolateral amygdala (McIntyre et al., 2005).
Of particular relevance to the ABC model, NE-induced activation of the amygdala also
spills over to affect memory outcomes for nearby neutral stimuli. For instance, in oddball
paradigms, people often show worse memory for inconspicuous neutral stimuli that appear just
before or after an emotional compared with a neutral “oddball” stimulus (Hurlemann et al., 2005;
Sakaki et al., 2014; Strange et al., 2003). Such emotion-induced retrograde memory
impairments do not occur in patients with amygdala damage (Hurlemann et al., 2007; Strange et
al., 2003) or in healthy individuals following β-adrenoreceptor blockade (Strange et al., 2003).
Taken together, these results suggest that beyond simply enhancing memory of emotional
material, NE-driven activation of the amygdala impairs processing nearby neutral material. But,
importantly, this is not always the case. Amygdala-NE interactions have also been shown to
retroactively enhance memory for non-arousing neutral information (e.g., Barsegyan et al.,
2014; Roozendaal et al., 2008).
Summary of arousal-related memory models
In summary, although there are many brain-based models of how arousal influences
cognition, these theories cannot simultaneously account for arousal’s enhancing and impairing
effects on perception, attention, and memory. Like our GANE framework, many of these models
recognize the noradrenergic system’s important role in emotion-cognition interactions under
arousal (Markovic et al., 2014; McGaugh, 2000, 2004; McIntyre et al., 2012). In particular, a
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 17
common theme across these studies is that β adrenergic receptors are critical for arousal-
enhanced memory consolidation. However, previous work has focused almost exclusively on
how NE enhances emotional memory and/or leads to neutral-item impairments by activating the
amygdala (e.g., Strange & Dolan, 2004; Strange et al., 2003). In contrast, we argue that
arousal-induced activation of the noradrenergic system enhances cognitive selectivity for any
prioritized stimulus, regardless of whether it is emotional or not. In this way, the GANE model
encompasses a broader range of arousal (e.g., emotion, novelty, motivation etc.) and priority
types (e.g., goal relevance or perceptual salience) to explain the full story of how arousal
shapes information processing in the brain.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 18
Locus coeruleus neuromodulation of arousal and cognition
Motivationally significant events that demand our attention, including stimuli associated
with reward/punishment, seeing a snake in the grass, or hearing an unexpected loud noise,
activate a small nucleus in the brainstem known as the locus coeruleus (LC). The LC is a small
bilateral nucleus located in the dorsal pontine tegmentum (13,000 per hemisphere in humans;
Foote & Morrison, 1987) and serves as the primary source of cortical NE (Berridge et al., 2012;
Berridge & Waterhouse, 2003; Samuels & Szabadi, 2008, 2008). Remarkably, despite its small
size, the LC is centrally involved in numerous cognitive and arousal processes, including
regulating cortical arousal states, wakefulness, and executive/memory function. Via its
widespread release under arousal, NE enhances learning (Ahissar et al., 1996; Chamberlain et
al., 2006; Harley, 1987), regulates synaptic plasticity (Ahissar et al., 1996; Neuman & Harley,
1983; Salgado et al., 2012), and optimizes higher-order cognitive processes, including working
memory (Arnsten & Li, 2005; Wang et al., 2007). Thus, the LC-NE system plays a fundamental
role in broadcasting learning signals across the brain that helps guide adaptive behavioral
responses and consolidates salient information into long-term memory.
Functional neuroanatomy of the LC-NE system
LC neuron activity is characterized by two different, but not mutually exclusive, modes of
firing. Tonic, or background, levels of LC activity help regulate overall levels of arousal and
wakefulness (Carter et al., 2010), and become elevated under stress (Berridge & Waterhouse,
2003). In contrast, phasic, or transient, bursts of LC activity occur in response to salient, novel
or stressful stimuli (Aston-Jones & Bloom, 1981; Foote et al., 1980; Grant et al., 1988; Sara &
Bouret, 2012; Sara & Segal, 1991; Vankov et al., 1995). The LC also responds phasically to
behaviorally relevant events associated with goal relevance or decision outcomes (Aston-Jones
& Cohen, 2005; Aston-Jones et al., 1999). Furthermore, emotionally arousing stimuli induce LC
phasic activity irrespective of whether they are aversive (Chen & Sara, 2007; Grant et al., 1988)
or appetitive (Bouret & Richmond, 2015; Grant et al., 1988). Taken together, the phasic mode of
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 19
LC activity is capable of orchestrating responses to either bottom-up, top-down, or emotional
sources of priority, thereby implicating the LC-NE system as a candidate neuromechanism of
ABC effects (Figure 3).
LC axons are distributed throughout most of the brain (Gaspar et al., 1989; Javoy-Agid
et al., 1989; Levitt et al., 1984; Swanson & Hartman, 1975). Importantly, noradrenergic fibers
send collateral projections to target regions along the same sensory processing pathways,
including specialized first-order nuclei in the thalamus, a centrally located sensory relay station
in the brain. For example, LC projections to somatosensory cortex are more likely to co-
innervate the thalamic somatosensory nucleus (i.e., ventral posteromedial nucleus; touch
sensation) rather than other first-order thalamic nuclei (e.g., lateral geniculate nucleus; vision).
This anatomical hierarchical organization of the noradrenergic system enables NE to adjust the
filtering of incoming sensory inputs at the level of the thalamic relays, and, more broadly,
coordinate salient information processing across large-scale brain networks (Coull et al., 1999).
Subcortical regions underlying memory, attention, and emotional processes, including
the hippocampus, frontal cortex, and amygdala, share dense reciprocal anatomical connections
with the LC (Aston-Jones & Waterhouse, 2016; Berridge & Waterhouse, 2003). During salient or
stressful events, frontal cortical regions, including the orbitofrontal cortex, dorsal anterior
cingulate cortex, regulate LC activity to coordinate appropriate behavioral responses (Aston-
Jones & Cohen, 2005; Samuels & Szabadi, 2008). Furthermore, as reviewed in previous
sections, NE-induced activation of the amygdala and hippocampus is centrally involved in
emotional memory enhancements (McIntyre et al., 2012; Strange & Dolan, 2004) and
declarative memory consolidation more generally (Harley, 1987; Sara, 2015).

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Figure 3. Functional neuroanatomy of the LC-NE system at the systems level. Higher structures
that evaluate the motivational significance of a stimulus (e.g., the amygdala and prefrontal
cortex) communicate this information to the LC and regulate its output. Autonomic state
information (e.g., pain signals) is also communicated to the LC via other brainstem nuclei,
including the nucleus gigantis cellularis (NGC), which then broadcasts salience signals to
cortical and subcortical regions to guide behavior adjustments accordingly. The release of NE
occurs broadly and diffusely across most of the brain under arousal, with the exception of the
basal ganglia. This broad organization of NE innervation makes the noradrenergic system well
equipped to modulate most on-going cognitive processes. Figure is from Sara and Bouret
(2012). Amy = amygdala; PFC = prefrontal cortex. LC = locus coeruleus. Blue lines = NE
efferents.

At the synaptic level, LC axon varicosities, or small swellings along axonal fibers,
release large volumes of NE into extracellular space, enabling NE to activate a broad swath of
receptors at its cortical targets (Beaudet & Descarries, 1978; Descarries et al., 1977; O'Donnell
et al., 2012). It is also noteworthy that phasic LC responses elicit larger volume release of NE
than tonic activity (Florin-Lechner et al., 1996), suggesting that NE’s effects on cognitive
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 21
selectivity may be more robust during a sudden surge in arousal. This possibility is supported by
evidence that phasic LC promotes the exploitation of rewards and enhances task-focused
attention (Aston-Jones et al., 1999; Aston-Jones et al., 1994). Moreover, this view is consistent
with the LC’s proposed role as a “circuit-breaker” that resets on-going functional network activity
in response to sudden, unexpected changes in the environment (Bouret & Sara, 2005).
At its cortical and subcortical targets, NE binds to multiple adrenoreceptor subtypes (i.e.,
α1, α2 and β receptors) that are located both pre- and post-synaptically on neurons and
astrocytes (Berridge & Waterhouse, 2003; O'Donnell et al., 2012; Terakado, 2014; Tully &
Bolshakov, 2010). Importantly, these adrenoreceptors exert different effects on both NE release
and neuron excitability: Whereas α2-adrenoreceptors inhibit NE release via autoreceptors and
reduce cell excitability, β-adrenoreceptors generally increase cell excitability, network activity,
and synaptic plasticity (Berridge & Waterhouse, 2003; Marzo et al., 2009; Nomura et al., 2014;
Starke, 2001). α1-adrenoreceptors recruit phospholipase activation and typically increase cell
excitability via the inhibition of potassium channels (Wang & McCormick, 1993). Thus, the
regional density and distribution of adrenoreceptor subtypes helps determine how NE influence
local neuronal excitation and inhibition.
NE regulation of signal-to-noise ratio in cortical processing
It has been long appreciated that NE enhances the signal-to-noise ratio (Hasselmo et
al., 1997; Salgado et al., 2016). However, these effects of NE on synaptic transmission are less
intuitive than the simple enhancement of strong inputs versus suppression of weak inputs. In
fact, most sensory network data point to a predominantly inhibitory role of NE on neuronal
activity, even for sensory-evoked signals (Foote et al., 1975; Freedman et al., 1977; Hasselmo
et al., 1997; Kuo & Trussell, 2011; Livingstone & Hubel, 1981; O'Donnell et al., 2012; Oades,
1985; Waterhouse & Woodward, 1980).
Generally, NE’s signal-to-noise altering properties are relative and involve greater
suppression of spontaneous activity compared to inhibition of evoked signals (Foote et al.,
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 22
1975). For example, recording from individual auditory cortical neurons in awake squirrel
monkeys revealed that NE application suppressed spontaneous activity more strongly than
activity evoked by species-specific vocalizations (Foote et al., 1975). In auditory cortex, NE
appears to sharpen frequency-tuning curves primarily by suppressing activity representing non-
preferred inputs (Manunta & Edeline, 2004; Manunta & Edeline, 1997). Thus, NE’s effects on
signal-to-noise processing are perhaps best characterized by the conjoint suppression of weak,
spontaneous, or non-preferred inputs, along with sparing or inducing a less robust suppression
of strong sensory inputs. It is noteworthy that, compared with auditory cortex, cell-recording data
in animal visual cortex is more contradictory. For instance, there have been cases where NE
application actually enhances the absolute excitability of visual neurons, while also suppressing
activity in other visual neurons (Kasamatsu & Heggelund, 1982).
Beyond influencing individual neurons, NE’s suppressive effects help modulate cortical
arousal states that enhance selective attention and perceptual acuity. In anaesthetized rodents,
LC activity precedes transitions from synchronized to desynchronized cortical states, which
corresponds with increased sensory processing efficiency (Fazlali et al., 2016). Other studies in
anaesthetized rodents indicate that NE’s affects on signal-to-noise processing may occur even
as early as sensory thalamus, a first-order relay nucleus located in the center of the brain,
where noradrenergic stimulation decreases spontaneous firing and focuses receptive fields
(Hirata et al., 2006).
The notion that LC stimulation or NE application primarily quiets cortical activity also
accords with electrophysiological recordings in awake animals. For instance, during locomotion,
NE mediates widespread depolarization of inhibitory neurons across rodent visual cortex, which
helps increase the signal-to-noise ratio (Polack et al., 2013). Thus, rather than broadly
increasing neuronal activity under heightened arousal, NE predominantly suppresses on-going
patterns of activity and reduces correlated fluctuations in neuronal activity across the brain (e.g.,
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 23
slow waves oscillations); in turn, this generates a less noisy (i.e., desynchronized) cortical state
that is conducive to selectively processing strong sensory inputs.
NE regulates gain modulation
Building on earlier neural network models of LC effects on cortical network processing,
researchers have argued that LC firing increases signal gain in task-relevant cortical networks,
such that the excitability of already active neurons is increased, whereas activity of inhibited
neurons is suppressed (Aston-Jones & Cohen, 2005). In addition to shaping representations
directly, computational models also demonstrate that LC stimulation enhances processing of
information streams transmitting strong inputs, leading to greater lateral inhibition of weaker
competing pathways (Eldar et al., 2013; Usher et al., 1999). Thus, consistent with NE’s role in
signal-to-noise modulation, LC activity helps increase the gain on competition between high and
lower priority representation. Interestingly, these patterns of enhanced neuronal gain during LC
activity are strongly evocative of the arousal-biased competition effects we find at the behavioral
level. This observation inspired us to target the LC-NE system as a candidate neuromechanism
underlying arousal’s dual effects on cognition (Figure 4).

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Figure 4. Canonical arousal-biased competition (ABC) effect in memory, whereby – under
arousal – memory of high priority information (e.g., goal-relevant) is enhanced, whereas
memory of lower priority information (i.e., less salient) is impaired (left panel). This dual effect of
arousal on memory outcome is strikingly similar to the gain modulation function of the
noradrenergic system, whereby – during LC phasic firing – activity in already activated neurons
is enhanced even further, while activity in less activated neurons is inhibited even further (right
panel; adapted from Aston-Jones and Cohen (2005)).

A common theme in physiological and computational work is that NE release amplifies
the effects of neuronal inhibition. Such widespread suppressive effects are generally attributed
to NE’s modulatory effects on inhibitory interneurons and GABAergic neurotransmission
(Salgado et al., 2016). Strong glutamate activity in cortical circuits stimulates local release of
GABA, the most widespread inhibitory neurotransmitter in the brain (Petroff, 2002); in turn,
GABA release enhances lateral inhibition of less active nearby cortical circuits (Xue et al.,
2014). NE release activates fast-spiking parvalbumin positive interneurons that support lateral
inhibition (Cox et al., 2008; Huang et al., 2013; Toussay et al., 2013) and amplifies inhibition
directly, with suppression being most robust at intermediate NE levels (Nai et al., 2009).
NE also affects subtypes of inhibitory interneurons differently in ways that should
enhance neuronal gain. For instance, while LC-NE system activity activates interneurons that
mediate lateral inhibition (Salgado et al., 2012), it can also inhibit interneurons with feedforward
projections (Brown et al., 2005). As a result, a strong signal will inhibit competing pathways
while enhancing activity in other neurons within its own processing pathway. This finding is in
line with one of the core tenets of biased competition theory: when an object representation
dominates competition in one part of a functional network (e.g., an area processing the object’s
lines or color features), other aspects of its representation will be enhanced elsewhere
(Desimone, 1998).

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Open questions in the LC-NE system literature
In summary, the functional neuroanatomy of the noradrenergic system makes it well
positioned to modulate any on-going selection process when arousal occurs. Until recently,
however, there were still open questions as to how NE could impact high and lower priority
representations differently in the brain:
1) The majority of physiological studies examining NE’s effects on sensory network processing
indicate that NE suppresses most activity, while sparing evoked activity (Foote et al., 1975).
Yet at the behavioral level, our empirical data show that arousal enhances perception of
salient, high priority information (Lee et al., 2014; Sutherland & Mather, 2012). If NE
predominantly inhibits sensory activity, what mechanism can account for enhanced
processing of strong inputs under arousal?
2) How does NE influence competing representations differently if it is released in a diffuse and
non-specific manner across most of the brain (Berridge & Waterhouse, 2003)? As the ABC
model predicts, the priority of a given stimulus is the critical factor that determines how
arousal modulates brain activity. This raises the question of whether the effects of arousal-
induced NE release differ according to the strength of a sensory or affective input. Indeed,
early recordings in monkey visual cortex support this idea by showing that local NE release
is correlated with the amount of coincident light-evoked neuronal activity (Marrocco et al.,
1987). This suggests that, despite being released broadly under arousal, NE levels might
also be regulated locally. It is unclear, however, how local synaptic regulation of NE occurs;
moreover, little is known about how such self-regulating processes influence specific
behavioral outcomes under arousal.
3) Another mystery concerns in vitro studies examining spike-timing-dependent long-term
potentiation (LTP), or the strengthening of synaptic weights, and long-term depression
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 26
(LTD), or the weakening of synaptic weights. For instance, LC stimulation alone does not
yield high enough concentrations of NE in the hippocampus and prefrontal cortex needed to
trigger LTP (e.g., ~twice baseline, Florin-Lechner et al., 1996; ~.5 micromolar,
Palamarchouk et al., 2000). Thus, in the living brain, what elevates NE concentration to
levels capable of promoting memory consolidation?
4) The influential “network reset” theory of LC modulation proposes that, during sudden
arousal, the LC reorganizes functional brain networks to respond to novel or unexpected
events (Bouret & Sara, 2005; Sara & Bouret, 2012). As a result, on-going processing of
other stimuli is disrupted in order to re-allocate attention to new biological imperatives. This
model, however, cannot clearly explain why phasic arousal induced by something emotional
can enhance processing of preceding neutral stimuli when they are goal relevant (Sakaki et
al., 2014). Again, our behavioral findings are difficult to reconcile with the notion that NE
primarily functions to suppress and/or reset sensory and cognitive network activity.

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Glutamate Amplifies Noradrenergic Effects (GANE) model: arousal’s core selectivity
mechanism in the brain

To address these gaps in the neuroscience literature, we recently proposed GANE, a
novel model of how NE released under arousal impacts high and low priority representations
differently despite its diffuse release across the brain. According to the GANE model, local
glutamate level is the key to determining whether NE release will further enhance or suppress
mental representations.
Our framework is built upon the idea that glutamate, the most prevalent excitatory
neurotransmitter in the brain (Meldrum, 2000), is the neurochemical substrate of priority.
Glutamate receptors, such as AMPA and NMDA receptors, mediate rapid excitatory synaptic
transmission, functional network connectivity, and long-term memory (Bliss & Collingridge,
1993; Lynch, 2004; Traynelis et al., 2010). In addition to point-to-point transmission across a
synapse, some glutamate escapes the synaptic cleft, leading to ‘glutamate spillover’ (Okubo et
al., 2010). According to GANE, arousal-induced amplification of strong activity occurs via “NE
hot spots,” where positive feedback loops between local NE and glutamate spillover up-regulate
prioritized signal transmission. This excitatory effect contrasts with more widespread NE-
induced suppression of activity in non-hot-spot regions transmitting weaker representations
(e.g., lower priority or spontaneous activity). The NE hot spot mechanism is composed of six
tenets:
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 28

Figure 5. The norepinephrine (NE) hotspot mechanism of the GANE model. Discrepant levels of
glutamate in “high” and “low” priority synapses interact with local NE released under arousal,
leading to opposing processing outcomes (i.e., gain modulation). Under arousal, NE axons
become depolarized and release large volumes of NE via varicosities, or small swellings along
the axon fiber; in turn, NE interacts with local glutamate transmission to generate “hotspots” of
even greater activity through the following effects: (1) Adrenergic receptor subtypes have
different thresholds for activation, with β-adrenergic receptors requiring the highest levels NE to
be engaged. Different adrenoreceptor subtypes also exert opposing influences on neuronal
activity: whereas β-adrenergic receptors potentiate neuronal responses and enhance synaptic
plasticity, α2-adrenergic receptors inhibit neuronal activity. Thus, accessing β-adrenergic
receptor effects is key to enhancing glutamatergic neurotransmission. (2) In highly active
synapses transmitting prioritized information, high levels of glutamate spill over and bind to
NMDA receptors on nearby depolarized NE axons; in turn, this enhances the release of NE
even further. (3A/B) In addition, adrenergic auto-receptors regulate NE release differently:
whereas β-adrenoreceptors self-enhance NE release, α2-autoreceptors inhibit local release of
NE. (4) The local up-regulation of NE at hotspots (that would otherwise not occur in less active
synapses) generates high enough NE levels to engage β-adrenergic receptors on glutamate
terminals, leading to even greater glutamate release. Elsewhere, low levels of glutamate are too
weak to recruit additional NE and engage β-adrenergic receptors; therefore, in these regions
α2-adrenoreceptor’s inhibitory effects prevail. In sum, only the most active synapses will garner
enough NE release to engage β-adrenergic receptors, thereby triggering self-regulating
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 29
excitatory processes that enhance signal transmission and enhance memory
encoding/consolidation.

1) α- and β-adrenoceptors exert different effects on neuron excitability and have
different activation thresholds. Compared to α-adrenoreceptors, β-adrenoreceptors
require relatively high NE concentrations to be engaged. By comparison, α1-adrenergic
receptors require more moderate levels of NE and α2-adrenergic receptors require the
lowest NE concentrations (Arnsten, 2000; Ramos & Arnsten, 2007). Thus, under arousal,
α2-adrenoceptor’s inhibitory effects should be widespread, whereas β-adrenoreceptors
should be only be engaged in regions with elevated NE concentrations. These different NE-
receptor-specific effects play an important role in shaping synaptic plasticity to favor
prioritized representations under phasic arousal (see tenet #4).
2) High glutamate activity recruits additional NE release from nearby NE varicosities.
Accumulated findings from in vitro and in vivo studies indicate that high glutamate evokes
additional NE release from nearby noradrenergic varicosities by binding to NMDA receptors
(Fink et al., 1989; Lehmann et al., 1992; Pittaluga & Raiteri, 1992; Vezzani et al., 1987).
Thus, strong glutamate signals privilege high-activity regions’ access to the sensory and
memory-enhancing effects of β-adrenoreceptors by up-regulating local NE levels.
3) Adrenergic β- and α2-adrenergic autoreceptors regulate local NE release.
Autoreceptors at NE varicosities amplify neuronal contrast by regulating additional NE
release as a function of local NE concentration. The primary presynaptic adreno-
autoreceptor in humans, the α2A-adrenoceptor, inhibits NE release when it detects low or
moderate NE levels (Delaney et al., 2007; Starke, 2001). In contrast, presynaptic β-
adrenoceptors on noradrenergic neurons increase local NE release when engaged at higher
NE concentrations (Ueda et al., 1983). Together with glutamate-evoked NE release (tenet
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 30
#1), the contrasting effects of these different auto-receptors at low and high NE
concentrations enable the noradrenergic system to amplify neural gain based on the degree
of local excitation.
4) Elevated NE at hot spots activates β-adrenoceptors on glutamate terminals. Such
engagement on presynaptic glutamate axons should lead to additional glutamate release
and, in so doing, enhance the high priority excitatory signal even further (Ferrero et al.,
2013; Gereau & Conn, 1994; Herrero & Sánchez-Prieto, 1996; Ji et al., 2008; Kobayashi et
al., 2009; Mobley & Greengard, 1985).
Past work shows that different NE levels regulate different forms of spike-timing-dependent
plasticity (STDP) depending on which adrenoreceptors are activated (and therefore, the
local level of NE). Whereas NE binding to moderate affinity α1-adrenergic receptors leads to
long-term depression and memory impairment (Huang et al., 2014; Salgado et al., 2012;
Treviño et al., 2012), NE binding to lower affinity β-adrenoreceptors (requiring higher NE
levels) leads to long-term potentiation and memory enhancement (Salgado et al., 2012;
Treviño et al., 2012). Furthermore, activation of β-adrenoreceptors in neuronal ensembles
triggers intracellular signaling cascades that help integrate those neurons into a memory
engram (Han et al., 2007). Thus, highly salient, prioritized perceptual or mnemonic traces
may be selected for memory storage via the excitatory actions of β-adrenoreceptors.
5) Phasic LC activity is the key trigger of hotspots and is most likely to occur within the
context of moderate tonic LC-NE system activity. Another key mechanism of the hotspot
involves the close relationship between tonic and phasic modes of LC firing. In line with
previous models of gain modulation (Aston-Jones and Cohen, 2005), we’d expect phasic LC
responses, such as motivationally significant events, to regulate hotspots most effectively
given that volume release of NE is greater during this mode of activity (Berridge and
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 31
Waterhouse, 2003). More importantly, glutamate’s ability to recruit additional NE via NMDA
receptor activation requires concomitant depolarization of the NE fiber (Lüscher & Malenka,
2012). It is well established that tonic LC activity constrains phasic LC responses to salient
events, with moderate background levels of activity being most permissive to transient LC
firing. This intermediate phasic-tonic ratio is also most likely to optimize task-focused
attention and cognitive selectivity, as NE’s effects on cognitive processing follow an
inverted-U function (Aston-Jones and Cohen, 2005). Thus, a moderate state of sympathetic
arousal (and background levels of NE release) is important for creating cognitive and
synaptic states that are most conducive to attracting local NE release; in turn, this should
up-regulate local activity above and beyond the initial intensity of signal transmission.
6) Elevated NE levels at hot spots briefly prolong neuronal excitation. β-adrenoreceptor
activation inhibits the slow after-hyperpolarization, a refractory phenomenon that suppresses
neuronal activity after sustained depolarizations (Madison & Nicoll, 1982; Nicoll, 1988).
Thus, reduced normalization at hot spots may provide an additional temporal processing
advantage to prioritized inputs, as this phenomenon is mediated by β-adrenoreceptors that
are selectively activated at hotspots. In contrast, activation of α-adrenoreceptors enhances
the long-lasting hyperpolarizations that would help filter out and suppress weaker inputs.
Together, these differential effects on neuronal normalization are thought to be one
mechanism by which NE enhances the signal-to-noise ration in cortical synapses.
Summary of the GANE model
According to the GANE model, the opposing actions of different adrenoreceptors provide
an efficient way for arousal to regulate signal gain based on the priority, or the degree of
activation, of a representation. At the core of GANE, the potentiating effects of lower-affinity β-
adrenergic receptors enable NE to ignite hot spots of even greater activity in regions
transmitting strong inputs. The resulting synergism between strong glutamate signals and
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 32
postsynaptic β-adrenergic effects trigger synaptic plasticity processes that support memory
consolidation. Additionally, the lateral inhibitory effects of β-adrenoreceptor activation help
strong signals propagate along their own processing pathways, while simultaneously
suppressing activity in weaker competing pathways. In other brain regions, the auto- and
postsynaptic inhibitory effects of high-affinity α2-adrenoreceptors inhibit weaker inputs where
glutamate signals are too low to recruit additional NE and ignite a hotspot. We argue that,
through these activity-dependent excitatory/inhibitory actions of NE, arousal enhances “winner-
take-more” and “loser-take-less” outcomes in perception and memory (i.e., arousal-biased
competition effects).

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 33
Biomarkers of LC-NE system structure and function
An important challenge for cognitive neuroscientists is that GANE largely focuses on
local synaptic processes, making it difficult to test this theory at the systems level. Despite this
limitation, emerging research has identified several indirect biomarkers of LC-NE system
activity, including hormones and pupillometry, which enable researchers to study how putative
LC responses modulate perception and memory in humans. Furthermore, pharmacological
manipulations can be used to examine the relationship between specific adrenoreceptor
subtypes and memory more directly. Finally, functional and structural magnetic resonance
imaging (fMRI) can also be utilized to measure local brain activity and, using specialized
sequences, visualize and delineate LC structure within individual subjects.
Salivary alpha-amylase (sAA). One candidate biomarker of endogenous noradrenergic
activity is the digestive enzyme salivary alpha-amylase (Nater & Rohleder, 2009). The release
of sAA is positively correlated with changes in central NE levels induced by exercise (Chatterton
et al., 1996), acute stress (Thoma et al., 2012), or pharmacological challenge (Ditzen et al.,
2014). Prior research also demonstrates that sAA is positively correlated with selective long-
term memory for emotional but not neutral images (Segal & Cahill, 2009). Although saliva
measures do not provide phasic measures of physiological arousal, they make it possible to
estimate overall changes in sympathetic nervous/noradrenergic system activity induced by an
emotionally arousing task. In particular, given that phasic LC responses are intrinsically tied to
overall levels of baseline LC activity, increased global NE release might create brain states that
are more conducive to enhancing selectivity under transient arousal (e.g., hearing a gunshot).
Pharmacology. Pharmacological challenge provides a more direct manipulation of the
noradrenergic system in humans (Chamberlain et al., 2006). Moreover, pharmacological
manipulations allows for causal inferences between specific adrenoreceptor subtypes effects
and cognitive outcomes to be made. Since we were specifically interested in the role of β-
adrenergic receptors in amplifying priority effects under arousal, in Study 2 we administered a β-
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 34
adrenergic receptor blocker, propranolol, to participants prior to performing an emotional oddball
task that has been shown to elicit ABC memory effects (Sakaki et al., 2014). Converging
research in humans and rodents show that propranolol administration abolishes emotional
memory enhancements (Cahill et al., 1994; Grillon et al., 2004; McGaugh, 2013) and blocks an
emotion-induced retrograde amnesia for preceding neutral stimuli (Strange et al., 2003).
Although drug effects are systemic rather than local, the administration of propranolol enabled
us to test GANE’s specific prediction that arousal-induced activation of β-adrenergic receptors
strengthens goal-relevant memories, while weakening lower priority, less-attended neutral
stimuli.
Functional magnetic resonance imaging (fMRI). Neuroimaging enables me to
measure local regional changes in brain activity as a function of stimulus priority, which is
central to testing GANE theory. Past work shows that different categories of visual stimuli are
represented in distinct regions of the ventral visual stream. For example, scene processing has
been localized to an area known as the parahippocampal place area (Epstein & Kanwisher,
1998), whereas object perception and recognition primarily activates the lateral occipital cortex
(LOC; (Grill-Spector & Malach, 2001). In a recent neuroimaging study, Lee et al., (2014) took
advantage of the fact that scenes and objects activate distinct cortical regions in the brain to
determine how arousal influenced brain processing differently when faces were prioritized and
scenes were not (Lee et al., 2014). Consistent with the ABC model, hearing a tone conditioned
to shock vs. a non-condition neutral tone enhanced activity in face-selective cortex (i.e., fusiform
face area), while suppressing activity in scene-selective cortex.
Physiological research suggests that NE may account for these selective changes in
blood flow. NE helps coordinate the delivery of the brain’s energy supplies, such as oxygen, and
glucose, to areas of high activity (e.g., O'Donnell et al., 2012; Toussay et al., 2013). One key
way that NE mobilizes energy is by increasing spatiotemporal synchronization of blood delivery
to oxygen demand within the brain. For instance, in mice, increasing NE levels constricts blood
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 35
vessels throughout most of the brain, but selectively dilates blood vessels in the vicinity of highly
active, stimulated brain regions (Bekar et al., 2012). It is possible that this physiological effect of
NE on blood oxygen delivery could underlie the arousal-related changes in blood-oxygen-level-
dependent (BOLD) signal observed using functional magnetic resonance imaging (fMRI). For
instance, in the study by Lee et al. (2014), NE potentially released when hearing a shock-
conditioned tone may have helped direct blood flow towards highly active regions representing
the prioritized face stimulus (i.e., fusiform face area), while directing blood flow away from less
active brain regions representing non-salient scenes (i.e., parahippocampal place area).
To capitalize on this finding in Study 3, we manipulated the top-down priority of two
different visual categories to examine whether arousal-by-priority interactions in memory also
correspond to similar patterns underlying brain activity. Specifically, we examined how threat of
monetary punishment-induced arousal selectively modulates mnemonic processing in category-
selective visual cortex and the LC.
Pupillometry. Previous work suggests a link between LC-NE system activity and
changes in pupil diameter. For instance, single cell and electrophysiological recordings in
monkeys show that phasic LC neuron activity is tightly coupled with pupil dilations (Joshi et al.,
2015; Varazzani et al., 2015). Furthermore, electrical stimulation of LC neurons in monkeys also
elicits pupil dilation responses (Joshi et al., 2015). Consistent with animal findings,
pharmacological manipulating LC-NE system activity in humans also affects pupil diameter
(Koss et al., 1984). More recent neuroimaging studies show that brainstem activity in a location
consistent with the LC relates to non-luminance-related changes in pupil dilation during
cognitive tasks (Alnæs et al., 2014; Critchley et al., 2005; Murphy et al., 2014). Together these
findings support the notion that pupil diameter changes may be an effective non-invasive
biomarker of LC activity, with pupil dilation relating to the phasic mode of LC neuron responses
and baseline pupil size relating to the tonic, or background, mode of LC firing (Eldar et al., 2013;
Murphy et al., 2011).
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 36
Several fMRI studies have identified activity in the vicinity of the LC during events known
to stimulate the LC, such as stress, conflict/error processing, and motivationally significant cues
(Krebs et al., 2013; Liddell et al., 2005; Mohanty et al., 2008; Qin et al., 2012). However, the
reliability of these findings is questionable due to the low spatial resolution of functional images
and potential cardiac pulsation artifact (Astafiev et al., 2010). To help localize cognitive task-
related brainstem activity to the LC, researchers have begun combining pupil diameter
measurements with fMRI using parametric modulation analyses (Alnæs et al., 2014; Murphy et
al., 2014; Sterpenich et al., 2006).
For instance, one recent fMRI study linked fluctuations in pupil diameter both at rest and
during a cognitive task to BOLD signal changes within an anatomical location consistent with
the LC (Murphy et al., 2014). Importantly, these putative LC-related patterns of activation
remained even after physiological noise correction and spatial smoothing, suggesting that
integrating pupil measures helps circumvent the issues of brainstem pulsation and the LC’s
small size. Activity in peri-LC regions associated with pupil diameter was also greater during
oddball detection vs. standards, which is consistent with the established role of the LC in goal-
relevant processing (Aston-Jones et al., 1994; Nieuwenhuis et al., 2005).
In Study 3 of this dissertation, we took a similar integrative approach by weighting the
BOLD response on a given trial according to the magnitude of pupil dilation to a neutral visual
stimulus (Sterpenich et al., 2006). This technique allowed us to examine how event-related
changes in pupil dilation, a non-invasive index of phasic LC activity, parametrically modulated
regional brain activity during encoding of goal-relevant versus distracting visual stimuli.
Neuromelanin-sensitive weighted MRI. Until recently, imaging the human LC was
notoriously difficult due to its small size of ~2-15 mm (Chan‐Palay & Asan, 1989) and low MR
signal in conventional T1-weighted anatomical images. However, a growing number of studies
have taken advantage of the fact that - unlike most other structures in the brainstem - the LC
contains neuromelanin, a byproduct of NE metabolism (Sasaki et al., 2006). Using MR
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 37
sequences sensitive to neuromelanin, the LC can be visualized effectively (Shibata et al., 2006).
For instance, 3T fast spin-echo (FSE) T1-weighted MRI sequences can enhance MR signal
contrast between the LC and neighboring brainstem tissue (Keren et al., 2009; Sasaki et al.,
2006; Shibata et al., 2006; Takahashi et al., 2014). Thus, in Study 3, we used FSE imaging to
localize and delineate subject-specific LC masks for fMRI region-of-interest analyses.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 38
Dissertation Aims

The central aim of this dissertation was to link biomarkers of human noradrenergic
system activity to “winner-take-more” and “loser-take-less” memory outcomes under arousal.
Specifically, we aimed to test GANE’s global, receptor-specific, and local activity-related
predictions regarding how emotional and motivational arousal differentially impact processing
goal-relevant versus lower priority mental representations. Using a combination of
pharmacological, physiological, and neuroimaging techniques, three studies were conducted to
test the GANE model in healthy young adults:
Study 1: Effects of tonic noradrenergic activity. The goal of this behavioral study was
to examine whether task-irrelevant emotionally arousing sounds amplified competition effects in
memory between spatially overlapping goal-relevant and distracting images. Specifically, we
were interested in determining whether task-induced increases in salivary alpha-amylase (sAA),
a global biomarker of noradrenergic activity, moderated emotional arousal’s trial-by-trial
influence on memory trade-offs between top-down prioritized and distracting neutral images.
Based on a growing literature demonstrating sex differences in arousal responses and memory,
our secondary goal was to examine whether the sex steroid hormones estradiol and
progesterone moderated any of these effects in women.
Study 2: Effects of β-adrenoreceptors. The goal of this double blind, placebo-
controlled pharmacological study was to test GANE’s key prediction that arousal-induced
activation of β-adrenoreceptors amplify the effects of goal-relevance in memory. We
administered 40mg of propranolol hydrochloride, a β-adrenoreceptor blocker, or 40mg of vitamin
E placebo to healthy young adults. After pill intake, participants completed an emotional oddball
task in which they were asked to prioritize a neutral object appearing just before an emotional or
neutral black-framed image within a sequence of 7 neutral objects. We hypothesized that β-
adrenoreceptor blockade would attenuate both arousal-induced memory enhancements for
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 39
preceding goal relevant images and arousal-induced memory decrements for subsequent lower
priority images.
Study 3: Local cortical effects of LC-NE system activity. The goal of this functional
magnetic resonance imaging (fMRI) study was to test GANE’s prediction that threat-induced
activation of the LC selectively enhances successful encoding-related activity in a brain region
transmitting a goal-relevant visual stimulus. In addition, we were interested in determining
whether motivational incentives parallel the effects of emotional arousal on biased competition
effects in memory. During a monetary incentive encoding fMRI task, participants explicitly
prioritized a background scene in attention and memory while ignoring a transparent foreground
object. On some trials, arousal was induced by threatening to deduct money from a preset
account if participants forgot loss-cued scenes during a subsequent memory test. Pupil dilation
was measured on a trial-by-trial basis as a proxy of LC activity, and was used to test two
hypotheses: at the behavioral level, we hypothesized that threat-induced pupil dilation would be
associated with enhanced memory for goal-relevant scenes. In the brain, we hypothesized that
the magnitude of pupil dilation would parametrically modulate successful encoding-related
activity in the LC and parahippocampal place area (PPA), a scene-selective region located in
the ventral visual stream.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 40
Chapter 2. Emotional arousal amplifies goal-relevant memory selectivity under
elevated noradrenergic activity in women but not men

1. Introduction
Emotionally arousing events form some of our most vivid and enduring memories (LaBar
& Cabeza, 2006; Sharot et al., 2004). Yet arousal also affects processing of spatially or
temporally adjacent neutral information (Anderson et al., 2006; Bocanegra & Zeelenberg, 2009;
Kensinger et al., 2007; Knight & Mather, 2009; Mather, 2007; Mather & Sutherland, 2011). For
example, when eyewitnesses to a crime recall their experience, they often remember the
weapon itself – the source of arousal - at the cost of memory for the perpetrator’s face, a
phenomenon known as the “weapon focus effect” (Steblay, 1992). The adaptive significance of
such trade-offs is that processing of seemingly inconsequential information is suppressed to
allocate limited mental resources to a motivationally significant stimulus. As exemplified by the
weapon focus effect, however, this memory narrowing is maladaptive when other nearby
information, such as a perpetrator’s face, is important to remember. Thus, understanding the
factors that determine when emotion will enhance or suppress memory is of critical importance.
According to Arousal-Biased Competition (ABC) Theory, arousal amplifies the effects of
stimulus priority, thereby enhancing processing of highly salient information at the expense of
processing competing, less salient information (Mather & Sutherland, 2011). In line with ABC’s
predictions, seeing an emotional image can enhance memory for a preceding goal relevant
neutral object, while impairing memory for another nearby but less-attended neutral object
(Sakaki et al., 2014). Likewise, hearing an emotional versus neutral sound suppresses memory
for neutral scenes paired with a salient foreground object that was seen just beforehand (Ponzio
& Mather, 2014). Emotional arousal therefore seems to amplify competitive processes more
generally, leading to “winner-take-more” and “loser-take-less” effects in memory regardless of
whether priority is determined by emotional factors or not.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 41
Presently, most brain-based models of arousal’s selective influence on cognition are
problematic, because they fail to account for arousal’s dual effects on cognition. To account for
how arousal interacts with priority in the brain, the Glutamate Amplifies Noradrenergic Effects
(GANE) model proposes that activity-dependent increases in norepinephrine (NE) enable
arousal to enhance already highly activated, prioritized mental representations even further
(Mather et al., 2015). At the same time, relatively modest increases in NE should suppress
processing of relatively lower priority (i.e., less active) mental representations via local auto-
inhibition. Together these dichotomous influences of NE on local regional activity amplify the
gain on competition between high and lower priority information processing, providing a
neuromechanism by which arousal optimizes selectivity in perception and memory.
Consistent with GANE, previous research suggests that NE biases attention to favor
affectively salient information, as determined by the history of reward and punishment, over
non-emotional information (Markovic et al., 2014). Thus, emotional stimuli recruit the LC-NE
system to garner more resources for their representation. Other evidence indicates that NE’s
memory-boosting effects also extend to emotional stimuli when sympathetic arousal is
manipulated externally. For instance, in healthy young women, increasing NE levels using
isometric handgrip (IHG) enhances selective memory for negative over positive emotional
stimuli (Nielsen et al., 2015). GANE’s core prediction, however, that arousal-induced NE release
will amplify the effects of goal-relevance for something neutral has yet to be tested.
Notably, Nielsen et al. (2015) also found that the memory-biasing effects of NE only
occurred in women with lower estradiol and progesterone levels at encoding, suggesting that
sex hormones may moderate NE’s influence on memory selectivity. These findings are in line
with evidence that sex steroid hormones modulate emotional memory enhancements both in the
presence and absence of stress (Andreano et al., 2008; Ertman et al., 2011) and may potentiate
NE release (Bangasser et al., 2015). Additionally, elevated sex hormone levels are also
associated with enhanced brain activity during viewing of negative emotional images in limbic
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 42
brain regions, such as the amygdala and hippocampus, that are known to support emotional
memory enhancements (Andreano and Cahill, 2010). Taken together, these findings raise the
possibility that sex hormone levels in young women also moderate GANE effects on prioritized
neutral information.
The goal of Study 1 was to determine whether arousal-induced NE release, as indexed
by sAA change across a cognitive task (Ditzen et al., 2014), amplifies the effects of top-down
priority in cognition such that memory for goal-relevant neutral images is enhanced at the
expense of memory for competing neutral distracters in men and women. We also examined
whether estradiol and progesterone moderated the strength of NE’s effects on memory trade-
offs in young women.
2. Methods
2.1 Participants. One hundred and two healthy young adults were recruited from the
USC Psychology Subject Pool to participate in this experiment. All participants provided written
informed consent approved by the University of Southern California Health Science Campus
IRB and were awarded course credits for their participation. All eligible individuals had normal or
normal-to-corrected vision and hearing. In addition, recruited females were randomly sampled,
and as a result, they were either naturally cycling with regular menstrual cycles or using
hormonal contraception.
To avoid contamination of saliva samples, participants were instructed to: 1) Refrain
from eating, gum chewing, or teeth brushing for at least one hour prior to the experiment, and 2)
Refrain from cardiovascular exercise and alcohol/caffeine intake 24 hours prior to the start of
the experiment.
Prior to analysis, we applied several exclusion criteria: 1) failure to comply with saliva
collection criteria (n = 21; most of which were due to the strict 24-hour enforcement with respect
to aerobic exercise); 2) categorization performance on the task was below chance (n = 7); 3)
memory for target stimuli was below chance (n = 14); 4) experimenter error (e.g., script
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 43
crashing) or participant failed to follow instructions (n = 5), and 5) missing distraction ratings (n =
3). Note that some of these overlap since some participants violated multiple criteria. After
exclusions based on these criteria, a total of 59 participants remained and were analyzed in this
study (31 females: M
age
= 21 years, SD = 2.62; 28 males: M
age
= 21 years, SD = 1.89).
2.2 Procedure. Upon arrival participants were consented and then completed a variety
of behavioral questionnaires. Participants then drank an 8-oz bottle of water and answered the
saliva screening questions. A few minutes later, participants provided a 1-mL saliva sample via
passive drool and then performed a competitive visuo-attention task.
2.3 Competitive visuo-attention task
2.3.1 Stimuli. Visual stimuli were composed of 48 neutral object and 48 neutral scene
grayscale images selected from previous datasets (Kensinger et al., 2006); Gabrieli et al., 1997)
and the Internet. Half of the object images depicted kitchen utensils, while the other half
depicted animals; half of the scene images depicted indoor scenes, while the other half depicted
outdoor scenes. With these images, 48 “overlap” stimuli were created by overlaying each object
image on top of a scene image using Adobe Photoshop 5.0. The object image was then
rendered transparent such that the foreground object and background scene images were
equally discernable. Each of the un-merged object and scene stimuli were yoked with a
semantically related image that served as a foil during the recognition memory test.
Emotional sound stimuli consisted of 16 neutral (M
valence
= 5.74, SD
valence
= 0.26; M
arousal
=
4.27, SD
arousal
= 0.097), 16 positive (M
valence
= 1.96, SD
valence
= 0.066; M
arousal
= 7.59, SD
arousal
=
0.094), and 16 negative (M
valence
= 7.38, SD
valence
= 0.069; M
arousal
= 6.24, SD
arousal
= 0.17) audio-
clips from the International Affective Digital Sounds (Bradley & Lang, 2007). The most salient 2s
portion of each original 6s audio-clip was selected for the task using Audacity. For neutral
sounds, we clipped the original audio files to exclude any brief periods of silence.
2.3.2 Competitive visuo-attention task. To examine how emotional arousal influences
memory selectivity, participants performed a competitive visuo-attention task consisting of an
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 44
encoding phase and a memory test (Figure 1). The overall structure of the experiment was a 2
(Prioritized: object vs. scene) x 3 (Valence: neutral vs. negative vs. positive) within-subjects
design. The task consisted of four blocks of 12 trials (i.e., overlap images), with three trials of
each valence in each block. The order of the overlap stimuli was randomized within each block.
Prior to the start of each block, participants were cued to which visual stimulus category (object
or scene) to focus on and memorize. Object-focused and scene-focused blocks were always
staggered and their order was counterbalanced across participants. The prioritized visual
category and valence associated with each overlap stimulus was also counterbalanced across
participants. To familiarize participants with the experiment, they were given six practice trials
and one example memory trial prior to the main task.

Figure 1. An example trial from the competitive visuo-attention task and subsequent recognition
memory test. Each trial consisted of an “overlap” image of a transparent object overlaid on a
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 45
background scene. Participants were cued to categorize and memorize either the scene or
object stimulus while ignoring the other. Following the overlap image, participants heard an
emotional or neutral sound and were prompted to rate how arousing they found each sound via
button press.

2.3.3 Encoding Phase. During the encoding phase, participants were presented with a
series of grayscale “overlap” images of a transparent, centered object overlaid on a background
scene (Figure 1). Each trial commenced with a 2s verbal reminder of which visual category to
prioritize (“focus on object” or “focus on scene”) followed by a 3s inter-stimulus-interval (ISI)
consisting of a fixation cross in 24-point black font. Following this fixation period, an overlap
image appeared in the middle of the screen for 1.75 seconds.
To manipulate stimulus priority, or goal relevance, participants were instructed to focus
on and memorize the exact image of either the object or background scene while ignoring the
other visual category. To facilitate encoding and ensure participants were prioritizing the correct
stimulus, they were also instructed to categorize the target stimulus (object: animal or kitchen
utensil; scene: indoor or outdoor) as quickly and accurately as possible via button press when
the overlap image appeared. In addition, participants were told that their memory would only be
tested for the scenes and objects they categorized (i.e., specifically focused on) during the task.
The overlap image was followed by a 1s fixation period. To manipulate arousal, a
negative, positive, or neutral emotional audio-clip was then played for 2s. After hearing the
sound, participants were prompted to rate how arousing they found each sound on a scale from
1 = not intense at all to 8 = extremely intense via button press. Participants were instructed to
provide their rating within the 4 seconds that the prompt was displayed on the screen.
Importantly, these subjective ratings were used as the primary trial-by-trial estimates of phasic
arousal in the hierarchical linear modeling (HLM) analyses rather than the pre-defined valence
categories or normative IADS arousal ratings. Each trial concluded with a 3s fixation cross inter-
trial-interval.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 46
2.3.4 Post-task delay period: Immediately following the visuo-attention task,
participants provided a second 1-mL saliva sample via passive drool. Participants then worked
on a number search puzzle for 5 minutes to introduce a delay between encoding and retrieval.
2.3.5 Recognition memory test. To examine the differential effects of arousal on
memory for high versus lower priority images, we tested memory for all target and distracter
images. In a two-alternative forced choice memory test, the scenes and objects from each
overlap stimulus were displayed individually. Each of these items was shown alongside a similar
but perceptually different (e.g., orientation) new image. Participants were tasked with identifying
which image they had seen during the encoding phase. After making each memory choice,
participants were prompted to rate their confidence in their memory accuracy on a scale ranging
from 1 = not confident at all to 6 = extremely confident. There were no time limits for either the
memory choice or confidence rating.
2.4 Emotional sound distraction ratings. After the memory test, we also acquired a
rating of how distracting the participants found the emotional sounds on a scale ranging from 1
= not distracting to 8 = very distracting. These distraction ratings provided an estimate of how
effectively participants prioritized goal-relevant stimuli relative to the task-irrelevant emotional
sound stimuli, with lower distraction scores being associated with better goal-directed attention.
We anticipated that lower distraction scores would therefore moderate a greater positive
influence of arousal on memory selectivity.
2.5 Saliva assay and analysis. Saliva samples were immediately frozen, and kept
frozen for a minimum of 24 hours to allow mucins to precipitate. Prior to the assays, they were
thawed and centrifuged at 3,000 x g for 15 min to extract particulates from saliva. Clear
supernatant was decanted into microtubes. Alpha-amylase levels were estimated using
Salimetrics, LLC (State College, PA) enzyme kinetic assay kits and measured optically using
Molecular Devices, LLC SpectraMax M3 Multi-mode Microplate Reader (Sunnyvale, CA). To
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 47
estimate task-evoked noradrenergic activity, we calculated a sAA change score by subtracting
sAA concentration values at baseline (sample 1) from values immediately post-task (sample 2).
The saliva samples were then analyzed for 17β-estradiol and progesterone levels using
Salimetrics, LLC (State College, PA) ELISA kits and optical measurements acquired from a
Molecular Devices, LLC SpectraMax M3 Multi-mode Microplate Reader (Sunnyvale, CA). We
assayed the first and the last saliva samples for 17β-estradiol and progesterone; from these
samples, we determined the average levels of these hormones. The observed ranges from the
assay of 17β-estradiol (M = 1.96, SD = 0.82) and progesterone (M = 121.33, SD = 104.9) were
also similar to the expected ranges for women (Salimetrics, LLC, State College, PA).
2.6 Memory codependency analysis. To examine how arousal influenced competitive
mnemonic processes on a trial-by-trial basis, we performed a memory codependency analysis.
Specifically, we examined how memory accuracy for the distracting stimulus (lower priority) in a
given overlap image differed as a function of memory accuracy for its corresponding target
stimulus (high priority). Each of the 48 trials in the encoding phase were coded post hoc
according to one of four possible memory codependency outcomes: 1) Remembered Target
and Forgot Distracter, 2) Forgot Target and Remembered Distracter, 3) Remembered both, or
4) Forgot both.
According to the ABC framework, emotional arousal biases mental resource allocation
towards prioritized representations, increasing memory for goal relevant stimuli at the expense
of memory for less salient distracters under arousal. Thus, an “optimal memory trade-off” was
operationalized as trials in which participants remembered the target stimulus while forgetting its
corresponding distracter. To increase statistical power, memory performance was not
differentiated by the specific visual category of the target (object-target trials versus scene-
target trials).

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 48
2.7 Hierarchical linear modeling analyses.
2.7.1 Trial-by-trial effects of emotional arousal on selective memory trade-offs. To
test our main prediction that NE facilitates arousal-biased competition memory effects during
encoding, we performed a hierarchical linear modeling (HLM) analysis with the glmer function in
the lme4 library (Baayen et al., 2008). The parameters were estimated with the maximum
likelihood method in R (R Core Team, 2012). Each trial was used as a Level 1 unit and each
participant was used as a Level 2 unit. Optimal memory trade-off scores were modeled as the
dependent variable with a binomial (logit) distribution (1 = Remembered
target
Forgot
distracter
, 0 =
other memory outcome). Subjective arousal ratings for the sounds were group-centered and
modeled as the level-1 trial-by-trial predictors, which were nested within three grand-mean-
centered level-2 predictors: Sex (1 = female, -1 = male), Sound Distraction Score, and Task-
Induced sAA Change. Trials with missing arousal ratings were excluded. The predicted two-way
and three-way interaction terms between arousal ratings, sAA change, and distraction scores
(see Table 1) were included in the model, and Sex was modeled as a main effect.
First, to determine the optimal HLM model, we tested whether there were random effects
in the slope across participants. A comparison (ANOVA) between models with and without a
random effects term was not significant (p > .05), suggesting that the effects of arousal on
memory trade-offs were not different across participants. Thus, a random slope effects term was
not included in the final model.
2.7.2 Interaction between trial-by-trial emotional arousal on selective memory
trade-offs in men. To interpret the directionality of the sex-related interaction effects in the first
HLM, we performed the same HLM analysis in men and women, separately. These models
were identical to the whole-group HLM, except the Sex predictor was removed (see Table 1).
2.7.3 Interaction between sex steroid hormones and trial-by-trial emotional arousal
on selective memory trade-offs in women. Next, we performed a female-only HLM analysis
to test whether sex steroid hormone levels moderated arousal’s influence on memory trade-offs.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 49
To simplify the model, continuous estrogen and progesterone values were multiplied to produce
a single ovarian hormone predictor.
As in the previous HLMs, optimal versus sub-optimal memory trade-offs were coded as
the binomial dependent variable. Subjective arousal ratings were group-centered and modeled
as the level-1 trial-by-trial predictor, which was nested within four grand-mean-centered level-2
predictors: ovarian hormone levels, sound distraction scores, and task-induced sAA change
scores. The HLM modeled the main effects of each of these predictors and the following
interaction terms: Ovarian Hormone x sAA Change, Ovarian Hormone x Arousal, sAA Change x
Arousal, sAA Change x Distraction, and the three-way interaction between Arousal, sAA
Change, and Distraction (see Table 2).
3. Results
3.1 Sound arousal and distraction ratings. Mean distraction ratings for the sounds did
not significantly differ between men (M = 3.25, SD = 1.14) and women (M = 3.36, SD = 1.66; p >
.05). Mean subjective arousal ratings for the sounds also did not significantly differ between
men and women in any of the three pre-defined valence categories. However, there was a
robust main effect of Valence on subjective arousal ratings, F(2,57) = 796.51, p < .001, η
p
2
=
.97, such that participants perceived negative (M = 7.09, SD = .091) sounds as being
significantly more arousing than positive (M = 5.09, SD = .16) and neutral sounds (M = 2.62, SD
= .09). Positive emotional sounds were also rated as being more arousing than neutral sounds
(all pairwise comparisons: ps > .001).
3.2 Mean memory performance. To verify that participants properly prioritized the goal-
relevant target images over distracters, we performed a one-way ANOVA on memory
performance, with Priority (target vs. distracter) as a within-subjects factor. As expected,
memory for target stimuli (M = .77, SEM = .01) was significantly better than memory for
distracters (M = .55, SEM = .011), F(1,58) = 196.11, p < .001, η
p
2
= .77.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 50
Table 1. Hierarchical linear model (HLM) examining interaction effects between trial-by-trial
arousal ratings, distraction ratings, and salivary alpha-amylase on memory selectivity across all
participants.
Predictors Estimate SE z p
Intercept -0.6080 0.0516 -11.7830 <2e-16
Emotional Sound Arousal Rating 0.0128 0.0179 0.7150 0.4747
Sex -0.0366 0.0515 -0.7110 0.4768
Task-Induced sAA Change 0.0013 0.0010 1.3140 0.1889
Emotional Sound Distraction Rating -0.0245 0.0404 -0.6080 0.5431
Arousal Rating x Sex 0.0160 0.0181 0.8820 0.3777
Arousal Rating x sAA Change 0.0003 0.0003 0.8960 0.3703
Arousal Rating x Distraction -0.0125 0.0134 -0.9360 0.3493
sAA Change x Sex 0.0004 0.0010 0.4530 0.6507
sAA Change x Distraction -0.0005 0.0007 -0.6730 0.5008
Distraction x Sex 0.0201 0.0404 0.4990 0.6181
Arousal Rating x Sex x sAA 0.0007 0.0003 2.0830 0.0372*
Arousal Rating x Distraction x sAA 0.0001 0.0003 0.4890 0.6248
Distraction x Sex x sAA Change 0.0000 0.0007 -0.0180 0.9852
Arousal x Sex x Distraction x sAA -0.0005 0.0002 -2.1900 0.0285*
Key: sAA Change: Change score in salivary alpha-amylase concentration (U/mL) levels from
baseline to immediately post-task. Sex (female = 1, male = -1). Significant results are bolded. *p
< .05.

3.3 The effects of phasic emotional arousal on memory selectivity. Statistical
results from the whole-group HLM analysis are displayed in Table 1. Across all participants,
there was a significant three-way interaction between Arousing Rating, Sex and sAA change, z
= 2.08, p = .037. In addition, the results revealed a significant four-way interaction between all
predictors, z = -2.19, p = .029.
3.4 Sex-dependent effects of phasic emotional arousal on memory selectivity. To
better understand the nature of these complex interactions found across all participants, we
performed two separate follow-up HLMs in men-only and women-only groups.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 51
In men, there were no significant main or interaction effects of the predictors on memory
selectivity (ps > .05; Table 2). In contrast, women with larger task-induced increases in sAA
showed greater trial-by-trial positive modulatory effects of emotional arousal on memory
selectivity, z = 2.55, p = .011 (Table 3). To better interpret the pattern of this interaction, we
calculated -1 and +1 standard deviation values for these two predictors and estimated the
corresponding optimal memory trade-off values using the HLM regression equation. Plotting
these results revealed that, in individuals who showed greater increases in sAA levels across
the task, higher subjective emotional arousal was associated with greater memory selectivity
(Figure 2).
The results also revealed a significant three-way interaction between sound arousal
ratings, task-related sAA change, and sound distraction ratings, z = -2.08, p = .038, such that
arousal-enhanced memory trade-offs were even greater in women who rated the task-irrelevant
emotional sounds as being less distracting (Figure 3). Together these findings suggest that,
under elevated putative NE release, women were significantly more likely to show arousal-
enhanced memory selectivity than men.
Table 2. Hierarchical linear model examining Interaction effects between trial-by-trial arousal
ratings, distraction ratings, and salivary alpha-amylase on memory selectivity in men only.

Predictors Estimate SE z p
Intercept
-0.5624947 0.0726027 -7.748 9.37E-15
Emotional Sound Arousal Rating
-0.0037477 0.0261639 -0.143 0.8861
Task-Induced sAA Change
0.0008469 0.0015237 0.556 0.5783
Emotional Sound Distraction Rating
-0.0482416 0.0660921 -0.73 0.4654
Arousal Rating x sAA Change
-0.000589 0.0005524 -1.066 0.2862
Arousal Rating x Distraction
0.0193602 0.023751 0.815 0.415
sAA Change x Distraction
-0.0004779 0.0012268 -0.39 0.6969
Arousal Rating x sAA x Distraction
0.0007389 0.0004443 1.663 0.0963
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 52
Key: sAA Change: Change score in salivary alpha-amylase concentration (U/mL) levels from
baseline to immediately post-task.

Table 3. Hierarchical linear model examining Interaction effects between trial-by-trial arousal
ratings, distraction ratings, and salivary alpha-amylase on memory selectivity in women only.
Predictors Estimate SE z p
HLM #3: Interaction effects between trial-by-trial arousal ratings, distraction
ratings, and salivary alpha-amylase on memory selectivity in women only
Intercept
-0.6563492 0.0730928 -8.98 <2e-16
Emotional Sound Arousal Rating
0.0255942 0.0245905 1.041 0.298
Task-Induced sAA Change
0.0016497 0.0011996 1.375 0.1691
Emotional Sound Distraction Rating
-0.000653 0.0456478 -0.014 0.9886
Arousal Rating x sAA Change
0.0011083 0.0004345 2.551 0.0108*
Arousal Rating x Distraction
-0.0236012 0.0159577 -1.479 0.1391
sAA Change x Distraction
-0.0004742 0.0006666 -0.711 0.4769
Arousal Rating x sAA x Distraction
-0.0005019 0.0002413 -2.08 0.0376*
Key: sAA Change: Change score in salivary alpha-amylase concentration (U/mL) levels from
baseline to immediately post-task. Significant results are bolded. *p < .05.

3.5 The moderating effects of sex hormones on arousal-induced memory
selectivity in women. Contrary to our second main prediction, adding a ovarian hormone
(estradiol/progesterone interaction) predictor to the HLM did not change the significance of
these effects (Table 4). However, we did find main effects of task-induced sAA change and
ovarian hormones on memory, such that elevated hormones were associated with enhanced
memory trade-offs, z = 1.96, p = .0498, z = 2.59, p = .0096, respectively. These findings indicate
that – irrespective of phasic arousal – greater ovarian and NE-related hormone levels predicted
better goal-directed attention and memory in women.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 53
Table 4. Hierarchical linear model examining interaction effects between trial-by-trial arousal
ratings, distraction ratings, salivary alpha-amylase and ovarian hormones
(estradiol/progesterone interaction) on memory selectivity in women only
Predictors Estimate SE z p
Intercept
-7.68E-01 8.48E-02 -9.057 <2.00E-16
Emotional Sound Arousal Rating
2.78E-05 3.07E-02 0.001 0.99928
Task-Induced sAA Change
2.49E-03 1.27E-03 1.962 0.0498*
Emotional Sound Distraction Rating
1.23E-03 4.22E-02 0.029 0.97679
Ovarian Hormone Level
2.34E-03 9.03E-04 2.592 0.00955**
Arousal Rating x Ovarian Hormone
4.65E-04 3.38E-04 1.374 0.16935
Ovarian Hormone x sAA Change
-1.65E-05 1.05E-05 -1.576 0.11493
Arousal Rating x sAA Change
1.05E-03 4.36E-04 2.405 0.01616*
Arousal Rating x Distraction
-2.49E-02 1.59E-02 -1.566 0.11743
sAA Change x Distraction
-5.94E-04 6.13E-04 -0.968 0.33303
Arousal Rating x sAA x Distraction
-5.18E-04 2.40E-04 -2.157 0.03098*
Key: sAA Change: Change score in salivary alpha-amylase concentration (U/mL) levels from
baseline to immediately post-task. Significant results are bolded. *p < .05; **p < .01.

Finally, to verify that the effects of sAA change and arousal on memory trade-offs did not
differ according to the prioritized visual category (object or scene), we performed two follow-up
HLMs examining each trial type, separately. As expected, the interaction between sAA change
and arousal rating showed similar patterns for the object-focused and scene-focused trials, z =
2.6, p = .0094, and z = 0.94, p = .35, respectively. Likewise, the three-way interaction showed a
similar patterns for the object-focused and scene-focused trials, z = -1.29, p = .20, and z = -
1.62, p = .10, respectively. Thus, the semantic properties of the target stimulus did not bias the
results, suggesting that our findings represent top-down attentional processes more generally.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 54

Figure 2. Estimation of the trial-by-trial influence of emotional arousal and task-induced sAA
change on optimal memory selectivity from the hierarchical linear modeling (HLM) analyses in
women only. To better interpret the directionality of interaction effects, the regression equation
from the women-only HLM without sex hormones was used to estimate memory trade-offs. The
dependent variable, optimal memory trade-off score, was operationalized as trials in which
participants correctly remembered the target visual stimulus (scene or object) while forgetting its
corresponding distracter. Optimal memory trade-off scores were estimated for sound arousal
rating and task-induced salivary alpha-amylase (sAA) change values that were relatively higher
or lower (+/- 1 SD, respectively) the mean. The black line represents higher sAA change,
whereas the gray line represents lower sAA change.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 55

Figure 3. Estimation of the trial-by-trial influence of emotional arousal, task-induced sAA
change, and task-irrelevant emotional sound distraction scores on optimal memory selectivity
from the hierarchical linear modeling (HLM) analyses in women only. To better interpret the
directionality of interaction effects, the regression equation from the women-only HLM without
sex hormones was used to estimate memory trade-offs. The dependent variable, optimal
memory trade-off score, was operationalized as trials in which participants correctly
remembered the target visual stimulus (scene or object), while forgetting its corresponding
distracter. In this plot, optimal memory trade-off scores were estimated for sound arousal
ratings, sound distraction ratings, and task-induced sAA change values that were relatively
higher or lower (+/- 1 SD, respectively) than the mean. Black and light gray lines represent +1
SD and -1 SD values for the sAA change predictor, respectively. Solid lines and broken lines
represent +1 SD and -1 SD values for the sound distraction rating predictor, respectively.

4. Discussion
Although it is well established that NE helps strengthen emotional memories (McGaugh
& Roozendaal, 2002), it is less clear whether NE also facilitates arousal’s dual effects on neutral
information processing. Given the broad and diffuse release of NE under arousal (Berridge &
Waterhouse, 2003; Foote & Morrison, 1987), the noradrenergic system is well positioned to
modulate any on-going selection processes when emotional events occur. Consistent with this,
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 56
β-adrenergic receptor activation in the amygdala enhances memory of emotional stimuli at the
expense of memory for preceding inconspicuous information (Strange & Dolan, 2004; Strange
et al., 2003). We aimed to test whether NE can also bias memory in favor of prioritized, goal-
relevant neutral information (Sakaki et al., 2014). Our results supported this prediction, revealing
that, in women with greater task-increased NE levels, emotional arousal enhanced memory of
goal relevant stimuli at the cost of memory for competing distracters. We also found evidence of
sex differences in NE’s effects on arousal-biased memory selectivity: young women showed
significantly greater modulatory effects of NE on arousal-related memory trade-offs than men.
Furthermore, we found that higher ovarian hormone levels in women were associated with
greater memory trade-offs regardless of the arousal induced by the subsequent sound.
Overall, our finding in the women supports the recent GANE model of emotion-cognition
interactions, which proposes that NE yields different memory outcomes as a function of stimulus
priority (Mather et al., 2015). According to GANE, goal-relevant representational activity triggers
a positive feedback loop with nearby NE axons that garners even higher levels of NE and brain
activity. In contrast, more modest NE release elsewhere suppresses task-irrelevant activity.
Although GANE focuses primarily on local synaptic interactions between brain activity and NE
release, its core prediction that NE amplifies cognitive selectivity is testable at the
behavioral/hormonal level. Specifically, global increases in NE, as indexed by task-induced
increases in sAA, should enhance NE’s local effects by fueling both priority-related activity (i.e.,
glutamate) and broad-scale inhibition of weaker lower priority-related activity.
Supporting this view, human genotyping studies demonstrate that ADRA2B deletion
carriers, who have less inhibition of NE release, show greater activity in the insula and
amygdala when viewing or encoding emotional versus neutral images (Cousijn et al., 2010;
Rasch et al., 2009). This NE modulation of limbic activity is believed to underlie the dominance
of emotional stimuli in perception (Todd et al., 2013) and memory (de Quervain et al., 2007) in
these individuals (Markovic et al., 2014). The present results extend these findings by showing
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 57
that higher NE levels also bias cognitive processing in favor of goal-relevant neutral stimuli and
not just emotional stimuli.
Akin to genotyping studies, a recent study showed that isometric handgrip-induced
increases in NE bias memory towards highly arousing negative information, suggesting that
incidentally elevating noradrenergic activity selectively amplifies the effects of phasic arousal on
memory (Nielsen et al., 2015). Critically, our results suggest that greater background NE levels
was also associated with memory costs induced by arousal for distracting information: women
with greater noradrenergic activity showed greater arousal-biased competition memory
outcomes on a trial-by-trial basis. These data are in line with the idea that the noradrenergic
system helps regulate neuronal gain in the brain (Aston-Jones & Cohen, 2005; Eldar et al.,
2013; Servan-Schreiber et al., 1990; Usher et al., 1999); that is, NE enhances the excitability of
active neurons, while further suppressing the excitability of inhibited neurons. LC recordings in
monkeys (Aston-Jones et al., 1999; Aston-Jones et al., 1994) showed that task performance is
optimal under conditions of moderate tonic (i.e., baseline/background) LC activity, which is most
permissive to LC phasic (i.e., rapid) responses that enhance goal-relevant attention (Aston-
Jones & Cohen, 2005). Consistent with this, our linear modeling analyses in women revealed a
significant main effect of task-induced sAA increase on memory selectivity, which could
correspond to the peak of the inverted-U function between arousal/NE and cognitive function
(Arnsten, 2009). Furthermore, given the significant NE-by-arousal interaction effect on memory
trade-offs, it is possible that GANE effects are particularly strong when phasic emotional arousal
influences on-going mental activity under conditions of higher background NE release.
Interestingly, we found significant sex-dependent effects of arousal and hormonal activity
on memory, such that noradrenergic activity (i.e., sAA increase) and emotional arousal biased
memory in favor of preceding top-down prioritized stimuli significantly more in women than in
men. A large body of work shows that men and women exhibit different responses to emotional
stimuli, which may alter arousal’s spillover effects on processing nearby neutral information. For
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 58
instance, women exhibit larger startle reflex amplitude – a putative index of amygdala
modulation - to emotional images than men (Bianchin & Angrilli, 2012). Past work shows that
noradrenergic activation of the amygdala mediates emotional arousal’s suppressive effects on
nearby information, while also enhancing memory of emotionally salient items (Strange & Dolan,
2004; Strange et al., 2003). Thus, it is possible that NE effects on the amygdala are the locus of
these sex-dependent memory effects. Consistent with this, emotionally arousing words elicit
larger suppressive effect on preceding inconspicuous words in women than in men (Strange et
al., 2003). It has been speculated that this may relate, at least in part, to sex differences in the
ability to recall emotional events (Seidlitz & Diener, 1998). In the current study, although ovarian
hormones did not affect the interaction between noradrenergic activity and phasic arousal on
memory, they did enhance overall selectivity in memory in favor of top-down prioritized
memoranda. This finding is intriguing given the paucity of evidence concerning the effects of
ovarian hormones on goal-directed memory trade-offs (i.e., competition) compared to
declarative memory more generally.
By highlighting the important role of priority in neuronal processing, the GANE model
provides predictions about how neutral information will fare under arousing conditions. However,
since priority is relative, it is difficult to determine whether the goal-relevance or perceptual
salience of a neutral stimulus is high enough to outcompete emotional stimuli, which tend to be
preferentially processed and attended to (LaBar & Cabeza, 2006). We attempted to limit such
competition using three strategies. First, we presented the goal-relevant stimulus (image) and
arousing stimulus (sound) in different sensory modalities. Second, we introduced a 1s lag
between the presentation of the neutral overlap and emotional stimuli, which is associated with
emotion-enhanced processing of standalone neutral information (Bocanegra & Zeelenberg,
2009). Third, we acquired ratings of how distracted participants were by the sounds to
approximate the competitive weight of the emotionally arousing stimuli. As predicted, we found
that arousal-enhanced memory trade-offs were greater in women who not only showed
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 59
increased NE levels but were also better able to prioritize the goal relevant neutral images over
the emotional sounds. Thus, an important consideration in future emotion-cognition research is
to measure how effectively participants are able to voluntarily focus their attention on a goal
amidst task-irrelevant emotional distraction.
Unlike previous studies examining the influence of NE on memory of emotional stimuli,
estradiol and progesterone did not moderate the strength of NE’s influence of memory
selectivity in young women. The menstrual cycle involves dynamic and complex interactions
between these hormones that can influence emotional responses and learning in women
(Sakaki & Mather, 2012). Furthermore, many studies show that sex hormone effects on
emotional memory differ by menstrual cycle phase (Nielsen et al., 2013; Pompili et al., 2016)
and differ between women who are on hormonal contraception and naturally cycling (Nielsen et
al., 2011). One limitation of this study is that we randomly sampled females who were on
hormonal contraception or naturally cycling, which may dilute any interaction effects between
these hormones and noradrenergic activity across the sample. Alternatively, it is possible that
ovarian hormones specifically affect memory of emotional stimuli rather than altering the
influence of emotion on proximal neutral information processing.
Another limitation was that we could not disentangle valence effects, because subjective
emotional arousal ratings were highly correlated with pre-defined valence categories. Previous
behavioral evidence indicates that positive emotional stimuli also amplify the effects of priority in
memory, suggesting that GANE effects are invariant to the valence of the arousal-eliciting
emotional stimulus (Sakaki et al., 2014). Yet other evidence shows that the spillover effects of
emotional arousal may differ by valence when background NE levels are manipulated
independently via isometric exercise (Nielsen et al., 2015) or pharmacology (Hurlemann et al.,
2005). A key distinction between these studies is that they did not manipulate the priority of the
neutral stimuli appearing near something emotional. Since we manipulated and focused on
arousal’s effects on competition between goal-relevant and distracting neutral stimuli, we would
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 60
also expect our results to be invariant to the valence of the emotional stimuli (Sakaki et al.,
2014). Nonetheless, future studies should focus on examining whether valence also modulates
arousal-biased competition effects.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 61
Chapter 3. Noradrenergic Mechanisms of Arousal-Biased Competition in Memory

1. Introduction
Selectivity is at the core of efficient cognitive processing, helping us to prioritize
significant information among competing sensory inputs. Years of research demonstrate that
emotional experiences dominate this competition for limited mental resources to ensure
behaviorally relevant/emotional events are preferentially processed and stored into long-term
memory (Dolan, 2002; LaBar & Cabeza, 2006; McGaugh, 2000, 2013). As demonstrated in
Study 1, however, the effects of arousal also spillover and influence other on-going selection
processes. Importantly, arousal’s effects don’t only influence processing of spatially competing
information but also impact competitive processes that unfold over time.
One particularly striking example of how emotional arousal influences temporally
adjacent neutral stimuli is provided by an oddball paradigm in which a perceptually deviant
emotional image is embedded within a sequence of neutral stimuli. Whereas some evidence
indicates that emotional stimuli enhance memory for preceding neutral items (Anderson et al.,
2006; Knight & Mather, 2009), other studies have yielded the opposite finding, reporting an
emotion-induced retrograde amnesia for preceding neutral stimuli (Hurlemann et al., 2005;
Hurlemann et al., 2007; Strange et al., 2003). According to the arousal-biased competition
(ABC) model posits, arousal will enhance rather impair memory of those items if they receive
more attention (priority) than they would otherwise (Mather & Sutherland, 2011).
In the first explicit test of this hypothesis, Sakaki et al. (2014) manipulated priority in a
visual oddball paradigm by altering the goal-relevance of neutral object images appearing just
before (oddball-1 objects) or after (oddball+1 objects) an emotional versus neutral oddball
image (Sakaki et al., 2014; Sakaki et al., 2014). As predicted, emotional arousal led to
retrograde amnesia for oddball-1 objects when the oddball image was prioritized (making the
oddball-1 low priority), whereas prioritizing the neutral oddball-1 image instead led to an
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 62
emotion-induced retrograde memory enhancement for the object. Other studies also support the
notion that priority determines how neutral perceptual and memory traces will fare under
emotional arousal. For instance, viewing an emotionally laden image can enhance subsequent
learning of the tilt of a perceptually salient versus less salient line (Lee et al., 2012). Similarly,
hearing an emotional sound or a tone conditioned to shock also enhances attention (Lee et al.,
2014) and memory (Lee et al., 2015; Sutherland & Mather, 2012; Sutherland & Mather, 2015)
for subsequently presented perceptually salient or goal-relevant visual stimuli, while
suppressing memory of less salient visual stimuli. In addition, hearing an emotional sound
immediately after seeing an object-scene pair leads to impaired memory for the less salient
background scene (Ponzio & Mather, 2014). Thus, emotional arousal seems to amplify the
effects of priority, biasing attention and memory to preference highly activated mental
representations over stimuli that are peripheral to the focus of attention.
It is widely recognized that norepinephrine (NE) released in the amygdala under
emotional arousal contributes to the superiority of emotional events in attention and memory
(Markovic et al., 2014; McGaugh, 2000, 2002; Strange & Dolan, 2004). In particular, numerous
studies demonstrate that this NE- and amygdala-dependent enhancement of emotional memory
relies on β-adrenergic receptor activation (Cahill et al., 1994; McGaugh, 2013; Strange & Dolan,
2004).
However, it has been less clear how NE release influences memory for nearby neutral
information. In addition to enhancing processing of emotional stimuli, β-adrenoreceptor
activation mediates an emotion-induced retrograde amnesia of inconspicuous neutral stimuli
(Hurlemann et al., 2005; Hurlemann et al., 2007; Strange & Dolan, 2004; Strange et al., 2003),
suggesting that these receptors can also account for the suppression of peripheral neutral
information under arousal. Yet, on the other hand, post-learning pharmacologically increased
NE levels in the rodent amygdala can enhance rather than impair memory consolidation of for
previously learned neutral information (Barsegyan et al., 2014; Roozendaal et al., 2008).
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 63
Furthermore, amygdala activation in humans is also associated with subsequent memory of
neutral items encoded in an emotional context (Erk et al., 2003). Thus it might not always be the
case that noradrenergic activation of the amygdala leads to emotion-induced memory
impairments of neutral information processing.
To account for the dual effects of NE on cognitive selectivity, the Glutamate Amplifies
Noradrenergic Effects (GANE) model proposes that the noradrenergic system amplifies the gain
of prioritized information processing under arousal irrespective of how priority is instantiated
(Mather et al., in press). According to GANE, under moderate arousal, NE is released diffusely
at levels that are not high enough to engage low-affinity (and therefore high threshold) β-
adrenergic receptors (Ramos & Arnsten, 2007). However, where there is already high neuronal
activity, glutamate stimulates additional NE release by binding to NMDA receptors on nearby
NE fibers. In these regions, NE concentrations then become high enough to engage β-
adrenergic receptors on presynaptic glutamatergic terminals, which in turn stimulate even
greater glutamate release. This glutamate-NE positive feedback loop, also known as an “NE hot
spot,” amplifies excitatory activity in neuronal ensembles transmitting prioritized information.
Most importantly, the synergistic interaction between stronger glutamate signals and
postsynaptic β-adrenoreceptor activation potentiates excitatory activity (Berridge & Waterhouse,
2003) and trigger synaptic plasticity processes that support memory consolidation (Marzo et al.,
2009; Salgado et al., 2012; Treviño et al., 2012). At the same time, high glutamatergic activity at
hot spots should also stimulate local GABAergic activity that inhibits weaker, competing
representations (Brown et al., 2005).
In sum, GANE posits that the sensory- and memory-enhancing effects of β-
adrenoreceptors are not specific to emotional stimuli, per se, but are selective to any highly
active region processing prioritized mental representations. In this way, GANE builds on earlier
theories of LC-NE system function (Aston-Jones & Cohen, 2005; Bouret & Sara, 2005; Markovic
et al., 2014; McGaugh & Roozendaal, 2002) by confronting the longstanding issue of how NE
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 64
could yield opposing processing outcomes for goal-relevant versus noisy or task-irrelevant
sensory inputs. Consistent with the GANE framework, in rodents neither footshock (arousal) nor
exploration of a novel environment alone is sufficient to elevate amygdala glutamate levels;
however, when these two events co-occur, amygdala NE levels rise substantially and remain
elevated for a sustained period of time (McIntyre et al., 2002). Similarly, infusing low doses of
glutamate and NE separately into the bed nucleus of the stria terminalis has no effect on
inhibitory avoidance learning; however, when co-infused, NE and glutamate led to memory
enhancements (Liu et al., 2009). Propranolol administration blocked the memory-enhancing
effects of higher glutamate infusion, suggesting that β-adrenoreceptors helped potentiate
glutamate’s effects on memory. However, previous human research has mostly focused on how
NE biases attention to favor emotional stimuli over non-emotional stimuli (Strange & Dolan,
2004; Strange et al., 2003; Markovic et al., 2014). Whether NE also enhances memory of top-
down prioritized neutral information under arousal is less clear (Sakaki et al., 2014).
The primary aims of this pharmacological study were to test two fundamental aspects of
GANE in humans: 1) LC-NE system activation, as measured by task-induced changes in
salivary alpha-amylase (Ditzen et al., 2014), under emotional arousal amplifies the effects of
top-down goals in memory (i.e., replicate Study 1), and 2) This increase in the gain of goal-
relevant stimuli over lower priority stimuli is specifically mediated by β-adrenoreceptor activation.
To test these hypotheses, we combined the emotional oddball paradigm used in Sakaki et al.
(2014) with the administration of 40 mg of propranolol, a β-adrenoreceptor blocker. Our main
hypothesis was that, under placebo, emotional oddball images would enhance memory of high
priority oddball-1 objects while impairing memory of lower priority oddball+1 objects. More
importantly, we predicted that β-adrenoreceptor blockade would attenuate the dichotomous
influence of emotional oddballs on high and lower priority object memory.
The second goal of Study 2 was to test another prediction of GANE that emotional
arousal leads to selective memory trade-off effects within each trial. According to the ABC
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 65
model (Mather & Sutherland, 2011), emotional arousal biases competition for mental resources
in favor of goal-relevant stimuli. GANE also posits that under arousal, NE optimizes energy
supplies in favor of high priority information over low priority information. Because mental and
energetic resources are limited (Desimone & Duncan, 1995), emotional arousal should therefore
intensify competitive processes between stimuli and, in so doing, elicit a memory trade-off
between high and lower priority stimuli. Thus, we expected that: 1) participants would be more
likely to forget the oddball+1 object when they remembered its corresponding prioritized
oddball-1 object on emotional versus neutral oddball trials, and 2) this effect would be
diminished by β-adrenoreceptor blockade. In short, this analysis mimicked the main memory
codependency analysis in Study 1. However, rather than examining competition between
spatially competing neutral stimuli, this study examined temporal competition between a
prioritized image preceding something emotional versus a less-attended image that appeared
just after.
2. Methods
2.1 Participants. Thirty-two healthy young adults were recruited from the University of
Southern California Psychology Subject Pool to participate in this two-day experiment. All
participants provided written informed consent approved by the University of Southern California
Health Science Campus Institutional Review Board. Participants were awarded course credits
for their participation. Of the enrolled participants, 27 individuals met all of the health screening
criteria, ensuring it was safe for them to take the drug. One participant in the placebo condition
was excluded due to a script error during the emotional oddball experiment. Thus, a total of 26
participants were included in the final analyses (19 F; M
age
= 20 years, SD = 0.25).
Prior to the main experiment session, participants were randomly assigned to either the
drug or placebo group using a pre-determined randomization scheme. This resulted in the
following drug group assignments: 12 Drug (10 F; M
age
= 20.08 years, SD = 0.36) and 14
Placebo (9 F; M
age
= 19.9 years, SD = 0.35).
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 66
2.2 General Procedure
2.2.1 Health Screening (Day 1): During the first 30-minute session, participants
provided written informed consent and were screened for general health and normal
cardiovascular function. Main contraindications of propranolol include: sinus brachycardia,
bronchial asthma, diabetes, low blood pressure, depression, problems with circulation, heart
disease, pheochromocytoma, and impaired hepatic or renal function. Participants who had a
history of any of these conditions were ineligible to participate in the experiment session on Day
2. Additional health-related exclusion criteria included: women who are currently nursing or
pregnant; known sensitivity to propranolol or other beta-blockers; psychoactive drug use; a
history of smoking; and participants without normal or normal-to-corrected vision and hearing.
Blood pressure was measured to ensure that participants did not exhibit hypertension or
hypotension according to definitions established by the National Heart Lung and Blood Institute.
Of the 32 participants that completed the health screening, 27 were deemed eligible to
participate in the main experiment session.
As part of session 1, participants were administered the Center for Epidemiological
Studies Depression questionnaire (Radloff, 1977) to assess depression, and the Behavioral
Inhibition System and Behavioral Activation System scale (BIS/BAS; (Carver & White, 1994) to
assess sensitivity to punishment and reward, respectively.
2.2.2 Experiment Procedure (Day 2): Participants were randomly assigned to double-
blind oral intake of a 40mg single dose of the β-adrenergic receptor antagonist propranolol
hydrochloride (N = 12) or a 40mg single dose of vitamin E placebo (N = 14). All pills were
compounded by the USC Health Science Campus pharmacy and appeared identical. To reduce
variability in salivary alpha-amylase (sAA) levels and control for other factors that might
influence performance or sAA concentration (see (Nater & Rohleder, 2009), participants were
instructed to refrain from exercise and eating food within 1 hour, sleep within 2 hours, caffeine
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 67
within 3 hours, and alcohol within 24 hours of the experiment. All participants complied with
these instructions. They were also instructed to remain seated for the entirety of the experiment.
Upon arriving for the 4-hr experiment, participants drank a 10-oz bottle of water and
were administered his/her assigned pill. Previous emotion-cognition work shows that
propranolol takes approximately 1-2 hours to reach peak plasma concentration (Hurlemann et
al., 2010; Strange et al., 2003); thus, we introduced a delay by having participants watch a
nature documentary for approximately 70 minutes. The oddball experiment commenced
approximately 80 minutes (M = 79 minutes, SD = 6.5 minutes) after pill administration to
maximize the memory-altering effects of β-adrenergic blockade. On average, participants
finished the emotional oddball task approximately 2 hours and 5 minutes (M = 125 minutes, SD
= 6.15 minutes) after pill administration.
In addition to the main oddball task, participants were administered the Positive and
Negative Affect Schedule (Watson et al., 1988) at three time points (baseline, pre-task, post-
task) to assess changes in positive and negative affect. Potential side effects of the drug were
assessed using a 16-item symptoms questionnaire immediately before and immediately after
the oddball task. Ratings were made on a scale ranging from 1 = not at all to 7 = a great deal,
and included questions related to common side effects of propranolol, such as dizziness,
headache, or sensation of numbness in limbs.
2.3 Emotional Oddball Task
2.3.1 Overall Procedure. The emotional oddball task was divided into an encoding
phase and a two-alternative forced choice recognition memory test.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 68

Figure 1. A schematic example of a trial from the encoding (A) and filler/memory phase (B) of
the emotional oddball task.

2.3.2 Encoding phase. During the encoding phase, participants viewed sequences of
seven images that were semantically unrelated. Each of these sequences was composed of six
non-oddball photo objects displayed on a white background with no black frame. The other
image, the oddball, was perceptually deviant in that it was displayed on a black screen and
randomly appears in the 3
rd
, 4
th
or 5
th
position in each sequence. Each image in the sequence
also contained an accompanying noun label. The labels were shown above the non-oddballs in
black arial font and above the oddball pictures in white arial font. Images were displayed for 1.5
seconds each, with a 500-ms inter-stimulus-interval containing a black string of plus signs (+++)
displayed on a white background.
Prior to beginning the task, participants were instructed to remember as many neutral
objects as possible for a later memory test. Stimulus priority, or goal relevance, was
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 69
manipulated by instructing participants to try especially hard to memorize the object appearing
just before the black-framed oddball image. To increase prioritization of the oddball-1 object and
ensure that participants were focusing on the oddball-1 item, they were prompted to report the
identity (label) of the oddball-1 target picture at the end of each sequence. Following this
response, participants also answered a forced-choice question concerning a perceptual feature
of the oddball-1 object. For example, if the oddball-1 image depicted grapes, participants were
asked, “Were they green?” and indicated either “Yes” or “No” by key press. This allowed us to
test the veracity of the oddball-1 memory representation in working memory and to increase its
top-down priority even further. Overall, there were 7 cycles containing 6 sequences each,
resulting in a total of 42 individual sequences (1/2 emotional). Each cycle had 3 negative
emotional oddball and 3 neutral oddball trials.
2.3.3 Recognition memory test. At the end of each cycle (i.e., after encoding neutral
objects from 6 separate sequences), participants were prompted to count backwards from a
three-digit number by increments of 3 for one minute. Participants then completed a two-
alternative forced choice recognition test containing pairs of old and new items. On each
memory trial, participants were presented with two different photographs of the same object
side-by-side. Their task was to indicate whether both images were new (not seen previously in
any sequence) or to pick the specific image they saw during the encoding phase. Each memory
test included 21 image pairs: 6 oddball-1 objects, 6 oddball+1 objects, 6 new object pairs, and 3
fillers (old objects used neither as oddball-1 nor as oddball+1 objects), which were used to
motivate participants to try to encode all objects. By testing short-term memory for both oddball-
1 and oddball+1 objects, we were able to determine how emotionally arousing oddballs
differentially influenced processing of high versus lower priority images. It also enabled us to
examine arousal-biased competition effects on a trial-by-trial basis (see Section 2.6.5).
As in Sakaki et al. (2014), this design was chosen so that we could assess the specificity
of memory for high and lower priority objects. Each response was coded as a correct response
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 70
for specific recognition when the participants chose the exact object image from the encoding
phase. Old responses assigned to either the exact image or other version of the same object
was also obtained as a measure for gist memory. To correct for false alarm rates (said “old”
when both were new), we analyzed the corrected recognition rate (specific recognition hit rate –
false alarm rate).
2.3.4 Stimuli. Oddball stimuli were composed of 21 emotionally negative (M
arousal
= 6.43,
SD = 0.59, M
valence
= 2.39, SD = 0.70) and 21 neutral (M
arousal =
3.47, SD = 0.55, M
valence
= 5.30,
SD = 0.45) pictures from the International Affective Picture System (Lang et al., 1999). Non-
oddball stimuli included 126 pairs of photographs of neutral (i.e., non-arousing) objects obtained
from a previous study (Kensinger et al., 2006) and other resources (e.g., the Internet). These
images were randomly assigned to the pre- or post-oddball position and further assigned to one
of the three conditions (negative, neutral or memory test distracter), which was counterbalanced
across participants. One of the object images from each pair was shown during the encoding
phase, while the other served as a foil in the memory test. The image that appeared during
encoding was counterbalanced across participants. We also included an additional 21 object
pairs for fillers. An additional 147 neutral object images were used in the remaining list
positions.
2.4. Cardiovascular measurements. To assess the cardiovascular effects of the drug,
three measures of blood pressure (systolic/diastolic) and heart rate (beats per minute; BPM)
were collected at the following time points relative to pill intake: 0 minutes (baseline), 69 +/- 2
minutes (pre-oddball-task), 125 +/- 6 minutes (post-oddball-task). Cardiac measures were
acquired using a Microlife 3MC1-PC Ultimate Automatic Blood Pressure Monitor with Irregular
Heartbeat Detection device (China).
Three separate 2 x 3 mixed Analysis of Variance analyses (ANOVAs) were used to
analyze drug effects on blood pressure and heart rate, with Condition (drug vs. placebo) as a
between-subjects measure and Time (baseline vs. pre-task vs. post-task) as a repeated-
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 71
measure. Follow-up two-tailed independent samples t-tests were used to examine significant
main effects of Condition at each of the three timepoints.
2.5. Salivary alpha-amylase (sAA) collection and analysis
2.5.1 Saliva samples. Saliva samples were immediately frozen for a minimum of
twenty-four hours to allow mucins to precipitate. Prior to the assays, the samples were thawed
and centrifuged at 3,000 x g for 15 min to extract particulates from saliva. Clear supernatant
was decanted into microtubes.
2.5.2 Salivary alpha-amylase measurement. Alpha-amylase levels were measured
using Salimetrics, LLC (State College, PA) enzyme kinetic assay kits and measured optically
using Molecular Devices, LLC SpectraMax M3 Multi-mode Microplate Reader (Sunnyvale, CA).
Both of these samples were collected using the passive drool method.
2.5.3 Salivary alpha-amylase analysis. To determine the effects of propranolol on
central noradrenergic activity, we collected and analyzed two samples of salivary alpha-amylase
(sAA), a candidate biomarker of LC activity (Ditzen et al., 2014). The first baseline sample was
collected immediately prior to the oddball task, whereas the second sample was collected
immediately after the oddball task.
The effects of the drug on sAA concentration were assessed using a Time (pre-task vs.
post-task) x Condition (drug vs. placebo) mixed ANOVA, with Time as a repeated-measure and
Condition as a between-subjects factor. Task-induced changes in sAA levels from pre-task to
post-task were also used to account for individual differences in ABC memory performance.
2.6 Memory Analyses
2.6.1 The effects of the drug and emotion on free recall of oddball-1 objects. To
assess how effectively participants prioritized object-1 object information in attention and
working memory, we examined the effects of the drug and emotional oddballs on the free recall
of the identity of the oddball-1 object and one of its perceptual features. Recall performance was
probed at the end of each trial (i.e., 7-item sequence of object images). The proportion of trials
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 72
with correctly recalled information were analyzed using two separate 2 x 2 mixed ANOVAs with
Emotion (negative vs. neutral) as a repeated-measure and Condition (drug vs. placebo) as a
between-subjects factor. Follow-up Bonferroni-corrected paired t-tests were used to examine
the effects of emotion on working memory performance within the drug and placebo groups,
separately.
2.6.2 Corrected specific recognition memory analysis. The critical prediction of
GANE is that β-adrenoreceptor blockade should attenuate emotional arousal’s dichotomous
influence on memory by diminishing an arousal-induced memory enhancement of the high
priority, oddball-1 item while preventing arousal-induced suppression of memory for the lower
priority, oddball+1 item. To test this hypothesis, we performed a 2 (Priority: high vs. low) x 2
(Emotion: negative vs. neutral) x 2 (Condition: drug vs. placebo) mixed ANOVA with Priority and
Emotion as repeated-measures and Condition as a between-subjects factor. Specific corrected
recognition memory performance was operationalized as the hit rate for the exact oddball-1 and
oddball+1 object images (participant said “old” and selected the exact object image) minus the
false alarm rate (participant said “old” for one of the objects when both objects were “new”).
2.6.3 Corrected gist recognition memory analysis. To examine whether emotional
oddballs amplified the effects of top-down priority in memory more generally, we conducted the
same statistical tests for gist memory. The dependent measure for gist memory was the
proportion of trials when participants correctly answered “old” on the memory test, but selected
the wrong object image. As before, gist memory performance was corrected for false alarm
rates.
2.6.4 Trial-by-trial memory codependency analysis. While the corrected recognition
memory analysis revealed the tendency of emotional arousal to influence object memory as a
function of goal relevance, we were most interested in examining arousal-biased competition
memory effects on a trial-by-trial basis. Thus, we performed a memory codependency analysis
in which we determined whether or not remembering a given oddball-1 object was contingent on
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 73
remembering its corresponding oddball+1 object.
In this trial-by-trial memory codependency analysis, we calculated the overall frequency
of four possible memory outcomes (R = Remembered and F = Forgot): 1) R
oddball-1
, F
oddball+1
; 2)
R
oddball-1
, R
oddball+1
; 3) F
oddball-1
, R
oddball+1
; and 4) F
oddball-1
, F
oddball+1
. These memory scores were
calculated for emotional oddball and neutral oddball trials, separately. We operationalized
arousal-biased competition (ABC) memory effects as the following interaction term:

ABC Memory Score = [Emotion (R
O-1
F
O+1
)

– (R
O-1
R
O+1
)] – [Neutral (R
O-1
F
O+1
)

– (R
O-1
R
O+1
)]

Specifically, these ABC memory scores signify how much more likely participants were
to remember the oddball-1 object and forget its corresponding oddball+1 object (selective
memory) as opposed to remembering both objects (global memory) on emotional relative to
neutral oddball trials.
To examine how β-adrenergic blockade affected this type of memory selectivity under
arousal, the ABC memory codependency scores were analyzed using a 2 (Emotion: negative
vs. neutral) x 2 (Memory Outcome: R
O-1
F
O+1
vs. R
O-1
R
O+1
) x 2 (Condition: drug vs. placebo)
mixed ANOVA, with Emotion and Memory Outcome modeled as repeated-measures and
Condition modeled as a between-subjects factor. Follow-up Bonferroni-corrected t-tests were
used to further examine which memory outcome types were driving any main effects or
interactions.
2.7 Association between task-induced changes in salivary alpha-amylase and
arousal-biased competition memory difference scores. Previous studies have shown that
emotionally arousing images can elicit increases in sAA levels (Segal & Cahill, 2009). Thus, to
examine whether task-induced changes in noradrenergic activity could account for emotion’s
effects on oddball-1 object memory, we first subtracted sAA levels for sample 1 (immediately
pre-task) from sAA levels for sample 2 (immediately post-task). These simple change sAA
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 74
scores were then correlated with corrected specific mean recognition performance for oddball-1
objects and oddball+1 objects.
In two separate analyses, we correlated emotion-induced effects on oddball-1 (high
priority) versus oddball+1 (lower priority) specific corrected recognition memory difference
scores to account for any individual differences in ABC memory effects. A Henze-Zirkler’s
multivariate normality test indicated that the data had a bivariate normal distribution (p > .05);
therefore, we conducted a Pearson’s coefficient correlation analysis.
3. Results
3.1 Drug effects on mood and self-reported side effects. Propranolol did not have a
significant effect on any of the self-reported symptoms or positive (Drug: M = 24.6, SEM = 1.8;
Placebo: M = 23.83, SEM = 1.67) or negative (Drug: M = 11.81, SEM = 1.05; Placebo: M =
12.69, SEM = 0.97) affect (ps > .1). This finding indicates that participants did not experience
any adverse physical or psychological/affective side effects of the drug or emotional oddball
task. In addition, paired t-tests revealed that depression levels (Drug: M = 19.33, SEM = 2.41;
Placebo: M = 17.71, SEM = 1.45) and sensitivity to punishment (BIS; Drug: M = 20.67, SEM =
1.28; Placebo: M = 20.29, SEM = 1.21) did not significantly differ between the drug and placebo
groups (ps > .05).
3.2 Drug effects on blood pressure. Across all participants, systolic blood pressure
significantly decreased after pill intake, F(2,23) = 9.32, p = .001, η
p
2
= .45, but did not
significantly differ between the drug and placebo conditions, F(1,24) = .68, p = .42, η
p
2
= .028
(Figure 2). Post hoc t-tests indicated that this main effect of Time was driven by systolic blood
pressure being significantly lower immediately before the oddball task (M = 108 mmHg, SEM =
1.82) compared to baseline (M = 115 mmHg, SEM = 1.78; p = .001), t(25) = -4.45, p < .001.
There was no significant Time x Condition interaction effect (p > .16) or significant differences
between drug conditions in systolic blood pressure at any of the three individual time points (ps
> .29).
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 75
Diastolic blood pressure also decreased over time, F(2,22) = 5.53, p = .011, η
p
2
= .34,
which, like systolic blood pressure, was driven by a large decrease from the baseline (M = 70
mmHg, SEM = 1.23) to the pre-task measurement (M = 66 mmHg, SEM = 1.03; p = .009).
There was a significant main effect of Condition, such that the drug group exhibited lower
diastolic blood pressure levels than the placebo group, F(1,23) = 4.82, p = .039, η
p
2
= .17. The
Time x Condition interaction effect on diastolic blood pressure was not significant, F(2,22) = 2.5,
p = .11, η
p
2
= .19. But a subsequent analysis revealed that the drug led to significantly lower
diastolic blood pressure immediately after the oddball task compared to placebo, t(24) = 2.63, p
= .015, but not during the baseline, t(23) = 1.48, p = .15, or before the task, t(24) = 0.87, p = .93.
Thus, propranolol effectively reduced diastolic blood pressure at the time it has been shown to
reach peak plasma concentration (Hurlemann et al., 2010).

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 76

Figure 2. Changes in systolic (A) and diastolic (B) blood pressure from baseline (intake) to
immediately before and after the oddball task. Dark gray bar refers to drug condition, whereas
the light gray bar refers to the placebo condition. *p < .05.

3.3 Drug effects on blood pressure and salivary alpha-amylase levels. Compared to
placebo, propranolol administration led to a marginally significant decrease in overall sAA
concentration across the oddball task, F(1,24) = 3.3, p = .082, η
p
2
= .12, which is consistent with
findings that β-adrenoreceptor activation is associated with sAA levels in humans (van Stegeren
et al., 2006). There was no significant Time x Condition interaction effect, F(1,24) = 0.11, p =
.74, η
p
2
= .005, or main effect of Time on sAA concentration, F(1,24) = 2.12, p =.16, η
p
2
= .081
(see Figure 3).
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 77

Figure 3. Salivary alpha-amylase (sAA) levels immediately before and immediately after the
oddball task by drug group.

3.4 Working memory accuracy for oddball-1 identity and perceptual features. To
determine how effectively participants prioritized the oddball-1 object in working memory
(feature binding) and attention, we probed memory for oddball-1 object identity and one of its
perceptual features at the end of each trial. A Emotion x Condition mixed ANOVA revealed that
emotional oddballs did not significantly influence free recall accuracy for oddball-1 object
identity, F(1,24) = 1.86, p = .19, η
p
2
= .072. Furthermore, performance was near ceiling for both
emotional oddball (M = .97, SEM = .013) and neutral oddball trials (M = .98, SEM = .006). There
was no significant interaction (p > .65) or main effect of propranolol (p > .19) on correctly
recalling the verbal label of the goal-relevant oddball-1 object. Likewise, emotion did not
significantly affect memory accuracy for perceptual details of the oddball-1 object, F(1,24) =
1.08, p = .31, η
p
2
= .043, nor were there significant interaction (p > .22) or main effect of
propranolol (p > .74). Follow-up one-way ANOVAs revealed that emotion did not significantly
affect either of these working memory measures in either drug group independently (ps > .1).
This finding that emotion did not affect working memory suggests that arousal did not impair
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 78
online memory maintenance processes or top-down prioritization of the oddball-1 object.

Figure 4. Yes/no response accuracy for the perceptual detail question about the goal-relevant
oddball-1 object (A) and free recall accuracy for that object’s identity (B) during the encoding
phase of the oddball task.

3.5 Corrected specific recognition memory analysis for oddball-1 and oddball+1
memory. To determine if emotional oddballs differentially influenced specific memory for
oddball-1 versus oddball+1 objects and if these effects were affected by propranolol, we
performed a 2 (Priority) x 2 (Emotion) x 2 (Condition) mixed ANOVA (Figure 5A). As expected,
high priority oddball-1 objects were recalled significantly better than lower priority oddball+1
objects, F(1,24) = 81.94, p < .001, η
p
2
= .77. Emotional oddballs significantly impaired overall
recognition memory for the objects tested, F(1,24) = 19.26, p < .001, η
p
2
= .45. There was no
significant main effect of propranolol on overall corrected recognition (p > .29).
The results also revealed a significant Priority x Emotion interaction effect on corrected
specific recognition memory across both drug conditions, F(1,24) = 5.34, p = .03, η
p
2
= .18,
which was driven by emotional oddballs significantly impairing memory for oddball+1 objects
(neutral > negative: t(25) = 3.92, p = .001. We also found marginally significant Emotion x
Condition, F(1,24) = 2.95, p = .099, η
p
2
= .11, and Priority x Emotion x Condition, F(1,24) = 3.05,
p = .093, η
p
2
= .11, interaction effects on specific memory.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 79
To determine whether this three-way interaction was driven by the effects of emotional
oddballs on oddball-1 or oddball+1 object memory, we performed two additional 2 x 2 mixed
ANOVAs with Emotion and Condition modeled as within- and between-subjects factors,
respectively. Corrected specific recognition memory performance was analyzed separately for
oddball-1 and oddball+1 objects.
For oddball-1 objects, contrary to our expectation, we did not find a significant effect for
memory enhancement under arousal (Sakaki et al., 2014). Instead we saw the opposite pattern
(i.e., emotion-induced retrograde amnesia for high priority objects), although it was not
significant, F(1,24) = 2.11, p = .16, η
p
2
= .081. There was no main effect of Condition or Emotion
x Condition interaction effect for oddball-1 objects (ps > .33). As expected, for oddball+1 objects
emotional oddballs significantly impaired memory of the lower priority oddball+1 objects, F(1,24)
= 16.09, p = .001, η
p
2
= .40. We also found a significant Emotion x Condition interaction effect,
F(1,24) = 4.47, p = .045, η
p
2
= .16, such that propranolol blocked this emotion-induced
anterograde amnesia for oddball+1 objects.
Separate follow-up one-way ANOVAs in each drug group indicated that emotion
significantly impaired oddball+1 object memory under placebo, F(1,13) = 29.53, p < .001, η
p
2
=
.69, but not under propranolol, F(1,11) = 1.22, p = .29, η
p
2
= .10. Together these results indicate
the significant ABC memory interaction effect was primarily driven by emotion-induced
anterograde amnesia for lower priority objects in the placebo group.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 80

Figure 5. Mean corrected specific recognition and gist recognition performance for the objects
appearing just before (high priority) and just after (lower priority) the emotionally arousing or
neutral oddball images. *p < .05; ***p < .001.

3.6 Corrected gist recognition memory analysis for oddball-1 and oddball+1
memory. Next, we examined how emotional oddballs and the drug affected gist memory based
on object priority (Figure 5B). The results were consistent with the corrected specific recognition
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 81
memory analysis: there were significant main effects of Priority, F(1,24) = 52.06, p < .001, η
p
2
=
.68, and Emotion, F(1,24) = 14.9, p = .001, η
p
2
= .38, and a significant Priority x Emotion
interaction effect on gist memory, F(1,24) = 15.34, p = .001, η
p
2
= .39.
Again, emotion did not significantly impair memory of oddball-1 objects in either drug
group (ps > .05), but did significantly impair memory for oddball+1 objects in the placebo group,
F(1,13) = 14.33, p = .002, η
p
2
= .52. This emotion-induced anterograde amnesia for the
oddball+1 objects diminished under propranolol, F(1,11) = 3.96, p = .072, η
p
2
= .27, indicating
that – like specific recognition – propranolol blunted the memory-impairing effect of emotional
arousal on lower priority information.
Lastly, to determine whether the effects of emotion and the drug differed based on the
specificity of memory (i.e., detail versus gist recognition), we performed an exploratory 2 x 2 x 2
x 2 mixed ANOVA with Priority, Emotion, and Memory Outcome (gist, detail) as repeated
measures and Condition as a between-subjects factor. None of the interactions between
Condition and/or Emotion and Memory Outcome were significant (ps > .1), indicating that the
moderating effects of propranolol on emotion-related memory did not differ based on the
specificity of the memory trace.
3.8 Trial-by-trial memory codependency analysis. To determine how emotional
oddballs influenced memory selectivity on a trial-by-trial basis, we performed a 2 x 2 x 2 mixed
ANOVA with Emotion (negative vs. neutral) and Memory Outcome (R
O-1
F
O+1
, R
O-1
R
O+1
) as
repeated measures and Condition (drug vs. placebo) as a between-subjects factor (Figure 6).
Overall, participants were more likely to remember both the oddball-1 and oddball+1 objects (R

O-1
R
O+1
: M = 52.59, SEM = 3.58) than to show a selective memory trade-off in favor of the high
priority oddball-1 object (R
O-1
F
O+1
: M = 34.2, SEM = 2.65), F(1,24) = 9.45, p = .005, η
p
2
= .28.
However, participants were more likely to remember the high priority oddball-1 object while
forgetting the corresponding oddball+1 object when the oddball was emotional, F(1,24) = 15.79,
p = .001, η
p
2
= .40. Furthermore, consistent with GANE theory, the results revealed a significant
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 82
Emotion x Memory Outcome x Condition interaction effect on trial frequency, F(1,24) = 4.82, p =
.038, η
p
2
= .17, such that emotion-induced memory trade-offs were more likely to occur in the
placebo than propranolol group. There were no other significant main or other interaction
effects.
An independent samples t-test indicated that the total proportion of neutral R
O-1
F
O+1

trials did not significantly differ between the placebo and the drug condition (p > .1). Thus, the
ABC memory effect observed in the placebo condition was driven by the relative enhancement
of memory trade-offs induced by emotional versus neutral oddballs rather than an emotion-
related change in neutral-associated memories only.
We performed two follow-up 2 (Emotion) x 2 (Memory Outcome) repeated-measures
ANOVAs to examine whether emotional arousal significantly enhanced memory trade-offs within
the placebo and drug groups, separately. In the placebo group, there were significantly fewer
memory trade-offs (R
O-1
F
O+1
), compared to more global memory effects (R
O-1
R
O+1
), F(1,13) =
10.9, p = .006, η
p
2
= .46. As predicted, there was also a significant Emotion x Memory Outcome
interaction effect, F(1,13) = 22.07, p < .001 η
p
2
= .63, such that oddball-1 objects were more
likely to be remembered at the expense of memory for their corresponding oddball+1 objects on
emotional versus neutral oddball trials. Follow-up paired t-tests indicated that, compared to
neutral oddball trials, emotion enhanced selective memory for prioritized objects (i.e., R
O-1
F
O+1

outcome), t(13) = 3.62, p = .003, while reducing the likelihood of remembering both oddball-1
and oddball+1 objects on a given trial (i.e., R
O-1
R
O+1
outcome), t(13) = -5.47, p < .001.
However, emotion did not affect memory outcomes for F
O-1
F
O+1
or F
O-1
R
O+1
memory outcome
types (ps > .29).
In contrast, there were no significant interaction or main effects under β-adrenoreceptor
blockade with propranolol (ps > .2). Furthermore, follow-up paired t-tests showed that emotional
oddballs did not alter the likelihood of showing any of the four memory codependency outcomes
compared to neutral oddballs (ps > .12). Thus, our results support the GANE model by
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 83
demonstrating that β-adrenoreceptor activation under emotional arousal biases encoding
processes in favor of prioritized objects and against lower priority (i.e., less attended) objects.

Figure 6. Results from the trial-by-trial memory codependency analysis. The plot indicates the
percentage of trials by valence that participants showed optimal selectivity (remembered
oddball-1, but forgot oddball+1) versus more global memory enhancements (remembered both
objects) in the drug versus placebo condition. R = Remembered; F = Forgotten; O-1 = oddball-1
object; O+1 = oddball+1 object. *p < .05.

3.9 Associations between task-induced sAA change and emotional arousal’s
influence on prioritized memories. Contrary to one of our main prediction, emotional arousal
did not significantly enhance memory for prioritized oddball-1 objects (see Section 3.5). To test
the possibility that individual differences could account for the lack of a main effect of emotion,
we performed a Pearson coefficient correlation analysis between emotion-induced effects on
oddball-1 object memory (negative-neutral oddball-1 memory difference scores) and sAA
concentration change from pre- to post-task (Figure 7).
Across all participants, oddball task-induced sAA change was positively associated with
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 84
greater emotion-induced enhancement of the high priority memory trace, r(24) = .42, p = .032.
In contrast, task-induced changes in sAA were not significantly associated with emotion-induced
suppression of the oddball+1 object or any other ABC memory measure (ps > .1).

Figure 7. Correlation between task-induced change in salivary alpha-amylase, a marker of NE
release, and emotion-related memory enhancements (relative to neutral oddballs) for the
oddball-1, goal-relevant objects.

4. Discussion
In this study, we combined a pharmacological manipulation with an emotional oddball
paradigm to test whether NE mediates the selective influence of emotional arousal on memory
for adjacent high (e.g., goal-relevant) and lower priority neutral stimuli (Mather et al., in press).
In particular, we tested the GANE model’s core prediction that emotion-induced activation of β-
adrenergic receptors facilitates arousal’s priority-specific effects on memory. Contrary to a
previous oddball study manipulating the priority of peri-oddball neutral items (Sakaki et al.,
2014), emotional arousal did not enhance memory of preceding goal-relevant neutral stimuli
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 85
under placebo. However, we found that β-adrenergic blockade attenuated an emotion-induced
anterograde amnesia of less-attended, lower priority neutral stimuli. Moreover, when accounting
for trial-by-trial memory contingencies, we found that propranolol blocked an emotion-induced
trade-off whereby prioritized oddball-1 objects were recalled at the expense of memory for their
corresponding lower priority oddall+1 objects. Additionally, a correlation analysis revealed that,
across participants, emotion conferred a memory advantage to goal-relevant objects in
individuals who showed greater task-induced increases in salivary alpha-amylase, a biomarker
of norepinephrine release. Together these results suggest that broader activation of the
noradrenergic system amplifies the mnemonic benefits of arousal for prioritized mental
representations. The finding that the sAA-memory relationship was apparent across both the
drug and placebo groups suggests that such enhancements do not rely exclusively on β-
adrenoreceptors; rather, β-adrenoreceptors appear to play a more critical role in mediating
emotion-induced memory impairments for less salient stimuli.
The current results replicate earlier findings that, via β-adrenoreceptor activation,
pictorial emotional oddballs proactively impair memory for subsequent neutral pictures
(Hurlemann et al., 2005). Past work implicates the amygdala as the critical locus NE-induced
memory deficits for neutral stimuli experienced near something emotional (Strange et al., 2003).
The most common interpretation of these emotion-related impairments is that the amygdala
selectively modulates cortical and hippocampal activity to favor processing of emotional stimuli
(Dolcos et al., 2004; Fastenrath et al., 2014; Kilpatrick & Cahill, 2003; Richardson et al., 2004;
Strange & Dolan, 2004; Vuilleumier et al., 2004), thereby leaving fewer resources available to
process less salient neutral information. Supporting this hypothesis, bilateral amygdala damage
is associated with poorer memory for gist but enhanced memory for visual details of aversive
versus neutral photographs (Adolphs et al., 2001), suggesting that this region suppresses
information that is peripheral to an emotional event.
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Since the observed memory interference for neutral objects occurred in an anterograde
fashion, we interpret these arousal effects as emotion impacting more rapid attention and
encoding processes rather than consolidation. Indeed, noradrenergic modulation of the
amygdala is not only critical for consolidating salient or emotion-laden events but also for
modulating initial perception, attention, and encoding processes (Fox et al., 2000; Hamann et
al., 1997; Hurlemann et al., 2005; Liddell et al., 2005; Markovic et al., 2014; Vuilleumier, 2005).
For example, both β-adrenergic blockade (De Martino et al., 2008) and amygdala lesions
(Anderson & Phelps, 2001) reduce the perceptual dominance of emotional stimuli that are
presented in close succession to neutral stimuli. Based on these data, our impairment finding
may signify a β-adrenergic and amygdala-dependent biasing of attention and encoding
resources away from relatively mundane information appearing just after something emotionally
significant.
Notably, other lesion studies suggest that the amygdala might not be the only
mechanism by which arousal biases attention and stimulus detection. For instance, unilateral
amygdala lesion patients exhibit similar emotion-enhanced visual cortical activity as healthy
controls (Edmiston et al., 2013). Amygdala lesion patients also show comparable levels of
attentional capture by emotional stimuli (Piech et al., 2011) and detection advantages for
emotional versus neutral target images (Piech et al., 2010). Additionally, two bilateral amygdala
lesion patients showed enhanced recall for emotional relative to neutral words during an
attentional blink task (Bach et al., 2011). Thus, from the perspective that the attention-grabbing
effects of emotion drive anterograde amnesia, β-adrenoreceptor expression in extra-amygdalar
regions may also contribute to emotion’s amnestic effects on subsequent neutral information.
If the amygdala only moderates part of the effects of arousal on memory, how else do
NE - and more specifically β-adrenoreceptors – modulate mnemonic processes during
emotional experiences? Noradrenergic axons project throughout most of the cortex, enabling
the LC to influence and coordinate cognitive and sensory processing across multiple levels of
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brain function (Berridge & Waterhouse, 2003; Chandler et al., 2014; Foote & Morrison, 1987;
Morrison & Foote, 1986; Samuels & Szabadi, 2008). While the bulk of human research focuses
on β-adrenoreceptor’s role in emotional memory enhancements, animal research demonstrates
that arousal-induced NE release can also inhibit activity in the sensory cortex (Waterhouse et
al., 1980), an effect that may involve β-adrenoreceptors (Waterhouse et al., 1982). For instance,
application of a β-adrenoreceptor agonist potentiates GABA-induced suppression of
somatosensory cortical neurons (Sessler et al., 1995). In rodents iontophoresis of NE amplifies
surround suppression of non-preferred inputs to visual cortical neurons (Waterhouse et al.,
1990). Taken together, β-adrenergic receptor activation appears to help enhance neuronal
contrast and increase the signal-to-noise ratio of synaptic neurotransmission. Additionally, in
humans β-adrenergic blockade disrupts attention reorienting by reducing activity in a ventral
attention network anchored in right frontal and parietal cortex (Strange & Dolan, 2007). Thus,
under arousal, β-adrenoreceptors may suppress lower priority inputs even further either by
potentiating cortical inhibitory signals or facilitating the reorienting of attention towards
motivationally relevant stimuli (e.g., emotional oddballs).
The lack of an emotion-induced memory benefit for prioritized oddball-1 object images
observed in this study could have resulted from emotionally salient oddballs garnering more
attention than the preceding goal-relevant object image. Much evidence indicates that emotional
stimuli are rapidly processed and attended to (Fox et al., 2000; Vuilleumier, 2005; Vuilleumier &
Schwartz, 2001), especially due to their immediate relevance to survival and wellbeing (Öhman
et al., 2001). Thus, insofar as the emotional stimulus was prioritized due to its saliency, our null
finding doesn’t necessarily contradict the GANE model. Instead, emotion-induced activation of
β-adrenoreceptors may have enhanced memory representations of the emotional oddball
images at the expense of processing surrounding neutral items (Strange & Dolan, 2004). We
did not test this possibility since we did not want to risk directing any additional attention towards
the emotional oddballs. Importantly, under placebo, emotional arousal amplified biased
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 88
competition outcomes such that prioritized oddball-1 objects were better remembered at the
cost of memory for their competing oddball+1 object. This finding indicates that, on a trial-by-
trial basis, arousal amplified the selectivity of memory since it did not suppress object memory
bidirectionally (i.e., regardless of goal relevance). Furthermore, this effect was abolished by β-
adrenergic blockade, suggesting that the noradrenergic system exerts different effects on high
and lower priority neutral information appearing near something emotional.
Although β-adrenoreceptor blockade did not affect emotion’s influence on goal-relevant
memories, an individual differences analysis revealed a positive association between more
global noradrenergic activity, as indexed by increased sAA levels across the task, and emotion-
induced memory enhancements of goal-relevant objects. This finding accords with previous
studies demonstrating that task-induced increases in sAA are selectively associated with recall
of emotional and not neutral images (Segal & Cahill, 2009). Our results are also in line with
pharmacological experiments targeting NE’s influence on the attentional blink. For example,
pharmacologically increasing NE levels with reboxetine makes emotional stimuli more resistant
to blink-related perceptual suppression (De Martino et al., 2008). Likewise, genotyping studies
show that carriers of the ADRA2B deletion variant, who purportedly have greater NE availability
due to reduced inhibition of noradrenergic signaling, show similar emotional “sparing” effects
(Todd et al., 2013). Deletion carriers also show greater amygdala and insula activity when
viewing negative emotional expression (Cousijn et al., 2010; Rasch et al., 2009) and greater
emotional memory enhancements compared to non-carriers (de Quervain et al., 2007).
Together these studies suggest that arousal biases attention towards affectively relevant
information when NE levels are elevated. Here, we expand on these findings by showing that
the mnemonic advantage of arousal can also extend to nearby neutral stimuli if they are
credited as goal relevant.
It is noteworthy that this GANE-like effect due to sAA did not differ based on drug group.
If β-adrenergic receptors were blocked by propranolol, how would emotional arousal enhance
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 89
memory of goal-relevant neutral stimuli? One possibility is that elevating overall NE levels
enhanced the phasic effects of arousal on task-focused attention, thereby increasing the
selectivity of memory (Aston-Jones & Cohen, 2005; Aston-Jones et al., 1999; Aston-Jones et
al., 1994). Intriguingly, Strange and colleagues (2003) found that propranolol administration was
associated with enhanced recall for the neutral word preceding an emotional versus neutral
oddball word (Strange et al., 2003). Our results are similar in that arousing oddballs still
strengthened high priority memory traces despite β-adrenergic blockade, suggesting the
mnemonic benefit of NE on goal-relevant information might involve different adrenoreceptor
subtypes. For instance, NE modulates cognitive flexibility and working memory processes in the
prefrontal cortex (PFC) by activating α2-adrenoreceptors (Ramos & Arnsten, 2007; Wang et al.,
2007). In turn, this may alter the strength of the PFC’s top-down inputs to posterior cortical
regions where goal-relevant stimuli are represented (Gazzaley & Nobre, 2012). Beyond the
PFC, α2-adrenoreceptor agonists have been shown to selectively enhance the distribution of
blood flow to stimulated sensory regions, thereby supplying the energy necessary to process
prioritized, task-relevant inputs (Bekar et al., 2012). Furthermore, in rodents, pairing NE with
local visual cortex stimulation enhances the responsiveness of nearby astrocytes, which help
facilitate metabolite delivery and synaptic plasticity (Paukert et al., 2014). Importantly, a α1-
adrenoreceptor antagonist blocked this noradrenergic regulation of local astrocytic gain (Paukert
et al., 2014). It may be the case, then, that the gain of prioritized information processing under
arousal relies on complex interactions between NE and multiple adrenoreceptor subtypes.
There are several limitations in this study that warrant consideration. The sample sizes
are modest; therefore, it is difficult to determine whether the lack of an arousal-biased
competition effect in memory (Sakaki et al., 2014) was due to insufficient power. Furthermore,
this issue limited us from investigating sex differences in emotions influence on top-down
priority. Previous work shows that women exhibit significantly larger amnestic effects of
emotional oddballs on preceding neutral stimuli (Strange et al., 2003). We also did not control
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 90
for menstrual cycle phase or birth control use, which have been shown to alter emotional
memory enhancements induced by elevated noradrenergic activity (Nielsen et al., 2015).
Finally, β-adrenoreceptor blockade during memory retrieval can also abolish emotional memory
enhancements (Kroes et al., 2010; Murchison et al., 2004). Since participants in this study
performed encoding and retrieval during the same session, it is unclear which stages of episodic
memory were affected by β-adrenergic blockade.
Another interesting open question is whether the arousal-enhancing effects of β-
adrenoreceptors on goal-relevant memories only emerge after longer periods of consolidation.
Accumulated evidence points to a key role of β-adrenoreceptors in long-term memory
consolidation of emotional information (Ferry et al., 1999; McGaugh & Roozendaal, 2002;
Southwick et al., 2002). Behavioral studies in humans indicate that memory-enhancing effects
of emotional oddballs on preceding neutral items receiving high attentional weight become
apparent after a 1-week delay (Anderson et al., 2006; Knight & Mather, 2009). Likewise, the
mnemonic advantage of emotional over neutral stimuli also increases over longer retention
intervals (Sharot & Yonelinas, 2008). Taken together, although we did not find β-adrenergic
effects on relatively short-term memory of goal relevant stimuli, it is possible that the effects of
arousal on recently formed memory traces increase during consolidation. Of relevance to this
hypothesis, recent work demonstrates that offline sleep-dependent consolidation mechanisms
selectively enhance memory of emotional information (Payne et al., 2012; Payne et al., 2008).
There are also some indications that NE may help enhance selective memory consolidation
during sleep (Gais et al., 2011; Sara, 2010), suggesting that endogenous increases in NE
activity during offline consolidation leads to the preferential retention of salient information that is
“tagged” by arousal at encoding (Cahill et al., 2003; Tully & Bolshakov, 2010). Future
pharmacological studies could examine whether activating β-adrenoreceptors either at encoding
or during consolidation affects arousal’s differential effects on long-term memory of goal-
relevant and lower priority neutral stimuli.
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In summary, Study 2 used pharmacology to test GANE’s prediction that β-adrenergic
receptor activation amplifies the selective effects of emotional arousal on memory of high (i.e.,
goal-relevant) and lower priority information (Mather et al., in press). Our results replicated
previous findings that β-adrenergic blockade prevents an emotion-induced anterograde
amnesia for relatively less attended stimuli (Hurlemann et al., 2005). While emotion did not
affect memory of goal-relevant stimuli in either the placebo or beta-blocker groups, memory for
goal-relevant stimuli under arousal was correlated with task-related changes in sAA, a proxy of
LC-NE system activity. We also found that propranolol reduced competition in memory between
high and lower priority objects: emotion no longer enhanced memory for a given oddball-1
object while suppressing memory for the subsequent oddball+1 object. Together these results
suggest that noradrenergic mechanisms mediate the selective influence of arousal on memory,
with β-adrenoreceptors being particularly important for suppressing memory of less important
information under arousal.

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Supplementary Table 1. Frequency of each trial-by-trial memory outcome by oddball valence
(negative vs. neutral), item priority (oddball-1 vs. oddball+1), and drug condition.

PLACEBO NEUTRAL Memory for Oddball-1
Memory for Oddball+1
Remembered Forgot
Remembered 63.95 5.78
Forgot 25.51 4.76

PLACEBO NEGATIVE Memory for Oddball-1
Memory for Oddball+1
Remembered Forgot
Remembered 47.62 5.44
Forgot 39.46 7.48

DRUG NEUTRAL Memory for Oddball-1
Memory for Oddball+1
Remembered Forgot
Remembered 52.38 7.94
Forgot 34.52 5.16

DRUG NEGATIVE Memory for Oddball-1
Memory for Oddball+1
Remembered Forgot
Remembered 46.43 8.73
Forgot 37.30 7.54

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Chapter 4. Locus coeruleus activity strengthens goal-relevant memories under
threat of punishment

1. Introduction

Adaptive behavior relies on the ability to select and store motivationally significant
information amidst distraction. Cognitive selectivity is particularly important during arousing
situations, such as imminent threat, when prioritizing behaviorally relevant inputs helps us learn
how to avoid danger in the future. Thus far, our data lend credence to the idea that arousal-
related increases in noradrenergic activity enhance the effects of goal-relevance in cognition,
such that memory for high priority representations is enhanced, while memory for lower priority
representations is suppressed (Mather & Sutherland, 2011). NE’s affects on memory, however,
aren’t unique to emotional and/or task-irrelevant arousing stimuli. The LC also responds
phasically to variety of motivationally significant events, including performance errors, reward or
punishment, and cognitively demanding tasks (Berridge & Waterhouse, 2003).
For instance, like emotional events, motivational incentives also narrow the scope of
attention and memory onto information relevant to our goals, such as attaining reward or
avoiding punishment (Chelazzi et al., 2013; Kaplan et al., 2012; Levine & Edelstein, 2009).
Motivation to gain rewards or avoid losses can enhance memory of stimuli that are intrinsically
non-arousing, or neutral (Montagrin et al., 2013; Murty et al., 2012). In light of these valence-
invariant effects of incentives on cognition, it has been suggested that motivation biases
selective attention based on the “motivational intensity” of a stimulus, a property that is
analogous to sympathetic arousal (Dolcos et al., 2014; Gable & Harmon-Jones, 2010). Together
these findings suggest that emotion and motivational incentives might enhance “winner-take-
more” and “loser-take-less” outcomes in attention and memory via a common arousal
mechanism in the brain (Sorensen and Barratt, 2014; Sorensen et al., 2014; Chiew and Braver,
2011).
Neuromodulatory systems, including the dopaminergic and locus coeruleus-
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 94
norepinephrine (LC-NE) systems, are essential for enhancing learning and memory of
motivationally significant information (Berridge & Waterhouse, 2003; Harley, 2004; Sara, 2009;
Shohamy & Adcock, 2010). During salient events, these neuromodulators broadcast learning
signals across the brain and work in concert to modulate memory and executive attention
processes in target brain regions, including the hippocampus, prefrontal cortex, and posterior
sensory/parietal cortices (Floresco, 2015; Harley, 2004; Ramos & Arnsten, 2007; Sara, 2009).
To date, however, the vast majority of research has focused on the dopaminergic system’s
central role in reward-motivated memory encoding (Adcock et al., 2006; Shohamy & Adcock,
2010). By comparison, considerably less attention has been paid to NE’s effects on motivated
declarative memory (for a review, see (Sara, 2009).
Characterizing NE’s influence on motivated cognition is particularly timely given new
evidence that the dopaminergic system supports encoding processes incentivized by reward but
not punishment (Murty et al., 2012). In contrast, learning motivated by threat relies on activation
of the amygdala, a region known to enhance emotional memory via arousal-induced release of
NE (McGaugh, 2013; McGaugh & Roozendaal, 2002; Strange & Dolan, 2004). Thus, NE
release may be closely linked to memories formed under threat.
Recent electrophysiological studies in monkeys also suggest that DA and NE play
distinct but complementary roles in goal-directed behavior: whereas the DA system mediates
the incentive effects of expected reward on cognition, the NE system appears to be more
involved in regulating arousal responses and energizing actions when faced with challenge
(Bouret et al., 2012; Bouret & Richmond, 2015; Varazzani et al., 2015). For instance, LC neuron
activity increases with pupil dilation, a putative index of mental resource allocation (Kahneman,
1973), and the amount of effort required to perform an action (Varazzani et al., 2015). Research
in humans also suggests that effort-related LC activity enhances the gain of activity in task-
relevant cortical networks. In one neuroimaging study, increased pupil dilation was associated
with increased activation of the LC and a goal-directed dorsal attention network during a
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 95
multiple object-tracking task (Alnæs et al., 2014). Recent neuroimaging evidence also indicates
that activation of the LC rather than the ventral tegmental area (VTA) helps regulate cognitive
control processes in the dorsal anterior cingulate cortex during stroop interference (Köhler et al.,
2016). Together these findings suggest that the LC enhances goal-directed attention processes
by increasing targeted resource allocation in the brain, particularly under conditions of elevated
arousal, conflict and/or effort.
Accumulated findings indicate that the LC-NE system helps modulate decision-making
and attention processes, such as target detection or stimulus discrimination/conflict (Aston-
Jones & Cohen, 2005; Aston-Jones et al., 1994; Krebs et al., 2013; Murphy et al., 2014;
Raizada & Poldrack, 2008; Strange & Dolan, 2007) and enhanced memory consolidation of
emotionally arousing events (Cahill et al., 1994; Markovic et al., 2014; Mather et al., in press;
McGaugh, 2013). But researchers have yet to bridge the attention and memory literatures to
test whether the human noradrenergic system activity also enhances goal-directed mnemonic
processes under motivational arousal.
Furthermore, past neuroimaging studies have only examined how punishment
motivation or actual shock enhances memory of neutral scene images in the absence of
competing visual inputs (Murty et al., 2012; Schwarze et al., 2012). Thus, another open question
is whether LC-NE system activity selectively enhances processing goal-relevant information
when it competes with distracters. Supporting this possibility, neural network models indicate
that LC-NE system serves to amplify the gain on neuronal processing under arousal, such that
strong inputs are enhanced, while weaker inputs are suppressed (Eldar et al., 2013;
Nieuwenhuis et al., 2005; Usher et al., 1999).
The GANE model posits that local glutamate signals, which are the neurochemical
substrate of priority, dictate whether arousal-induced LC activity will enhance or impair a neutral
mental representation (Mather et al., in press). According to the GANE framework, local NE-
glutamate interactions help up-regulate excitatory signals in regions transmitting motivationally
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relevant information, generating “NE hotspots;” in turn, hotspots enable arousal to selectively
enhance high priority representational activity, leading to “winner-take-more” outcomes in
memory under arousal. Whereas Study 1 and Study 2 provided evidence that global (i.e., not
trial specific) noradrenergic activity is conducive to arousal’s phasic effects on cognition, it is still
unclear a) whether these phasic effects were driven by LC activity specifically, and b) whether
arousal’s facilitating effects on goal-relevant memory manifest locally in high priority
representational cortex.
Using neuroimaging to study the human LC is challenging due to its small size of 2.5mm
in-plane by 12-17mm longitudinally (Fernandes et al., 2012), the low spatial resolution of
conventional MRI (~2-3mm isotropic voxels), and the presence of cardiac pulsation artifacts in
the brainstem (Astafiev et al., 2010). However, one recent study showed that combining
neuroimaging with pupil dilation measures could be used to pinpoint task-relevant brainstem
activity to the LC (Murphy et al., 2014). Converging evidence from animal (Varazzani et al.,
2015) and human (Alnæs et al., 2014; Murphy et al., 2014) research shows that LC activity is
tightly coupled with non-luminance-related changes in pupil dilation during cognitive tasks.
These functional data map onto anatomical evidence that the LC sends projections that inhibit
the parasympathetic oculomotor complex, resulting in pupil dilation (Samuels & Szabadi, 2008;
Wilhelm, 2008). Thus, scaling blood-oxygen-level dependent (BOLD) activity changes by
stimulus-evoked pupil dilations may be an effective non-invasive method of measuring the
effects of LC-NE modulation on cognitive activity (Murphy et al., 2014).
The goal of this fMRI study was to examine whether threat-induced LC activation
amplifies memory of goal-relevant neutral stimuli. Specifically, we used a monetary incentive
encoding task to examine how threat of monetary punishment influences encoding-related brain
activity differently as a function of stimulus priority. Goal-relevance was manipulated by
instructing participants to explicitly attend to and memorize a neutral background scene, while
ignoring a transparent foreground object. To induce arousal, some trials were cued to potential
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 97
monetary loss if participants forgot the background scene. At the behavioral level, we
hypothesized that motivation to avoid punishment would enhance memory of high priority
scenes, while suppressing memory of lower priority objects. We also hypothesized that trial-by-
trial magnitude of pupil dilation, a proxy of LC activity, would be associated with threat-related
memory enhancements for high priority scenes but not distracter objects.
To test our key prediction that LC activity modulates priority-specific activity in the brain
(Mather et al., 2015), we took advantage of the fact that different categories of visual stimuli
activate distinct representational regions in sensory cortex (Lee et al., in preparation; Lee et al.,
2014). Prior to the monetary incentive encoding task, we used a functional anatomical localizer
to localize and delineate the parahippocampal place area (PPA), a visual cortex region
specialized to process scenes (Epstein & Kanwisher, 1998), and the lateral occipital cortex
(LOC), a region specialized to process object information (Grill-Spector et al., 1999), in each
participant. This enabled us to determine how threat modulated encoding-related activity
according to the priority of the target scenes and their competing distracter objects (Lee et al.,
2014). We hypothesized that pupil dilation, an index of LC activity, would parametrically
modulate successful encoding activity for scenes in the PPA, hippocampus, and LC. In contrast,
we hypothesized that pupil responses to the overlap stimuli would not be associated with
successful object encoding activity in the LOC, indicating that NE’s memory-boosting effects are
selective to prioritized, goal-relevant stimuli.
2. Methods
2.1 Participants. Thirty-two healthy young adults were recruited from the University of
California’s Psychology Subject Pool and nearby community to participate in this experiment. All
participants provided written informed consent approved by the University of Southern California
Institutional Review Board and received monetary compensation for their participation and
performance on the task. All eligible individuals had normal or normal-to-corrected vision and
hearing and were not taking beta-blockers or psychoactive drugs. To limit the influence of sex
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 98
hormones (i.e., progesterone and estradiol) on the predicted memory and arousal effects,
female participants had to be: 1) using monophasic hormonal contraceptives and 2) using those
contraceptives for at least 3 months. Female participants only completed the experiment on
their active pill days (days 8-21).
Six participants were excluded from data analysis: two participants fell asleep during the
scan; one participant had excessive head movement during the scan (> 2mm); two participants
violated recruitment criteria on the day of their scan; and one participant failed to follow task
instructions. After exclusion, this resulted in 26 participants (M
age
= 22.15; SD
age
= 2.26; 13
female) for final analysis. In addition, four additional participants were not included in any pupil
analyses due to poor eye-tracking quality (e.g., pupil values were unattainable for > 50% of the
encoding trials).
2.2 Monetary Incentive Encoding Task
2.3.1 Stimuli. The visual stimuli consisted of 96 neutral objects and 96 neutral scenes
images selected from previous datasets (Gabrieli et al., 1997; Kensinger et al., 2006) and the
Internet. Objects consisted of animate (e.g., animals) or inanimate objects (e.g., kitchen
utensils) centered on a white background. Half of the scene images depicted indoor scenes
(e.g., bedroom), while the other half depicted outdoor scenes (e.g., landscape). Each of these
images was resized to be 300 x 300 pixels and rendered in grayscale. From these images, 96
“overlap” stimuli were created by overlaying one object image on top of one scene image; the
object image was rendered transparent using Adobe Photoshop 5.0, such that the foreground
object and background scene images were equally discernable. Each of the grayscale, un-
merged object and scene stimuli were yoked with a categorically similar but perceptually
different image that served as a foil during the recognition memory test. This resulted in a total
of 192 scene and object images.
To isolate cognitive-related changes in pupil dilation, image luminance was normed
across all of the task screens (i.e., overlap stimuli, fixation crosses, scrambled images, and
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money symbols) using the SHINE toolbox in MATLAB ((Willenbockel et al., 2010). In order to
model an “active baseline” (Stark & Squire, 2001) in the FMRI analyses, scrambled overlap
images were generated by shuffling a 0.27° x 0.27° box in a random fashion using MATLAB.
To further increase arousal in the monetary threat condition, each threat cue was
accompanied by a negative buzzer sound acquired from the International Affective Digitized
Sounds (Bradley & Lang, 2007); Sound ID #712; M
Arousal
= 7.98, SD
Arousal
= 1.62; M
Valence
= 2.42,
SD
Valence
= 1.62). No sound was paired with the neutral money cues.
2.3.2 Task. FMRI data was acquired during the monetary incentive encoding task. The
overall structure of the experiment was a 2 (Priority: scene vs. object) x 2 (Arousal: threat vs.
neutral) within-subjects design. There were a total of 96 encoding trials in the task, which were
subdivided into four blocks/runs of 24 trials each (i.e., overlap images). Half of these trials
belonged to the threat condition, while the other half was neutral. The order of the overlap
stimuli was randomized across all blocks and the order of the arousal trials was randomized
within each block.

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Figure 1. Schematic trial from the monetary incentive encoding fMRI task. The red square
denotes the 2.5s stimulus-encoding period when brain activity was analyzed. The top right panel
shows a schematic trial from the two-alternative forced choice memory test that was
administered outside the scanner. Note that images are not drawn to scale.

2.3.3 Encoding phase. During the task, participants were presented with a series of
grayscale “overlap” images of a transparent object overlaid on a background scene (Figure 1).
At the beginning of each trial, participants were shown one of two gray monetary symbols for 1s.
In the threat condition, a “no symbol” cued participants that they would lose 50 cents if they
forgot the upcoming scene on the subsequent memory test; in the neutral condition, a square
symbol cued participants that, while they should still try to memorize the background scene,
they would not be punished for forgetting it later on. To increase arousal on threat trials, each
threat cue was accompanied by a highly arousing buzzer sound. The monetary cue was
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followed by a jittered central fixation cross (2, 4 or 6s). After this variable anticipation period, the
overlap image appeared for 2.5 seconds.
To manipulate goal-relevance (i.e., top-down priority), participants were instructed to
only focus on and memorize each scene in the overlap images while ignoring the foreground
object. To reduce the salience/priority of the object even further, participants were told that their
memory would not be tested for any of the objects and that they were only meant to be a
distraction. To facilitate encoding and ensure participants were prioritizing the scenes, they were
also instructed to categorize each scene as indoors or outdoors as quickly and accurately as
possible when the overlap image appeared. Regardless of the cue, participants were instructed
to categorize and memorize the exact scene image on each trial.
Following the overlap stimulus, participants performed a brief scrambled-image detection
task, which was used to 1) prevent memory rehearsal of the scenes and 2) provide an active
baseline period that could be modeled in the neuroimaging analyses. In this task, participants
saw three scrambled overlap images for 1s each, which were separated by 1s fixation cross
screens in between. Participants’ task was to make a button response with their right hand as
quickly as possible when they saw each scrambled image: they pressed the left button when
they saw the first scrambled image, the right button when they saw the second scrambled
image, and the left button again for the third scrambled image. Each trial concluded with another
3s fixation cross, which served as the inter-trial-interval (ITI).
2.3.4 Memory test. Approximately 15 minutes after completing the monetary incentive
encoding fMRI task, participants were administered a two-alternative forced choice recognition
memory test outside the scanner (Figure 1). To examine the differential effects of threat-related
arousal on memory for high (scene) versus lower (object) priority images, we tested memory for
all scene and object images that appeared during the task. In the memory test, scenes and
objects from each overlap stimulus were displayed individually for a total of 192 memory trials
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(96 of each visual category). Prior to beginning the memory test, participants were told that their
memory would be tested for all objects and scenes and to try their best on every trial.
On each memory trial, one object or scene image from the previous encoding task was
displayed alongside a semantically similar but perceptually different (e.g., different shape,
orientation etc.) foil image. The order of stimulus presentation was randomized and an equal
number of old items appeared on the left and right of the screen. Participants were tasked with
identifying which image they had seen in the scanner.
After making each memory response via button press, participants were prompted to
rate their confidence in their memory accuracy according to one of three options: extremely
confident, somewhat confident, and just guessing. Participants had to make their memory
responses within 10 seconds and their confidence rating within 5 seconds. If they failed to
respond within these response deadlines, the screen automatically advanced to the next trial
and memory accuracy was recorded as a miss. To prevent fatigue, there were breaks 1/3 and
2/3rds of the way through the memory test.
2.4 Procedure. Upon arriving for the experiment, participants provided written informed
consent and completed several pen-and-paper questionnaires (see Table 1), including an in-
house demographics form, State-Trait Anxiety Inventory (Spielberger & Gorsuch, 1983), the
Positive and Negative Affect Schedule (Watson et al., 1988), Behavioral Inhibition System and
Behavioral Activation System scale (Carver & White, 1994), Center for Epidemiological Studies
Depression scale (Radloff, 1977), and Sensation-Seeking Scale (Zuckerman et al., 1978).
Next, participants were given instructions for the monetary incentive encoding task on a
computer. To familiarize participants with the experiment, they completed six task practice trials
and three memory test practice trials prior to entering the scanner.
Scanning procedures took approximately 1 hour. Inside the MRI scanner, participants
first completed the LOC/PPA functional localizer task (see 2.7 for details), which was followed
by a T1-weighted high-resolution neuroanatomical scan. The functional localizer scan was
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collected first to avoid any sensory interference during the main task and memory test. After the
structural scan, participants completed 4 runs of the monetary incentive encoding task, which
lasted approximately 24 minutes. Between each run, participants were reminded of the task
instructions and to keep their head as still as possible. Scanning concluded with a ~2 minute
neuromelanin-sensitive weighted scan, which was used to help localize and delineate the LC in
the brainstem (Clewett et al., 2016; Keren et al., 2009; Shibata et al., 2006).
After exiting the scanner, participants were administered a second PANAS questionnaire
to assess changes in positive and negative affect and a number search puzzle, which was used
to equalize the duration of the study-test delay across participants. Approximately 15 minutes
after completing the monetary incentive encoding task, participants were administered the self-
paced memory test on a computer. They then completed a post-questionnaire that assessed
several aspects of the task, including how arousing and unpleasant they found the buzzer
sounds on threat trials, and how arousing they found the threat and neutral money cues. All
ratings were made on a 7-point scale ranging from 1 = not at all to 7 = very.
To assess punishment-related motivation, participants also indicated how motivated they
were to memorize scenes on threat trials and neutral trials, separately, by making a vertical
hash mark along a line spectrum ranging from not motivated on the left to extremely motivated
on the right. Motivation responses were converted to scores by measuring the distance of the
hash mark from the leftmost point (not motivated) in cm. In addition, to ensure that participants
prioritized the scenes during the task, they were asked whether or not they tried to memorize all
of the scenes and objects. One male participant answered “yes,” so was excluded from all
analyses (his exclusion was already noted above in the participants section).

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Table 1. Behavioral questionnaire data
Behavioral Measure Mean (SD)
CES-D 16.42 (4.01)
PANAS #1 (Pre-Task): Negative/Positive Affect 12.12 (2.39) / 30 (7.32)
PANAS #2 (Post-Task): Negative/Positive Affect 11.69 (1.82) / 26.73 (9.32)
State Anxiety 31.96 (9.32)
Trait Anxiety 42.19 (9.6)
Behavioral Inhibition System (BIS) 20.35 (5.07)
Behavioral Activation System (BAS) 17.46 (2.18)
Sensation-Seeking Scale (SSS) 21.16 (3.84)
Buzzer Sound Arousal 5.31 (1.44)
Buzzer Sound Unpleasantness 3.69 (1.76)
Threat Cue Arousal 4.04 (1.31)
Neutral Cue Arousal 1.5 (0.86)
Motivation to Memorize Scenes on Threat Trials 5.23 cm (1.92)
Motivation to Memorize Scenes on Threat Trials 2.62 cm (2.14)

2.5 Encoding performance analysis. To examine aspects of attention during the
monetary incentive encoding task, we submitted scene categorization accuracy and reaction
times (RT) for threat and neutral trials to paired t-tests. We also examined arousal-related
differences in scrambled-image detection accuracy and RT using 2 (Arousal: threat vs. neutral)
x 3 (Time: first vs. second vs. third) repeated-measures Analysis of Variance(s).
2.6 Memory analysis
2.6.1 Mean memory performance. To determine how threat-related arousal
differentially influences memory for high and lower priority information, we performed a 2
(Priority: high vs. low) x 2 (Arousal: threat vs. neutral) repeated-measures ANOVA. Planned
follow-up paired t-tests were also used to test whether, compared to neutral trials, threat
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enhanced memory for scenes, while suppressing memory for objects. Memory performance
was calculated as the proportion of correctly remembered trials in each arousal trial type.
2.6.2 Memory codependency analysis. According to ABC theory, arousal biases
limited mental resources towards prioritized representations, such that goal-relevant stimuli are
even more likely to be remembered at the expense of memory for their competing distracters
(Mather and Sutherland, 2011). Thus, to examine how threat-induced arousal influenced
competitive mnemonic processes on a trial-by-trial basis, we performed a memory
codependency analysis; that is, we examined how memory accuracy for a scene in a given
overlap image differed as a function of memory accuracy for its corresponding object. Each of
the 96 trials from the monetary incentive encoding task were coded post hoc according to one of
four possible memory codependency outcomes: 1) remembered scene and forgot object, 2)
forgot scene and remembered object, 3) remembered both, or 4) forgot both. The frequencies of
each memory outcome were calculated for threat and neutral conditions, separately.
To test our main behavioral prediction that arousal enhances goal-relevant memory
selectivity rather than memory more globally (e.g., memory for a scene and its corresponding
object), we performed a 2 (Arousal: threat vs. neutral) x 2 (Memory Outcome:
remembered
scene
forgot
object
vs. remembered both) repeated-measures ANOVA. Follow-up paired
t-tests were used to examine the main effects of Arousal on selective versus global memory
outcomes.
In addition, we performed the same analysis after filtering out memory trials where
participants specified that they had only guessed on their memory judgment for the scene.
Although each overlap image had memory confidence ratings for both the scene and its
corresponding object, we performed this filtering based only on the scene’s confidence rating
since participants’ confidence should have been lower for the objects. We reasoned that scene
memoranda that were properly prioritized would lead to higher subjective memory confidence of
such items.
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2.7 Functional localizer. To examine how threat-induced arousal differentially
influenced encoding-related activity in visual category-selective cortex, we performed a
functional localizer task to delineate the parahippocampal place area (PPA; high priority region)
and lateral occipital cortex (LOC; low priority region) in the left and right hemisphere of each
participant. The localizer scan consisted of 48 scrambled objects, 48 objects, and 48 scene
images. Scrambled images of the intact stimuli were generated using the shuffling procedure
described in Section 2.3.1. All images were resized to 300 x 300 pixels, gray-scaled, luminance-
normed and displayed on a gray background. Image presentation was divided into 6 blocks,
which lasted 60 seconds each. These blocks were further subdivided into 3 mini-blocks lasting
20s each, in which a series of images from one of the three visual categories was displayed on
the screen (e.g., scrambled-only, object-only, or scene-only). Each mini-block contained 8
images lasting 10s and were separated from each other by a 10s fixation cross. Each image
was displayed for 1s, followed by 250ms fixation cross inter-stimulus-interval (ISI). The order of
mini-blocks was counterbalanced across participants. Participants were instructed to passively
view the images as if they were watching movie. Importantly, none of the stimuli from the
localizer task were used in the monetary incentive encoding task.
2.8 Eye-tracking. During FMRI scanning, pupil size was measured continuously at 60
Hz using an infrared ASL model 504 eye-tracker system (Applied Science Laboratories,
Bedford, MA). Trials contaminated by eye blinks were discarded from the analyses. Stimulus-
evoked pupil dilation responses were examined for two events of interest: the monetary cue and
the overlap stimulus. For each of these events, mean pupil size was estimated beginning 1s
after stimulus onset to 2s after stimulus onset (Sterpenich et al., 2006).
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To measure dilation, mean pupil size during this time window was baseline-normed by
subtracting the average pupil size during the 500 ms window prior to event onset (money cue or
overlap image). To examine the influence of threat-related arousal on pupil dilation, we
performed paired t-tests comparing mean pupil dilation responses between threat and neutral
trials during the money cue and overlap stimulus periods, separately. In addition, we examined
how threat influenced mean pupil size across four different time points of interest (pre-cue,
during money cue, pre-stimulus, during overlap stimulus), since baseline pupil size likely
constrained the magnitude of the subsequent dilation response.
2.9 The relationship between arousal-evoked pupil responses and memory
selectivity. To examine whether threat-induced pupil dilation was associated with threat-
enhanced high priority memory, we performed hierarchical linear modeling (HLM) analyses
using the glmer function in the lme4 library (Baayen et al., 2008). The parameters were
estimated with the maximum likelihood method in R (R Core Team, 2012). Each trial was used
as a Level 1 unit and each participant was used as a Level 2 unit.
Previous work shows that arousal’s beneficial carryover effects on perception and recall
of salient stimuli may last for up to 3 seconds (Sutherland & Mather, 2012). This finding raises
the question of whether NE’s effects on memory encoding are driven by LC responses to a) the
arousing event (e.g., monetary cue), b) the target stimulus itself (e.g., background scene in
overlap image) or c) some combination of the two. Thus, pupil dilation to both the monetary cue
and overlap stimulus were modeled as predictors, along with their interaction. Arousal condition
(1 = threat, -1 = neutral) and mean pupil dilation responses were group-centered and modeled
as the level-1 predictors of trial-by-trial memory outcomes. The main effects of Arousal, Overlap
Stimulus Dilation, and Money Cue Dilation were included in the model, along with all two- and
three-way interaction terms. In two separate HLMs, we examined how threat-induced pupil
dilation responses to the money cue and overlap stimulus influenced scene (high priority) and
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object memory (lower priority) independently (dependent variables: 1 = Remembered
Scene/Object; 0 = Forgot Scene/Object).
2.10 Neuroimaging Analyses
2.10.1 MRI data acquisition. All neuroimaging data were acquired on a 3T Siemens
PRISMA scanner located at the University of Southern California Dana & David Dornsife
Neuroimaging Center. The visual stimuli were displayed on a liquid crystal display monitor (1024
x 768 pixels at 60 Hz), which participants viewed via a mirror attached to a 32-channel matrix
head coil. Scanning commenced with the functional localizer task. Localizer functional imaging
data were acquired using one echo-planar imaging sequence lasting 6 minutes and 28 seconds
(TR = 2000 ms, TE = 25 ms, 41 slices, slice thickness = 0 mm, FOV = 192; flip angle = 90
degrees; number of volumes = 183; isotropic voxel size = 3mm
3
).
Next, a high-resolution T1-weighted anatomical image (MPRAGE) was acquired to aid
with functional image co-registration (slices = 176 axial; TR/TE/TI = 2300ms/2.26ms/1060ms;
FOV = 256mm; in-plane resolution = 1mm
2
; slice thickness = 1mm with no gap; bandwidth =
200Hz/Px; GRAPPA with acceleration factor = 2; duration: 10 minutes and 42 seconds). Four
runs of the same EPI sequence were then used to collect fMRI volumes during the monetary
incentive encoding task, with each run lasting 6 minutes and 14 seconds.
Following the monetary incentive encoding task, we collected on neuromelanin-sensitive
weighted MRI scan using a T1-weighted fast spin echo (FSE) imaging sequence (TR = 750 ms,
TE = 12 ms, flip angle = 120°, 1 average to increase SNR, 11 axial slices, FOV = 220 mm,
bandwidth = 220 Hz/Px, slice thickness = 2.5 mm, slice gap = 3.5mm; in-plane resolution =
0.429 x 0.429 mm
2
, scan duration: 1 minute and 53 seconds).
2.10.2 Image preprocessing. Image preprocessing was performed using FSL Version
5.0.4 (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). The functional volumes were
preprocessed according to the following steps: motion correction using MCFLIRT, removal of
non-brain tissue using BET, spatial smoothing using a Gaussian kernel of 6mm full-width-at-
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half-maximum (FWHM), grand-mean intensity normalization of the entire 4D data set by a single
multiplicative factor and a high-pass temporal filter of 100s. Structured noise and physiological
artifacts, such extreme head motion and white matter/CSF signal, were identified and removed
from the dataset using a single-session independent component analysis (ICA; (Beckmann et
al., 2005). Special care was taken not to remove components containing brainstem signal near
the LC, since removing noise-correlated brainstem activity risked removing signal of interest.
The criteria used for classifying noise components are described in more detail in Clewett et al.
(2013) (Clewett et al.). Each participant’s denoised mean functional volume was co-registered to
his or her T1-weighted high-resolution anatomical image using brain-based registration (BBR).
The anatomical image was then co-registered to the 2mm

isotropic MNI-152 standard-space
brain using an affine registration with 12 degrees of freedom.
2.10.3 Image preprocessing.
2.10.4 Subsequent memory effect general linear models (GLMs). Functional images
were analyzed to examine how threat-induced arousal affected perceptual and successful
encoding-related activity across the whole brain using a parametric general linear model (GLM).
FMRI analyses were focused on the period when participants viewed the overlap image. We
create separate event-related regressors by modeling the onset times of the monetary cues and
the overlap stimuli with durations of 1s and 2.5s, respectively. Each task regressor was
convolved with a dual-gamma canonical hemodynamic response function and their temporal
derivatives were used to model data.
Subsequent memory effects (i.e., successful memory) were examined by coding each
trial post hoc according to whether each scene was remembered or forgotten. A lower-level
GLM was then constructed for each participant using 4 task regressors: 1) threat scene
remembered, 2) threat scene forgotten, 3) neutral scene remembered, and 4) neutral scene
forgotten.
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To examine how putative LC activity modulated brain activity encoding, four
complementary task regressors were modeled to examine the parametric relationship between
pupil dilation and brain activity during encoding; these parametric regressors were scaled
according to the magnitude of the pupil dilation response to overlap stimulus on a trial-by-trial
basis. As a result, the BOLD response was weighted more strongly on trials with larger
stimulus-evoked pupil responses than on trials with smaller pupil responses, thereby providing
an index of phasic noradrenergic modulation of local brain activity (Murphy et al., 2011). Pupil
dilation values across each block of the fMRI task were mean-centered and then input into the
“strength” column of the three-column format EV files in FSL. Finally, eight additional regressors
were included in the GLM: 2 task regressors modeled activity during the money cue (threat or
neutral) and 6 nuisance regressors described residual head motion.
Three separate contrasts were created to test for main effects of Memory (remembered
vs. forgot), Arousal (threat vs. neutral), and an Arousal x Memory interaction effect: [threat
(remembered > forgotten) – neutral (remembered – forgotten)]. Whole-brain statistical
parametric brain maps were acquired for both the pupil-modulated and non-pupil-modulated
regressors. This entire procedure was repeated to examine the effects of threat on object
processing as well.
From the lower-level statistical parametric maps, a second-level fixed-effects analysis
was performed across each participant’s functional runs. The resulting contrast images were
analyzed in higher-level mixed-effects analysis using FMRIB’s local analysis of mixed effects
(FLAME 1 + 2; (Beckmann et al., 2003). A single group average for each of the contrasts-of-
interest was calculated using a one-sample t-test. In both the lower-level and higher-level
analyses, statistical parametric maps were corrected for multiple comparisons with clusters
determined by Z > 2.3 voxel-wise thresholding and a family-wise error-corrected cluster
significance threshold of P < .05 (Worsley, 2001).
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2.10.5 Region-of-interest (ROI) analyses. Region-of-interest (ROI) analyses were
performed to determine how threat modulated successful scene and object encoding-related
activity in high (PPA) and lower priority (LOC) visual category-selective cortex. Using FSL
Featquery, percent signal change values were extracted from each participant’s second-level
statistical parametric maps for the left/right LOC and left/right PPA. The ROI masks for each
LOC and PPA region were defined individually from the localizer session as 6mm-radius
spheres centered upon peak voxels in the ventral occipital and temporal cortex that were most
selective for objects (object block > scrambled objects + scene blocks; Z = 2.57, uncorrected)
and for scenes (scene block > scrambled objects + object blocks; Z = 2.57, uncorrected),
respectively. Using this approach, the ROIs were definable for all participants for both LOC
(mean peak MNI voxel coordinates: Left [-43 -69 -8]; Right [47 -69 -8]) and PPA (mean peak
MNI voxel coordinates: Left [-27 -47 -6]; Right [27 -43 -9]). These peak coordinates for the L/R
PPA and LOC are displayed in standard space in Figure 8.
To determine how threat influenced the strength of scene and object memory traces
formed at encoding, percent signal change values from these ROIs were submitted to 2
(Arousal: threat vs. neutral) x 2 (Memory: remembered vs. forgot) repeated-measures ANOVAs.
To test our main hypothesis that successful scene encoding relates to enhanced activity in the
prioritized representational region (PPA) and reduced activity in the lower priority
representational region (LOC) under arousal, we also performed a 2 (Region: PPA vs. LOC) x 2
(Arousal: threat vs. neutral) x 2 (Memory: remembered vs. forgot) repeated-measures ANOVA.
The same ROI analyses were conducted for the pupil-modulated statistical parametric maps.
A final ROI analysis for the LC was conducted using the same statistical procedures as
above. To define the LC masks, ROIs were hand-drawn on each participant’s neuromelanin-
sensitive weighted image using procedures described in Clewett et al. (2016) (Clewett et al.,
2016). An illustration of the four-step procedure is displayed in Figure 2. Bilateral LC ROIs were
manually defined as a ~1.29 mm wide by ~1.29 mm high (i.e., 3 x 3 voxels) in an axial slice
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located 7mm below the inferior boundary of the inferior colliculus. These masks were centered
on the left and right voxels with the highest signal intensities neighboring the corners of the
fourth ventricle. In the instances where the peak voxel was located immediately adjacent to the
fourth ventricle, the center of the ROI was moved one voxel further away (i.e., laterally) from the
peak intensity voxel.
The FSE-space hand-drawn LC ROIs were written into 2mm standard space using the
transformation matrices acquired from the fMRI analyses. We were unable to delineate LCs in 6
participants due to a scan not being collected (n = 2) or inter-space registration issues (n = 4).
For those participants, we used a standard-space 2SD LC mask derived from a previous study
to extract percent signal change estimates (Keren et al., 2009).

Figure 2. Locus coeruleus (LC) anatomical tracing protocol. Step 1) In the axial plane, the
inferiormost slice of the inferior colliculus (IC) was located; we then moved down 7 mm (2 slices)
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into a slice where LC signal intensities were most apparent. Step 2) Left and right LC regions-of-
interest (ROIs) appeared as high (bright) signal intensities neighboring the corners of the fourth
ventricle. Step 3) A small cross (3 x 3 voxels; yellow) of approximately the width of the LC (~1-2
mm) was placed on the voxels with peak signal intensity. Step 4: A dorsal pontine tegmentum
(PT) reference ROI (orange) was defined as a 10 x 10 voxel square located 6 voxels above the
more ventral (higher in the MRI axial image) of the two LCs and equidistantly between them. L =
left hemisphere.

3. Results
3.1 Arousal and motivation ratings. As expected, participants rated the threat money
cues as significantly more arousing than neutral money cues, t(25) = 8.61, p < .001. Participants
also rated being significantly more motivated to memorize scenes on threat trials compared to
neutral trials, t(25) = 4.53, p < .001.
3.2 Encoding and detection performance results. Analysis of scene categorization
accuracy and RT’s revealed that threat did not affect how quickly or accurately participants
categorized each scene (ps > .05; Figure 3). Threat of monetary punishment did not affect
detection accuracy for the three scrambled images, F(1,25) = .10, p = .92, η
p
2
= .00. However,
there was a significant main effect of Time, such that detection accuracy for the first scrambled
image appearing after the overlap image was decreased compared to the second and third
scrambled images, F(2,24) = 17.87, p < .001, η
p
2
= .60 (Figure 3). Importantly, there was no
significant Time x Arousal interaction effect on scrambled-image detection accuracy, F(2,24) =
1.4, p = .27, η
p
2
= .11, nor any main effects of Arousal on detection accuracy for any of the three
scrambled images (ps > .05).
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Figure 3. Mean scene categorization accuracy (i.e., judging the scene to be indoors or
outdoors).
Across the detection task, threat did not significantly slow down detection speed, F(1,25)
= 3.08, p = .091, η
p
2
= .11. Unsurprisingly, detection was slower for the first scrambled image
compared to the second and third scrambled images, F(2,24) = 109.97, p < .001, η
p
2
= .90.
There was no Time x Arousal interaction on detection speed, F(2,24) = .31, p = .73, η
p
2
= .02
nor any main effects of Arousal on RT for any of the three scrambles images (ps > .05; Figure
4). Together these results suggest that, while participants may have had difficulty re-orienting
attention from the overlap images to the first scrambled image, this effect did not differ by
Arousal condition nor did it persist across the remainder of the detection task.
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Figure 4. Mean scrambled image detection accuracy and reaction times (RTs) during the
distracter task.

3.3 Memory Performance
3.3.1 Mean memory performance results. To determine how threat-related arousal
differentially influences memory of high and low priority information, we performed a 2 (Priority:
high vs. low) x 2 (Arousal: threat vs. neutral) repeated-measures ANOVA. Participants
remembered significantly more scenes than objects, F(1,25) = 85.96, p < .001, η
p
2
= .78 (Figure
5, top panel). Consistent with our main behavioral prediction, threat-induced arousal enhanced
memory of prioritized scenes, while impairing memory of distracting objects, F(1,25) = 21.64, p
< .001, η
p
2
= .46. This arousal-by-priority interaction effect was predominantly driven by a
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significant threat-induced enhanced memory of high priority scenes, t(25) = 4.92, p < .001; in
contrast, threat-induced suppression of memory of low priority objects was marginally
significant, t(25) = -1.7, p = .10. A one-tailed, one-sample t-test revealed that object memory on
threat trials was not significantly above chance, t(25) = 1.23, p = .13.

Figure 5. Memory performance on the monetary incentive encoding fMRI task. Top panel:
proportion of correctly remembered high priority scenes and lower priority objects by Arousal
condition (threat of punishment versus neutral). Bottom panel: proportion of trials where
participants showed a selective memory trade-off in favor of the high priority scene (left) or
remembered both the scene and object stimuli in the overlap images (right). *p < .05, **p < .01,
***p < .001.

3.3.2 Memory codependency results. To determine how threat-related arousal
differentially influences memory selectivity on a trial-by-trial basis, we performed a 2 (Memory
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Outcome: RF vs. RR) x 2 (Arousal: threat vs. neutral) repeated-measures ANOVA. Participants
were significantly more likely to remember both stimuli rather than remember the scene and
forget its corresponding object, F(1,25) = 4.53, p = .043, η
p
2
= .15 (Figure 5; bottom panel).
Follow-up paired t-tests revealed that this interaction effect was predominantly driven by
threat significantly enhancing memory for scenes at the cost of memory for competing objects,
t(25) = 3.69, p = .001, whereas threat had no effect on successful memory for paired scene and
object stimuli, t(25) = 1.02, p = .32. Together these results indicate that participants were more
likely to remember scene-object pairs on neutral trials than on threatening trials. Filtering out
trials where participants guessed on the high priority scenes rendered the main effect of
Memory Outcome insignificant, F(1,25) = 2.12, p = .16, η
p
2
= .078, whereas the predicted
Memory Outcome x Arousal interaction effect on memory frequency remained marginally
significant, F(1,25) = 3.82, p = .062, η
p
2
= .13.
3.4 Pupil Results. We examined how mean pupil size fluctuated across 4 different time
points in the task by arousal condition (Figure 6). A 2 (Condition: threat vs. neutral) x 4 (Time:
pre-monetary cue vs. during monetary cue vs. pre-overlap stimulus vs. during overlap stimulus)
repeated-measure ANOVA revealed that mean pupil size was significantly larger during threat
compared to neutral trials of the monetary incentive encoding task, F(3,19) = 29.01, p < .001,
η
p
2
= .82.
Paired t-tests indicated that mean pupil size was significantly larger in the threat versus
neutral condition during the money cue, t(21) = 9.19, p < .001, prior to the overlap stimulus,
t(21) = 4.89, p < .001, and during the overlap stimulus, t(21) = 3.25, p = .004, suggesting that
physiological arousal was sustained after participants saw the threat cue. Importantly, mean
pupil size did not differ by Arousal condition just prior to the money cue, t(21) = .11, p = .91,
indicating that arousal-induced pupil dilation to the money cue was not biased by baseline
differences in pupil diameter between conditions.
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Examination of pupil dilation responses (i.e., the change from the immediately prior
event) indicated that, as expected, the threatening cues elicited significantly larger pupil
dilations than the neutral cues, F(1,21) = 83.25, p < .001, η
p
2
= .80 (Figure 6). In contrast, pupil
dilation responses to the overlap stimulus were significantly smaller on threatening trials
compared to neutral trials F(1,21) = 4.99, p = .037, η
p
2
= .19, which likely was influenced by the
already high pupil size in the threat condition in the pre-stimulus phase.

Figure 6. Mean pupil diameter across four periods-of-interest, including the money cue, overlap
stimulus and their 500 ms baselines. Statistical significance results (asterisks) shown between
time points indicate whether pupil dilation was significant at the money cue and overlap stimulus
was different between conditions. Statistical significance results reported at each of the four
time points indicate whether mean pupil size was different between conditions. Pupil size is in
arbitrary units. *p < .05, **p < .01, ***p < .001.

3.5 Relationship between pupil dilation and memory. Consistent with GANE’s main
prediction, the first HLM analysis revealed a significant overlap-related pupil dilation-by-
condition interaction effect on scene memory accuracy, z = 2.17, p = .03 (Table 2). To interpret
the directionality of this effect, we extracted +/-1 standard deviation values for each predictor in
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the HLM. We then used the linear regression equation (betas) to estimate scene memory
performance when overlap pupil dilation and condition were relatively higher or lower than the
mean. Plotting this interaction revealed that threat-evoked pupil dilation responses to the
overlap stimulus predicted successful scene (high priority image) encoding (Figure 7). The
second HLM analysis revealed no effects of pupil dilation on successful object memory,
indicating that the mnemonic effects of pupil responses are unique to the high priority stimulus
(Table 3).

Figure 7. Plot of hierarchical linear modeling (HLM) results examining the trial-by-trial influence
of threat-evoked pupil dilation to the overlap stimulus on accurate scene (goal-relevant stimulus)
memory.

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Table 2. Hierarchical linear modeling results for the relationship between pupil dilation
responses to the money cue, overlap stimulus and arousal condition (threat or neutral) on scene
memory.
Predictors Estimate SE z p
Intercept 0.80 0.084 9.55 <2e-16
Money Cue Pupil Dilation 0.0048 0.011 0.44 .66
Overlap Pupil Dilation 0.0048 0.011 0.42 .68
Condition 0.21 0.053 3.98 7xe-5***
Money Cue Dilation x Condition -0.005 0.011 -0.46 .65
Overlap Dilation x Condition 0.025 0.011 2.17 .03*
Money Cue Dilation x Overlap
Dilation
-0.00039 0.0023 -0.17 .86
Money Dil. x Overlap Dil. x Cond 0.00098 0.0023 0.43 .67
Notes: Arousal Condition is coded as Threat = 1 and Neutral = -1. Scene memory was coded as
Remembered = 1 and Forgot = 0. Significant results are bolded. *p < .05; **p < .01; ***p < .001.

Table 3. Hierarchical linear modeling results for the relationship between pupil dilation
responses to the money cue, overlap stimulus and arousal condition (threat or neutral) on object
memory.
Predictors Estimate SE z p
Intercept 0.1313829 0.0487403 2.696 0.00703
Money Cue Pupil Dilation -0.0079466 0.0100273 -0.792 0.42808
Overlap Pupil Dilation 0.0001051 0.0106007 0.01 0.99209
Condition -0.0335967 0.0487403 -0.689 0.49063
Money Cue Dilation x Condition 0.0013716 0.0100273 0.137 0.8912
Overlap Dilation x Condition 0.011331 0.0106007 1.069 0.28512
Money Cue Dilation x Overlap
Dilation
0.0011246 0.0021245 0.529 0.59656
Money Dil. x Overlap Dil. x Cond -0.0036814 0.0021245 -1.733 0.08313
Notes: Arousal Condition is coded as Threat = 1 and Neutral = -1. Object memory was coded as
Remembered = 1 and Forgot = 0. Significant results are bolded. *p < .05; **p < .01; ***p < .001.

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3.6 FMRI results for non-pupil-modulated regressors.

3.6.1 Main effect of arousal on perceptual processing. To determine how threat of
punishment modulated brain activity, we compared brain activation patterns during overlap
stimulus presentation between the threat and neutral conditions. Threat elicited greater activity
in the dorsal LOC and intraparietal sulcus (Table 4; Figure 8 top panel). In contrast, threat
suppressed activity in regions encompassing nodes of a right fronto-parietal network, including
right frontal cortex, right dorsolateral parietal cortex and the precuneus. In addition, threat
suppressed activity in the postcentral gyrus, left parietal operculum, and primary auditory cortex
(A1).
3.6.2 Main effect of successful scene and object encoding. To identify brain regions
associated with successful memory encoding, we separately contrasted activation patterns
between trials when participants subsequently remembered vs. forgot target scenes and
distracting objects. Successful scene memory was associated with enhanced activity in the
dorsal parietal cortex, LOC, intraparietal sulcus (IPS), PPA, parahippocampal gyrus, and
nucleus accumbens (Figure 8 top panel). Significant successful memory-related clusters also
encompassed bilateral amygdala and hippocampus. There were no significant memory-related
activations for objects.

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Figure 8. Whole-brain patterns of activity related to main effects of Arousal (threat vs. neutral)
and Memory (remember vs. forgot) on scene processing for the pupil-unmodulated (top panel)
and pupil-modulated (bottom panel) analyses. SFG = superior frontal gyrus; LOC = lateral
occipital cortex; PPA = parahippocampal cortex; Nacc = nucleus accumbens; IFG = inferior
frontal gyrus; SMA = supplementary motor area. All clusters survived multiple comparison
correction at Z > 2.3 voxel-wise thresholding and a family-wise error-corrected cluster
significance threshold of P < .05 (Worsley, 2001).

3.6.3 Interactions between threat of punishment and scene/object encoding
success on whole-brain activity. Next, we examined brain activation patterns associated with
the interaction between threat-induced arousal and successful memory encoding for scenes and
objects, separately. There were no significant arousal-by-memory interactions for scenes or
objects.
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Table 5. Arousal and memory effects on whole-brain activity during scene encoding.
MNI Peak Coordinates (mm)
Brain Region H X Y Z Z-max Voxels
Threat > Neutral
Intraparietal Sulcus L -28 -74 24 4.56 1706
Intraparietal Sulcus R 34 -80 26 4.88 1084

Neutral > Threat
Middle Temporal Gyrus R 70 -36 0 4.95 9889
Frontal Pole R 16 46 48 4.65 6429
Parietal Operculum L -64 -24 16 4.47 5512
Precuneus R 2 -72 40 4.81 4544
Postcentral Gyrus -- 0 -34 76 3.39 756

Scene Remembered > Forgotten
Lateral Occipital Cortex/Superior
Parietal Lobule
R 28 -70 48 4.53 9595
Parahippocampal Gyrus R -28 -38 -18 5.26 8987

Threat (R > F) – Neutral (R > F)
N/A -- -- -- -- -- --
Notes: H = hemisphere; L = left; R = right.

3.7 Region-of-Interest (ROI) analysis results for non-pupil-modulated regressors. To
examine how threat of monetary punishment affected successful encoding activity in category-
selective cortex ROIs, we extracted percent signal change values from each participant’s
left/right PPA and left/right LOC identified by the functional localizer. We also performed an ROI
analysis using LC mask derived from 20 participants’ neuromelanin-sensitive weighted images
and LC masks from Keren et al. (2009). Since we did not have specific predictions concerning
laterality, all analyses were collapsed across the left and right hemispheres.
Consistent with a previous fMRI study (Lee et al., 2014), there was a significant Region x
Arousal interaction effect on perception-related brain activity, such that threat influenced activity
in the PPA and LOC differently, F(1,25) = 14.42, p = .001, η
p
2
= .37 (Figure 9). Follow-up paired
t-tests indicated that this interaction was predominantly driven by threat enhancing perceptual
activity in the PPA, t(1,25) = 2.7, p = .012, but having no significant effect on perceptual activity
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in the LOC, t(1,25) = -0.81, p = .43. Thus, arousal induced by threat did not enhance brain
activity indiscriminately but rather was selective to the sensory cortical region specialized to
process scenes, the goal-relevant stimulus.
Successful scene encoding was associated with enhanced activity in the PPA, F(1,25) =
21.46, p < .001, η
p
2
= .46 (Figure 10). However, contrary to our prediction, successful scene
encoding was also associated with enhanced activity in the LOC, F(1,25) = 12.58, p = .002, η
p
2
= .34, suggesting that – although the LOC predominantly responds to objects – it also makes
contributions to processing spatial information (Dilks et al., 2013). There were no significant
arousal-by-memory interactions in either of the category-selective ROIs. For the LC ROI
analyses, there were no main or interaction effects of Arousal or Memory.

Figure 9. Region-of-interest (ROI) analysis of visual category-selective cortex. Top panel:
Results of the functional localizer scan revealed the location of the left/right lateral occipital
cortex (LOC; lower priority region), specialized to process objects, and the parahippocampal
place area (PPA; high priority region), specialized to process scenes. Coordinates represent the
average peak voxel across all participants for each of the four ROI masks. Bottom panel: The
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main effect of Arousal (threat vs. neutral) on perception-related activity in the LOC and PPA.
Mean percent signal change values represent brain activity levels when participants viewed the
overlap image. **p < .01.

Figure 10. Subsequent memory ROI analysis for the category-selective cortical regions
(LOC/PPA). Mean percent signal change values represent brain activity levels when participants
viewed the overlap image and either remembered or forgot the goal-relevant scene (left two
bars) or remembered or forgot the distracting object (right two bars). Red bars represent the
threat condition, whereas gray bars represent the neutral condition. *p < .05; **p < .01; ***p <
.001.

3.8 FMRI results for pupil-modulated regressors.

3.8.1 Main effect of arousal on perceptual processing. There were no significant
perception-related activations corresponding to the contrast of threat vs. neutral trials.
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3.8.2 Main effect of successful scene and object encoding. Successful pupil-
modulated scene memory was associated with reduced activity in the left central operculum,
right supplementary motor cortex, and right inferior frontal gyrus (Table 6; Figure 8 bottom
panel). There were no significant successful encoding activations modulated by pupil dilation for
objects.
3.8.3 Interactions between threat of punishment and scene/object encoding
success on whole-brain activity. In the final whole-brain analysis, we tested our main
hypothesis that pupil dilation relates to threat-enhanced mnemonic processing of scene stimuli
in scene-selective cortex. Consistent with this prediction (i.e., GANE model), pupil dilation
parametrically modulated successful scene encoding activity in the left posterior
parahippocampal cortex and a brainstem/cerebellum cluster encompassing the LC and VTA/SN
(Figure 11A). There were no significant interaction effects for object memory, indicating that
neuromodulatory effects of putative LC activity were specific to high priority scenes.

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Table 6. Arousal and memory effects on whole-brain activity during scene encoding
parametrically modulated by pupil dilation responses to the overlap image.

Scene Memory Pupil-Modulated MNI Peak Coordinates (mm)
Brain Region Hemi X Y Z Z-max Voxels
Threat > Neutral
N/A -- -- -- -- -- --

Scene Forgotten > Remembered
Central Operculum L -46 -8 16 3.55 2279
Supplementary Motor Area R 8 0 68 3.54 1269
Inferior Frontal Gyrus R 58 16 18 3.44 992

Threat (R > F) – Neutral (R > F)
Cerebellum R 10 -56 -30 3.53 1721
Posterior Parahippocampal Gyrus L -12 -40 -6 3.7 1016
Notes: H = hemisphere; L = left; R = right.

3.9 Region-of-Interest (ROI) analysis results for non-pupil-modulated regressors.
There were no significant main or interaction effects of Arousal and Memory on PPA/LOC
activity. However, confirming our prediction that LC activity relates to threat-enhanced high
priority memory, a 2 x 2 ANOVA revealed that pupil-modulated LC activity was significantly
greater when participants remembered threat-motivated scenes versus neutral-related scenes,
F(1,21) = 4.87, p = .039, η
p
2
= .19 (Figure 11C).
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Figure 11. (A) Whole-brain results of the pupil-modulated memory general linear model
analysis. Orange clusters signify regions where, relative to the neutral condition, pupil dilation
responses to the overlap image positively modulated successful scene encoding activity.
Orange functional clusters represent regions where, relative to the neutral condition, threat-
induced pupil dilation to the overlap image modulated successful scene encoding activity. (A)
Significant arousal-by-memory interactions were observed in the locus coeruleus (LC),
parahippocampal gyrus (PHG) and the ventral tegmental area/substantia nigra (VTA/SN). (B)
Functional-structural overlap between results from the pupil-modulated scene memory analysis
anatomical references for the LC. Blue voxels in left panel reflect location of an LC 2SD
standard-space mask from Keren et al. (2009). Second panel from left shows the location of the
arousal-by-memory functional cluster. Red voxels in middle-right image signify locations where
the functional cluster and LC standard-space mask intersected. As illustrated here with one
participant’s sample neuromelanin-sensitive MRI image (far right; bright white dots under red
arrows) there was a high degree of overlap between the functional cluster and the anatomical
position of the LC. (C) Region-of-interest analysis for the LC. The red and gray bars signify
successful scene trace activity in the threat and neutral conditions, respectively. *p < .05. R =
Scene Remembered; F = Scene Forgotten.

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4. Discussion
Neuromodulatory signals are fundamental for enhancing declarative memory of
behaviorally relevant events. Yet most research has focused on how the dopaminergic system
facilitates reward-motivated learning and goal-directed attention (Sara, 2009); by comparison,
the LC-NE system’s role in motivated memory encoding is less understood. Here, we combined
measures of pupil dilation, a non-invasive biomarker of phasic LC activity (Murphy et al., 2014),
with fMRI to demonstrate that LC-NE system activity selectively enhances goal-relevant
memories under arousal.
Consistent with a previous study that used threat of shock to motivated encoding (Murty
et al., 2012), we first found that threat of monetary punishment led to enhanced memory for
target scene images. Importantly, this memory-boosting effect of aversive arousal was specific
to scenes, as threat induction suppressed rather than enhanced memory of concurrent
distracting objects. Next, hierarchical linear modeling analyses revealed that greater pupil
dilation to the overlap stimulus was associated with threat-enhanced memory of the prioritized
scenes but not the competing lower priority objects. In the brain, threat selectively enhanced
perceptual activity in the scene-selective visual cortex, while suppressing activity in both task-
irrelevant auditory cortex and bottom-up fronto-parietal attention networks. Punishment-
motivation enhancements in scene memory were associated with greater activity in the
PPA/LOC and several key memory regions, including the amygdala, hippocampus, and nucleus
accumbens. Most importantly, pupil dilation modulated threat-related scene encoding activity in
the parahippocampal gyrus, a cortical region implicated in scene processing. Together these
findings suggest that suggest that NE and DA may interact synergistically during high phasic
arousal to facilitate goal-directed memory enhancements.
It is well established that motivation enhances memory for behaviorally relevant stimuli
and can narrow the focus of attention onto stimuli relevant to our goals (Levine & Edelstein,
2009; Montagrin et al., 2013), at least when incentives precede goal attainment (e.g., earning a
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reward or successfully avoiding punishment; (Kaplan et al., 2012). However, less is known
about how motivational arousal influences competition in memory between concurrent neutral
stimuli. Past work shows that experiencing emotional arousal can lead to retrograde memory
enhancements of preceding goal-relevant stimuli, suggesting that arousal biases consolidation
processes (Sakaki et al., 2014). Extending this work, we demonstrate that arousal induced by
threat of punishment can differentially impact encoding processes. This finding is in line with the
arousal-biased competition model, which posits that arousal impacts high and lower priority
representations differently depending on their goal-relevance (Mather & Sutherland, 2011).
Furthermore, our results accord with evidence that affective states with high “motivational
intensity,” which is homologous to sympathetic arousal, narrow the scope of attention (Harmon-
Jones et al., 2012).
Using pupil dilation as an indirect biomarker of human LC activity (Murphy et al., 2014),
we showed that enhanced pupil dilation responses to to-be-remembered target images are
associated with better memory for those images when they are encoded under threat. In a
similar finding, enhanced memory for high-value versus low-value-associated words was
associated with greater pupil dilation to high-value words during encoding (Ariel & Castel, 2014).
Likewise, pupil dilation evoked by events known to activate the LC-NE system, including target
detection or oddball sounds, has been linked to enhanced memory for concurrent task-irrelevant
background scenes (Hoffing & Seitz, 2015). Here, we used pupil-weighted GLM analysis to
validate the putative link between the LC, pupil dilation, and selective learning. Past work also
shows that pupil dilation predicts subsequent perceptual stability of one percept over another
(i.e., Necker cube) in a perceptual rivalry paradigm (Einhäuser et al., 2008). This perceptual
rivalry finding is intriguing given that bottom-up salience was held constant in our experiment
and participants had to dissociate scene versus object visual inputs in a top-down manner.
From the perspective that phasic LC activity amplifies decision outcomes (Aston-Jones &
Cohen, 2005), our results suggest that LC responses also enhance mnemonic processing of
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top-down prioritized information.
Our current results contrast with recent evidence that “high gain” states, as indexed by
greater baseline pupil size, bias individuals to learn from trial-and-error better based on their
intrinsic learning preferences (Eldar et al., 2013). This high gain state, however, did not predict
better learning performance overall, but just better learning when rewarded features adhered to
participants’ predisposed learning style, such as learning based on perceptual or semantic
stimulus features. In fact, the degree to which task performance corresponded with individual
predisposed learning styles was strongly anti-correlated with average pupil dilations across all
participants. In contrast, we found that phasic pupil dilations rather than baseline pupil size was
associated with better learning for top-down prioritized scenes. Our finding is therefore
consistent with the view that transient LC activity enhances online top-down selection processes
(Aston-Jones & Cohen, 2005). This phasic memory effect may be distinguishable from more
habitual, hard-wired learning effects that manifest under high tonic LC/arousal states, such as
stress, when goal-directed inputs from the PFC are impaired by high tonic NE release (Arnsten
et al., 2015; Arnsten, 2011; Schwabe et al., 2012).
By simultaneously modeling pupil dilation responses to both the monetary cue and the
overlap stimulus, we were also able to pinpoint the mnemonic benefit of putative LC activity to
the stimulus-encoding period. Why would NE’s effects on memory be most evident during
selective attention rather than the arousing cue? Recent work in monkeys shows that – although
pupil dilation occurs in responses to reward cues – LC activity was more closely correlated with
dilations during action onset of subsequent goal-directed responses (Varazzani et al., 2015).
Furthermore, the magnitude of pupil dilation and LC activity were both correlated with the
amount of effort required to perform the action. Our data expand upon theoretical and empirical
work implicating LC activity in effortful, goal-directed attention (Alnæs et al., 2014; Aston-Jones
& Cohen, 2005; Bouret et al., 2012; Floresco, 2015; Raizada & Poldrack, 2008).
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Physiologically, the LC-NE system is well equipped to direct resource allocation based
on its ability to selectively direct blood flow towards areas of high activity (Bekar et al., 2012)
mobilize glucose metabolism (O'Donnell et al., 2012), and supply additional energy via local
gain in astrocyte activity (Paukert et al., 2014). Together these findings suggest that the positive
relationship between pupil dilation and scene memory may signify participants’ increased effort
to encode target scene images when motivated by threat.
Consistent with previous imaging studies using tones conditioned to shock, we found
that threat of punishment yielded brain activity patterns consistent with increased neuronal gain,
where arousal modulated category-selective visual cortical activity differently based on priority
(Lee et al., 2014). Specifically, when viewing the overlap stimulus, threat-induced arousal
selectively enhanced activity in the parahippocampal place area (PPA), a region that is highly
selective for scene stimuli, but had no effect on activity in the lateral occipital cortex (LOC), a
region that tends to respond more selectively to objects. This lack of an arousal-induced
suppression in the lower priority region is not surprising given that, in addition to processing
object shape information, the LOC processes spatial information (Dilks et al., 2013). Moreover,
we could not distinguish between LOC activities corresponding to the distracting object versus
objects that were part of the scene, so LOC responses could have been driven by the task-
relevant percept as well. In contrast, the study by Lee et al. (2014) measured activity in face-
selective cortex when face stimuli were prioritized versus lower priority, spatially adjacent
scenes, thereby increasing the spatial and categorical selectivity of attention-modulated brain
activity.
The GANE model predicts that arousal interacts with sensory processing based on the
current level of local activity; thus, unlike the LOC, arousal-induced suppression of brain activity
should most likely to occur in brain regions processing truly task-irrelevant inputs. Indeed, we
found that threat led to suppressed activity in primary auditory cortex during encoding,
suggesting that arousal modulates sensory activity according to its goal-relevance. Threat of
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monetary punishment also led to suppressed activity in a right fronto-parietal network, including
the temporoparietal junction (TPJ), which has been implicated in processing salient bottom-up
sensory inputs and reorienting attention (Corbetta et al., 2008; Corbetta & Shulman, 2002). In
contrast, threat led to enhanced perceptual activity in dorsal LOC near the posterior intraparietal
sulcus, a key region that helps regulate top-down selective attention (Corbetta & Shulman,
2002). Previous work demonstrates that these fronto-parietal attention networks antagonize
each other in order to compete for the focus of attention and that enhanced ventral attention
network activity is associated with episodic encoding failures for information selected in a top-
down manner (Corbetta & Shulman, 2002; Uncapher et al., 2011). Thus, our results
demonstrate that threat of punishment selectively enhances task-relevant functional network
activity, while attenuating activity in regions processing task-irrelevant - in this case, bottom-up -
sensory inputs.
Successful scene encoding was associated with increased activity in ventral visual
areas, including scene-selective visual cortex (i.e., PPA) and the LOC. However, contrary to our
expectation, threat only marginally significantly strengthened scene memory traces in the PPA
relative to the neutral condition. One interesting finding was that the amygdala facilitated
memory for goal-relevant scenes irrespective of threat. Emerging research indicates that the
amygdala may play a more general role in salience detection rather than simply biasing
processing of emotionally salient information (Ousdal et al., 2008; Sander et al., 2003).
Furthermore, others have argued that amygdala activity guides feature-based attention, which,
in our current experimental design, would be essential for resolving perceptual and attention
competition between each scene and its overlapping, transparent object (Jacobs et al., 2012).
We also found that nucleus accumbens activity was associated with scene encoding across
both conditions, which coincides with evidence that behaviorally-relevant but not behaviorally-
irrelevant emotional stimuli not only enhance activity in the amygdala and nucleus accumbens
but also functional interactions between these two regions (Ousdal et al., 2012). As expected,
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threat did not elicit any significant patterns of activity corresponding to successful object
memory, but this may have been due to object memory performance being close to chance.
In accordance with the GANE model, we found that stimulus-evoked pupil dilations
modulated scene-encoding activity in the LC and a PHG, a predominantly scene-selective
cortical region (Köhler et al., 2002; Staresina et al., 2011). Thus, our results demonstrate that
such LC neuromodulation strengthens goal-relevant mnemonic traces under arousal by
modulating local representational activity selectively. Interestingly, the results from the pupil-
memory HLM and pupil-weighted FMRI analyses were highly consistent: they both
demonstrated that threat moderated the relationship between pupil/LC activity and memory.
Although it is conceivable that any non-luminance evoked pupil dilation relates to activation of
the LC, our findings imply that there may be other mechanisms at play when learning is
motivated by threat.
As our pupil-weighted fMRI results showed, one such mechanism could be the midbrain
dopaminergic nuclei, the SN/VTA, which co-activated with LC when threat enhanced scene
memory relative to the neutral condition. The LC and VTA are reciprocally connected and form
highly interdependent networks that work in concert to optimize cognitive processing,
particularly under highly motivated states (Mejias-Aponte, 2016; Sara, 2009). Much research
indicates that midbrain dopamine activity enhances declarative memory (Lisman & Grace, 2005;
Shohamy & Adcock, 2010). To our knowledge, this is the first study showing that pupil dilation
modulates the relationship of VTA/SN activity with subsequent memory. Activation of LC leads
to burst firing in the VTA (Sara, 2009) and vice versa (Deutch et al., 1986), suggesting that the
two systems likely interact in enhancing motivated memory.
Another key finding was that the memory-boosting effects of LC and VTA/SN activity
only emerged after accounting for trial-by-trial variability in pupil responses (i.e., but not in the
un-modulated conventional GLM). This result suggests that the LC-NE and DA systems co-
activate to strengthen prioritized memory traces when participants were most sympathetically
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aroused under threat. As discussed above, large pupil dilations may represent moments when
participants were maximally engaged or invested in the task; this “exploitative” mode of LC firing
should in turn amplify top-down processing of scene memory traces in task-relevant brain
regions, such as the PHG. From a methodological perspective our results further validate the
technique of linking task-evoked changes in pupil size to BOLD signal fluctuations in order to
localize brainstem activity to the LC (Murphy et al., 2014). Furthermore, despite controversy
surrounding which neuromodulators regulate pupil dilation, our data support the leading theory
that non-luminance-related pupil dilations are driven by phasic LC activity.
There are several limitations in this study to consider. First, influential models of LC-NE
system function predict that NE released under arousal enhances the gain on competition
between task-relevant and task-irrelevant representations. However, we did not find evidence of
arousal-induced suppression in the lower priority object-selective region, the LOC, or in memory
performance. Future studies could use more distinct categorical stimuli, such as the FFA (Lee et
al., 2014), to examine whether arousal-biased competition processes in memory also map onto
gain effects in category-selective visual cortex.
Second, although the pupil-modulated GLM approach helped localize memory-relevant
activity to an area consistent with the LC, the BOLD signal in the brainstem is confounded by
cardiac pulsation artifact and low spatial resolution of conventional MRI (Astafiev et al., 2010).
We note, however, that it is unlikely that our findings resulted from threat enhancing the
confounding physiological effects on brainstem signal via increased sympathetic arousal,
because we did not observe a main effect of threat on brainstem activity. Even so, our results
should be interpreted with caution given current methodological constraints on imaging
brainstem activity in humans.
Third, it is possible that VTA/SN recruitment was not driven by aversive responses to
threat, per se, but from the ambiguity of using a secondary reinforcer, such as money, to
motivate learning. For instance, since all participants ultimately earned money for their memory
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performance (chance was 50%, or $8.00), they may have framed punishment-cued trials as an
opportunity to win rather than lose money. Moreover, potentially avoiding negative outcomes
could serve as an intrinsic reward signal. Using primary reinforcers, such as shock, might
therefore be a cleaner method of manipulating threat specifically and would allow for clearer
interpretations regarding the influence of aversive contexts on encoding processes (Murty et al.,
2012). In the same vein, future studies should explicitly examine valence-specific effects in this
paradigm by providing rewarding incentives as well. Using both impending gain and losses
would helps dissociate whether the DA systems influence on motivated learning pertains more
to reward or motivated attention more generally.

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Supplementary Figures

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Supplementary Figure 1. Cross-subject variance in peak L/R PPA (red/yellow) and L/R LOC
(blue) coordinates identified using the functional localizer.

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Chapter 5. General Discussion
Decades of emotion-cognition research demonstrate that the superior status of
emotional memories is predominantly driven by arousal-induced activation of the noradrenergic
system. Here, we demonstrated that, beyond strengthening emotional memories, noradrenergic
mechanisms also amplify memory selectivity for top-down prioritized neutral stimuli under
arousal. Thus, NE’s neuromodulatory effects on cognitive processing are not unique to emotion;
rather, the LC-NE system appears to play a more fundamental role in enhancing memory of any
high priority representation under arousal, irrespective of its emotionality.
In this dissertation, we used a combination of pharmacological, physiological, and
neuroimaging techniques to test global, local and adrenoreceptor-subtype-specific predictions of
GANE theory: 1) increased tonic (or global) NE activity at encoding interacts with phasic, or
transient, arousal to influence memory selectivity for neutral stimuli; 2) β-adrenoreceptor
activation mediates the dichotomous influence of arousal on memory for task-relevant versus
task-irrelevant information; and 3) phasic LC activity under arousal strengthens goal-relevant
memory traces via the local enhancement of high priority excitatory signals. Together, our
results validated many of these predictions, while also suggesting more nuanced NE
mechanisms and moderating factors, including sex hormones, involved in enhancing goal-
relevant representations under arousal.
Main Finding 1: In women, tonic increases in noradrenergic activity promote
arousal-enhanced memory selectivity

In Study 1, we were interested in examining how task-irrelevant emotional arousal
enhances memory trade-offs between spatially competing high and lower priority neutral visual
stimuli. Previous work shows that hearing emotional sounds can impair memory for background
sounds paired with a neutral foreground object (Ponzio & Mather, 2014). However, the study
conducted by Ponzio and Mather (2014) did not find evidence of competition effects: During
emotional trials, memory for central foreground objects wasn’t enhanced at the cost of memory
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for relatively inconspicuous background scenes, at least not on average (trial-by-trial
competition was not examined). One possibility is this null effect was due to sensory
competition/interference being too low between the neutral visual stimuli. Thus, for Study 1 we
amped up object-scene competition by rendering the foreground object transparent. Since
bottom-up perception was held constant, we could also specifically examine the top-down
biasing effects of goal-directed attention on memory. Moreover, having participants toggle their
attention between the two visual categories (object/scene) enabled us to determine whether
arousal’s effects generalize to any stimulus type, which is characteristic of phasic LC activity
(Sara, 2009).
Study 1 also tested the possibility of sex differences in emotion-induced memory trade-
offs. Sex differences in emotional responses, memory, and underlying brain activity in limbic
regions, such as the amygdala, are ubiquitous in the emotion-cognition literature (Andreano &
Cahill, 2009). For instance, women show even stronger memory enhancements for emotional
versus neutral stimuli and tend to recall emotional material more quickly and more vividly than
men (Hamann, 2005; Hamann & Canli, 2004). Women also exhibit greater emotion-induced
retrograde amnesia for inconspicuous neutral words preceding an emotional versus neutral
oddball word (Strange et al., 2003).
Converging theoretical and empirical work suggest that women’s differential sensitivity to
arousal’s effects on memory may relate to interactions between estradiol, progesterone and NE
(Andreano & Cahill, 2009; Ertman et al., 2011). Consistent with this possibility, higher levels of
progesterone and estrogen have been shown to moderate the effects of exercise-increased NE
levels on biased memory outcomes for emotionally salient information in healthy young women
(Nielsen et al., 2015). Furthermore, the amygdala is rich in estrogen and progesterone
receptors; given this region’s central role in mediating arousal-induced suppression of
inconspicuous information, this suggests that the convergent mnemonic effects of NE and
hormone influences in the amygdala may lead to sex differences in memory trade-offs.
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The aim of Study 1 was to expand on these findings and determine the neurohormonal
mechanisms that mediate emotional arousal’s influence on memory selectivity for goal-relevant
information. To this end, we investigated how task-induced changes in sAA, a candidate
biomarker of global (non-trial-specific) noradrenergic activity, and sex steroid hormone levels
were associated with arousal-biased memory trade-offs. In a competitive visuo-attention task,
participants viewed images of a transparent object overlaid on a background scene and were
instructed to explicitly memorize one of these stimuli while ignoring the other. Following each
image, participants heard emotional or neutral audio-clips and provided a subjective arousal
rating for those sounds. These ratings were then used as a predictor of the trial-by-trial effects
of emotional arousal on preceding neutral information processing.
Hierarchical linear modeling (HLM) analyses revealed that emotional sounds enhanced
memory selectivity - that is, remembering the goal-relevant image at the cost of remembering its
corresponding distracter - significantly more in women than in men who showed greater pre-to-
post task increases in sAA. In women, this arousal-by-priority interaction was not only significant
but also significantly greater in those who rated the emotional sounds as less distracting,
suggesting they were better able to prioritize goal-relevant images over the task-irrelevant
emotional sounds. Higher sex steroid hormone levels were also associated with greater memory
selectivity in women irrespective of emotional arousal ratings. Yet we did not find evidence that
sex hormone levels mediated the positive relationship between NE and selective memory
outcomes. Together these suggest that emotion-induced NE release amplifies the effects of
goal relevance in memory for healthy young women but not men.
In sum, Study 1’s results highlight that elevated background noradrenergic activity (i.e.,
non-trial-specific) might promote GANE-like effects in memory. We speculate that emotional
sounds elicited phasic LC activity on a trial-by-trial basis, which would indicate that a surge in
arousal interacts with overall noradrenergic tone to modulate on-going cognitive selection
processes. Additionally, this experiment showed that, compared to a similar previous study
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(Ponzio & Mather, 2014), the selective effects of emotional arousal may be more apparent when
spatial interference between neutral stimuli is increased (i.e., by rendering the object
transparent). Indeed, past work suggests that NE’s modulatory effects on perceptual acuity and
memory are most apparent under conditions of high inter-stimulus competition, or sensory
overlap.
One previous fMRI study examined brain activity while participants performed an
audiovisual simultaneous detection task with varying levels of difficulty (Raizada & Poldrack,
2008). In this task, participants had to discriminate whether a flashing white disc and burst of
noise occurred simultaneously or successively. Challenge-driven attention was invoked by
increasing the unpredictability of task demands from moment-to-moment, such that the onsets
of the visual and auditory stimuli varied between being relatively close or farther apart in time.
Imaging analyses revealed that more difficult trials (i.e., greater audio-visual stimulus overlap)
were associated with co-activation of the LC and right frontal cortex. Furthermore, the LC
demonstrated greater functional connectivity with primary auditory, primary visual and parietal
cortex throughout the task, suggesting that the LC-NE system augmented sensory processing
to maximize stimulus discrimination. Rodent research also indicates that NE is important for
discrimination performance on tasks involving temporal unpredictability and high distracter
interference (Carli et al., 1983; Cole & Robbins, 1992). Thus, NE appears to modulate cortical
activity to maximize discrimination between optimal (i.e., dominant or high priority) and sub-
optimal synaptic inputs, particularly in the presence of noise or distracter interference.
Main Finding 2: β-adrenoreceptors help amplify arousal-biased competition
outcomes in memory, particularly via the suppression of weak visual representations.

The aim of Study 2 was to examine how emotional arousal’s selective effects on
cognition spillover to influence memory competition between high and lower priority object
images presented in succession. More critically, this human pharmacological study was
designed to test GANE’s key prediction that, under arousal, local activity-dependent increases
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in NE help activate low-affinity β-adrenoreceptors and, as a result, enhance memory processing
of prioritized information at the expense of processing less salient information (Mather et al., in
press). We also anticipated that β-adrenergic blockade would reduce memory decrements for
less-attended stimuli, based on the important role of β-adrenoreceptors in facilitating cortical
inhibition (Waterhouse et al., 1982).
This human pharmacological experiment had a double-blind, placebo-controlled,
randomized design. A total of 26 healthy younger adults were administered 40 mg of
propranolol, a β-adrenoreceptor blocker, or 40 mg of placebo. After pill administration,
participants completed an emotional oddball task in which they were asked to prioritize a neutral
object appearing just before an emotional or neutral oddball image within a sequence of 7
neutral objects. We hypothesized that, based on previous findings using the same oddball
experiment, increasing the goal-relevance of the oddball-1 image would lead to its enhancement
rather than suppression in memory by emotional arousal under placebo (Sakaki et al., 2014).
Our results replicated previous findings that β-adrenergic blockade prevents an emotion-
induced anterograde amnesia for relatively less attended stimuli (Hurlemann et al., 2005).
However, contrary to our main prediction, emotion did not affect memory of goal-relevant stimuli
in either the placebo or beta-blocker groups. Similar to Study 1, we found that memory for goal-
relevant stimuli under arousal were correlated with task-related changes in sAA, a proxy of LC-
NE system activity. We also found that propranolol reduced competition in memory between
high and lower priority objects, such that emotional oddballs no longer enhanced memory for
oddball-1 objects at the cost of memory for their corresponding oddball+1 objects on a trial-by-
trial basis. Together these results suggested that noradrenergic mechanisms mediate the
selective influence of arousal on memory, with β-adrenoreceptors being particularly important
for suppressing memory of less important information under arousal. Unfortunately, we could
not explore the possibility of sex differences in β-adrenoreceptor effects on memory, because
the sample size was too small.
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Notably, our finding of a positive relationship between task-induced sAA change arousal-
biased memory enhancements for the prioritized object images paralleled the mnemonic effects
of NE observed in Study 1: The results from both of the studies suggest that elevated
noradrenergic tone may drive selectivity effects that occur on a trial-by-trial basis. Consistent
with this, theoretical models of LC modulation propose that moderately high tonic levels of
arousal/NE are most permissive to phasic LC responses and enhanced task-focused attention
(Aston-Jones & Cohen, 2005). Such tonic-phasic interactions in LC-NE system activity aligns
with the long-held theories that arousal influences cognitive processing via an inverted-U
function, with behavioral performance being optimized at moderate levels of overall arousal
(Yerkes & Dodson, 1908).
Importantly, interpreting our results as high-sAA-increase individuals reaching the peak
of this inverted-U rests on the assumption that the degree of task-induced sAA increase did not
push individuals to the far right of the curve (i.e., stressful conditions). Nonetheless, we
speculate that this is a valid assumption given that task-evoked increases in sAA enhanced top-
down prioritization processes, which are thought to arise in the PFC: Neurobiological models of
NE-PFC interactions suggest that, if sympathetic arousal levels were too high, goal-directed
attention and memory processes should have been impaired rather than enhanced via
disruptions in PFC activity (Arnsten, 2011; Schwabe et al., 2012). Furthermore, previous studies
demonstrate that emotional images from the IAPS database generally don’t induce high levels
of stress, as index by negligible increases – or even decreases - in the stress hormone cortisol
(Henckens et al., 2009). Thus, it is unlikely that task-induced sAA changes led to stress levels of
arousal that typically reduce memory specificity (Qin et al., 2012).

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Main Finding 3: Phasic LC activity works in concert with the dopaminergic system
to enhance local cortical representations of a prioritized stimulus; moreover, this pattern
of increased local cortical activity corresponds with enhanced memory of goal-relevant
neutral stimuli under threat of punishment.

Although Study 1 and Study 2 demonstrated that global noradrenergic activity sets the
stage for stimulus-evoked arousal-by-priority interactions, these studies used emotional sounds
and images as a behavioral proxy for phasic LC activity. In the GANE model, we predicted that
a high phasic-to-tonic ratio in LC activity should have the largest impact on selectivity since: 1)
LC responds phasically to motivationally significant stimuli and 2) compared tonic arousal,
phasic LC activity leads to larger volume release of NE in target cortical and subcortical regions
(Berridge & Waterhouse, 2003). To test this hypothesis, Study 3 used a variation of the overlap
paradigm from Study 1 to examine the LC-NE system’s transient effects on memory selectivity
via a combination of pupillometry and fMRI.
Study 3’s design was a slight departure from the first two studies, since we chose to use
aversive motivation – threat of monetary punishment - as opposed to an emotional stimulus,
such as the image of mutilated body, to elicit sympathetic arousal. We chose this approach, in
part, to show that LC-NE system activation may constitute a common brain mechanism by
which arousal enhances selective attention and memory regardless of whether sympathetic
arousal is increased via salient external stimuli or a combination of extrinsic incentives and
internal drives. In so doing, this helped us generalize our findings to models of incentivized
learning, and may help account for how motivation incites effective, goal-directed learning in the
brain.
According to the GANE model, arousal-induced activation of the noradrenergic system,
such as during aversive motivation, selectively enhances local activity in regions supporting
salient representations, thereby leading to winner-take-more outcomes in memory. To date,
however, most motivation-cognition research has focused on the dopaminergic system’s role in
enhancing reward-motivated memory. Thus, whether the LC-NE system also influences
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motivated memory encoding of goal-relevant information is less clear. Study 3 combined fMRI
with pupillometry to replicate previous findings that arousal further stratifies perceptual
processing-related activity between task-relevant and task-irrelevant cortical regions/networks
(Lee et al., in preparation; Lee et al., 2014). Most importantly, we were interested in examining
whether threat-induced LC activity enhanced neuronal gain, such that mnemonic traces of the
high priority scene stimulus were strengthened via modulation of scene-selective cortex, the
PHG and place area.
To test these hypotheses, I used a monetary incentive encoding fMRI task in which
participants explicitly prioritized a background scene in attention and memory while ignoring a
transparent foreground object. In this overlap design, the scene image was always prioritized,
because Study 1 showed that top-down attention enhance selectivity regardless of the target
stimulus category; scenes were also selected because it is harder to ignore the object than the
background scene, thereby increasing the likelihood of a residual object memory trace. As such,
we had an opportunity to examine arousal-induced suppression in hopes that distracter memory
would be above chance performance.
In the monetary incentive encoding task, arousal was induced by threatening to deduct
money from a preset account if participants forgot loss-cued scenes during a subsequent
memory test. The results revealed that threat of monetary punishment enhanced memory of
scenes, while impairing memory of objects. Arousal also intensified competition between high
and lower priority information, such that participants were even more likely to remember a scene
and forget its corresponding object. On a trial-by-trial basis, stimulus-evoked pupil dilation, a
biomarker of LC activity, was significantly more associated with successful scene memory in the
threat than the neutral condition.
As predicted, threat of punishment yielded brain activity patterns consistent with
increased neuronal gain, with arousal strengthening activity in the parahippocampal place area
(PPA), not affecting activity is less relevant object-selective cortex (LOC), and robustly
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suppressing activity in task-irrelevant auditory cortex. Threat-related arousal also suppressed
activity in a large-scale functional network, the right frontoparietal network, which prioritizes
bottom-up inputs (Corbetta & Shulman, 2002), while enhancing activity in dorsal occipito-
parietal regions that mediate spatial attention. Confirming our main GANE hypothesis, the
magnitude of pupil dilation to the target stimulus parametrically modulated the strength of
successful scene encoding-related activity in the LC and PHG. We also found that pupil-
modulated memory activity was related to activation of the dopaminergic midbrain, a
neuromodulatory system that also plays a key role in enhancing motivated declarative memory
(Shohamy & Adcock, 2010). Together these results provide novel evidence that threat-induced
LC-NE activity interacts with dopaminergic motivational brain systems to amplify encoding of
goal-relevant information.

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Updating the GANE model

Overall, our results supported our main hypothesis that LC-NE system activity mediates
arousal’s selective effects on goal-relevant vs. goal-irrelevant memory. However, it is important
to stress that GANE is a necessarily simplified framework of multifaceted biological
mechanisms. For instance, the GANE model does not incorporate all known adrenoreceptor
functions. In its present form, the hotspot mechanism is built upon the idea that different
thresholds of receptor activation lead to different effects on signal transmission and plasticity.
But at what point on the inverted-U do elevated levels of tonic LC activity impair hotspot
generation and cognitive performance (at least for top-down priority)? Under stress, does NE
ignite hotspots more indiscriminately since baseline NE concentrations are elevated more
globally? Or are hotspots less likely to emerge under stress since high tonic LC activity might
constrain phasic LC responses that promote selectivity?
This dissertation provided key initial evidence supporting some of GANE’s basic tenets.
However, other interesting findings in the current studies, such as sex differences in memory
selectivity under arousal (Study 1), a lack of β-adrenergic enhancement of the prioritized
stimulus (Study 2), and the involvement of the dopaminergic midbrain system in threat-
motivated encoding (Study 3), highlight complex biological realities that go beyond the current
parameters of our model. In the next few sections, we will discuss how our results align with or
conflict with findings in the NE literature, and propose potential revisions/updates to the GANE
model.
Diverse effects of different adrenoreceptor subtypes. Currently, GANE ascribes
specific cellular and cognitive effects to different adrenoreceptors: whereas β-adrenergic
receptors enhance synaptic plasticity and glutamate effects, α2-adrenoreceptors suppress
neuronal activity. Yet there are indications in the literature that the actions of these receptor
subtypes are more diverse. For instance, some physiological studies point to a role of β-
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adrenergic receptors in impairing rather than enhancing neuronal activity according to an
inverted-U function (Devilbiss & Waterhouse, 2000).
α1-adrenoreceptors also exert more complex effects on synaptic transmission than
simply inducing synaptic long-term depression. Co-activation of these receptors and β-
adrenoreceptors in the basolateral amygdala enhances long-term memory consolidation (Ferry
et al., 1999). Furthermore, other studies demonstrate that activating α1-adrenoreceptors can
potentiate sensory cortical activity (Mouradian et al., 1991) and recruit local astrocytic/vascular
effects that help mobilize energy resources in the vicinity of highly active neurons (O'Donnell et
al., 2012; Paukert et al., 2014). Study 2 of this dissertation showed that the association between
sAA increase and arousal-related memory enhancements for the prioritized oddball-1 objects
occurred across the placebo and beta-blocker groups. Thus, it is possible that the α1-
adrenoreceptors rather than β-adrenergic receptors contributed to enhanced goal-relevant
processing.
It is important to emphasize that any region with high enough NE concentrations to
engage β-adrenergic receptors is also capable of activating higher-affinity α1 and α2-
adrenoreceptors, since β-adrenoreceptors have the lowest binding affinity for NE (Arnsten,
2000). In scenarios where NE is only moderately released across the brain, local prioritized
signals should be privileged to access the facilitating and energy-mobilizing effects of both α1
and β-adrenergic receptors. By comparison, in less active regions of the brain, lower levels of
NE release might only be sufficient to engage inhibitory α2-adrenoreceptors.
Anterior versus posterior cortical effects of adrenoreceptors. In its current form, the
GANE model does not address a key distinction between NE’s effects on “classic “ synapses in
subcortical and posterior sensory regions versus newly evolved circuits in layer 3 of the
dorsolateral prefrontal cortex (Arnsten, 2000). Based on animal and human research, we
believe hot spot effects are most likely to occur in sensory and subcortical (e.g., amygdala,
hippocampus) synapses, where β-adrenergic receptors typically potentiate neuronal responses
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and synaptic plasticity (Berridge & Waterhouse, 2003; Lacaille & Harley, 1985). Supporting this
notion, the results from Study 3 showed that threat-related LC activity is associated with
enhanced high priority memory traces in sensory cortical regions specialized to process scenes.
In contrast, adrenoreceptor effects in the prefrontal cortex appear to flip. For instance, β-
adrenoreceptor activation impairs rather than enhances postsynaptic neuron activity via
increased cAMP signaling (Arnsten et al., 2012). Additionally, whereas α1-adrenoreceptors
enhance sensory neuron activity, they tend to disrupt PFC function and goal-directed behavior
(Ramos & Arnsten, 2007). Likewise, whereas α2-adrenoreceptors inhibit sensory cortical
activity, their activation strengthens DLPFC functional network connectivity and promotes
working memory (Arnsten, 2011).
We argue that the inverted rules of PFC adrenoreceptor function have important
implications for how NE interacts with different forms of priority (e.g., bottom-up vs. top-down) in
ways that support adaptive behavior. A surge in NE release in the PFC tends to disrupts goal-
selection processes that depend on working memory. The adaptive significance of such local
impairments may be to destabilize current representations and reorient attention to unexpected
events (Bouret & Sara, 2005). For instance, if an individual suddenly hears a gunshot while
working on his/her computer, it’s more important to direct attention towards the source of threat
rather than continue the task at hand. To update internal models of the environment (i.e., attend
to where the gunshot sound came from), it is instead more optimal for arousal to enhance
subsequent perceptual processing to process new sources of priority.
The importance of the timing of arousal and priority type. Fundamentally, the GANE
model predicts that arousal-induced NE release will bias competition in favor of whatever
information has the highest priority at that moment. According to a recent proposal by Warren’s
et al. (2016), perceptually salient information will be enhanced when arousal and NE release
occurs just beforehand, whereas goal-relevant representations, which require executive
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resources and take longer to evolve, will be enhanced when they are activated prior to arousal
induction.
Indeed, these predictions are well supported by empirical data from our lab. For
example, emotional oddballs or tones conditioned to shock amplify memory of preceding goal-
relevant information but not subsequent goal-relevant information (Sakaki et al., 2014). In
contrast, hearing emotional sounds or a tone conditioned to shock enhance subsequent
perception of perceptually salient stimuli (Lee et al., 2014; Sutherland & Mather, 2012).
Thus, at first glance, the contrasting effects of NE in posterior versus anterior cortical
regions seem difficult to reconcile with the GANE model. Yet, on the contrary, we believe that
this neuroanatomical distinction makes useful predictions about whether arousal-induced NE
release will enhance or impair information processing. Through its differential effects on
sensory/subcortical and prefrontal cortical activity, NE release may also favor different forms of
priority based on the timing of arousal: Experiencing arousal after goal implementation should
amplify the effects of top-down priority since cognitive control has had sufficient time to be
implemented; on the other hand, experiencing arousal should bias subsequent processing favor
of perceptually salient, bottom-up sensory inputs.
As Study 3 demonstrated, motivational incentives are a likely exception to this rule, since
– in this case – physiological arousal is intrinsic to achieving one’s goals. Whereas reorienting
attention is adaptive during unexpected arousing events, motivational arousal energizes
targeted cognitive processes necessary to win rewards or avoid punishment. In the context of
motivation, increased arousal therefore augments top-down effects in perception and memory
proactively. This facilitative effect of arousal on cognitive selectivity contrasts with previous ABC
studies where emotional arousal is induced incidentally by a task-irrelevant emotional stimulus
(e.g., oddball or hearing a disturbing sound), which – due to its high salience - may outcompete
the target neutral stimulus for limited mental resources.
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The effects of other hormones and brain systems on selectivity. Our neuroimaging
results from Study 3 showed that, under threat, concurrent activation of the LC and mesolimbic
dopamine system strengthened goal-relevant memory traces under threat. This finding
highlights the importance of different brain systems in moderating NE’s effects on cognitive
processing. It is well established that different neuromodulatory systems, such as the midbrain
dopamine system, are activated by arousal and interact with NE in target cortical and
subcortical regions (Sara, 2009). A large body of work indicates that these neuromodulators
play similar roles in regulating selective attention and learning (Harley, 2004; Noudoost &
Moore, 2011). At the cellular level, various neuromodulators, including acetylcholine, also
amplify signal-to-noise processing, alter receptive field size, and adjust synaptic weights in
response to motivationally significant inputs (Hasselmo et al., 1997). Like dopamine and
norepinephrine, acetylcholine also facilitates memory consolidation. For instance, blocking
muscarinic receptors in the basolateral amygdala abolishes emotional memories (Paré, 2003).
As demonstrated in Study 1, sex steroid hormones may also play an important role in
moderating the strength and efficacy of hotspots. In particular, the findings from Study 1 suggest
that elevated ovarian hormones in women help promote selective memory and goal-directed
attention. Although female participants were randomly sampled, or not excluded based on birth
control use or a specific menstrual cycle phase, this raises the intriguing possibility that naturally
cycling womens’ ability to maintain selectivity in their thinking may differ across the menstrual
cycle. Indeed, this idea is supported by evidence that different cognitive abilities, such as
working, spatial, verbal and emotional memory, vary with ovarian hormone levels (Andreano &
Cahill, 2009; Nielsen et al., 2013).
Anatomical segregation and specialization in the LC-NE system. GANE is built upon
the long-standing notion that, under arousal, NE is released in a uniform manner across most of
the neocortex. Recent anatomical and electrophysiological work, however, point to more
specialized effects of NE in different cortical terminal fields (Chandler et al., 2014). Some
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evidence suggests that NE’s influence on neuronal function might even differ according to the
layer of cortex being innervated (Salgado et al., 2016). Furthermore, new evidence reveals that
different subsets of LC neurons project to distinct regions of cortex (i.e., motor versus frontal
cortex) and elicit non-parallel release of NE (Aston-Jones & Waterhouse, 2016). Thus, future
research is needed to determine whether hotspots are generated and/or sustained differently
depending on the region of cortex that is activated and/or which subsets of LC neurons are
recruited under arousal.

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Implications for the aging brain
Neuromodulatory systems play a fundamental role in guiding adaptive attention and
converting motivationally significant experiences into durable long-term memories. But the
functional and structural integrity of these brain systems also change with age, suggesting that
altered neuromodulation is likely to correspond with patterns of cognitive decline. Thus, our
finding that LC-NE system activation relates to greater memory selectivity under arousal has
important implications for healthy and pathological aging.
To date, most cognitive aging research has focused on how declines in certain brain
regions, such as the lateral prefrontal cortex and hippocampus, or the dopamine system relates
to cognitive impairments and age-related pathology. Yet aging is also characterized by
decreased LC volume and cell density (Chan‐Palay & Asan, 1989; German et al., 1988;
German et al., 1992; Lohr & Jeste, 1988; Manaye et al., 1995; Vijayashankar & Brody, 1979).
In aging animals, several types of memory impairment, including deficits in spatial and
inhibitory avoidance learning, are associated with reduced NE neurotransmission and LC
neuron loss (Collier et al., 1987; Leslie et al., 1985). Recent autopsy evidence in humans
indicates that lower neuronal density in the LC is associated with cognitive decline ~6 years
prior to death, even after controlling for neuron density in other aminergic nuclei, including the
dorsal raphe, ventral tegmental area and substantia nigra (Wilson et al., 2013). Thus, the
functional and structural integrity of the LC appears to play an essential – and largely
underappreciated - role in healthy cognitive aging (Mather & Harley, in press; Robertson, 2013).
Based on the findings from this dissertation, what can be inferred about how NE and arousal
might interact with priority signals in the aging brain?
To understand how aging affects arousal’s ability to enhance selectivity, it is important to
consider three factors that may alter how NE interacts with local excitation/inhibition: 1) the
efficacy of primary neurotransmitter (glutamate/GABA) transmission; 2) the integrity of
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noradrenergic system structure and function, and 3) the ability of evaluative brain structures,
such as the PFC and amygdala, to detect and communicate salience signals to the LC and
shape cortical representations directly. Age-related decrements in any of these factors should
alter NE’s ability to ignite hotspots and amplify the effects of priority in perception and memory.
Neurotransmitters. Glutamate neurotransmission remains relatively intact with age,
though it’s unclear whether this is due to compensatory reductions in synaptic glutamate re-
uptake (Segovia et al., 2001). In contrast, much research indicates that NMDA receptor
expression declines throughout most cortical regions in the aging brain (Segovia et al., 2001).
Interestingly, given that NMDA receptors on NE varicosities are a critical component of the NE
hotspot mechanism, it is possible that such reductions in glutamate receptor density alter local
NE release as well. Indeed, animal research demonstrates that, in the hippocampus of aged
rodents, glutamate is less able to recruit additional NE release due to diminished NMDA
receptor function (Pittaluga et al., 1993). This finding suggests that older adults might fail to up-
regulate prioritized signals via NE hotspots during arousing events. On the inhibitory side of the
equation, GABAergic transmission becomes less efficacious with age and is associated with
less competitive (lateral) inhibition between high and lower priority processing pathways (Goh,
2011; Leventhal et al., 2003). This lack of cortical inhibition is likely to lead to a noisier cortex
and disrupted selectivity in neuronal processing.
LC-NE system structure and function. Central adrenoreceptor expression changes in
normal aging, but these alterations appear to vary by receptor-subtype. For instance, working
memory and selective attention deficits in older age have been attributed to reduced α2A-
adrenoreceptors function (Arnsten & Goldman-Rakic, 1985; Arnsten, 1993). By comparison, the
effects of aging on β-adrenergic receptors are less clear. Whereas some studies indicate that
there are no effects of aging on overall β-adrenergic receptor density in the brain (Kalaria et al.,
1989), other studies demonstrate a proliferation of β-adrenergic receptors in the brains of aged
mice and rats (Santulli & Iaccarino, 2013). Given the important opposing roles of α and β-
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adrenergic receptors in amplifying the signal-to-noise ratio, it is possible that such differential
age-related changes in adrenoreceptor expression alter how effectively hotspots are formed and
sustained under arousal.
One possibility is that arousal does not enhance memory selectivity as effectively due to
deficient LC-NE system function. In support of this hypothesis, lower extracellular NE levels in
the hippocampus of aged rats are associated with deficits in hippocampal LTP for contextual
fear memories (Luo et al., 2015). However, these emotional memories can be rescued by bath
application of NE to the hippocampus. In humans, a single bout of post-learning aerobic
exercise in healthy or mild cognitively impaired older adults enhances memory for positive
emotional images via increased LC-NE system activity (Segal et al., 2012). Likewise, performing
isometric handgrip prior to viewing emotional and neutral images also enhances incidental
memory for highly negative arousing images in healthy older women (Nielsen et al., in
preparation).
Together these findings suggest that externally manipulating noradrenergic activity in
older adults may be a safe and effective therapeutic method for enhancing cognitive selectivity
when it is most adaptive (e.g., maintenance of emotional wellbeing; avoiding threat or earning
reward etc.). Without intervention, baseline LC-NE system may be insufficient to enhance
selectivity in older adults; but when the noradrenergic system is engaged by external
manipulations, NE signaling may recover to produce memory trade-offs comparable to younger
adults. It may even be the case that – due to decreases in overall noradrenergic output – the
aging brain compensates by reducing NE re-uptake mechanisms, such as NE transporters,
and/or increasing adrenoreceptor expression to maintain stable signal transmission. This
possibility is supported by evidence that, in Alzheimer’s patients, β2-adrenergic receptors are
up-regulated cerebral microvessels of the frontal cortex (Kalaria et al., 1989). Perhaps synaptic
homeostatic mechanisms drive such compensation when noradrenergic input declines, thereby
sensitizing older adults’ brains to the phasic impact of arousal on cognition.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 157
Cortical processing efficiency and structural integrity. Because GANE predicts that
arousal-induced NE release influences processing according to local activity levels, it’s also
important to consider how the efficiency of local cortical processing changes with age. A
common finding in cognitive aging research is that activity in brain regions specialized to
process different visual categories becomes less distinct, or dedifferentiated, in older adults
(Goh et al., 2010; Park et al., 2004). Neuroimaging studies have linked this reduced neural
selectivity in older adults to greater activity in target-irrelevant visual processing regions, which
also corresponds with memory deficits for goal-relevant information (Gazzaley et al., 2005).
Thus, it is possible that arousal exacerbates distracter interference by enhancing high and lower
priority representations less discerningly; in turn, failure to suppress noisy activity would likely
reduce memory and perceptual selectivity.
Computational modeling of the dopaminergic system suggests that age-related
reductions in dopamine release are associated with reduced neuronal gain; as a result, this
impairs the fidelity and distinctiveness of mental representations in the aging brain (Li &
Rieckmann, 2014). NE neuromodulation shares many characteristics with dopamine,
suggesting that similar declines in the LC-NE system might also relate to reduced selectivity
with age. Within the context of Study 3’s findings, older adults might therefore exhibit even more
task-irrelevant LOC activity, which may correspond with reduced goal-relevant memory
enhancements and/or non-selective enhancements in both distracter and goal-relevant memory
traces.
Intriguingly, post-learning isometric exercise has been shown to enhance recognition
memory for previously studied highlight (salient) words in young adults and in older adults on
non-beta-adrenergic hypotensive medication; however, these benefits of exercise on memory
consolidation did not occur in older adults on beta-blockers (Nielson & Jensen, 1994). Beyond
enhancing consolidation processes, the results from Study 2 showed that β-adrenoreceptors are
also important for suppressing processing of neutral information.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 158
In past work, older adults showed similar emotional memory trade-offs as younger
adults, such that memory for emotional objects was enhanced, while memory for neutral
background scenes was impaired (Kensinger et al., 2007). Such emotion-induced memory
trade-offs are consistent with β-adrenoreceptor regulation of arousal-biased encoding processes
(Strange et al., 2003). Furthermore, the amygdala – the known locus of beta-mediated
emotional memory enhancements and peri-emotional suppression - also shows relatively less
age-related decline compared to other brain regions (Good et al., 2001). Taken together, these
findings suggest that β-adrenoreceptor function remains intact in normal aging and continues to
play an important role in consolidating emotional memories throughout the lifespan. Whether β-
adrenoreceptor activation under arousal also promotes memory selectivity for prioritized neutral
information in older adults is unclear.
Interestingly, in the emotional memory study conducted by Kensinger et al. (2007),
directing participants’ attention to the background scenes diminished emotion-induced memory
trade-offs in younger adults but not older adults. If β-adrenergic receptors remain intact in
normal aging, why would older adults not show a similar reduction in memory selectivity for
emotional objects when neutral information receives greater attentional weight? One possibility
is that these age differences are due to age-related deficits in top-down prioritization processes
(Gazzaley & D'Esposito, 2007). Aside from significant cortical decline in the PFC (Good et al.,
2001), there is also age-related decline in α2-adrenoreceptors that have been linked to impaired
distracter filtering. Perhaps older adults only exhibit arousal-enhanced selectivity when
information is prioritized in a stimulus-driven manner. This idea is supported by evidence that
hearing an emotional sound enhances perception of salient versus non-salient letters in an
array in both younger and older adults (Sutherland & Mather, 2015). Age differences in cognitive
processing might therefore not only relate to differences in noradrenergic signaling but also in
how effectively higher cortical and subcortical structures provide prioritized inputs to sensory
cortex.
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 159
Sex hormones. Finally, the results from Study 1 demonstrated that estradiol and
progesterone enhance goal-relevant memory selectivity regardless of arousal level. Menopause
is associated with a sharp decline in estrogen levels, suggesting that older women might be less
able to maintain selectivity in their thinking. The dorsolateral prefrontal cortex, a region that is
critical for higher-order cognitive operations, is especially sensitive to the beneficial effects of
estrogen replacement therapy in post-menopausal women (Herrera & Mather, 2015; Morrison et
al., 2006). Thus, estrogen replacement therapy may preserve and/or protect goal-directed
attention and memory encoding processes in older women, much in the same way that high sex
steroid hormone levels in younger women facilitated top-down priority effects in declarative
memory (Study 1).

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 160
Concluding Remarks
In summary, this dissertation provided systems-level evidence that noradrenergic
system activity enables arousal to narrow the focus attention onto our goals and cast this
information into memory more effectively. The current studies lay important groundwork for
understanding how intrinsic (e.g., motivation/effort) or extrinsically (e.g., emotional stimuli)
triggered sympathetic arousal regulates central information processing. Our results suggest that
noradrenergic activity is a shared mechanism by which seeing or hearing something emotionally
disturbing, effortful studying information, and threat avoidance enhance our ability to focus on
things that matters, while tuning out the rest. In so doing, this dissertation sheds new light on a
fundamental neurobiological mechanism of adaptive learning and memory.
Recent advances in neuroimaging, pharmacology (e.g., DREADDs), and microdialysis
technologies provide exciting future avenues for testing the GANE model. In humans, magnetic
resonance spectroscopy (MRS) enables in vivo measurements of glutamate and GABA
metabolites in the cortex. Thus, researchers could examine whether an arousing stimulus can
elicit a local, activity-dependent increase in glutamate levels in prioritized representational
cortex and whether this relates to specific memory outcomes. The development of fluorescent
glutamate indicators also makes it possible to measure extra-synaptic (spillover) glutamate
activity in active synapses (Okubo & Iino, 2011; Okubo et al., 2010). A key test of GANE will
involve examining the cognitive impact of combining the local infusion of β-adrenergic agonists
or antagonists with measures of cue/arousal-evoked glutamate activity.
Ultimately, understanding how the LC-NE system amplifies priority effects in perception
and memory has broad implications for disorders of emotion related to noradrenergic activity,
such as post-traumatic stress disorder (PTSD), in which the inability to restrict arousing stimuli
to the appropriate context leads to intrusive thoughts and distress (Southwick et al., 2002). In
addition, expanding our knowledge of how NE regulates cognitive selectivity is particularly
timely given the stresses and ever-evolving sensory landscape of our modern environment. The
RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 161
Information Age has placed an unprecedented challenge on the brain’s ability to extract and
remember important information. With the advent of social media and Internet advertising, our
minds are constantly over-stimulated and hyper-aroused, which makes it difficult to focus on
specific goals.
Deficits in selective attention become even more pronounced in older age, rendering an
increasing number of older adults vulnerable to distractions that disrupt their thinking and day-
to-day activities. LC neurons decline with age, suggesting that altered noradrenergic signaling
may underlie certain aspects of age-related cognitive decline. The LC has also attracted
increasing attention as a potential locus for neuronal pathology and memory deficits in age-
related neurodegenerative diseases, such as Alzheimer’s and Parkinson’s disease (Braak et al.,
2011; Gesi et al., 2000; Robertson, 2013). Recently, we demonstrated in healthy older adults
that LC structural integrity, as indexed by neuromelanin signal intensity, is positively associated
with factors known to protect healthy cognition, including education, occupational complexity
and verbal intelligence (Clewett et al., 2016). Thus, uncovering links between the noradrenergic
system and memory may offer new insights into targeted therapeutic interventions that can
improve older adults’ cognition, health, and wellbeing.
In conclusion, this dissertation sheds new light on the LC-NE system’s role in cognitive
selectivity, with broader implications for healthy cognitive aging, eyewitness testimony, and
disorders of emotion. Although additional research is needed to further elucidate the
neuromechanisms that regulate adaptive memory, one thing is clear: it is time to shift our focus
to the locus.

RUNNING HEAD: NE ENHANCES MEMORY SELECTIVITY 162
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Creator Clewett, David Vaughn (author)

Core Title Noradrenergic mechanisms of arousal-enhanced memory selectivity

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Publication Date 08/02/2016

Defense Date 06/02/2016

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag arousal,cognition,emotion,locus coeruleus,memory,Motivation,neuroimaging,Norepinephrine,OAI-PMH Harvest,pupil

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Language English

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Advisor Monterosso, John (committee chair), Levitt, Pat (committee member), Mather, Mara (committee member), Petzinger, Gizelle (committee member)

Creator Email clewett@usc.edu,davidclewett@gmail.com

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