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The impact of emotion, valence, and arousal on differential memory processes in younger and older people
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The impact of emotion, valence, and arousal on differential memory processes in younger and older people
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i
THE IMPACT OF EMOTION, VALENCE, AND AROUSAL ON
DIFFERENTIAL MEMORY PROCESSES IN YOUNGER AND OLDER PEOPLE
by
Allison Rose Ponzio
______________________________________________
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GERONTOLOGY)
AUGUST 9, 2016
COPYRIGHT 2016 ALLISON ROSE PONZIO
ii
DEDICATION
This dissertation is dedicated to the undergraduate and high school students that I have had the
absolute pleasure of mentoring over the course of my time at the University of Southern
California. Without you, not a single word of this dissertation would have come to be. You have
taught me far more than I ever could have hoped to teach you.
Thank you for being a constant source of joy, magic, inspiration, and light in my life.
Thank you for trusting my particular brand of crazy and supporting me every step of the way.
Christine Meinders, Shelby Bachman, Emily Chug
Ringo Huang, Sarah Hiramatsu, Madeline Ponzio
Joshua Faskowitz, Jaime Castrellon, Tess Levinson
Thea Weiss, Laurel Barrios, Kendall Amon
Jessica White, Hannah Nordberg, & Christina Deirmenjian
iii
ACKNOWLEDGEMENTS
Thank you, first and foremost, to my Emotion & Cognition lab mates (past and present) for their
support over the last five years, especially my mentor and the captain of our ship, Dr. Mara
Mather. Mara, thank you for providing me with the tools, model, and vision I needed to
succeed. I will always appreciate the chance you took on me what seems like a million years ago
now. Thank you for opening your lab to me and providing me with a home and a lab family. To
my lab mates, I will never be able to express enough in words how much you mean to me. I
hope part of that expression came through in the massive amount of baked goods we’ve shared
through out the years.
Thank you to former postdoctoral fellows, Dr. Philipp Opitz, Dr. Shawn Nielsen and Dr. Sarah
Barber, who guided me at several very critical moments in my graduate studies. The three of
you helped me change and grow in ways I didn’t think I ever would. Thank you.
Thank you to my committee, Dr. Jonas Kaplan, Dr. Tara Gruenewald and Dr. Mara Mather for
their comments and feedback through out this whole candidacy process.
Thank you to the USC Davis School of Gerontology, especially Maria Henke and Linda Broder.
Also, thanks to the USC Office of the Provost, the USC Graduate School (especially Dr. Meredith
Drake and Kate Tegmeyer), the National Institute of Health, the National Institute of Aging, and
the National Science Foundation for their generous funding support, including NIH grants
RO1AG025340 and K02 AG032309, NSF grant DGE-0937362, and the USC Provost’s Fellowship
for Incoming Graduate Students.
Thank you to my friends and family for their unwavering love and encouragement. Your
powerful laughter and welcome supply of salty meats and cannoli could never be
underestimated. I truly would have been lost without the warmth you bring to my life. Special
thanks also goes to Cathy Levi and my amazing cousins. Also special thanks to Garrett Longley,
Johanna Gruen, Rebecca Bevans, Lisa Cannizzaro, and Maddy Bohanon. You knew how to fix me
every time I stumbled or fell. Thank you.
Thank you to my Soul Cycle community, especially Lisa Moloshok. Never in my life have I found
a way to keep my mental and physical health on the forefront of my mind and my stress at bay.
Thank you for teaching me that strength and happiness are choices. And that we not only have
to choose them, but fight for them. And it’s an everyday fight. Thank you for helping me find
my voice.
And lastly, and from the deepest place my heart; I thank my forever cheerleaders Dena Ponzio
and Andria Foertsch. Without your patience, support, encouragement, laughter, comfort and
love I never would have survived this or any other difficult time in my life. You are perfect. You
are angels. A million times over. Thank you.
iv
TABLE OF CONTENTS
Dedication ii
Acknowledgements iii
Table of Contents iv
List of Figures vi
Abstract vii
Introduction 1
Pattern Separation & Pattern Completion 1
Hippocampal Communication & Changes with Age 3
Emotion & Cognition 5
The Components of Emotion 7
Valence 7
Arousal 9
Aims of the Dissertation 11
Study 1: How Emotion Impacts Pattern Separation 13
Method 13
Results 16
Discussion 20
Study 2: How Valence Impacts Pattern Separation 24
Overview 24
Method 25
v
Results 27
Discussion 33
Study 3: How Arousal Impacts Pattern Separation 36
Overview 36
Method 36
Results 39
Discussion 44
General Discussion 46
References 52
vi
LIST OF FIGURES
Figure 1.1 Trial structure for Study 1 14
Figure 1.2 Correctly identified similar items, by emotion and age for Study 1 17
Figure 1.3 False alarm rate for similar items, by emotion and age for Study 1 18
Figure 1.4 Pattern separation bias score, by emotion and age for Study 1 20
Figure 2.1 Trial structure for Study 2 26
Figure 2.2 Correctly identified similar items, by valence and picture type for
Study 2 29
Figure 2.3 Correctly identified similar items (scenes images only), by valence
and age for Study 2 30
Figure 2.4 Pattern separation bias score (scene images only), by valence and
age for Study 2 31
Figure 2.5 Pattern separation bias score when dividing the group by RBANS
performance, by valence and age for Study 2 33
Figure 3.1 Trial structure for Study 3 39
Figure 3.2 Correctly identified similar items, by condition and age for Study 3 40
Figure 3.3 Pattern separation bias score, by condition and age for Study 3 43
vii
ABSTRACT
Pattern separation and pattern completion are memory processes localized to the
hippocampus. Pattern separation involves the transformation of similar neural representations,
or memories, into non-overlapping representations. Essentially, it involves making similar
memories more distinct from each other. This is opposite to pattern completion, which involves
combining representations into one. Emotion has been shown to impact memory in both
younger and older adults due to the high amount of norepinephrine receptors in the
hippocampus. Norepinephrine, a neurotransmitter, is responsible for our “fight or flight”
response and is closely tied to an increase in emotional arousal. As we age, we tend to rely on
more gist-based processing, and therefore are more likely to pattern complete than pattern
separate. Previous research has suggested that emotion may influence pattern separation, but
it is unclear exactly what the mechanisms of this process are. The experiment in Study 1 tested
the impact of emotion on pattern separation and pattern completion. Study 2 and Study 3
contain experiments that tested the impact of the “quality” and “quantity” of emotion
separately. Study 2 tested how pattern separation and pattern completion are impacted by
valence, while Study 3 tested how pattern separation and pattern completion are impacted by
arousal. In general, these findings are related to the decline in the health of the hippocampus,
as well as the impact of norepinephrine on hippocampal processes. Older adults made
marginally more mistakes during emotional blocks. The findings for older adults may be due to
norepinephrine system acting on impaired hippocampal processes. Additionally, the findings for
younger adults relate to arousal biased competition theory. Emotion enhanced younger adults’
viii
memory accuracy for high priority information. The results may also be indicative of high levels
of arousal increasing memory due to norepinephrine acting on hippocampal processes.
1
INTRODUCTION
Pattern Separation & Completion
Pattern separation is a brain process of transforming similar representations or
memories into highly dissimilar, non-overlapping representations (Bakker, Kirwan, Miller, &
Stark, 2008). Alternatively, if the brain is treating the stimulus as an old stimulus, it will instead
complete partial patterns or combine similar patterns of activity into a single representation
(pattern completion). Subregions of the hippocampus participate in both pattern separation
and pattern completion. The dentate gyrus (DG)/CA3 region activates during pattern
separation, while the CA1 region tends to activate during pattern completion or mixed signals
(Duncan, Curtis & Davachi, 2009; Kumaran & Maguire, 2007). Bakker (2008) and colleagues
hypothesized that if activity in a particular brain region of the hippocampus region was altered
in any way by repetition (either suppression or enhancement), one could use the activity in a
particular region to infer whether that region was participating in pattern separation or
completion. The discrimination between pattern separation and pattern completion is
important, because they represent two important features of episodic memory (Yassa & Stark,
2011). Previous tests of pattern separation have commonly used either passive viewing of
images, or a behavioral pattern separation task. One study required participants to view
objects, and indicate if the object was typically an outdoor or indoor object. Sometimes objects
were repeated. Other times, similar, but not identical objects were presented on a second
presentation (Bakker et al., 2008). These initial tasks helped localize pattern separation in the
brain.
2
Elevated hippocampal activation is a noted hallmark in several disorders that often lead
to Alzheimer's disease, for example amnestic mild cognitive impairment (Dickerson, Salat,
Greve, Chua, Rand-Giovannetti, Rentz, et al., 2005). Pattern separation tasks are an ideal test
for hippocampal function, because the task activates several hippocampal subregions (Bakker,
Krauss, Albert, Speck, Jones, Stark, et al., 2012). The DG/CA3 network has pattern separation
functions, a finding which is supported by behavioral and neurophysiological data from animal
studies (Leutgeb, Leutgeb, Moser, & Moser, 2007; McHugh, Jones, Quinn, Balthasar, Copparu,
Elmquist, et al., 2007) which also show a weakening of pattern separation and a shift to pattern
completion in age-related memory loss when CA3 neurons are hyperactive (Wilson, Gallagher,
Eichenbaum & Tanila, 2006; Wilson, Ikonen, Gallagher, Eichenbaum & Tanila, 2005; Wilson,
Ikonen, McMahan, Gallagher, Eichenbaum, & Tanila, 2003). This has also been tested in
humans. One group tested the effect of a low dose antiepileptic in a group with amnestic mild
cognitive impairment, as compared to a group of healthy controls (Bakker et al., 2012).
Participants were asked to judge the item as "new", "similar", or "same". The drug was dosed
so that the levels of hippocampal activation in the amnestic mild cognitive impairment group
were similar to that of the healthy controls. Task performance was significantly better in the
drug group as compared with performance without drug treatment. This suggests that targeting
excess hippocampal activation may have therapeutic potential (Bakker et al., 2012). Consistent
with those findings, behavioral data obtained in the same three-choice recognition memory
tasks from older adults as compared with younger adults show a shift in performance toward
pattern completion. Older adults were just as good as younger adults at identifying new items,
but older adults were impaired when identifying objects as similar. This indicates that pattern
3
separation may be less efficient in older adults resulting in poorer recognition memory
performance due to increased interference (Toner, Pirogovsky, Kirwan, & Gilbert, 2009).
Older adults show a reduced ability to perform pattern separation tasks. In fact, real
world memory complaints often mirror errors in pattern separation processing. For example,
Toner and colleagues (2009) showed that when studying pattern separation bias in younger and
non-demented older adults, younger adults outperformed the older adults on the task. These
results suggest that older adults are more likely to rely on pattern completion processes rather
than pattern separation processes. Pattern separation tasks have often been used in order to
examine cognition problems with age, specifically when studying Alzheimer’s disease and mild
cognitive impairment (for examples see Bakker, et al. 2008; Yassa & Stark 2011). These pattern
separation problems with age may be related to older adults’ inability to correctly identify
source information when the possible sources are quite similar to each other (e.g., Henkel,
Johnson, & De Leonardis, 1998; Johnson, De Leonardis, Hashtroudi, & Ferguson, 1995), again
suggesting that older adults may rely more on pattern completion processes.
Hippocampal Communication & Changes with Age
As mentioned previously, the hippocampus is important for forming new associations
after cleaning up and reorganizing previous neural representations. This center for memory is
also responsible for storing independent memories, retrieving memories, and assisting in recall
and applying old information to new situations. Memory processes are highly vulnerable to the
affects of aging, and this decline may be due to changes in the communication within the
hippocampus (Yassa & Stark, 2011). The hippocampus, rather than other regions important for
memory like the parahippocampal gyrus and the entorhinal cortex, is the prime source of this
4
pattern completion and pattern separation distinction (Norman, 2010). Recurrent collaterals,
axons within one particular region that form a recursive feedback loop, were first identified by
Marr (1971) as being crucial to the pattern separation process. Within the hippocampus, these
recurrent collaterals allow for this process of making similar, overlapping representations more
distinct (pattern separation) or more similar (pattern completion). Being able to complete this
process would create interference and impact any and all new storage of information (Norman
& O’Reilly, 2003). Neurons project from the entorhinal cortex to the DG on a perforant path
with other neurons projecting to the CA3 via a mossy fiber pathway (Marr 1991; McNaughton &
Morris, 1987; Shapiro & Olton, 1994; Norman & O’Reilly, 2003). Cells in the DG project onto the
CA3 by way of granule cells, where pyramidal cells then project onto themselves heavily via the
previously mentioned recurrent collaterals. They also project onto CA1 through Schaffer
collaterals (Witter, 1993). This tri-synaptic circuit, based on the rat brain, provides the
foundation for understanding how pattern separation and completion happen in the brain.
Under certain circumstances the CA3 demonstrates pattern separation, and under other
circumstances pattern separation (Yassa & Stark, 2011). Electrophysiological studies in rodents
have shown that firing patterns indicative of pattern separation were consistent in CA3, rather
than CA1. Activation in CA1 shows patterns that are more consistent with pattern completion
(Lee & Kesner, 2004). Neurogenesis, the process by which new neurons are generated, declines
with aging (Seki & Arai, 1995; Kuhn, Dickinson-Anson, & Gage, 1996; Kempermann, Gast, &
Gage, 2002). This has potential to change the role of the DG in pattern separation (Nakashiba,
Cushman, Pelkey, Renaudineau, Buhl, McHugh, et al. 2012). Nakashiba and colleagues (2012)
showed that the granule cells born through adult neurogenesis are required for pattern
5
separation. These cells, which are crucial for communication between DG and CA3, are
produced in fewer amounts with older age. Older granuale cells contribute to pattern
completion in lieu of pattern separation, providing a neural basis for older adults’ neural and
behavioral results showing a shift towards more pattern completion processes rather than
pattern separation processes (Nakashiba, et al. 2012; Clelland, Choi, Romberg, Clemenson,
Frafniere, Tyers, et al. 2009).
Emotion & Cognition
Emotion influences cognitive and physiological processes. Even emotional stimuli shown
for a few seconds or less can influence physiological responses (i.e., heart rate, galvanic skin
response, and pupil dilation; Bradley, Miccoli, Escrig, & Lang, 2008). Viewing emotionally
arousing stimuli increases brain activity in areas important for object recognition relative to
emotionally neutral stimuli, suggesting emotional stimuli have greater priority in processing
(Sabatinelli, Flaisch, Bradley, Fitzsimmons, & Lang, 2004). During the presentation of neutral
and emotional stimuli, participants’ eyes are more likely to fixate first and longest on the
arousing stimuli (Knight, Seymour, Gaunt, Baker, Nesmith & Mather, 2007). Even in studies
where participants are directed to ignore arousing stimuli, those stimuli are the preferred
target of attention (Nummenmaa, Hyona, & Calvo, 2006). Auditory stimuli with emotionally
arousing content also seem to have priority in processing, leading to increased activity in the
auditory cortex (Zald & Pardo, 2002). The hippocampus has many noradrenergic receptors,
especially in the CA3 and dentate gyrus regions (Duncan, et al., 2009), making it quite
responsive to emotional arousal and suggesting this may be the cause of an increase in memory
due to emotion.
6
Although it seems clear that emotion influences cognitive and physiological processes,
the specific nature of this impact seems unclear. One example of this is memory for details vs.
gist, an ability related to pattern separation. Some research suggests that emotional arousal
may enhance memory for the gist (general idea; more of a pattern completion process), but not
the details of stimuli (more of a pattern separation process). For example, in one study
emotional images were interspersed in a slide show of neutral images. Participants were asked
to recall the images they saw, and were more successful at describing the gist of the slides than
the details (Adolphs, Gosselin, Buchanan, Tranel, Schyns & Damasio, 2005), with similar results
found when a neutral slide show was followed by emotional versus neutral videos (Liu, Graham,
& Zorawski, 2008). Detail memory did not show the same enhancement. Alternatively, results
of another study suggested that specific, emotional details are better remembered than the
general gist (Kensinger, Garoff-Eaton, & Schacter, 2006). It may be that the results depend on
the attentional resources a person may be able to or is allocating to a particular stimulus
(Kensinger, Garoff-Eaton, & Schacter, 2007).
One other study has attempted to test how emotional arousal impacts pattern
separation processes. Segal and colleagues (Segal, Stark, Kattan, Stark, & Yassa, 2012)
demonstrated that alpha-amylase (a marker of norepinephrine and therefore arousal) increases
after viewing emotional pictures, and this correlated with enhanced pattern separation
performance. But this study was not without limitations. First, the arousal manipulation
occurred at the beginning of the task, and not throughout, so arousal was likely to steadily
decrease. In addition, only 13 participants contributed to the correlation. In an effort to test
7
these effects in younger and older people I developed a task that interleaved emotion and the
pattern separation task.
The Components of Emotion
Emotion has two main components, which can be described as the “quality” and the
“quantity” of emotion. Quality, meaning the type or characteristics of emotion, can be referred
to as valence. Quantity, meaning the amount or magnitude of emotion, can be referred to as
arousal. These two components together make up the core of what emotion is. Evidence
suggests that while both valence and arousal impact cognitive performance (i.e. memory), they
operate through separate neural pathways (Kensinger & Corkin, 2004).
Valence
When reviewing how emotion and emotion processing changes with age, a strong vision
of late-life emotional stability emerges (Mather & Ponzio, 2016). In fact, well-maintained
positive emotions and somewhat decreased negative emotions are hallmarks of this stage of
life. Additionally, it turns out that there are also age-related positivity effects in attention and
memory (Mather & Carstensen, 2005). For example, one study incorporating multiple age
groups (younger, middle aged, and older adults) showed that after completing a recall test of
positive, negative, and neutral pictures, younger adults remembered far more negative relative
to positive images and older adults remembered far more positive relative to negative images
with middle-aged adults falling in between. Essentially, the ratio of positive to negative images
increased along with the age of the participant (Charles, Mather, & Carstensen, 2003).
Other age-by-valence interactions have been shown. A meta-analysis of over 100
studies incorporating both negative and positive stimuli and testing both younger and older
8
adults showed that older adults were more likely to favor positive over negative information in
attention and memory (Reed, Chan & Mikels, 2014). This has been commonly referred to as a
positivity effect. The mechanisms underlying this pattern in emotion and cognition are not
clear. One possibility is that an increase in positive affect could promote memory of positive
information. Studies have shown though, that the current mood and negative affect levels of
the participant do not account for this positivity effect (Charles et al., 2003; Kennedy, Mather,
& Carstensen, 2004; Mather & Carstensen, 2003; Mather & Knight, 2005). Another possibility is
that given the neural, cognitive, and physical declines associated with aging, older adults’
positivity effects may indicate some sort of decline. For example, patients with damage to the
amygdala, a brain region that helps will attention to and processing of threatening or negative
information, fail to show the advantages in processing negative stimuli that healthy younger
adults show (Cacioppo, Berntson, Bechara, Tranel, & Hawkley, 2011). Older adults also show
greater amygdala activity in response to positive than to negative information (Mather, Canli,
English, Whitfield, Wais, Ochsner, et al., 2004; Leclerc & Kensinger, 2011; Waldinger, Kensinger,
& Schulz, 2011). The amygdala is also critical in order for us to acquire and retain lasting
memories of emotional experiences (McGaugh, 2004). Contrary to the belief that the amygdala
declines with age, older adults show less structural decline in the amygdala than other brain
regions (Nashiro, Sakaki, & Mather, 2012), and show some increased threat detection abilities
(Leclerc & Kensinger, 2008; Mather & Knight, 2006). Older adults also show more prefrontal
activation relative to younger adults when processing emotional rather than neutral stimuli
(Mather, 2012), suggesting that the plasticity of the aging brain may contribute to continued
intact processing. Though older adults show decreased activity in prefrontal regions, this may
9
be due to what they are attending to, and not necessarily some sort of decline in neural
processing or structural integrity. Positivity effects are also stronger in participants who show
better scores on measures of cognitive decline (Mather & Knight, 2005; Petrican, Moscovitch, &
Schimmack, 2008), suggesting that higher cognitive function helps positivity effects to emerge.
In addition, in studies where participants cognitive resources are depleted, positivity effects no
longer appear (Knight et al., 2007; Mather & Knight, 2005). In summary, though the aging brain
experiences declines, when healthy cognitively able older adults cognitive resources are able to
operate normally, positivity effects are more likely to occur.
Socioemotional Selectivity Theory (SST) offers one of the strongest answers for why
these positivity effects occur (Carstensen, Isaacowitz, & Charles, 1999). SST posits that as we
age, our perception of time changes. When we are younger, we have limitless potential. When
we are young, we view our time as expansive, and focus on knowledge acquisition, making new
friends, adding social connections and investing in our futures. As we age, we experience a shift
in goals, to ones that focus on things that are more emotionally meaningful. For example, we
prune relationships to spend the most time with people we like the most in order to feel more
socially connected (Carstensen, 2006). This change in perspective could be why older adults
have better emotion regulation, a better emotional well-being profile, and the positivity effect
outcome in memory and cognition (Carstensen, Mikels, & Mather, 2006; Reed & Carstensen,
2012).
Arousal
When considering how arousal changes with age, the first element that must be
addressed is how many different systems that rely on arousal that are impacted by age. There
10
are several aging ailments that could impact how arousal is measured. For example, peripheral
neuropathy and other skin conditions could impact how we measure skin conductance. Or
cataracts could change the way we assess pupil dilation. As we age, our arteries become less
flexible and more fibrous, which weakens cardiac muscles, causes greater peripheral resistance
and poorer blood circulation (Lakatta, 1990). These changes in the cardiovascular system
influence some psychophysiological measures of arousal, like blood pressure and heartbeat. We
also experience changes in our electrodermal system (Porges & Fox, 1986), primarily a decrease
in the quantity of sweat glands, the amount of sweat produced, which impacts how skin
conductance is measured. Thus, not surprisingly, age-related decreases on measures such as
heart beat interval, skin conductance, respiration period, ear pulse transmission, and systolic
blood pressure have been found in people’s responses to emotional cues (Kunzmann,
Kupperbusch, & Levenson, 2005; Levenson, Friesen, Ekman, & Carstensen, 1991; Tsai,
Levenson, & Carstensen, 2000; but for studies finding no significant age differences for skin
conductance see Denburg, Buchanan, Tranel, & Adolphs, 2003; Neiss, Leigland, Carlson, &
Janowsky, 2009)
Even though our psychophysiological responses to arousal may change with age, there is
little to no evidence to suggest that our brains also experience a change in arousal. As
previously noted, the amygdala, which is highly implicated in feeling arousal as well as negative
valence, doesn’t decline nearly as much as other structures. And also, amygdalae integrity is
associated with better cognitive function. Additionally, several studies have indicated that the
structural integrity of the locus coeruleus, a structure in the pons that is also important for
physiological arousal and norepinephrine, is predictive of a slower rate of decline (Wilson, Nag,
11
Boyle, Hizel, Yu, Buchman, et al., 2013). During emotional event, norepinephrine is mainly
released from neurons originating in the locus coeruleus. It has been suggested that the
integrity of emotional arousal can be indicative of cognitive decline in older adults (Watson,
Bernhardt, Reger, Cholerton, Baker, Peskind, et al., 2006). It is possible that the neuroprotective
effects of noradrenaline could add to cognitive reserve. Additionally, in rodent studies looking
at the role of norepinephrine in hippocampal activity, results suggested that using a drug to
promote upregulation of norepinephrine was successful in reversing impaired emotional
memory (Luo, Zhou, Li, Wu, Hu, Ni, et al. 2015; Izumi & Zorumshi, 1999). In fact, another study
successfully transplanted norepinephrine neurons into older rats, which improved learning and
memory (Collier, Gash & Sladek, 1988). Noradrenaline has been shown to increase long term
potentiation in the hippocampus (Izumi & Zorumshi, 1999), therefore impacting the
communication within the hippocampus and providing a neural basis for studying how an
increase in arousal may impact memory. In addition, some of the most plentiful connections
within the hippocampus for norepinephrine are located within the DG, making the study of
pattern separation and pattern completion specifically relevant within the study of emotion
and memory (Harley, 2007).
The Aims of the Dissertation
Though Segal and colleagues (Segal, Stark, Kattan, Stark, & Yassa, 2012) demonstrated that
alpha-amylase increases after viewing emotional pictures, and this correlated with enhanced
pattern separation performance, this study had limitations (i.e., the arousal manipulation
occurred at the beginning of the task, and not throughout, so arousal was likely to steadily
decrease, and only 13 participants contributed to the correlation). In an effort to test these
12
effects in younger and older people, I developed a task that interleaved emotion and the
pattern separation task, something completely novel to previous research. Therefore, the aims
of the dissertation are as follows: Study 1 will discuss my attempt to provide a consistent
impact of emotion throughout the duration of the pattern separation task, as well as provide an
opening to Study 2 and Study 3. The goals of Study 2 and Study 3 will be to separately assess
the impact of valence (Study 2) and arousal (Study 3) on pattern separation. My original task (as
used in Study 1) was developed into two separate tasks, one to identify the effects of valence
(using separate positive, negative, and neutral blocks), and another to identify the effects of
arousal (using totally neutral blocks, but incorporating an isometric exercise task). Together,
these studies provide insight into the mechanisms of how emotion, and its separate
components (arousal and valence) influence memory abilities (pattern separation and pattern
completion) in younger and older people.
13
STUDY 1: HOW EMOTION IMPACTS PATTERN SEPARATION
Method
Participants. Forty younger adults (18-35 years of age, M
age
= 20.33, SD
age
= 3.07, 14
males) and 40 older adults (60-85 years of age, M
age
= 68.43, SD
age
= 5.08, 17 males) participated
and were compensated $15 per hour or with course credit for their participation. Younger
adults were recruited from the University of Southern California SONA Psychology Pool. Older
adults were recruited from the University of Southern California Healthy Minds database. Four
additional participants were unable to complete the task and so were not included in the
analyses. Participants were required to have normal or corrected-to-normal vision and hearing,
be free of chronic illnesses, have no cognitive impairment, and not be on any beta-blocker
medication.
Procedure. All participants consented and were debriefed in accordance with University
of Southern California Institutional review guidelines. Participants also completed depression,
mood, demographic and verbal fluency questionnaires.
Task. The main task was a continuous memory paradigm similar to Toner and colleagues
(Toner et al., 2009). Participants were presented with images one at a time (see Figure 1 for an
example). The images were either single objects or complex scenes (another novel component
from previous pattern separation studies). Some pictures were only presented once, others
were presented twice, with the repetition item being either exactly the same or very similar. As
the picture list played, participants were asked to indicate if the image was "new", "similar" or
"same." An image was considered "new" if it was the first time the participant had seen it
within the context of the study. Some "new" images were boxed with a red border, indicating
14
that those images were especially important and corresponded with an upcoming "similar" or
"same" image. The red borders were included to emphasize to participants that they should
consider those items to have high priority. An image was considered "same" if the exact same
image had been previously presented in the study. An image was considered "similar" if it was
not exactly the same as a previously presented item, but instead had a changed characteristic.
Images were presented for two seconds, during which time participants made their “same”,
“similar”, or “new” judgment, followed by a 1.5 second inter-trial-interval (see Figure 1.1).
Figure 1.1
These images were presented in blocks, which alternated between having emotional filler items
or neutral filler items. Images with a "similar" or "same" counterpart were presented between
10 and 40 images apart. Each block contained 128 images, with equal proportion of similar
pairs, identical pairs, unrelated novel items, and neutral or emotional filler items. In order to
increase the importance of the initial "new" image, we incorporated both bottom-up salience
and top-down goals. We surrounded target items with a red border to both increase the
bottom-up salience and to provide the top-down goal of focusing on that particular item since it
would have a pair later in the set (either a "same" or "similar" item). Participants were
15
instructed about this top-down rule during a practice session, and were reminded at the
beginning of every block. Each participant completed 6 blocks. After each block, participants
were told that they could forget all of the previously seen items, and instead focus on the next
block.
Equipment and Stimuli. The experiment was presented using PsyScope experimental
software (Cohen, MacWhinney, Flatt M, & Provost, 1993). Stimuli were assembled in-house
using Adobe Photoshop and images acquired through various sources, including the Internet
and an object and scene database (Goh, Chee, Tan, Venkatraman, Hebrank, Leshikar, et al.
2007). The emotional images were a part of the International Affective Picture Set (IAPS; Lang,
Bradley, & Cuthbert, 2008). The images were chosen based on previously assessed valence
(M
negative
= 2.63, SD
negative
= 0.76, M
neutral
= 4.99, SD
neutral
= .56, M
positive
= 6.37, SD
positive
= 0.85,)
and arousal (M
negative
= 6.46, SD
negative
= 1.39, M
neutral
= 2.20, SD
neutral
= 1.05, M
positive
= 4.21,
SD
positive
= 0.91) ratings.
Data Analysis. All data was analyzed using SPSS statistical software. I used analysis of
variance (ANOVA) to test for all main effects and interactions for several measures including:
correct responses (i.e. hits) for same items, correct responses (i.e. hits) for similar items,
incorrect responses (i.e. false alarms, or misidentifying information) for similar items, and
pattern separation bias. Essentially, I compared responses (new, similar, or same) to each
picture type (new [non-repetition new items and first presentation of similar/same items],
similar, or same) in emotional and neutral blocks for younger and older adults following the
results presented in Yassa, et al. (2010) and Tonner, et al. (2009), which are most similar to this
experiment.
16
In addition to hit and false alarm rates, the pattern separation bias score is another way to
illustrate pattern separation success. This calculation takes into account the probability of
calling a correctly pattern separated item "similar", while subtracting out the probability that an
individual will call an item only presented once "similar". P("Similar" | Similar) - P("Similar" |
Only). A higher score here indicates better pattern separation, and a lower score indicates a
high likelihood of calling non-memory items "similar".
Results
Hits and False Alarms. Similar to Yassa, et al. (2010), I wanted to examine how age
difference and emotion might impact hits for the different types of images. Using a repeated-
measures ANOVA, I investigated hit rates for similar items. Although I did not find main effects
of block type or age, I did find a significant interaction of block type and age, F(1,78) = 6.31, p =
.01, partial η
2
= .08. Splitting the data by age group indicated that older adults’ ability to
recognize similar items in emotional blocks (M = .52, SE = .03) were negatively impacted
relative to neutral blocks (M = .57, SE = .03), t(39) = -3.05, p < .01, but younger adults’ ability
was not affected by emotion, t(39) = 0.67, p = .51 (see Figure 1.2).
17
Figure 1.2
When comparing hits for same items, I found a significant main effect of age, F(1,78) = 7.83, p <
.01, partial η
2
= .09. This indicates that older adults were better at recognizing same items (M =
.32, SE = .03), as compared with younger adults (M = .22, SE = .03). This may be due to a
tendency of older adults to indicate “same” more often than “similar.” For example, when
comparing false alarms for similar items (indicated "same" for a similar item) in another
repeated-measures ANOVA, I found a main effect of age, F(1,78) = 16.79, p < .001, partial η
2
=
.18, and a significant block type by age interaction, F(1,78) = 9.04, p < .01, partial η
2
= .10. This
indicates that younger adults made fewer false alarms in emotional blocks (M = .16, SE = .02)
than neutral blocks (M = .20, SE = .02), t(39) = -2.60, p = .02. Older adults, on the other hand,
18
made marginally more false alarms in emotional blocks (M = .34, SE = .03), than neutral blocks
(M = .31, SE = .03), t(39) = 1.78, p = .08 (see Figure 1.3).
Figure 1.3
Because most pattern separation experiments use only object images, I also performed all hits
and false alarm analyses by coding the images by whether they are simply an object image or a
more complex scene image. Splitting the analyses by picture type did not reveal any additional
results.
Separation Bias. As Yassa and colleagues (2010) did, I performed a separation bias score
analysis. This calculated the difference between calling a similar item "similar" and calling a new
item "similar" in emotional blocks, and then separately for neutral blocks. Using a repeated-
19
measures ANOVA, I examined this separation bias score and compared block type (emotional
and neutral) for the age groups (younger adults and older adults). I found both a significant
main effect of age, F(1,78) = 6.32, p < .01, partial η
2
= .08, and a significant block type by age
interaction, F(1,78) = 10.71, p < .001, partial η
2
= .12. In general, younger adults had a higher
separation bias score (M = .53, SE = .04) than older adults (M = .36, SE = .04). Younger adults
had a higher separation score in emotional blocks (M = .54, SE = .04) as compared with neutral
blocks (M = .52, SE = .03), though this difference was not significant, t(39) = 1.22, p = .23. In
contrast, older adults had lower separation score in emotional blocks (M = .34, SE = .04) as
compared with neutral blocks (M = .38, SE = .04), and this difference was significant, t(39) = -
2.386, p = .02 (see Figure 1.4). Again, splitting the analyses by picture type did not show any
additional results.
20
Figure 1.4
Discussion
The goal of this study was to look at the impact of emotion on pattern separation and
pattern completion processes in younger and older adults. In general, the results suggest that
younger adults’ pattern separation abilities, which are largely intact, are more differentially
impacted than those of older adults when the younger adult experiences emotion. Younger
adults performed better in the emotional blocks relative to the neutral blocks. The older adults,
on the other hand, not only consistently performed worse overall, but also performed even
worse in the emotion condition.
21
In the analyses, I examined four measures. Three measures give us a clue as to how
emotion is impacting pattern separation abilities. The first, correctly identified similar items (hit
rate for similar items; participant correctly identified an item as being “similar”, but not exactly
the same, as a previously seen item), showed us that older adults’ abilities to recognize similar
items were negatively impacted when in the emotion condition as compared with the neutral
condition, but younger adults’ abilities were not. The second, incorrectly identifying similar
items (false alarm rate for similar items; participant incorrectly identified an item as being
“same” rather than “similar”), showed us that emotion was helpful for younger adults, who
made fewer false alarms in the emotion condition relative to the neutral condition. On the
other hand, older adults made marginally more false alarms in the emotional condition, relative
to the neutral condition. As seen in Toner, et al. (2009), older adults had far more false alarms
to similar items, indicative of decreased pattern separation abilities as compared with younger
adults, which I replicated here. The higher false alarm rate among older adults here, is
indicative of older adults relying on more gist-based processing. They are more likely to identify
something as being old, rather than “similar”. Essentially, older adults are more likely to pattern
complete, rather than pattern separate. Additionally, in line with findings from Segal et al.
(2012), emotion did enhance pattern separation abilities for younger adults. What extends
these findings is that emotion impacted these abilities for younger adults, while it caused the
opposite effect in older adults. Looking at the pattern separation bias score (the calculated
difference between correctly calling a similar item "similar" and incorrectly calling a new item
"similar") is the third way to look at pattern separation abilities. The pattern separation bias
score is important, because it can inform us as to whether the participant is indiscriminately
22
calling items “similar” incorrectly or using the “similar” response appropriately. Results
suggested that there was no difference in younger adults’ score during emotional blocks as
compared with neutral blocks. For older adults, the emotion condition caused a lower score
relative to the neutral condition, indicating that older adults more appropriately called items
“similar” in emotional blocks than neutral blocks. Emotion did not serve to enhance these
abilities for older adults, but did for younger adults.
The final measure gives us insight into pattern completion processes. Analysis of the last
measure, correctly identified same items (hit rate for same items; participant correctly
identified an old item as being the same as a picture they had previously seen), showed that
older adults were more likely to correctly identify these images than younger adults. This is not
surprising, again, given the fact that older adults are more likely to pattern complete, rather
than separate, and thus are more likely to be liberal in the use of calling any item “same”.
Impaired hippocampal pattern separation processes may lead to greater gist-based
processes under arousal. The evidence that older adults were less likely to discriminately use
the “similar” key, and were therefore incorrectly using that response for other images, supports
this. Indeed, the fact that older adults were worse in general during the emotion blocks bolsters
this argument. Therefore, the results of this study show that pattern separation abilities not
only decline with age, but also are not enhanced by emotion for older adults. Younger adults,
on the other hand, show enhancement while experiencing emotion. Declines in both the
hippocampus and the norepinephrine system could be source of these findings for older adults.
Therefore, due to the increased amount of norepinephrine receptors in the hippocampus as
23
compared with other brain regions (Duncan, Curtis, & Davachi, 2009), younger adults are doing
better on this memory task when experiencing emotion.
This study is not without its limitations. One main limitation is that I do not have
individual ratings of arousal and valence for each participant. Having those individual,
quantifiable amounts of valence and arousal felt by the participant could have allowed us to do
a more trial-by-trial basis of analysis, instead of using purely “emotional” or “neutral” blocks.
For example, stimuli could have been divided into different categories based on high and low
arousal, or high and low valence. Additionally, in this study, valence and arousal cannot be
teased apart, which leaves open the question of whether those two aspects of emotion are
both responsible for the effects I have seen here, or if only one is at play. This will be further
discussed in Study 2 and Study 3.
24
STUDY 2: HOW VALENCE IMPACTS PATTERN SEPARATION
Overview
Study 1 showed us that emotion does influence pattern separation abilities in both
younger and older adults. The question then was asked; do valence and arousal operate
separately? Are the effects due to valence or arousal? This study will discuss an experiment
specifically looking at the impact of valence.
As previously discussed, emotional memory tends to change with age. For example, in
one study looking at the recall of positive, negative and neutral pictures across younger,
middle, and older adults, the ratio of positive to negative images correctly recalled increased
with age (Charles, et, al., 2003). According to SST (Carstensen, et al., 2003), older adults have a
tendency to focus on more positive information, due to a desire to have goals more focused on
emotion regulation and the maintenance of positive affect. Older adults not only show a
preference in memory for positive information, they tend to look at it longer (Isaacowitz,
Wadlinger, Goren, & Wilson, 2006). In terms of emotional information processing, younger
adults tend to focus most on negative stimuli (Baumeister, Bratslavsky, Fickenauer, & Vohs,
2001; Rozin & Royzman, 2001) whereas older adults focus relatively more on positive stimuli
(Knight et al., 2007). This preference for positive information that older adults show could then
influence attention to the stimuli during the pattern separation processing. In keeping with
previous findings, a main effect of age was predicted, indicating that older adults would be
worse at the task overall. It is likely that older adults’ preference for positive information would
impair their pattern separation abilities during positive blocks. An age by valence interaction
was predicted, such that older adults will have poorer pattern separation in positive blocks than
25
negative blocks, whereas the reverse will be true for younger adults. Positive images should
elicit less of an arousal response in both groups as compared with negative images (Garavan,
Pendergrass, Ross, Stein, & Risinger, 2001), and therefore cause less pattern separation
enhancement. Also, older adults showing a preference for positive information should distract
from the task during positive blocks, resulting in poorer pattern separation.
Method
Participants. Forty-six younger adults (18-26 years of age, M
age
= 20.92, SD
age
= 1.82, 16
males) and 42 older adults (60-80 years of age, M
age
= 68.70, SD
age
= 6.11, 14 males) participated
and were compensated $15 per hour or with course credit for their participation. Younger
adults were recruited from the University of Southern California SONA Psychology Pool. Older
adults were recruited from the University of Southern California Healthy Minds database. Five
additional participants were excluded from the analyses because they either did not complete
the experiment, or noted during debriefing that they did not understand the task instructions.
Participants were required to have normal or corrected-to-normal vision and hearing, be free of
chronic illnesses, have no cognitive impairment, and not be on any beta-blocker medication.
Additionally, all older adults were screened for participation using the Telephone Interview of
Cognitive Status (TICS; Brandt, Spencer, & Folstein, 1988).
Procedure. All participants consented and were debriefed in accordance with University
of Southern California Institutional review guidelines. Participants also completed the same
depression, mood, demographic and verbal fluency questionnaires as Study 1. All participants
also participated in the Repeatable Battery for the Assessment of Neuropsychological Status
(RBANS; Randolph, Tierney, Mohr, & Chase, 1998) at the end of all experimental procedures.
26
Task. The main task was a continuous memory paradigm similar to the experiment
presented in Study 1. In that study, I incorporated mixed emotion blocks, with both positive and
negative images. In order to assess if one valence or the other differentially affects younger and
older adults’ ability to pattern separate, in this study, I presented the valences in separate
blocks (2 negative, 2 positive, 2 neutral; see Figure 2.1).
Figure 2.1
Equipment and Stimuli. All equipment and stimuli used during the experimental
procedure were the same as Study 1. The images were chosen based on previously assessed
valence (M
negative
= 2.63, SD
negative
= 0.76, M
neutral
= 4.99, SD
neutral
= .56, M
positive
= 6.37, SD
positive
=
0.85,) and arousal (M
negative
= 6.46, SD
negative
= 1.39, M
neutral
= 2.20, SD
neutral
= 1.05, M
positive
=
4.21, SD
positive
= 0.91) ratings.
Data Analysis. Consistent with Study 1, all data was analyzed using SPSS statistical
software. Once again I used analysis of variance (ANOVA) to test for all main effects and
interactions for several measures including: correct responses (i.e. hits) for “same” items,
correct responses (i.e. hits) for “similar” items, incorrect responses (i.e. false alarms) for
27
“similar” items, and pattern separation bias in terms of age and valence. In addition to the
memory results, I incorporated analyses featuring the results from the RBANS
neuropsychological testing as a grouping variable.
Results
Hits and False Alarms. Based on the analyses performed in Study 1, I wanted to focus on
how age differences and valence might impact hits for the memory items, both “similar” and
“same”. Using a repeated-measures ANOVA, I examined hits for “same” items and compared
block types (negative, positive, and neutral) for the age groups (younger adults and older
adults). I did not find any significant effects, F(2,172) = 0.19, p = .83, partial η
2
= .002. Splitting
the data by age group also did not indicate any differences for younger adults, F(2,90) = 0.19, p
= .83, partial η
2
= .004, or older adults, F(2,82) = 0.19, p = .82, partial η
2
= .005. When examining
hits for “similar” items and comparing block types (negative, positive, and neutral) for the age
groups (younger adults and older adults), I found a significant main effect of age, F(1,86) = 4.39,
p = .04, partial η
2
= .05, but no other significant effects, F(2,172) = 0.03, p = .97, partial η
2
=
.000. This indicates that older adults had a lower hit rate (M = .39, SE = .02) as compared with
younger adults (M = .45, SE = .02). Again, splitting the data by age group also did not indicate
any differences for younger adults, F(2,90) = 0.25, p = .78, partial η
2
= .006, or older adults,
F(2,82) = 0.06, p = .94, partial η
2
= .002. When examining false alarms for “similar” items and
comparing block types (negative, positive, and neutral) for the age groups (younger adults and
older adults) no significant effects were discovered, F(2,172) = 0.93, p = .40, partial η
2
= .011,
other than a main effect of age, F(1,86) = 13.31, p < .001, partial η
2
= .134. This suggests that
older adults (M = .30, SE = .02) made more errors overall than younger adults did (M = .22, SE =
28
.02). Finally, splitting the data by age group also did not indicate any differences for younger
adults, F(2,90) = 0.83, p = .44, partial η
2
= .02, or older adults, F(2,82) = 0.76, p = .47, partial η
2
=
.02.
Hits and False Alarms and Picture Type. I was also interested in examining differences
between different types of images (i.e. objects and scenes), so three other repeated-measures
ANOVAs, were calculated for each of the memory variables using picture type (objects and
scenes), block types (negative, positive, and neutral), and age groups (younger adults and older
adults) for comparison. For “same” hits, I identified a main effect of picture type, F(1,86) =
43.90, p < .001, partial η
2
= .34, indicating that scenes were better recognized (M = .70, SE =
.01) as being “same” than objects (M = .63, SE = .01). For “similar” hits, I identified a main effect
of picture type, F(1,86) = 42.34, p < .001, partial η
2
= .33, indicating that scenes were better
recognized (M = .48, SE = .02) as being old than objects (M = .39, SE = .02). An interaction
between picture type (objects and scenes) and valence (negative, positive, and neutral),
F(2,172) = 5.74, p = .004, partial η
2
= .063, showing that memory over all was better for scenes,
but that this varied by valence with scenes shown during negative blocks being the best
remembered (M = .52, SE = .03), as compared with scenes showed during neutral blocks (M =
.45, SE = .02) or positive blocks (M = .46, SE = .03). Follow up t-tests confirmed that the
difference between memory for scenes in negative blocks as compared with neutral blocks was
significant, t(87) = 2.67, p = .009, and the difference between memory for scenes in negative
blocks as compared with positive blocks was marginal, t(87) = -1.91, p = .06 (see Figure 2.2).
29
Figure 2.2
When looking only at the scene images, younger adults were better at correctly identifying
similar items (pattern separation) during the negative blocks (M = .55) as compared with the
neutral blocks (M = .46; t(45)= 2.57, p < .05) and positive blocks (M = .47; t(45)= 2.57, p < .05) ,
F(2, 90) = 3.70, p < .05. Older adults showed no impact of valence, F(2, 82) = 0.48, p = .62 (see
Figure 2.3).
30
Figure 2.3
For “similar” false alarms, I identified a main effect of picture type, F(1,86) = 71.02, p < .001,
partial η
2
= .45, indicating that scenes were less likely to cause a false alarm (M = .19, SE = .01)
than objects (M = .30, SE = .01), consistent with the hit rates presented previously.
Separation Bias. As with the previous experiment, I performed a separation bias score
analysis. Again, this calculated the difference between calling a similar item "similar" and calling
a new item "similar" in the different block types (negative, positive, and neutral) between the
age groups (younger and older adults). Using a repeated-measures ANOVA, I examined this
separation bias score and compared block type (negative, positive, and neutral) for the age
groups (younger and older adults). I found a significant main effect of age, F(1,78) = 6.32, p <
.01, partial η
2
= .08, and a significant valence by age interaction, F(1,86) = 21.22, p < .001,
31
partial η
2
= .20. In general, younger adults had a higher separation bias score (M = .53, SE = .04)
than older adults (M = .36, SE = .04), meaning they were better able to discriminately use the
keypress of “similar”. Older adults showed a greater bias of using “similar” for new images.
Results did not differ when the analysis was split by age group. Again looking only at the scene
images, younger adults were better at pattern separating during the negative blocks (M = .52)
as compared with the neutral (M = .41; t(45)= 3.119, p <.01) and positive blocks (M = .41; t(45)=
-2.52, p <.05) , F(2, 90) = 5.61, p = .01. Older adults showed no impact of valence, F(2, 82) =
1.09, p = .34 (See Figure 2.4).
Figure 2.4
RBANS. In general, older adults (M = 102.21, SE = 2,10) performed worse overall on the
total score of the RBANS as compared to younger adults (M = 108.76, SE = 2.26), t(86) = 2.11, p
= .038. Within each age group, a median split was performed based on the calculated total
32
score for the RBANS testing, sorting out higher performing individuals from lower performing
individuals. Analyses were repeated using this coded RBANS value. Follow up t-tests confirmed
that for both the younger adults, t(44) = -10.98, p < .001, and the older adults, t(44) = -9.95, p <
.0001, there were differences in the high and low performing groups created by the median
split. When looking at hits for “same” items, a main effect of RBANS score was discovered,
F(1,84) = 4.97, p = .03, partial η
2
= .06, suggesting that higher performing individuals made
more correct responses (M = .70, SE = .02) than lower performing individuals (M = .63, SE = .02).
Results were consistent when looking at hits for “similar” images. A main effect of RBANS score
was discovered, F(1,84) = 11.88, p < .001, partial η
2
= .124, suggesting that higher performing
individuals made more correct responses (M = .48, SE = .02) than lower performing individuals
(M = .37, SE = .02). No other parts of the analysis were significant. In addition, looking at false
alarms for “similar items” I only identified a previously discussed main effect of age. Finally,
when considering the pattern separation bias score, a valence by age by RBANS score three-
way interaction was discovered, F(2,168) = 3.50, p = 0.32, partial η
2
= .04 (see Figure 2.5).
33
Figure 2.5
This analysis showed that in general, low performing older adults had the hardest time
appropriately using the “similar” keypress. High performing older adults performed about the
same as low performing older adults, while high performing younger adults had the easiest
time appropriately using the “similar” keypress.
Discussion
The goal of this study was to look at the impact of valence on pattern separation and
pattern completion in younger and older people and to further understand the results
presented in Study 1.
34
This study showed several interesting findings in terms of how valence interacted with
pattern separation and pattern completion processes, and in general, how pattern separation
and pattern completion changes with age. First, when looking at correctly identified similar
items (hits for similar items), older adults showed a lower hit rate as compared with younger
adults. When the image types were taken into account, a difference in memory for objects and
scenes was found, such that scenes were better recognized as similar than objects. This
difference also interacted with valence, such that scenes shown during negative blocks were
better remembered than during neutral blocks, and marginally better than during positive
blocks. There was no difference in the valence category for objects. When looking further into
these effects for the scene images, it was discovered that this was where the impact of age was
appearing. Younger adults were better able to pattern separate in negative blocks relative to
positive blocks and neutral blocks, but older adults showed no impact of valence. The negative
images interspersed between the items that were to be pattern separated aided memory for
younger adults, but not older adults. This was consistent with both previous research and with
findings from Study 1. Also, this is related to similar findings about how emotion influences
memory (for example Sakaki, Fryer, & Mather, 2014). Second, when looking at incorrectly
identified similar items (false alarms to similar items), results showed that older adults made
more errors than younger adults, again, consistent with previous research and with the results
presented in Study 1. Third, when considering the pattern separation bias score, results were
similar to that of Study one. Younger adults had a higher score, indicative of being better able
to appropriately use “similar” and not just arbitrarily use it, as it seemed was more common
with the older adults. When looking at the data separately for younger and older adults, and
35
only at the scene images, younger adults were best at appropriately using the “similar”
keypress in the negative blocks as compared with both the neutral blocks and the positive
blocks. Older adults, on the other hand, did not show any impact of valence. These results are
similar to previous studies (Toner, et al. 2009), but extend the findings to show an impact of
negative valence. Additionally, when looking at the pattern separation bias score in conjunction
with the scores on the RBANS test, I found that low performing older adults were the worst at
being able to appropriately utilize the response of “similar” further insinuating the connection
between lower hippocampal function and pattern separation success.
And lastly, when considering the measure of pattern completion (hits for same items),
results suggested that there were no age differences in memory for these items, and memory
did not differ by valence category. When looking at the two types of images (objects and
scenes) separately, I discovered that memory was overall better for scenes than objects, but
that these results did not differ by valence category. These results were consistent with Study 1
results, which showed that younger and older adults are differentially impacted by emotion,
and in this case, valence.
This study is not without limitations. Similar to Study 1, we do not have individualized
ratings of valence and arousal for each emotional image shown throughout the experiment.
Instead, I am relying on predetermined ratings in order to assess valence and arousal levels. In
addition, there are many ways to induce emotion; specifically different types of valence, and
this study only incorporated one method.
36
STUDY 3: HOW AROUSAL IMPACTS PATTERN SEPARATION
Overview
Study 1 showed us that for younger adults, pattern separation abilities were better
when experiencing emotion as compared with not experiencing emotion. In Study 2, negative
valence impacted memory performance more so than either neutral or positive valence.
Changes in our psychophysiology are common with aging, as previously discussed. That
being said, older and younger adults respond similarly to several psychophysiological arousal
response measures (Denburg, et al., 2003; Neiss, et al., 2009). Several regions important for
arousal show structural integrity with age, including the amygdala (Nashiro, et al., 2012) and
the locus coeruleus (Watson et al., 2006), suggesting a relationship between the integrity of
emotional arousal processes and cognitive performance during late life (Watson et al., 2006;
Luo, et al. 2015; Izumi & Zorumshi, 1999). By evaluating measures of pattern separation in
response to an arousal manipulation and individual level psychophysiological responses, we will
be able to investigate the connection between integrity of emotional arousal and pattern
separation abilities as we age.
The isometric handgrip procedure, a type of stationary exercise used to investigate the
cardiovascular response to stress (Mitchell & Wildenthal, 1974), impacts the cardiovascular
system through sympathetic pathways (specifically increasing arterial blood pressure and heart
rate; Garg, Malhotra, Dhar & Tripathi, 2013), therefore making it an excellent tool to
understand the underlying mechanisms of sympathetic nervous activity (Christensen & Galbo,
1983). Isometric handgrip reliably increases plasma norepinephrine (Lake, Ziegler, & Kopin,
1976; Palmer, Ziegler, & Lake, 1978; Nielsen & Mather, 2015). Salivary alpha-amylase (sAA) is a
37
known biomarker for norepinephrine, as changes in sAA correlate with changes in plasma
norepinephrine (Chatterton, Vogelsong, Lu, Ellman, & Hudgens, 1996; Segal, Cotman, & Cahill,
2012; Nielsen & Mather, 2015).
Method
Participants. Thirty-seven younger adults (18-25 years of age, M
age
= 20, SD
age
= 1.49, 19
males) and 32 older adults (60-83 years of age, M
age
= 71.56, SD
age
= 6.54, 14 males) participated
and were compensated $15 per hour or with course credit for their participation. Younger
adults were recruited from the University of Southern California SONA Psychology Pool. Older
adults were recruited from the University of Southern California Healthy Minds database.
Participants were required to have normal or corrected-to-normal vision and hearing, be free of
chronic illnesses, have no cognitive impairment, not be smokers, and not be on any beta-
blocker medication. Younger female participants had to have been taking hormonal birth
control and have a normal menstrual cycle. Prior to the experiment, participants were asked to
refrain from eating, gum chewing, or teeth brushing for at least one hour. They were also asked
to refrain from exercise, alcohol, and caffeine twenty-four hours prior to the start of their
appointment. Twelve younger adults and eight older adults were excluded from the analyses
for not following task instructions, not following pre-task instructions (i.e. eating within one
hour of the first or second session), not participating in the second session of the experiment,
or for not being able to complete some parts of the experiment (i.e. saliva samples or handgrip
procedure).
Procedure. Participants completed two experimental sessions. Each session included
either an isometric handgrip task or a rest task as well as three neutral blocks of the main task
38
from Study 1 and Study 2. Whether the participant received the handgrip protocol or the rest
protocol was counterbalanced across individuals and sessions. Each session also included the
same depression, mood, demographic and verbal fluency questionnaires as were completed in
Studies 1 and 2. All participants consented and were debriefed in accordance with University of
Southern California Institutional review guidelines.
Isometric Handgrip Task and Rest Task. Arousal was manipulated between sessions,
either utilizing an isometric handgrip task or a rest task before the continuous memory
paradigm. The isometric handgrip task (Nielsen & Mather, 2015; Topolovec, Gati, Menon,
Shoemaker, & Cechetto, 2004) involved a maximum squeeze of a dynamometer for 18 seconds
with the dominant hand, followed by 60 seconds of resting the dominant hand palm up on the
table. This was then repeated three times. The rest task involved resting the dominant hand on
the grip of the dynamometer, followed by 60 seconds of resting the dominant hand palm up on
the table. This was then repeated three times.
Salivary α-Amylase Samples. Passive salivary α-amylase (sAA) samples were taken
before the experiment began (sAA Sample 1; after the participant had been seated for ten
minutes), after the instructions and practice for the memory task (sAA Sample 2), after either
the isometric handgrip task or the rest task (sAA Sample 3), and after the final block of the
memory task (sAA Sample 4) in order to measure indication of emotional arousal. This is a
common method of testing states of emotional arousal (Buchanan, Bibas, & Adolphs, 2010). As
compared with other measures of psychophysiological arousal, α-amylase, though attenuated,
remains mostly stable with age (Strahler, Mueller, Rosenloecher, Kirschbaum, & Rohleder,
2010).
39
Memory Task. The main task was a continuous memory paradigm similar to the
experiment presented in Study 1 and Study 2. In those studies, I incorporated mixed emotion
blocks, with both positive and negative images, or split valence blocks, with positive and
negative images presented separately. In order to assess if psychophysiological arousal
influences pattern separation, all blocks presented in this experiment were neutral (3 neutral
blocks presented at session 1, 3 neutral blocks presented at session 2; see Figure 3.1)
Figure 3.1
Equipment and Stimuli. Other than the omission of the emotional pictures, all
equipment and stimuli used during the experimental procedure were the same as Study 1 and
Study 2.
Data Analysis. Consistent with Study 1 and Study 2, all data was analyzed using SPSS
statistical software. Like in the previous two experiments, I used analysis of variance (ANOVA)
to test for all main effects and interactions for several measures including: correct responses
(i.e. hits) for “same” items, correct responses (i.e. hits) for “similar” items, incorrect responses
(i.e. false alarms) for “similar” items, and pattern separation bias. In addition to memory
variables, I also analyzed variables related to the sAA levels.
40
Results
Hits and False Alarms. For Study 3, I followed the same analysis plan as Study 1 and
Study 2. To start, I analyzed how age differences and arousal might impact hits for the memory
items, both similar and same. Using a repeated-measures ANOVA, I examined hits for same
items and comparing task types (handgrip and rest) for the age groups (younger adults and
older adults). For same hits, I identified a marginal main effect of task type, F(1,67) = 3.20, p =
.078, partial η
2
= .046, indicating that memory was marginally better during handgrip (M = .68,
SE = .01) rather than rest tasks (M = .60, SE = .01). Using another repeated-measures ANOVA, I
examined hits for similar items and comparing task types (handgrip and rest) for the age groups
(younger adults and older adults). I identified a main effect of task type, F(1,67) = 5.90, p = .018,
partial η
2
= .081, indicating that memory was better during handgrip (M = .48, SE = .02) rather
than rest tasks (M = .42, SE = .02). There was also a main effect of age, F(1,67) = 12.85, p < .001,
partial η
2
= .16, indicating that younger adults (M = .50, SE = .02) performed better than older
adults (M = .40, SE = .02). When the analyses was broken into individual age groups, t-tests
showed that the effect of task was significant for the younger adults, t(36) = -2.02, p = .05, but
not for the older adults, t(31) = -1.45, p = .16 (see Figure 3.2).
41
Figure 3.2
To assess similar false alarms, I used another repeated-measures ANOVA and compared task
types (handgrip and rest) for the age groups (younger adults and older adults). There was a
main effect of age, F(1,67) = 20.92, p < .001, partial η
2
= .24, indicating that younger adults (M =
.19, SE = .02) outperformed older adults (M = .30, SE = .02).
Hits and False Alarms and Picture Type. Similar to Study 2, breaking the data down by
picture type revealed interesting findings. Using a repeated-measures ANOVA, I examined hits
for same items and comparing task types (handgrip and rest), picture types (objects and scenes)
for the age groups (younger adults and older adults). I identified a main effect of picture type,
F(1,67) = 4.18, p = .045, partial η
2
= .06, indicating that scenes were better recognized as being
42
old (M = .68, SE = .02) than were objects (M = .65, SE = .02). There was also a significant picture
type by task type interaction, F(1,67) = 34.40, p < .001. When comparing memory for objects in
the rest and the handgrip condition, the handgrip condition improved memory, t(68) = -6.67, p
<.001. This handgrip effect did not occur for scenes, t(68) = -1.61, p =.11. Next, I examined hits
for similar items and comparing task types (handgrip and rest), picture types (objects and
scenes) for the age groups (younger adults and older adults). This revealed a main effect of
picture type, F(1,67) = 33.52, p < .001, partial η
2
= .33, such that scenes were better identified
(M = .49, SE = .02) as being similar than objects (M = .42, SE = .02). Also, a picture type by age
interaction was found, F(1,67) = 9.146, p = .004, partial η
2
= .12, suggesting that while older
adults overall remembered less, they were better able to identify similar scenes better than
similar objects. Results also reiterated the main effects of task type and age, previously
reported. When assessing the false alarms to “similar” items, a very consistent main effect of
picture type was again discovered, F(1,67) = 49.00, p < .001, partial η
2
= .42, such that
participants made more incorrect responses to objects (M = .28, SE = .01) rather than scenes (M
= .19, SE = .01).
Pattern Separation Bias. As in Study 1 and Study 2, I performed a separation bias score
analysis. Again, this calculated the difference between calling a similar item "similar" and calling
a new item "similar" in the different task type (handgrip and rest) between the age groups
(younger and older adults). Using a repeated-measures ANOVA, I found that there was a main
effect of task type, F(1,67) = 4.90, p = .03, partial η
2
= .07, such that pattern separation scores
were higher during the handgrip task (M = .37, SE = .02) relative to the rest task (M = .31, SE =
.02). There was also a main effect of age, F(1,67) = 20.17, p < .001, partial η
2
= .23. The
43
difference in task conditions was significant for older adults, t(36) = -2.08, p = .05, but not
younger adults, t(36) = -1.3, p = .20, therefore the main effect is driven by the older adults and
not the younger adults (see Figure 3.3).
Figure 3.3
sAA Levels. Results suggested that sAA levels were not differentially impacted by the
arousal manipulation, by the instructions and practice, or by the main pattern separation task.
Self reported baseline stress levels showed that while older adults reported less stress than
younger adults, participants did not report a significantly higher amount of stress on either of
the session days. Both age groups reported that their stress on both of the session days was
typical of their normal amount of stress. There was also not a significant difference between
44
the first or second session days regardless of whether it was the handgrip task session or the
rest task session.
Discussion
The goal of this study was to continue to tease apart the impact of emotion that was
observed in Study 1. Study 2 indicated findings related to valence, specifically negative valence.
The experiment in this Study served to test the impact of arousal.
Several interesting results were found in the measures for pattern separation. When
examining correctly identified similar items (hits for similar items), I found that memory was
better during the handgrip task session as compared with the rest task session. The main effect
of age alluded to the fact that this result was significant for younger adults, and was not
significant for older adults. This was consistent with Experiment 1, and with previous studies
(Segal, et al. 2012). An increase in arousal, as provided by the handgrip session, increased
pattern separation success for the task that followed. When examining incorrectly identified
similar items (false alarms to similar items), I found there was no main effect of task type, only a
main effect of age. This suggests that older adults were worse at this measure overall. The third
measure of pattern separation success, the pattern separation bias score, showed that the use
of “similar” was better during the handgrip task session then the rest session, and when age
groups were examined separately, this effect of handgrip was significant for older adults, but
not younger adults.
When considering the measure of pattern completion (hits for same items), I found that
memory was marginally better in the handgrip task, but not significant. When analyzing the
45
data for the object and scene images separately, memory was enhanced in the handgrip
condition for objects but not for scenes.
The limitations of this study mostly lie within the specific measure of arousal. sAA did
not reliably increase due to the manipulation for either the younger or the older adults. This is
in opposition to the study by Segal and colleagues (2012), which showed that sAA increased
after viewing an emotional picture slideshow. Although the manipulation in this study was
different than that study, we also predicted an increase in sAA. The lack of consistent sAA
effects in this study could be due to several reasons. For the younger adults, the manipulation
might not have been strong enough. In the demographic questionnaires given to participants,
most younger adults acknowledged that they exercise 3-5 hours per week. Exercise can
attenuate the arousal response in individuals who exercise regularly (Ebbesen, Prkachin, Mills,
& Green, 1992; Mierau, Hülsdünker, Mierau, Hense, Hense & Strüder, 2014), thus a
manipulation may need to be stronger. As far as the older adults, a different manipulation, such
as a squeeze ball instead of a dynamometer might have been more effective (Nielsen & Mather,
2015). Some older adults noted that the dynamometer was difficult to use because of the
firmness of the handle. Again, a squeeze ball may have allowed for a deeper squeeze and a
higher arousal response. Additionally, cortisol may prove to be another interesting assay to
perform to see if individual stress levels over the course of the session correlated with pattern
separation and pattern completion success.
46
GENERAL DISCUSSION
In general, the results contained in Study 1, Study 2 and Study 3 suggest that emotion
enhanced memory, both pattern separation processes and pattern completion processes in
younger adults but not in older adults. The exception to this is Study 3, which showed some
positive relationships for older adults. For younger adults, Study 1, Study 2, and Study 3 all
showed that emotion, valence, and arousal have the potential to enhance memory in both the
case of pattern separation and pattern completion. These results were different in older adults,
suggesting that emotion might not enhance memory or actually be harmful. Older adults did
not show the same results as younger adults for any of the studies presented in Study 1, Study
2 or Study 3 (with the exception of the pattern separation bias score in Study 3). In sum, for
older adults, most of the time emotion, valence, or arousal either did not change any of the
results (as was the case in Study 2 and Study 3, with the exception of the pattern separation
bias score) or was shown to be harmful (correctly pattern separated items, pattern separation
bias score, and marginally for incorrectly identified pattern separated items in Study 1).
Several studies have shown results that are in line with my findings for younger adults,
suggesting that emotion enhances memory for neutral items in younger adults (Nielson &
Powless, 2007; Anderson, Wais, & Gabrieli, 2006; Knight & Mather, 2009; Finn & Roediger,
2011; Nielson & Arentsen, 2012). Even animal research suggests that emotional arousal after
learning enhances memory for the experience of having learned (McGaugh, 2000; McGaugh,
2004). Though those results are strong, there has been some debate in the literature about this
being a true phenomenon, as some studies suggest that emotion impairs memory (Bornstein,
47
Liebel, & Scarberry, 1998; Strange, Hurlemann, & Dolan, 2003; Strange, Kroes, Fan, & Dolan,
2010; Knight & Mather, 2009).
Arousal-biased competition theory (ABC) provides a framework to understand this
phenomenon for the younger adults (Mather & Sutherland, 2011). Essentially, when high
priority information is presented alongside emotion, it is enhanced by that emotion due to an
increase in arousal. For example, in a series of studies looking at how emotion facilitates
memory for preceding neutral items, results suggested that if the item was high priority,
memory was enhanced (Sakaki, Fryer, & Mather, 2014). In this study, memory (specifically
recognition) of these images was enhanced because they were considered to be high priority.
Across all three of the studies presented in Study 1, Study 2, and Study 3, participants were told
that when an initial image came around (with the second image that was either similar or same
to come along later) it would have a red border around it. The red border served as both a top-
down and bottom-up clue that the image was important and therefore high priority to other
images that passed by. It was considered to have top-down priority because participants were
verbally instructed about the relevance of the border, and to have bottom-up priority because
of the increased perceptual salience of a bright color. Additionally, when asked after the task
about the meaning of the border, we ensured all participants knew that the border meant the
image was high priority and would need to be remembered in order to make accurate
responses for the pictures following it. The framework of ABC helps us further understand the
results for younger adults presented in Study 1, and set a framework to understand the results
in Study 2 and Study 3.
48
For younger adults, the results of Study 2 and Study 3 have similar underpinnings. In
Study 2, negative valence was shown to increase memory, and in Study 3, arousal was shown to
increase memory. These two results are driven by the same source, which is an elevated
arousal level. Negative valence has been shown to be more arousing than positive valence
(Garavan, et al., 2001). Additionally, it has been shown that younger adults pay attention to
more negative than positive stimuli (Baumeister, et al., 2001; Rozin & Royzman, 2001), thus
insinuating that younger adults could have been paying closer attention during the negative
blocks, relative to the neutral blocks, which resulted in increased processing and better
memory. Additionally, the stimuli that were selected to be included in both Study 1 and Study 2
(but presented differently in each study) had varying amount of valence and arousal. For Study
2, the negative stimuli were high in negative affect and high in arousal. The positive stimuli, by
comparison were high in positive affect, but lower in arousal. They were more arousing than
the neutral stimuli, which were mid range in valence and low in arousal. For Study 3, using a
purely arousal based measure, after the handgrip task, pattern separation success was
enhanced, further bolstering the claim that higher levels of arousal are responsible for the
increased pattern separation success.
These findings could be related to the high number of norepinephrine receptors in the
areas of the hippocampus responsible for pattern separation (Duncan, Curtis, & Davachi, 2009).
More support for these findings can be found within the structure of the Glutamate Amplifies
Noradrenergeric Effects (GANE) model (Mather, Clewett, Sakaki, & Harley, in press). This model
suggests that the locus coeruleus releases a widespread low to moderate amount of
norepinephrine that causes the brain to inhibit information due to receptors with a low
49
threshold for activation. During most arousal responses, the brain then experiences a lower
amount of activity. In brain regions where activity is already higher, due to being in operation
for some sort of cognitive process, glutamate from the synaptic activity initiates norepinephrine
release by the locus coeruleus axons that run throughout the brain. This causes an amplifying
effect and causes arousal to focus resources to the process at hand. In the hippocampus,
arousal would then be amplifying the on-going processes, causing a boost in memory for the
younger adults. Older adults on the other hand, who have a lower locus coeruleus neuron
density (Manaye, McIntire, Mann, & German, 1995; Sladek Jr & Sladek, 1978; Vijayashankar &
Brody, 1979), would therefore experience less of this boost. An intact locus coreleus
norepinephrine system would lead to an increase in memory, which could be why the study in
Study 3 showed an increase in pattern separation success (due to the intense nature of the
arousal component) but the other studies did not. An individual’s rate of cognitive decline is
intertwined with this locus coreleus norepinephrine system, such that lower locus coeruleus
density is related to lower cognitive function (Wilson, et al. 2013). In addition, aging has an
impact on how glutamate triggers additional release of norepinephrine (Pittaluga, Fedele,
Risiglione, & Raiteri, 1993; Gonzales, Brown, Jones, Trent, Westbrook, & Leslie, 1991), which
would make norepinephrine less likely to cause the amplifying effect and instead perhaps cause
an inhibitory effect, as seen in Study 1.
Overall, and in line with previous research (Toner, et al. 2009), older adults had a harder
time identifying similar items as compared with younger adults. This is related to the literature
on source monitoring, which also shows that older adults have an impaired ability to identify
things that are similar to things previously seen because of a deficiency in source accuracy
50
(Henkel, et al., 1998; Collier). It is also in line with the general findings about hippocampal
decline (Bakker, et al. 2008; Yassa & Stark, 2011; Toner et al. 2009) and norepinephrine declines
in the brain (Gallagher & Nicolle, 1993; Manaye, et al. 1995; Sladek Jr & Sladek, 1978;
Vijayashankar & Brody, 1979). Looking at individual results in terms of objects and scenes
allowed us to get a more nuanced understanding about how pattern separation experiments
not only impact objects, but impact scenes as well. Also, evidence has suggested that objects
that are presented cause an adaptation signal in the aging brain, meaning that an increase in
activation due to an element of novelty is less likely for objects (Chee, Goh, Venkatraman, Tan,
Gutchess, Sutton, et al. 2006). The studies presented in this dissertation indicate that pattern
separation is less efficient in older adults, which is highlighted as poorer recognition memory
performance due to increased interference (Toner, et al., 2009). In Study 1, emotion was shown
to negatively impact pattern separation success in older adults, providing us with the best
evidence that emotion could cause worse memory for older adults. Study 2 and Study 3
unfortunately did not show many results in terms of how valence or arousal would impact
pattern separation success. In general, these findings relate to both a decline in the
norepinephrine system and in the hippocampus associated with aging. Declines in the locus
coeruleus norepinephrine system (causing issues with how arousal amplifies memory) coupled
with an aging hippocampus (essentially causing older adults to rely more on pattern completion
processing than pattern separation processing) triggered the presented results.
Research on specific memory processes has direct relevance to the study of gerontology
and to the study of cognitive neuroscience. In terms of studying pattern separation and pattern
completion specifically with older adults, these studies suggest more nuanced changes in the
51
aging brain and address a problem many older adults struggle with. Studies have indicated that
over 34% of older adults 65-74 have reported memory problems. This increases to 88% after
age 85 (Reid & MacLullich, 2006). Understanding how memory processes, specifically emotional
memory processes, change over the life course could advance understanding of a problem that
impacts most older adults at some point in their lives. Older adults’ memory complaints often
center on pattern separation and similar source monitoring problems. Where did I park my car?
Is that my nephew or his brother? Did my friend change her hairstyle? Where did I leave my
keys? The set of studies presented here, as well as more studies to further understanding how
pattern separation is impacted by emotion could provide valuable insight into helping older
adults overcome pattern separation related problems.
52
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Abstract (if available)
Abstract
Pattern separation and pattern completion are memory processes localized to the hippocampus. Pattern separation involves the transformation of similar neural representations, or memories, into non-overlapping representations. Essentially, it involves making similar memories more distinct from each other. This is opposite to pattern completion, which involves combining representations into one. Emotion has been shown to impact memory in both younger and older adults due to the high amount of norepinephrine receptors in the hippocampus. Norepinephrine, a neurotransmitter, is responsible for our “fight or flight” response and is closely tied to an increase in emotional arousal. As we age, we tend to rely on more gist-based processing, and therefore are more likely to pattern complete than pattern separate. Previous research has suggested that emotion may influence pattern separation, but it is unclear exactly what the mechanisms of this process are. The experiment in Study 1 tested the impact of emotion on pattern separation and pattern completion. Study 2 and Study 3 contain experiments that tested the impact of the “quality” and “quantity” of emotion separately. Study 2 tested how pattern separation and pattern completion are impacted by valence, while Study 3 tested how pattern separation and pattern completion are impacted by arousal. In general, these findings are related to the decline in the health of the hippocampus, as well as the impact of norepinephrine on hippocampal processes. Older adults made marginally more mistakes during emotional blocks. The findings for older adults may be due to norepinephrine system acting on impaired hippocampal processes. Additionally, the findings for younger adults relate to arousal biased competition theory. Emotion enhanced younger adults’ memory accuracy for high priority information. The results may also be indicative of high levels of arousal increasing memory due to norepinephrine acting on hippocampal processes.
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The impact of emotion, valence, and arousal on differential memory processes in younger and older people
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