University of Southern California Dissertations and Theses

Crowding in peripheral vision

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Crowding in peripheral vision

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Content i

Crowding in Peripheral Vision

by

Kilho Shin

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
(PSYCHOLOGY)

May 2017

Copyright 2017 Kilho Shin
ii

Epigraph

I have told you all this so that you may have peace in me.
Here on earth you will have many trials and sorrows.
But take heart, because I have overcome the world.

John 16:33

iii

Dedication

To Dr. Bosco S Tjan

iv

Acknowledgments
The completion of this dissertation is impossible without the help and assistance of
many individuals.
First of all, I would like to express all my thankful heart to Dr. Bosco S Tjan, my
advisor and great mentor. His enthusiasm, professionalism, scholarship and dedication to
research have been very influential to me through this journey. I also appreciate his
patience, support and guidance for my graduate studies. Bosco was a very distinct person
who had a brilliant brain and warm heart at the same time. Whenever I faced difficult
situations as a graduate student, as an international student, as a husband and as a father
of two kids, he tried to help me in various ways. It was a wonderful chance for me to
have a mentor like him.
I would like to thank Dr. Irving Biederman, Dr. Mara Mather, Dr. Steven Lopez,
and Dr. Laurent Itti for being on my thesis committee. Special thanks to Irving and Mara
for their thoughtful inspiration and criticism on my thesis. Also, special thanks to Steven
for his considerable encouragement.
I would like to thank Dr. Jason Zevin and Dr. Morteza Dehghani for helping me
for recovery from the tragedy and finishing my PhD.
I also would like to thank to my TLAB friends. Thanks to Anirvan, Julian,
MiYoung, Ben, Rachel, Pinglei, Nihong, Jeiran, Helga, and Chris. I will never forget the
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past 8 years of my life with outstanding labmates. Also, I would like to thank to
Jiancheng, and Jared for helping me a lot for fMRI experiments.
Also, I would like to express my thankful heart to my Sharon, Joseph, Joanna and
my parents in Korea. Without their support, I couldn’t finish my PhD. Especially,
Sharon’s warm and thoughtful support is always like water in a desert.
Finally, I give all my thanks to Father in heaven. I glorify and praise our Lord,
Jesus Christ who is worthy of my praise.

vi

List of Figures
Figure 1.1: Crowding demonstration ············· ··········· ··················· ······ · · · · · · 3
Figure 1.2: Summary of representative researches for the locus of crowding·· · · · · · · · 8
Figure 2.1: Procedure and experimental conditions of Exp.2.1· · ·········· ···· · · · · · · · · 22
Figure 2.2: Procedure and experimental conditions of Exp.2.2· · · ········· ··········· · 26
Figure 2.3: Procedure and experimental conditions of Exp.2.3· · · ········· ··········· · 28
Figure 2.4: Results of Exp.2.1· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 32
Figure 2.5: Results of Exp.2.2· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 34
Figure 2.6: Results of Exp.2.3· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 35
Figure 3.1: Stimuli and task design of Exp. 3··· ············ · ··················· ········· 49
Figure 3.2: Results of PRL induction ··· ·········· ··········· ··················· ········· · 51
Figure 3.3: Results of crowding zone measurements ··· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 52
Figure 3.4: Results of crowding zone measurements for control experiment·· · · · · · · 53
Figure 3.5: Visual search performance· · · ··········· ·········· ··················· ········· 54
Figure 4.1: An example trial of Exp.4· · · ·········· ··········· ··················· ········· · 61
Figure 4.2: Estimating the crowding zone for elbow and threshold from a
psychometric function· · · ················· ········· ··················· ········· 63
Figure 4.3: Number of negatives as a function of p-value· · · ················· ········· · 66
Figure S1: Frequency of the number of perceived flankers in Exp.2.1. · · · · · · · · · · · · · 90
Figure S2: Reanalysis of Exp.2.1 using partial data sets as in Wallis & Bex (2011)
··················· ········· ··················· ········· ··················· ········· 91
Figure S3: Reanalysis of Exp.2.2 using partial data sets as in Wallis & Bex (2011)
··················· ········· ··················· ········· ··················· ········· 92
Figure S4: Reanalysis of Exp.2.3 using partial data sets as in Wallis & Bex (2011)
··················· ········· ··················· ········· ··················· ········· 93

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List of Tables
Table 2.1: Studies on the effects of visual awareness on crowding and the inferred
loci of crowding · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 16
Table 4.1: Descriptions of the Exp.4· · · · ········· ··········· ··················· ········· ··· 59
Table 4.2: Number of pairs· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 65

viii

Table of Contents
Epigraph· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · ii
Dedication· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · iii
Acknowledgements · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · iv
List of Figures· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · vi
List of Tables· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · vii
Chapter 1: Overview· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 1
Background for research questions· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 2
Properties of crowding··················· ········· ··················· ········· ··· ·· · 2
Studies on the mechanism of crowding· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · ··· ··· 4
Studies on the locus of crowding· · · · · · · · · · · · · · · · · · · · · · · · · ··········· ········ · · · · · · 5
Research questions and summary· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 10
Where is the neural locus of crowding? ········ ··············· · · · · · · · · · · · · · · · · ·11
How is peripheral vision changed through perceptual learning? ········ ····12
How should the crowding zone be measured? · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 13
Chapter 2: Where is the neural locus of crowding? · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 15
Introduction· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 15
Methods· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 20
Experiment 2.1· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 20
Observers· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 20
Stimuli· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 20
Procedures· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 22
Experiment 2.2· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 24
Observers· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 24
Stimuli· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 24
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Procedures· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 25
Experiment 2.3· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 27
Observers· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 27
Stimuli· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 27
Procedures· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 27
Data Analysis· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 29
Results· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 31
Discussion· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 36
Conclusion· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 40
Chapter 3: How is peripheral vision changed through perceptual learning?······· ····· · · · · · 41
Introduction· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 41
Methods· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 43
Observers· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 43
PRL induction· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 44
Stimuli and Apparatus· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 44
Eye Movement Recording and Simulated Scotoma· · · · · · · · · · · · · · · · · · · 44
Procedures· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 45
Data Analysis· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 46
Crowding measurement: psychophysics· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 47
Stimuli and Task· · · · · · · · · · · · · · · · ·········· ········· ··················· ·····47
Measuring the crowding zone· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 48
Results· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 50
PRL Induction· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 50
Crowding measurement: psychophysics· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 52
Crowding measurement - control: psychophysics· · · · · · · · · · · · · · · · · · · · · · · · · · · · · 53
Visual search performance· · · · · · · · · · · · · · · · · · · · · · · · · · · · · ·············· ········· ····54
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Discussion· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 55
Chapter 4: How should the crowding zone be measured? ············ ·· ··················· ···· 57
Introduction· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 57
Methods· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 59
Observers· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 59
Stimuli and Apparatus· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 60
Procedures· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 62
Analysis· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 62
Results · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 65
Discussion · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 66
Chapter 5: Conclusion· · · · · · · · · · · · · · · · · · · · · · · · · · · · · ··················· ········· ··················· ··69
References · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 71
Appendix· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 90

1

Chapter 1: Overview
Our visual system provides critical information for functions in our daily life such
as reading, object recognition and driving. We rely heavily on central visual information
captured by the fovea, which occupies only 0.02% of visual field area. However, over 8
million patients in the United States are forced to rely exclusively on their peripheral
vision for daily tasks as a result of foveal malfunction due to diseases such as age-related
or juvenile macular degeneration. There is no clinical therapy to restore their central
vision after disease.
Mechanisms of human form vision processing in the peripheral visual field remain
mysterious, despite decades of research. Peripheral vision, which occupies 99.98% of
visual field area, cannot successfully substitute the role of the central vision. Cluttered
objects in the periphery are difficult to recognize. For example, you can easily recognize
a letter S of USC when you see directly through your fixation, however, it becomes
harder when the trigram USC is in your periphery. The impaired performance is far less
than is predicted by the reduced spatial resolution alone. The excess limiting factor in
peripheral form vision responsible for this impairment is known as the crowding effect.
What is the mechanism of crowding? Although a large number of studies have
been done to reveal the mechanism of crowding, such as it is still unclear. Knowing the
mechanism of crowding not only helps us to develop effective rehabilitation regimens
2

and adaptive technologies for central vision loss patients, but also helps us to understand
the process of peripheral form vision.
Background for research questions
Properties of crowding
Visual crowding is defined as an impaired ability to identify an object due to the
presence of nearby objects (which are called flankers) in peripheral vision (Bouma, 1970;
Flom, Heath, & Takahashi, 1963). An example of crowding is shown in Fig.1.1a.
Crowding is a ubiquitous limitation and a fundamental bottleneck for object recognition
in peripheral vision. This phenomenon is not able to be explained only by lack of spatial
resolution, suggesting that crowding is a perceptual limiting factor. According to the first
report of crowding, nearby letters appear to be under pressure, such that the middle letter
is “squashed” (Korte, 1923). After Korte’s paper, many experiments have been conducted
to study the crowding effect (Bouma, 1970; Flom et al., 1963; Louie, Bressler, &
Whitney, 2007; A. S. Nandy & Tjan, 2007; D. G. Pelli, Palomares, & Majaj, 2004; Toet
& Levi, 1992; Wilkinson, Wilson, & Ellemberg, 1997). Despite this extensive research,
there is no one accepted explanation of the mechanism for crowding. Theories range
from lower-level feature misintegration models (Levi, 2008) to higher-level lack of
attentional resolution (Intriligator & Cavanagh, 2001).
The crowding zone, or area around a target in which flankers induce crowding, has
several robust characteristics (Fig.1.1b): a linear scaling with eccentricity (which is called
3

Bouma’s law), inward-outward asymmetry (Petrov, Popple, & McKee, 2007), and the
radial elongation to foveal direction (Toet & Levi, 1992). There is a lack of consensus
about the cause of these properties of the crowding zone.

Fig.1.1. Crowding demonstrations: (a) Demonstration of crowding: ‘K’ in the left
side of ‘-’ is easier to identify than ‘K’ in clutter. (b) An example of crowding
zone. Size of the crowding zone is linearly enlarged with eccentricity of the
target (Bouma’s law). A red circle shows inward-outward asymmetry - a flanker
closer to the fovea than the target (din) affects target identification more than one
further from the fovea (dout). Two purple lines indicate the crowding zone
elongation radially with respect to the fovea (radial-tangential anisotropy),
adapted from Toet and Levi (1992).
4

Studies on the mechanism of crowding
There are many theories for crowding, which range from optical level to attention
level (see comprehensive review by Levi, 2008). Although the underlying mechanism of
crowding is still under debate, most theories of crowding are based on a failure of feature
integration beyond the stage of simple feature detection (Chung, Levi, & Legge, 2001;
He, Cavanagh, & Intriligator, 1996; Levi, Hariharan, & Klein, 2002; Levi, 2008; Pelli et
al., 2004). It is assumed that object recognition in the visual system has two stages: a
feature detection stage, followed by a feature integration stage (Robson & Graham, 1981;
Treisman & Gelade, 1980). He et al. (1996) have shown that lower-level orientation of
Gabor a patch caused the adaptation effect even though the orientation was failed to be
identified due to the crowding, suggesting that the lower-level orientation was well
detected and registered at the feature detection stage. However, mis-integration of target
and flanker features at the feature integration stage resulted in a perceptual error in
identifying the target orientation. Thus, crowding would occur when the target and
flankers are mis-combined at the second stage for object recognition.
Several studies support the argument that such inappropriate feature integration
occurs at pre-attentive stages of visual processing (Dakin, Bex, Cass, & Watt, 2009;
Greenwood, Bex, & Dakin, 2010; Parkes, Lund, Angelucci, Solomon, & Morgan, 2001).
Parkes et al. (2001) provided evidence for feature mis-combination. They used Gabor
stimuli for the target and flanker stimuli. When the target and flanker were presented,
observers were not able to report the orientation of target, but report the average
5

orientation of target and flankers. This suggests that the orientation of the target, which
was not reported, was combined and averaged with flanker orientations rather than
discarded or suppressed. Thus, finding how features are mis-integrated in the feature
integration stage would be a crucial part to study the mechanism of crowding. In order to
study how features are mis-combined, finding where the neural locus of crowding would
provide a fundamental evidence in terms of hierarchical visual processing.
Studies on the locus of crowding
Many studies have been conducted to infer the locus of crowding; however, the
locus of crowding is still in debated. A study by Flom et al. (1963) might be the first
attempt to find the locus of crowding. They found that crowding occurs when target and
flankers are presented to a different eye. It therefore suggests that origin of crowding
would not be in the retina, but in the visual cortex, after binocular cells emerge.
V1 could be the earliest neural locus for crowding. Like the Flom et al.’s study,
Tripathy and Levi (1994) also have shown that crowding occurs after information from
two eyes combine together, and V1 is the first neural locus in which binocular cell
emerges. Kennedy & Whittaker (2010) have shown that crowding occurs after color
opponent processing takes place, suggesting the crowding starts to occur in V1.
Parkes et al. (2001) found that orientations of target and flanker stimuli (Gabor)
were compulsory pooled and averaged, and that subject was reliably able to report the
averaged orientation, but the study failed to identify the specific orientation of the target.
6

It suggests that crowding occurs just after emerging representation of local orientations,
and before the local orientation (i.e. target) enters the conscious perception (i.e. V1). He,
Cavanagh, and Intriligator (1996) found that simple feature (orientation) was able to
induce the adaptation effect, even when the orientation was not identifiable crowding,
suggesting that 1)simple feature was well registered in the lower level visual area, such as
V1, 2)crowding would take place beyond V1 which is the later stage of feature binding
sets in. However, the adaptation effect was diminished when the contrast of adapting
stimuli was lower, suggesting that crowding would take place even in the feature
registration stage of V1 (Blake, Tadin, Sobel, Raissian, & Chong, 2006).
Other groups showed that letter flankers produce crowding effect, regardless of
letter configurations, suggesting that the critical feature for the crowding is not the intact
letter, but the low-level feature of letter (Yu, Akau, & Chung, 2012). It also suggests that
the locus of crowding for the low-level feature would be beyond V1. Recently, a reduced
fMRI BOLD signal, which is associated with crowding, was found from V1 (Millin,
Arman, Chung, & Tjan, 2013).
Neuroimaging and neurophysiology studies suggested that crowding would occur
in V2/V3 (Bi, Cai, Zhou, & Fang, 2009). Bi et al., found that a reduced BOLD signal by
adaptation was shown in V2/V3. And also, Merigan (2000) have shown that crowding
occurs even in the case of V4-lesion, suggesting that crowding reflects neural interaction
in the lower level visual area, such as V1-V3.
7

V4 could also be a strong candidate that indicates the locus of crowding for
several supporting reasons. It was suggested that crowding would emerge at the locus of
the first- and second-order signal combining (Chung, Li, & Levi, 2007). This finding was
supported with a following argument: crowding zone was matched with the size of
receptive field size of V4(Smith, Singh, Williams, & Greenlee, 2001). The crowding
zone showed anisotropy between radial and tangential direction (Toet & Levi, 1992),
which was supported by the fact that anistropy was shown in V4 receptive fields (Piñon,
Gattass, & Sousa, 1998). The cortical distance between the target and flanker
representations could be a determinant for the locus of crowding (Liu, Jiang, Sun, & He,
2009). Although they found that visually equal distance stimuli, they concluded that
further distance in cortical area showed less crowding effect, suggesting that crowding
would occur at V4.
Some studies suggested even higher regions (V5 or above) as the origin of
crowding. Aghdaee (2005) have shown that spiral motion induced the motion aftereffect,
even the spiral motion was not perceived by crowding. It suggests that the crowding
would occur after the motion-induced aftereffect takes place in V5a (Haug, Baudewig, &
Paulus, 1998). Adapting apparent motion with crowded condition also showed the same
adaptation effect, suggesting that crowding would occur after in V5 (Rajimehr, Vaziri-
Pashkam, Afraz, & Esteky, 2004).
8

I summarized researches on the locus of crowding in Fig.1.2. The crowding seems
to related with the primary visual area to higher visual area, such as MT or V5. Reasons
for failing to localize the locus of crowding could be that the crowding could be a multi-
level phenomenon (Whitney & Levi, 2011), and also it could be due to the confounding
effect of bottom-up and top-down processing.
Plasticity in the peripheral vision
Macular degeneration (MD) is one of the major causes of central vision loss in
developed countries. As predicted, patients with age-related macular degeneration
Fig.1.2. Summary of representative researches for the locus of
crowding: Yellow bars indicate the starting locus of crowding
9

(AMD), a form of MD, increased to 17.8 million in US (Rein et al., 2009). AMD causes a
scotoma in the central visual field. Patients with AMD have to perform form-vision tasks
such as object recognition, face recognition, and reading through their peripheral vision.
However, the performance for these tasks in the peripheral vision is much inferior to that
of a foveal vision, even with compensating for the reduced-spatial resolution. Developing
efficient rehabilitation regime and technique for central vision loss patients are
proceeding with difficulty because the mechanism of form-vision in the peripheral vision
is still unknown.
AMD patients are forced to develop a pseudo-fovea, which is called a preferred
retinal locus (PRL), at the near of border of the scotoma for a substitution for their fovea
(Fletcher & Schuchard, 1997; Guez, Le Gargasson, Rigaudiere, & O’Regan, 1993;
Schuchard, 2005; von Noorden & Mackensen, 1962; White & Bedell, 1990). PRL should
keep three important characteristics for the quality of visual performance of the AMD
patients, such as retaining a discrete and static retinal image (fixational stability), tracking
moving objects (pursuit), and abrupt movement from one fixation position to other
position (saccadic eye movement).
PRL could be developed after training (Nilsson, Frennesson, & Nilsson, 2003), or
developed spontaneously. Even AMD patients try to be adopt the PRL as a new fovea,
but form-vision tasks like reading in the peripheral vision is much slower than the normal
fovea (Chung, Mansfield, & Legge, 1998). Fixation stability in PRL is far less than that
of the original fovea, which ranges from 1 deg to 8deg (Schuchard, Naseer, & de Castro,
10

1999). As the eccentricity of PRL increases, the fixation stability decreases (Whittaker,
Budd, & Cummings, 1988).
The fact that elderly AMD subjects were able to develop the PRL suggests a
possibility that the peripheral form-vision processing could be improved by training
(Chung, 2007; Chung, Levi, & Tjan, 2005; Dosher & Lu, 1998; Huckauf & Nazir, 2007;
Hussain, Webb, Astle, & McGraw, 2012; Sun, Chung, & Tjan, 2010). Chung (2007) have
shown that observer, who had learned to identify the crowded letter, was able to show
less crowding effect, but was not able to increase the reading speed. Chung (2012) also
have shown that AMD patients were able to produce less crowding effect with a larger
spacing crowded letters, but showed no effect on the reading speed. These studies suggest
that simply changing the letter size or spacing would not help enhancing the reading
efficiency for AMD patients, even though it can help them to recognize an individual
letter. However, Tarita-Nistor, Gonzalez, Markowitz, and Steinbach (2009) have shown
that the fixation stability at the PRL of people with AMD was able to increase by
training, increasing the reading speed.
Research questions and summary
To study the mechanism of crowding, I address three critical questions through a
series of psychophysical experiments: 1) we inform on the neural locus of crowding by
investigating the relationship between awareness and crowding, 2) the degree to which
11

peripheral vision can be changed through long-term training, and 3) the relationship
between two methods for measuring the crowding zone.
Where is the neural locus of crowding?
In the first study, we studied the neural origin of crowding. By knowing the neural
locus of crowding, we can constrain theories on mechanism of crowding. Determining
whether crowding depends on subjective awareness of the flankers can provide
information on the neural origin of crowding, when underlying mechanism of awareness
manipulation is known. However, recent studies that manipulated flanker awareness have
yielded conflicting results. To better understand the interaction between flanker
awareness and crowding, we suppressed flanker awareness with two methods: interocular
suppression (IOS) and adaptation-induced blindness (AIB). We tested two different types
of stimuli: gratings and letters. With IOS, we found that the magnitude of crowding
increased as the number of physical flankers increased, even when the observers did not
report seeing any of the flankers. In contrast, when flanker awareness was manipulated
with AIB, the magnitude of crowding increased with the number of perceived flankers.
Our results show that whether crowding is contingent on awareness of the flankers
depends on the method used to suppress awareness. In addition, our results imply that the
locus of crowding is upstream from the neural locus of IOS and close to or downstream
from that of AIB. Neurophysiology and neuroimaging studies jointly implicate mid-to-
high level visual processing stages for IOS, while direct evidence regarding the neural
12

locus of AIB is limited. The most consistent interpretation of our empirical findings is to
place the neural locus of crowding at an early cortical site, such as V1 or V2.
How is peripheral vision changed through perceptual learning?
In the second study, we studied how much the crowding zone is malleable through
perceptual learning. To identify the mechanism underlying crowding, understanding
plasticity of the peripheral vision should be necessary. It is natural to assume that the
visual system, including the peripheral vision, are developed to process visual
information optimally by natural scene statistics. Would it be possible to measure any
changes on the peripheral visual processing through changing the statistical properties? It
would help to figure out the property of the mechanism. We trained normally sighted
adults with occluded central vision to induce a preferred retinal locus (PRL), which
becomes a new locus in the periphery for fixation and the saccade reference. Here we
asked whether the development of a PRL in normally sighted people facilitates peripheral
vision by changing the crowding effect. We found that PRL induction led to a shrinkage
of the crowding zone along the radial axis at the PRL location. These findings suggest
that human crowding zones dynamically reorganize relative to the reference point of
saccade, which is consistent with the saccade-confounded image-encoding theory (Nandy
& Tjan, 2012). This study reveals the residual plasticity in adult peripheral vision and
sheds light on new mechanisms of rehabilitation for patients with central field loss.

13

How should the crowding zone be measured?
In the third study, we tested whether crowding zones from different methods are
measuring the same crowding zone or not. A complication in identifying the mechanism
underlying crowding is in deciding how crowding should be measured. Previous studies
that have sought to inform on the mechanism using psychophysics have used different
measurements, making conflicting results difficult to interpret. Crowding zone refers to
an interaction zone between a peripheral target and nearby flankers. In literature, there
are two different ways to measure the crowding zone, based on two different definition of
crowding zone: 1) a typical crowding zone, which is defined the starting point of target-
flanker distance to interact, and 2) an operational crowding zone, which is defined a
certain target-flanker distance to yield a certain target identification performance. To
compare the properties of the crowding zone across studies, it would be important to
know whether crowding zones from different methods are comparable or not. In here,
when accuracy in identifying target stimuli as a function of distance between target and
flankers is a dependent variable, the crowding zone can be determined by two-line fit, the
intersection between a slope of a psychometric function and high asymptote for no-
flanker condition. The crowding zone is also measured as the spacing associated with a
specific level of accuracy where at the slope of the psychometric function is maximum.
We tested whether the crowding zone measured with different methods preserves
monotonicity within experiments or observers. We found the monotonicity of crowding
zone from two different methods was statistically preserved within experiments and
14

observers. This suggests that previous studies using these two methods to measure the
crowding zone can be compared, and that the crowding zone is robust across different
measurements.

15

Chapter 2: Where is the neural locus of crowding?
Introduction
In peripheral vision, object recognition in a cluttered scene is difficult. This
phenomenon, known as crowding, is thought to be a key factor limiting peripheral form
vision (Levi, 2008) and is thought to provide an important avenue toward a basic
understanding of visual processing associated with object recognition (Balas, Nakano, &
Rosenholtz, 2009; Freeman & Simoncelli, 2011; Nandy & Tjan, 2012; Pelli & Tillman,
2008; Whitney & Levi, 2011). Although the underlying mechanism of crowding is not
well understood, it is commonly suggested that crowding is due to a breakdown in feature
integration necessary for object perception. This faulty feature integration theory suggests
that the detection of simple visual features is not impeded in clutter (Chung, Levi, &
Legge, 2001; He, Cavanagh, & Intriligator, 1996; Levi, Hariharan, & Klein, 2002; Levi,
2008; Nandy & Tjan, 2007; Pelli, Palomares, & Majaj, 2004); rather, clutter prevents the
detected features from being properly integrated (Levi et al., 2002; Nandy & Tjan, 2007;
Pelli et al., 2004). A natural question to ask with respect to this theory is, what are the
features that the visual system detects but fails to integrate? Knowing the neural
locus/loci of crowding will provide the answer. In general, the locus of crowding will
inform on the mechanism of crowding.
One approach to identify the locus/loci of crowding is to study the relationship
between awareness and crowding. Such an approach can be effective if the underlying
16

mechanism of a given manipulation of visual awareness is known. In the context of a
crowding experiment, visual awareness of the target or the flankers can be manipulated.
Table 2.1 summarizes several representative studies that manipulated awareness to infer
the neural locus of crowding.
Table 2.1. Studies on the effects of visual awareness on crowding and the inferred loci of
crowding.
Studies Stimuli Awareness
Awareness
manipulation
Measurements Findings
Locus of
crowding
He,
Cavanagh, &
Intriligator,
1996
Gabors Target Crowding
Adaptation to
orientation
Orientation-specific
adaptation is not
affected by
crowding.
After V1
Rajimehr,
Vaziri-
Pashkam,
Afraz, &
Esteky, 2004
Dots Target Crowding
Adaptation to
apparent
motion
Selective adaptation
to apparent motion
is not affected by
crowding.
After V5
Aghdaee,
2005
Spiral
grating
Target Crowding
Adaptation to
spiral motion
Adaptation to spiral
motion is not
affected by
crowding.
After
V5/MT
Moutoussis &
Zeki, 2006
Moving
gratings
Target Crowding BOLD signal
Crowded stimuli
enhance neural
signals in V3A, V5,
and parietal cortex.
-
Veenemans,
Cavanagh, &
Chakravarthi,
2009
Letters Flankers
Pre- and
post- masks
Letter
identification
Impaired target
identification even
when the flankers
are made invisible
by masking.
-
Cham &
Cheung, 2009
Gabors
flankers
and target
Continuous
flash
suppression
Adaptation to
invisible target
orientation
Adaptation to target
orientation is
reduced when
invisible flankers
are presented.
-
Wallis & Bex,
2011
Letters Flankers
Adaptation-
induced
blindness
Letter
identification
Less crowding
when the flankers
are invisible.
After V1
Ho &
Cheung, 2011
Gratings Flankers
Continuous
flash
suppression
Adaptation to
orientation
Contrast threshold
of orientation
discrimination of a
grating is elevated
even when flankers
are not visible.
V1
17

Crowding reduces awareness of target stimuli such as static gratings (He et al.,
1996), moving dots (Rajimehr et al., 2004), spiral gratings (Aghdaee, 2005) and moving
gratings (Moutoussis & Zeki, 2006). He et al. (1996) showed that target orientation made
invisible by crowding was still able to induce an orientation-specific contrast threshold
elevation for a subsequent test Gabor, suggesting that crowding takes place after V1,
where orientation information is processed. Rajimehr, Vaziri-Pashkam, Afraz, & Esteky
(2004) showed that direction-selective adaptation to random-dot motion occurred even
when the adapting stimulus was made invisible by crowding. They concluded that
crowding occurred after the locus of motion processing, such as V5/MT. Similarly, the
rotation selective adaptation to spiral motion also occurred even when it was made
invisible by crowding, suggesting that crowding may occur after the locus of spiral
motion, such as V5/MT (Aghdaee, 2005). Moutousis and Zeki, (2006) found that a
moving grating that was removed from awareness by crowding evoked neural activity in
V3/V5 and the parietal cortex. Their finding suggested that the locus of crowding was
downstream from these cortical areas
Several studies have also investigated the effects of flanker awareness on
crowding (Chakravarthi & Cavanagh, 2009; Cham & Cheung, 2010; Ho & Cheung,
2011; Veenemans et al., 2009; Wallis & Bex, 2011), with conflicting results. Wallis and
Bex (2011) showed that flanker awareness was necessary to elicit crowding. They found
that letter flankers made invisible through adaptation-induced blindness (AIB) did not
interfere with the target letter in a letter-identification task. They concluded that crowding
18

requires awareness of flankers, and the locus of crowding would be downstream from the
neural locus of adaptation-induced blindness. Other studies showed that flanker
awareness is not necessary for crowding. Making flankers invisible with pre- and post-
masks (Veenemans et al., 2009) , interocular suppression (a.k.a continuous flash
suppression) (Cham & Cheung, 2010; Ho & Cheung, 2011) or noise and metacontrast
masking (Chakravarthi & Cavanagh, 2009) led to crowding. For example, Ho et al.
(2011) showed that Gabor flankers rendered invisible by interocular suppression (IOS)
interact with target Gabors. They concluded that crowding occurs regardless of
awareness, and the neural locus of crowding is upstream from the neural locus of these
awareness manipulations.
Various methods have been used to produce invisible flankers in crowding
experiments, including interocular suppression (Blake & Fox, 1974; Tsuchiya & Koch,
2005; Wolfe, 1984) and adaptation-induced blindness (Motoyoshi & Hayakawa, 2010).
With interocular suppression (IOS), two different stimuli are presented to each eye, and
compete for awareness. If one stimulus is much more salient than the other, because it
has a higher contrast or is flashing, then the more salient stimulus can continuously
suppress the weaker stimulus. In the case of high-contrast continuous flash suppression,
numerous physiological and neuroimaging studies have implicated lower level visual
areas as the probable locus of the suppression (Haynes, Deichmann, & Rees, 2005;
Leopold & Logothetis, 1996; Logothetis & Schall, 1989; Polonsky, Blake, Braun, &
Heeger, 2000; Tong & Engel, 2001; Wunderlich, Schneider, & Kastner, 2005). With
19

adaptation-induced blindness (AIB), adapting to a drifting vertical grating (mask) at a
temporal frequency of 10 Hz has been found to make a stimulus invisible when it was
presented at the same location as the drifting mask (Motoyoshi & Hayakawa, 2010).
Unfortunately, little is known about the neural basis of AIB.
Why did studies yield conflicting results on the relationship between crowding and
visual awareness? Different methods for suppressing awareness (e.g. AIB vs. IOS) may
rely on different neural substrates with different cortical loci. Differences in stimuli (e.g.
Gabor vs. letters, narrowband vs. broadband) may also explain the discrepancy, as
different stimuli may require distinct stages of processing and recruit different neural
machinery. In the current study, we used two different methods to make flankers
invisible: AIB and IOS. We also tested two different types of stimuli: narrowband Gabor
patches and broadband letters. We first tested whether Gabor flankers rendered invisible
by IOS (Exp.2.1) or AIB (Exp.2.2) would induce crowding. We then tested crowding
with invisible letter flankers, induced with IOS (Exp.2.3). To preview, we found that
flankers made invisible with IOS continue to cause crowding, whereas flankers made
invisible with AIB do not. These results suggest that visual awareness is not a necessary
factor for crowding, and that the locus / loci of crowding are at the later stage from that of
IOS (i.e. up-stream from that of IOS) and at the earlier stage from that of AIB (i.e. down-
stream from that of AIB). Incidentally, our findings also place the neural loci of AIB to
be up-stream from those of IOS.

20

Methods
Experiment 2.1
Observers
Four healthy volunteers, including the first author, participated in Exp.2.1. For this
and other experiments, all observers, excluding the first author, were naive to the purpose
of the experiments. All observers had normal color vision and normal or corrected ‐to‐
normal visual acuity. The study was carried out in accordance with the regulations of the
Institutional Review Board of University of Southern California.
Stimuli
Gaussian-windowed sinusoidal gratings (Gabors) were used as targets and
flankers. For the Gabors, the Gaussian window had a standard deviation of 0.35°. The
spatial frequency of the grating was 2.85 cycles/deg. The peak Weber contrast of the
Gabors was 60%. The target was presented at an eccentricity of 5° below fixation, and
the center-to-center spacing between the target and flankers was 1.25°.
Four Gaussian-windowed flickering radial square-wave gratings were used as
interocular suppressors to render the flankers invisible. The suppressors were at 100%
contrast and alternated between clockwise and counterclockwise rotation (angular
velocity = 144°/sec) while alternating between shrinking and expanding (0.25°/sec) at 1
Hz. This type of suppressor has been shown to minimize motion and orientation
aftereffects (Blake et al., 2006). Their size ( 𝜎 = 0 .5°) was slightly larger than the
21

flankers’ in order to fully mask the flankers and minimize any mixed percepts (Blake,
2001). These suppressors were presented to the dominant eye of an observer, which was
determined by the Porta test (Roth, Lora, & Heilman, 2002): observers extended a hand
and aligned the thumb to a distant object with both eyes opened. By closing one eye at a
time, the dominant eye was identified as the one that retained the alignment.
The stimuli were created in the MATLAB (MathWorks, version XX) using the
Psychophysics Toolbox 3 (Brainard, 1997; Denis G. Pelli, 1997). They were presented on
a Dell P1230 19” monitor (resolution 1024 X 768 at 100Hz) driven by a MacBook
computer (OSX version 10.5.8) via a passive video attenuator (Denis G. Pelli & Zhang,
1991) to provide 11 bits of linearly spaced contrast levels achieved using custom-built
calibration and contrast-control software implemented in MATLAB
(https://github.com/usc-tlab/LinearFineContrast.git). Only the green channel of the
monitor was used.
An observer’s head was held fixed with a chin-and-head rest to maintain a viewing
distance of 70 cm. At this viewing distance, a pixel subtended approximately 0.031° of
visual angle. Background luminance of the display was 25.7 cd/m
2
. Observers viewed the
left ‐ and right ‐halves of the screen through a mirror stereoscope. Two small fixation
crosses were presented to the left- and right-halves of the screen to aid binocular fusion.
Upper and lower nonius lines were also displayed to allow for self-monitoring of
vergence.
22

Procedures
Figure 2.1 shows the timing and stimuli for Exp. 2.1. One target and up to 4
flankers were presented in an observer’s peripheral visual field. An observer fixated on
the fixation mark, and the target was presented at 5° in the lower visual field. For each
trial, the primary task was to discriminate the orientation of a target – clockwise (CW) vs.
counter-clockwise (CCW) relative to 45°) and respond with a key press. In addition, the
observer reported the number of flankers perceived.

Figure 2.1. Procedure and experimental conditions of Experiment 2.1: a) An example
trial. The left and middle series of images were presented to the non-dominant and
dominant eyes, respectively. The right series of images indicates an observer’s percept. A
fixation point and a nonius line were shown to each eye. A small square appeared 100 ms
prior to the stimulus to indicate the eccentric location of the target (5° below from the
fixation point). Observers had to maintain fixation on the cross and a constant vergence
state indicated by the alignment of the nonius lines. 100 ms after the small square
disappeared, a target, flankers and suppressors were presented for 100 ms simultaneously.
The observer was asked to report the orientation of the target (CW or CCW from 45°)
23

and the number of Gabors seen. b) Conditions used in Exp. 2.1. The first row indicates
the stimuli that were presented to the non-dominant eye, the second row indicates the
stimuli that were presented to the dominant eye, and the third row indicates an observer’s
percept. The left-most column indicates a target-only condition, which was measured in
separate blocks. The four flanking conditions were with 1 to 4 flankers and (always) 4
suppressors. It should be noted that an observer’s percept of these 4 conditions was
usually the same: 82% of total trials were fully suppressed trials (Figure S1 in Appendix).

Both the target and flankers were shown to an observer’s non-dominant eye, and
four suppressors, at 100% contrast, were presented to the observer’s dominant eye
regardless of the actual number of flankers.
For each trial, there could be 1 to 4 flankers for Exp. 2.1 (Figure 2.1). These
flanking conditions were randomly presented within each experimental block, and each
flanker condition was presented for 5 trials in each block. Each block consisted of 20
trials. 4 blocks were performed in one run, and each observer completed 5 runs. Thus,
total trial is 400 trials and 100 trials for each conditions. The observer was required to
take a break (minimum 5 minutes) between each run. The entire experiment was
completed in a single session of approximately two hours.
The flankers, which were of a lower contrast (60%), were removed from visual
awareness with continuous flash suppression (see Stimuli). The observers were unaware
of the flankers in 82% of the trials (Figure S1). We also measured accuracy in a target-
only condition before the main experiment to the effect due to the suppressors alone. The
target, flankers and suppressors were presented simultaneously for a duration of 100ms.
24

The orientations of the flankers were independently jittered within ±2x the observer’s
angular threshold about 45°, which was obtained prior to the main experiment.
Pre-measure: Orientation threshold
Individual observer’s orientation-discrimination threshold for the Gabor stimuli
was obtained prior to Exps.2.1 and 2.2. Specifically, we presented a single Gabor target
with one of five orientation offsets (±1°, ± 1.8°, ± 3.1°, ± 5.6°, and ±10°) from 45° at an
eccentricity of 5° below the fixation point. The observer had to discriminate the tilted
direction of the target (CW or CCW relative to 45°). The orientation-discrimination
threshold corresponded to a discrimination accuracy of 75% correct.
Experiment 2.2
Observers
Six normally sighted volunteers, including the first author, participated in Exp.2.2.
Among six observers, two observers (including the first author) also participated in Exp.
1.
Stimuli
The Gabor target and flankers were the same as in Exp.2.1, except that the
observers viewed them with both eyes. AIB, as opposed to IOS was used to suppress
visual awareness of the flankers. During adaptation, 4 adaptors were presented at the
flanker locations. The adaptors were the same Gabor patches as the flankers, and the
25

orientation of adaptors was randomly selected within ±4x the individual angular
threshold about 45° and replaced every 100 ms.
Procedures
Figure 2.2 shows the timing and stimuli for Exp.2.2. Observers fixated at a
fixation cross. The main task was identical to that of Exp.2.1. There could be 0 to 4
flankers. For conditions with 0 to 3 flankers, each condition was presented for 4 trials per
block; the 4-flanker condition was presented for 12 trials per block. Each block consisted
of 28 trials. 8 blocks were performed in one session, and each observer completed 2
sessions, one session per day. Thus, total 448 trials were done by each observers. Before
the first trial of each block, there was a 1 minute adaptation. In addition, a 5 s top-up
adaptation was shown before each trial. Following the top-up adaptation, the target and
flankers were simultaneously and smoothly ramped on and off according to a Gaussian
temporal profile with 𝜎 = 200 ms, following Wallis & Bex (2011). Adaptors were
presented again during the response period to maximize adaptation effects. The 5-s top-
up adaptation for the next trial commenced immediately after the observer had
responded.
26

Figure 2.2. Procedure and experimental conditions of Experiment 2.2: a) A sample trial
of Exp. 2.2. Eccentricity and center-to-center distance between a target and flankers were
the same as for Exp.1. During adaptation, the adaptors were presented dynamically at the
same position as the flankers to induce “blindness” to the flankers. The orientations of
adaptors were randomly selected from within ±4x of the individual’s orientation
threshold relative to 45° and changed every 100 ms. Following adaptation, target and
flankers were smoothly ramped on and off following a Gaussian time course with 𝜎 =
200ms. After that, observers had to report the orientation of the target (CW or CCW)
relative to 45° and the total number of Gabors seen simultaneously with the target
(including the target). Each block of 280 trials began with a 1 minute initial adaptation,
and each trial started with a 5 s top-up adaptation. b) Conditions used in Exp. 2. The first
row depicts the five flanking conditions; the second row depicts an observer’s dominant
percept.

27

Experiment 2.3
Observers
Four normally sighted volunteers, including the first author, participated in
Exp.2.3. One observer (the first author) participated in both Exp. 2.1 and 2.2, and another
participated in Exp.2.2.
Stimuli
Gaussian-windowed Sloan letters (H, K, N, V, and Z) were used in Exp.2.3. The
Gaussian window had a standard deviation of 0.2°. Circular gratings were used as
suppressors. Unlike the letter flankers, the suppressors were devoid of any sharp edges.
This allowed an observer to more easily detect incomplete suppressions. The suppressor
alternated between shrinking and expanding (0.25°/sec) at 1 Hz. Other attributes of the
stimuli were identical to those used in Exp.2.1.
Procedures
Figure 3 shows the timing and stimuli for Exp.2.3. The procedures were similar to
those of Exp.2.1. Each flanking condition, with number of letter flankers ranging from 0
to 4, was presented in 6 trials per block. A block consisted of 30 trials. 4 blocks were
performed in one run, and each observer completed 3 runs over 1 or 2 days. Before each
run, an observer adjusted the contrast of the letter target and flankers with key press such
that the flankers became invisible in the presence of the continuous flash suppressors.
The purpose for this procedure was to find an effective contrast that is enough for target
28

and flankers to be identifiable for the letter identification task and suppressed by
suppressors. If the contrast of target and flanker was too low, then the flankers will be
suppressed well, however at the same time, the target letter will not be identified well.
The average selected (Weber) contrast ranged from 0.62 to 1. The primary task was to
identify the target letter and respond with a mouse click on a response screen. The
secondary task was to report the total number of letters (target and flankers) perceived.

Figure 2.3. Procedure and experimental conditions of Experiment 2.3: a) An example
trial of Exp. 2.3. The left and middle series of images were presented to the non-dominant
and dominant eyes, respectively. The right series of images indicates an observer’s
percept. A fixation point and nonius lines were shown to each eye. A small square
appeared 100 ms prior to the stimulus to indicate the location of the target (5° below from
the fixation point). Observers had to maintain fixation and vergence state (keeping the
nonius lines aligned). 100 ms after the small square disappeared, the target, flankers and
suppressors were presented simultaneously for 200 ms. The observer was asked to report
the identity of the target and the number of letters seen on screen. b) Conditions used in
29

Exp.3. The first row depicts the stimuli associated with the five flanking conditions that
were presented to the non-dominant eye; the second row depicts the suppressors that
were presented to the dominant eye, and the third row indicates an observer’s dominant
percept.
Data analysis
We wanted to assess separately the effects of numbers of presented and perceived
flankers on object identification performance. However, these two effects are inevitably
correlated with each other because it was more likely for a participant to miss a presented
flanker than to hallucinate a non-existent flanker. The conventional approach is to hold
one effect constant by considering only a subset of trials. For example, to assess if the
number of perceived flankers had an effect on performance, Wallis & Bex (2011) used
only those trials with all flankers were presented. This approach is inefficient because it
throws away a considerable number of trials under conditions where 1, 2 or 3 flankers
were presented
To fully utilize the data set for each experiment, two mixed-effect logistic
regression models were fitted to the data using the lme4.0 package in R (Bates, Mächler,
Bolker, & Walker, 2014; R Core Team, 2013). One model was used to examine the effect
of the physical number of flankers, and the other to identify the effect of the perceived
number of flankers. Each of these models treats the effect of interest as a fixed effect on
the slope of the linear model and the effect of no interest as a random effect on that slope.
For example, when we assessed the effect of the number of physically presented flankers
on performance, we considered this effect as a slope of the linear model separately for
30

trials in which the participant reported seeing 0, 1, 2, 3, or 4 flankers. The slopes due to
the number of presented flanker are allowed to vary randomly across these five types of
trials. We tested against the null hypothesis that these slopes were not significantly
different from zero. More specially, in terms of mixed effect models, the model for
testing the effect of physical flankers consisted of a fixed effect for the number of
flankers presented, and two random effects: by-observer random intercepts and by-
perceived-number-of-flankers random slope. We specified this model in R as:
Physical_model = glmer(correct~presented+(0+presented|preceived)+(1|subject),
data=full_data, family=binomial(link="logit"))
That is, the linear predictor of the generalized linear model with a logit link function is
η = ( C
i
+ β
1
) X
j
+ β
0
+ S
k
+ ϵ
h

where the 𝛽 ’s are the parameter estimates of the fixed effects, 𝑖 , 𝑗 , 𝑘 , ℎ index the
perceived number of flankers, the presented number of flanker, the subject, and the
observation, respectively, and 𝐶 , 𝑆 , 𝜖 are Gaussian random variables drawn from different
distributions, representing the random effects due to the number of perceived flankers,
subject, and measurement, respectively.
Similarly, the model for testing the effect of perceived flankers consisted of a
fixed effect for the number of perceived flankers, and two random effects: by-observer
random intercepts, and by-presented-number-of-flankers random slope:
31

Perceptual_model = glmer(correct~preceived+(0+preceived|presented)+(1|subject,
data=full_data, family=binomial(link="logit"))
These mixed effect models are statistically superior to approaches that require the
selection of a subset of trials for a given analysis (Baayen, Davidson, & Bates, 2008),
such as using only trials with four presented flankers to test for the effect of perceived
flankers, as was done in earlier studies (e.g. Wallis et al., 2011). For completeness, we
also carried out analyses using these earlier methods and showed that the method of
analysis did not affect our main conclusion (see Appendix).
Results
The purpose of Exp.2.1 was to test whether flankers made invisible with
interocular suppression (IOS) could lead to crowding. If orientation discrimination is
impeded by the invisible flankers, we may conclude that the orientation features of the
target and flankers were compulsory over-integrated, causing crowding, before the stage
where IOS impedes flanker visibility. Conversely, if performance of the orientation
discrimination task is affected only by the number of subjectively visible flankers, we
may conclude that crowding occurs downstream from the locus of IOS.
We found that the accuracy of orientation discrimination decreased significantly
with increasing number of physical flankers (Figure 2.4a; slope: β = -0.23, SE = 0.06, z =
-4.11, p = 3.96e-5), similar to a finding by Põder (2008). In contrast, we did not find any
significant effect due to the perceived number of flankers (Figure 2.4b; slope: β = -0.1,
32

SE = 0.12, z = -0.93, p = 0.35). Our finding is that physically presented flankers,
regardless of visual awareness, cause crowding and suggest that crowding occurs
upstream from the neural locus of interocular suppression. This pattern of findings is
consistent with that of Ho & Cheung (2011) and extends the earlier study by varying the
number of flankers.

Figure 2.4. Results of Exp.2.1: a) Accuracy of orientation discrimination as a function of
the number of presented flankers. Accuracy decreases with increasing numbers of
presented flankers (thick maroon line, p = 3.96e-5). b) Accuracy of orientation
discrimination as a function of the number of perceived flankers. Accuracy does not
change significantly with increasing numbers of perceived flankers (thick blue line, p =
0.35). The dotted lines indicate chance performance. Different observers’ data are
33

represented with different colors. Size of the square symbols shows the number of trials
of a given condition for a given observer. Error bars represent standard error. Gray dots
(clustered into gray bars) indicate fitted values generated from the logistic mixed-effect
models.

When we used a different method (AIB) to manipulate flanker visibility, the
results were very different. We found that both the presented (physical) and perceived
numbers of flankers had a significant effect on target orientation discrimination (z = -
2.37, p = 0.0179, Figure 2.5a; z = -5.49, p = 4.04e-8 Figure 2.5b; respectively), but the
perceived number of flankers had a much stronger effect (slope: β = -0.08, SE = 0.04 for
physical, vs and slope: β = -0.19, SE = 0.03 for perceived). Regarding the effect of
flanker awareness, we replicated the result of Wallis & Bex (2011). Namely, performance
(and crowding) depends on the number of perceived flankers. However, while Wallis &
Bex did not observe any significant effect of the physical number of flankers only, we
did. This difference is likely due to our use of a more sensitive statistical model. We were
able to replicate the qualitative results of Wallis & Bex by fitting the same logistic
models they used in their study (Appendix). In other words, despite the different stimulus
types (letters in Wallis & Bex, Gabors in the current study), the perceived number of
flankers had a strong effect on crowding when visual awareness was manipulated with
adaptation-induced blindness.

34

Figure 2.5. Results of Exp.2.2. a) Accuracy of orientation discrimination as a function of
the number of presented flankers. Accuracy decreases with increasing number of
presented flankers, but the effect size was small (thick maroon line, slope: β = -0.08, SE
= 0.04, z = -2.37, p = 0.0179). (Note that by design, there were more 4-flanker trials than
other trial types – see Methods.) b) Accuracy of orientation discrimination as a function
of the number of perceived flankers. Accuracy again decreases with increasing number of
flankers perceived, but with a much larger effect size (thick blue line, β = -0.19, SE =
0.03, z = -5.49, p = 4.04e-8). The dotted lines indicate chance performance. Different
observers’ data are represented with different colors. Size of the square symbols shows
the number of trials in a given condition for a given observer. Error bars represent
standard error. Gray dots (clustered into gray bars) indicate fitted value generated from
the logistic mixed effect models.

35

In Exp. 2.3, we returned to IOS and tested if, as for AIB, the pattern of results
observed with Gabors (narrow in spatial-frequency bandwidth) generalized to letters
(broadband). We found that it did. Figure 6 shows the results of Exp.2.3. Similar to Exp.
2.1, we found that letter-identification accuracy decreased with the number of presented
(physical) flankers (slope: β = -0.54, SE = 0.08, z = -6.97, p = 3.08e-12, Figure 2.6a) but
was not significantly modulated by the perceived number of flankers (slope: β = -0.32,
SE = 0.38, z = -0.86, p = 0.35, Figure 2.6b).

Figure 2.6. Results of Exp.2.3. a) Accuracy of orientation discrimination as a function of
the number of presented flankers. Accuracy decreases with increasing number of
presented flankers (thick maroon line, p = 3.08e-12). b) Accuracy of orientation
36

discrimination as a function of the number of perceived flankers. Accuracy decreased
numerically with increasing number of flankers perceived, but the effect is not
statistically significant (thick blue line, p = 0.35). The dotted lines indicate chance
performance. Different observers’ data are represented with different colors. Size of the
square symbols shows the number of trials of a given condition for a given observer.
Error bars represent standard error. Gray dots (clustered into gray bars) indicate fitted
value generated from the logistic mixed effect models.

Discussion
We found that flankers rendered invisible with interocular suppression (IOS)
continued to crowd a target and impede target identification. The number of presented
flankers, regardless of whether an observer is aware of them, monotonically affects target
identification accuracy. This result appears to be independent of the stimulus type
(narrowband Gabors in Exp.2.1 or broadband letters in Exp.2.3). In contrast, when
flankers were made unperceivable by adaptation-induced blindness (AIB), target-
discrimination performance depended on the number of perceived flankers, rather than
the number of presented flankers. This was the case for Gabors, as shown in Exp.2.2 and
for letters, as shown in Wallis & Bex (2011). Therefore, it is the method used to
manipulate visual awareness that determines whether invisible flankers have any effect
on crowding.
These results resolve the apparent conflict in findings between Ho & Cheung
(2011), Chakravarthi & Cavanagh (2009) and Wallis & Bex (2011), and attribute their
opposing conclusions to the difference in methods used to manipulate visual awareness –
37

Ho & Cheung used IOS, while Wallis & Bex used AIB. While these two studies also
differed in the stimuli they used, our results show that stimulus class is unlikely to be a
factor.
Unlike in Wallis & Bex, we did find a small but significant effect of the number of
presented flankers on crowding (when controlling for visibility). We attribute this to the
more sensitive mixed-effect logistic model that we used. Specifically, we modeled the
factor of no interest (e.g. the number of presented flankers in the perceived-number-of-
flanker model) as a random effect and thereby increased the sensitivity to the factor of
interest (i.e. the number of perceived flanker in the parenthetic example above). We must
note that the number of perceived flankers is bounded by and correlates with the number
of presented flankers. There is, therefore, a fundamental limit on any experiment’s ability
to tease these two apart. We were further limited by our ability to generate sufficient
variance in the number of flankers perceived with interocular suppression – the
suppression was so strong that in most cases the number of perceived flankers was zero.
It would be true that IOS produced lack of data points to compare the effect of perceived
flankers in Exp.2.1 and 2.3. However, focusing our attention on the effects due to the
number of physically presented flankers avoids these limitations, revealing a distinct,
clear result: the number presented flankers, regardless of awareness, always has an effect
on crowding with a strength dependent on how awareness is manipulated.
Neural loci of crowding, IOS and AIB
38

Our findings imply that the primary neural locus of crowding is downstream from
that of AIB but upstream from that of IOS, if we are to assume that there is a primary
locus of crowding and that it does not change across these experiments. By using the
same stimuli (Exp.2.1 vs. Exp.2.2), we have removed stimulus class as a potential
confound that is present in the current literature. These results force a re-evaluation of
some of the earlier conclusions regarding the loci of crowding, IOS and AIB. Indeed, the
neural loci of IOS and AIB are not as clear as once thought.
The neural locus of IOS is unlikely to be in V1 or V2. While IOS was found to
reduce the neural activity associated with a target stimulus in V1 (Sengpiel, Baddeley,
Freeman, Harrad, & Blakemore, 1998; Sengpiel, Blakemore, & Harrad, 1995; Sengpiel &
Blakemore, 1994), the subjective awareness of the target stimulus did not correlate with
firing rates, LFP power or fMRI BOLD response in V1 (Maier et al., 2008; Wilke,
Logothetis, & Leopold, 2006). Rather, a strong correlation between the subjective percept
under IOS and neural activity was only found in higher visual areas, such as V3a and
LOC (Fang & He, 2005; Hesselmann & Malach, 2011). Likewise, in binocular rivalry, a
form of interocular suppression, a subjectively suppressed stimulus was found to still
evoke neural activity in about 80% of the cells in V1/V2, 60% of cells in V4/MT, and
10% of cells in IT (Leopold & Logothetis, 1996; Logothetis & Schall, 1989; Sheinberg &
Logothetis, 1997). In behavioral studies, the lower-level features of a stimulus, such as
spatial frequency (Blake & Fox, 1974; Blake et al., 2006), orientation (Wade &
Wenderoth, 1978), and color (K. D. White, Petry, Riggs, & Miller, 1978) are found to be
39

less affected by binocular rivalry. Taken together, these results suggest that while IOS
has a measureable impact on the neural response to a target stimulus in the early stages of
visual processing, such as V1 and V2, its impact in these early stages is insufficient to
affect a target’s percept.
The current study found that flankers subjectively suppressed by IOS continue to
cause crowding. Hence the primary locus of crowding is unlikely to be in the high-level
visual areas. Recent studies that found crowding-related fMRI response suppression as
early as V1 (Chen et al., 2014; MiYoung Kwon, Bao, Millin, & Tjan, 2014; Millin et al.,
2013) further support this conclusion.
In contrast to IOS, there have been few studies regarding the neural origin of
adaptation-induced blindness (AIB). Motoyoshi and Hayakawa (2010) considered the
possibility that AIB was due to gain reduction in early visual areas but rejected this as a
complete explanation of AIB since they found that an AIB-suppressed target was able to
induce a measurable amount of tilt induction and surround suppression. In contrast,
Wallis and Bex (2011) showed that AIB-suppressed flankers did not cause crowding. By
using a more sensitive statistical model, the current study has refined the results of Wallis
and Bex. We replicated their primary finding that under AIB crowding is related to the
number of perceived flankers. However, we also found that the number of physically
presented flankers, after having discounted any effect due to the number of perceived
flankers, also has an effect on crowding. In other words, relative to the neural locus of
AIB, the neural locus of crowding is more similar to those of tilt induction and surround
40

suppression than the results of Wallis and Bex implied. A low-level locus of crowding is
most consistent with the totality of these results.
Conclusion
Visual awareness is not a necessary condition for crowding. Using two classes of
stimuli and two paradigms, we showed that crowding of a target may or may not depend
on visual awareness of its flanking stimuli, depending on how visual awareness is
manipulated. Our results imply that the primary neural locus of crowding is at an earlier
stage of visual processing than that of interocular suppression but at a similar or later
stage than that of “adaptation-induced blindness”. These findings are incompatible with
theories implicating a high-level origin of crowding.

41

Chapter 3: How is peripheral vision changed through
perceptual learning?
Introduction
The human fovea is critical for high resolution vision. It comprises less than one
percent of retinal area but its cortical projection takes up over one third of the visual
cortex in the brain (Manson and Kandel, 1991). Our oculomotor system continually
brings targets of interest into the fovea via saccades. Thus, the fovea serves both as the
locus for fixations and as the oculomotor reference for saccades.
When conditions in which the fovea cannot provide high resolution input to the
cortex, peripheral vision appears to be malleable. Clinical observations of patients with
prolonged central vision loss often establish one or a small number of eccentric fixation
loci in the peripheral retina termed preferred retinal locus/loci or PRL), typically close to
the edge of their central scotoma for better acuity (Crossland, Culham, Kabanarou, &
Rubin, 2005; Crossland, Engel, & Legge, 2011; Tarita-Nistor et al., 2009). It has also
been demonstrated that in normally sighted individuals whose central vision is blocked
by an artificial scotoma, the oculomotor system adapts rapidly and persistently to a newly
induced PRL (Kwon, Nandy, & Tjan, 2013). Here we tested whether the experimentally
induced PRL would lead to changes in peripheral vision in normally sighted adults.
42

When people are using their peripheral vision to recognize objects, their
performance is largely reduced. In the periphery, surrounding objects that are within a
critical distance of the target impair target identification beyond what can be fully
explained by the low spatial resolution in the periphery, a phenomenon termed crowding.
As a hallmark of peripheral vision, crowding refers to the inability to identify objects in
clutter, which has been shown to be the primary limitation in spatial vision (Levi, 2008;
Whitney & Levi, 2011) Crowding usually happens when the spacing between objects is
less than half the eccentricity of the object (Bouma, 1970). Flankers of equal distances
from the target can induce different extents of crowding -- the outward flanker induces a
stronger crowding effect than the inward flanker (Petrov et al., 2007), and radially
positioned flankers produce stronger crowding effects than tangentially positioned
flankers (Toet & Levi, 1992).
Among these characteristics of crowding, the radial-tangential anisotropy is
recognized as the most intriguing phenomenon. If we plot the spatial extent over which
flankers affect target identification, the resulting crowding zone would not be circular,
but is markedly elongated along the radial axis. Such anisotropy cannot be accounted for
by cortical geometry and few models could predict it in a principled way (Whitney &
Levi, 2011). It is worth noting that the elongation along the radial axis is always toward
the anatomical fovea, which is also the trajectory for typical saccades that bring the target
in the periphery to the fovea. The radial-tangential anisotropy appears to be reduced,
mostly through shrinkage of the radial meridian, at the PRL location for AMD patients
43

(Chung, 2013). A recent computational model for the crowding effect (Nandy & Tjan,
2012) proposed that the shape of the crowding zone is due to distorted image statistics
acquired during saccades. According to this model, there would be a change in the shape
of the crowding zone if the saccade was no longer referenced to the fovea.
To test this hypothesis, we trained subjects in object-following and object-search
tasks with a simulated scotoma. During the 3-4 weeks training, we tracked their
development of a PRL, and compared the crowding zones before and after training in
psychophysical tests by measuring the crowding effect in two flanker configurations
(Radial/Tangential) at two locations (PRL/Sideways).
Methods
Observers
Eighteen healthy subjects (14 Female, 21-33 years old) participated in the
experiment (9 in the main group and 9 in the control group). They were naï ve to the
purpose of the experiment and had never participated in any long-term training experiment
before. All subjects had normal or corrected-to-normal vision. They had no known
neurological or visual disorders. They gave written, informed consent in accordance with
the procedures and protocols approved by the review committee of University of Southern
California.

44

PRL Induction
Stimuli and Apparatus
All stimuli were high-contrast 24 bit RGB color images of indoor scenes and
objects. Images of 49 indoor scenes were selected from an image database (Luo, Satgunam,
& Peli, 2012). Images of 140 objects were selected from a commercially available set of
photographs of real objects from www.thinkstockphotos.com. The stimuli were generated
and presented with MATLAB and PsychToolbox (Brainard, 1997; Pelli, 1997). The
display was a 19’’ CRT monitor (refresh rate, 85 Hz; resolution, 1,024 × 768). The stimuli
were presented at a viewing distance of 57 cm with their head stabilized by a chin and
headrest. The displayed scenes subtended visual angles of 39° × 29°.

Eye Movement Recording and Simulated Scotoma
Subjects’ eye movements were monitored with an infrared video-based eye tracker
sampled at 2,000 Hz (EyeLink 1000 Tower Mount monocular eyetracker, SR Research)
with a maximum spatial resolution of 0.02 . A nine-point calibration and/or validation
sequence was performed at the beginning of every block, and drift correction was made at
the beginning of every trial. Calibration and/or validation were repeated until the
validation error was smaller than 1  on average. The average gaze error was 0.5 , ranging
from 0.1  to 1 . A gaze-contingent visual display was used to simulate central visual field
loss in normally sighted subjects. The real-time gaze position was sent to the display
computer through a high-speed Ethernet link. The average delay between actual eye
45

movement and screen update was ~10 ms. Continuous gaze information was used to draw
a scotoma on the display screen at a refresh and update rate of 85 Hz. The scotoma, as
shown in Figure 3.1a, was a circular disc with a radius of 5°, rendered as a uniform gray
patch (luminance 18 cd/m
2
) on the screen. A gray cross at 6.5° eccentricity in an 11 o’clock
direction to the fixation served as the gaze marker for the induced PRL.

Procedures
During the PRL induction training phase, subjects performed two visual tasks in
one trial: object-following and object-searching (Figure 3.1a). Each block started with the
calibration and/or validation sequence described earlier (~5 min) followed by drift
correction. Subjects were trained with 40-45 blocks over 3-4 weeks. Each block consisted
of 40 trials and took ~25 min to complete.
In the object-following task, subjects followed a target object as it was randomly
repositioned against a cluttered background. The center of the object was uniformly
distributed within the central 31° × 22° region of the display. To encourage eccentric
fixation, each move was initiated only when the onscreen position of the subjects’ scotoma
did not occlude the target object for at least 1.5 s. Subjects were told to examine the target
object as accurately as possible, given that it was the search target for the upcoming visual
search phase. One third of the time, the target was repositioned sideways from the PRL in
order to induce a systematic bias in the saccade trajectory.
46

In the object-searching task, subjects searched for the same target object in a
cluttered background with multiple distractors. Both the target and distractors were
superimposed on the scene. Subjects were asked to report the presence or absence of the
search target as quickly and as accurately as possible by a key press. The probability of
the target being present was 0.5. Auditory feedback, a tone was provided for a correct
response and no feedback for an error.

Data Analysis
Oculomotor performance (fixational PRL and saccadic reference) was assessed
during the two tasks under the assumption that the target object was the intended target of
gaze. A parsing algorithm (Fischer, Biscaldi, & Otto, 1993; Gitelman, 2002) was applied
to the pre-processed gaze position data in order to classify saccades and fixations while
excluding microsaccades (Engbert & Mergenthaler, 2006). See Kwon et al., (2013) for
details.
Fixation density maps were derived from the retinal positions of the target objects
during periods of fixation via kernel density estimation with a bivariate Gaussian kernel
(Botev, Grotowski, & Kroese, 2010). The PRL was defined as the peak of the density
distribution. The variance of fixation was defined as the BCEA (bivariate contour ellipse
area) that encompassed 68% of fixations around the mean and was used as an indicator of
fixation stability (Crossland, Sims, Galbraith, & Rubin, 2004). BCEAs were calculated
from the density maps. The density maps for the first saccade landing site were obtained
47

in a similar manner from the retinal positions of the target objects at the end point of the
first saccade after object movement.

Crowding measurement: psychophysics
Stimuli and Task
Target and flankers were tumbling “T”s, in radial or tangential configuration, with
the center letter at 6.5° eccentricity (Fig. 3.1). The line defined by the center letter and
fixation point was 30° clockwise (Sideways location) or counterclockwise (PRL location)
from the vertical axis. The size of letter “T” was 0.75° and the contrast was 100%.
Background luminance of the display is 25.7 cd/m
2
. In a trial, one target and two flankers
were presented for 150 ms. After a 500 ms blank interval, subjects were asked to identify
the orientation of the target “T”, by clicking, via the mouse, on one of the four tumbling
“T”s. Feedback for correct answers was provided by a tone. The next trial began 1 s after
feedback.
Four conditions [2 configurations (radial/tangential) × 2 locations (PRL/Sideways)]
were tested in a counterbalanced order. Four blocks were completed for each condition.
Each block consisted of 30 trials. The first 4 blocks were target only blocks to measure
each participant’s target only performance.

48

Measuring the crowding zone
To measure the crowding zone, we developed a quick method by using a psi-
marginal method (Kontsevich & Tyler, 1999; Prins, 2013). For each flanker condition and
each subject, threshold, slope and lapse rate of a log-Weibull function were estimated to
maximize the posterior probabilities by observer’s response in each trial. Threshold was
calculated at the level of 63% of a normalized log-Weibull function. The crowding zone
was determined by the intersection between slope and a high asymptote. Slope of the
psychometric function is determined at the point of inflection at the threshold. The high
asymptote was estimated by weighted average of lapse rate from the psi-marginal method
and finger-errors from no-flanker trials. We assumed the intersection of the two lines as
the crowding zone at where the identification performance started to drop. We set the
guess rate 25% as the number of possible target stimuli.

49

Figure 3.1. Stimuli and task design of Exp. 3: (A) PRL induction tasks. Subjects’ central
visual field was blocked by the gray circular disc. In the Object-Following task, subjects
were asked to follow a target object with a gaze marker (a gray cross outside the gray disc)
as it was repositioned in a cluttered background. In one third of the trials, the target was
repositioned from PRL to Sideways location. In the other trials, the target was randomly
positioned. In the Object-Searching task, subjects searched for the same target object in a
cluttered background with an array of distractors. (B) Schematic description of a four-
alternative forced-choice (4-AFC) trial in a staircase for measuring the thresholds of
crowding zone.

50

Results
PRL Induction
For all 9 participants, the variance of the fixational PRL decreased rapidly during
the explicit PRL induction period (Figure 3.2A). The variance (defined as a bivariate
contour ellipse area, BCEA) at the end of the induction block was significantly reduced
from that of the first block (70% reduction, t(8) = 8.31, p < 0.001). Furthermore, the
variance of the first saccade landing site was also significantly reduced over the induction
period (Figure 3.2B, 70% reduction, t(8) = 11.04, p < 0.001. The reduction of the first
saccade landing site after the target moved at or near the induced PRL demonstrated a
shift in oculomotor reference from the fovea to the induced PRL.

51

Figure 3.2. Results of PRL induction
(A) Development of preferred retinal locus for fixation. Left panel: Probability density
maps showing the retinal positions of a target object at fixation. Polar plots represent the
visual fields. The gray patch depicts the central scotoma. The red dot marks the location
of peak density of the PRL. (B) Development of referenced saccades measured by the first
saccade landing site. Left panel: probability density maps showing the retinal position of
a target object at the completion of the first saccade after target movement. Right panel:
Variance of the first saccade landing site as a function of the number of training blocks.
Variance was defined as the bivariate contour ellipse area that encompassed 68% of
fixations around the mean. Shaded areas denotes ±1 SD across subjects.
52

Figure 3.3. Results of crowding zone measurements
(A) Crowding zones measured at the PRL and Sideways locations before (blue) and after
(red) training, depicted in a visual field polar plot. Line thickness denotes ±1 SE across
subjects. The black dot inside the diamond indicates the location of the target letter.
(B) Training induced changes in the critical spacing along the radial and tangential axes
at the PRL location, and along the radial and horizontal axes at the Sideways location.
Error bars denote ±1 SE across subjects. Asterisks indicate significant changes (* p<0.05,
** p<0.01).

Crowding measurement: psychophysics
To compare the subjects’ crowding zones before and after PRL training, we
measured the critical spacing between target and flankers in radial and tangential axis, at
the PRL and Sideways locations (Figure 3.3). A repeated-measures ANOVA with flanker
configuration (radial/tangential) and locations (PRL/Sideways) as within-subject factors
showed a significant main effect of flanker configuration, F(1, 8) = 13.04, p = 0.0069). A
53

post-hoc paired-t test showed that the reduction of the crowding effect was larger in the
radial condition than in the tangential condition at Sideways (5.33° to 3.78°) locations (t(8)
= -3.0266, p = 0.016).

Figure 3.4. Results of crowding zone measurements for control experiment
(A) Crowding zones measured at PRL and Sideways locations twice with 2 day time
difference; pre (blue) and post (red) without any PRL induction. Other denotes are
identical with that of Figure 3.3.
(B) No changes without PRL induction in the critical spacing along the radial and
tangential axes at both PRL and Sideways location.

Crowding measurement - control: psychophysics
To compare the subjects’ crowding zones before and after without PRL training as
a control experiment, we conducted the same crowding measurement experiment with
different participants (Figure 3.4). The control experiment was identical to the crowding
54

measurements experiment except for the PRL induction. A repeated-measures ANOVA
with flanker configuration (radial/tangential) and locations (PRL/Sideways) as within-
subject factors showed no significant main effect of flanker configuration (F(1, 8) =
0.2097, p = 0.6592), nor locations (F(1, 8) = 1.0923, p = 0.3265). Post-hoc paired-t test
showed that the reduction of crowding effect was larger in the radial condition than in the
tangential condition at Sideways (5.33° to 3.78°) locations (t(8) = -3.0266, p = 0.016).

Visual search performance
All 9 participants showed a gain in accuracy after 5 blocks of PRL induction (Figure 3.5).
Performance significantly increased from the 1
st
block to reach a plateau after the 3rd
block, (t(9) = -3.813, p=0.0051). From the 3rd block to the end of PRL induction, there
was no significant change in performance accuracy.

Figure 3.5. Visual search performance: (A) Averaged proportion correct for the visual
search performance (n=9) as a function of block number. (B) Time taken to finish to judge
for target presented or absent as a function of number of block. (C) Number of saccades
to judge for target presented or absent as a function of block number. Only correct trials
were used for (B) and (C). Error bar represented ± 1SE.

55

Discussion
We found that inducing a prescribed PRL with a simulated central-field scotoma
can lead to a shrinkage of the crowding zone along the radial axis at the PRL location,
causing a reduction in the aspect ratio of the elliptical shape of the crowding zone (i.e., to
become more circular). It suggests that human crowding zones dynamically reorganize
relative to the reference point of the saccades, which is consistent with the saccade-
confounded image-encoding theory (Nandy & Tjan, 2012).
Our finding was partially consistent with a prediction of Nandy and Tjan’s model
(2012) on re-orientation of an elongated crowding zone. They proposed a computational
model to explain that an anisotropic crowding zone is due to the erroneously confounded
orientation information by saccadic eye movement with spatial attention, resulting in an
elongation of the crowding zone towards the natural fovea. The model predicted that 1)
the aspect ratio of the elongation of the crowding zone at the natural fovea will be reduced
in a direction of being more circular and 2) the elongation of the crowding zone at the
other location will be re-oriented by a fixation re-reference towards the induced fovea (i.e.,
the PRL). Our results, and the results of a patient study (Chung, 2013), confirmed the first
prediction, that the shape of the crowding zone at the PRL will became more circular with
PRL experience. For the second prediction, we failed to find clear evidence for the re-
orientation of the crowding zone in the Sideways condition, however, the elongation of
the Sideway’s crowding zone also became less elongated. At this point, it is still unclear
whether the reduced anisotropy in the Sideway’s condition will become elongated later
56

toward the PRL or not. Since we studied with normally sighted observers, they have an
intact natural fovea and keep using the fovea for daily tasks except during our PRL training
times. Thus, compared to the PRL patients, it could be lack of time to fully present the re-
orientation of the crowding zone at the Sideways location.
Our finding also suggests that oculomotor system in normally sighted observers
rapidly adapted to an induced fovea with a simulated central scotoma. Using a similar
training paradigm to that of Kwon et al. (2013), we showed that PRL training with a gaze
marker improves the fixation stability at the PRL and the precision of the targeting
saccades over 3-4 weeks. Visual search performance improved and reached a high
asymptotic level of accuracy over 5-10 blocks. This enhanced visual performance is
closely correlated with the fixation stability at the PRL (Falkenberg, Rubin, & Bex, 2007;
Tarita-Nistor et al., 2009).
The present study provides evidence that peripheral vision in a normally sighted
observer adapts to new functions under the environment of simulated central vision loss
by inducing a PRL. Our study thus adds to the theoretical foundation for the investigation
of not only the mechanisms of form-vision in the periphery, but also for the development
of an efficient rehabilitation regime and technique for central vision loss patients.

57

Chapter 4: How should the crowding zone be
measured?
Introduction
The crowding zone is typically defined in terms of the minimum center-to-center
spacing area between a target and flanker which yields a similar target-identification
performance as for an unflanked target (Levi, Song, & Pelli, 2007; Martelli, Majaj, &
Pelli, 2005; Pelli et al., 2007; Scolari, Kohnen, Barton, & Awh, 2007; Strasburger, 2005;
van den Berg, Roerdink, & Cornelissen, 2007; Yeshurun & Rashal, 2010). The crowding
zone can also be operationally defined in terms of the center-to-center distance between a
target and flanker which shows a certain level of performance for target-identification
(Chakravarthi & Cavanagh, 2007a; Chung, 2007; Chung, Li, & Levi, 2012; Chung &
Mansfield, 2009; Hussain, Webb, Astle, & McGraw, 2012; Kooi, Toet, Tripathy, & Levi,
1994; Louie, Bressler, & Whitney, 2007). Because of these two different definitions for
the crowding zone, two different methods have been used for measuring the crowding
zone. In the first case, where accuracy for target identification is the dependent variable, a
typical method for measuring the crowding zone is to get the accuracy for each target-
flanker distance levels and to find the intersection of a two-line fit; the first line is a high
asymptote where accuracy is similar to that for isolated target identification, and the other
is a maximum slope at an inflection point where the second derivative is zero. In here, the
typical crowding zone is called as elbow. Another way to measure the operational
58

crowding zone is to find a point of a psychometric function (for example, Weibull
function) that yields a certain performance level with an adaptive procedure (e.g.
QUEST). Here, we call this operational crowding zone as threshold.
Little consideration has been given to whether these two methods would yield the
qualitatively same crowding zone. Without clarifying this matter, many researchers have
measured the crowding zone using only one of two methods without disentangling elbow
method and threshold method.
Akin to measuring temperature in Fahrenheit and Celsius, a monotonicity would
be a crucial feature for judging whether the elbow and the threshold are qualitatively the
same crowding zone. The monotonicity is a characteristic that an entire function is only
increasing or decreasing as an independent variable is increasing. If two methods
measure the same crowding zone qualitatively, the monotonicity should be preserved
across various experimental conditions, such as stimuli and observers. For instance, if the
elbow increases with a change of stimuli while the threshold decreases with the same
change of stimuli, it would be hard to tell these two methods would measure qualitatively
the same crowding zone or not. In this study, we investigated whether the monotonicity
between the elbow and threshold is preserved within observers and experiments.
To achieve this, we first developed a method to measure the elbow and threshold
from one psychometric function, using a psi-marginal method (Kontsevich & Tyler,
1999; Prins, 2013).
59

We conducted 8 experiments using letter stimuli (for details, see Table 4.1). We
found that the monotonicity between elbow and threshold across observers and
experiments was statistically preserved, implying that two methods for measuring
crowding zone would measure the same qualitative crowding zone.
Exp Observers Target Flanker Location (polar
angle)

4.1 A 7 9 letters
(D,H,K,N,O,R,S,V,
and Z)
Tumbling E (6.5,245) (6.5, 65)
(10,245) (6.5 305)

B 1 (6.5 295) (6.5, 115)
(10, 295), (6.5, 235)

C 14 (6.5 295) (6.5, 115)
(10, 295), (6.5, 235)
6 letters
(D,H,R,S,V,
and Z) used
for updating a
psychometric
function
D 2 (6.5,245) (6.5, 65)
(10,245) (6.5 305)
4.2 A 14 9 letters
(D,H,K,N,O,R,S,V, and Z)
(6.5,245) (6.5, 65)
(10,245) (6.5 305)
B 1 (6.5 295) (6.5, 115)
(10, 295), (6.5, 235)
C 13 6 letters
(D,H,Z,S,V, and Z)
(6.5,240) (6.5, 300)
4.3 2 Tumbling T (6.5,245) (6.5, 65)
(10,245) (6.5 305)

Table 4.1. Descriptions of the Exp.4. 8 experiments were conducted, falling into 3
categories based on the type of target and flakers. Exp.4.1 used letter target and tumbling
E as a flanker. Exp.4.2 used letter as a target and flanker both. Exp.4.3 used tumbling T
as a target and flanker both. Exp.4.1c-d and Exp.4.2a-b used 6 letters among 9 letters
were used for updating a psychometric function (letters, such as K, N and O, were
excluded to minimize confusion not necessarily due to crowding).
Methods
Observers
Total 49 observers, including one author, participated in one or more experiments
(24 for Exp.4.1, 28 for Exp.4.2 and 2 for Exp.4.3. For details, see Table 4.1). For all
60

experiments in this study, all observers, excluding the author, were naive to the purpose
of the experiments. All observers had normal color vision and normal or corrected ‐to‐
normal visual acuity. This study was carried out in accordance with the regulations of the
Institutional Review Board of University of Southern California.

Stimuli and Apparatus
In all experiments, stimuli were Sloan letters. In Exp.4.1, letters were used for the
target and tumbling E for flankers. In Exp 4.2, letters were used for target and flankers. In
Exp 4.3, tumbling Ts were used for target and flankers. For details, see Table 4.1.
The size of stimuli was 0.75° and the contrast was 100%. The stimuli were created
using MATLAB and the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997). They
were presented on a SONY monitor (resolution 1024 X 768 at 100Hz) driven by a
MacBook laptop via a passive video attenuator (Pelli & Zhang, 1991) to provide 11 bits
of linearly spaced contrast levels achieved with custom-built calibration and contrast-
control software implemented in MATLAB. Only the green channel of the monitor was
used.
Observers’ head was held fixed with a chin-and-head rest to maintain a view
distance of 70 cm. At this viewing distance, a pixel subtended approximately 0.031° of
visual angle. Background luminance of the display is 25.7 cd/m
2
. Responses were made
by using a keyboard and a mouse.
61

Figure 4.1. An example trial of Exp.4. A black dot served as a fixation. A red dot was
appeared 1000ms prior to the stimulus to indicate the eccentric location of the target (6.5°
lower left or right from the fixation point). An observer had to fixation the black dot.
1000 ms after observer’s initiating, a target and two flankers were presented for 150 ms
simultaneously. The observer was asked to report what letter was seen at the response
screen by using a mouse.

62

Procedure
Figure 4.1 shows the timing for all experiments. Procedures across all experiments
were identical. Trials began with a black fixation dot, and a red dot that indicated the
position in which the target letter would appear. One letter target and two letter flankers
were presented at 6.5 degrees in an observer’s peripheral vision. Stimuli duration was
100 ms. After the stimuli presentation, a display with all possible letter targets what
shown, and observers had to choose the target letter they saw using a mouse. There was
feedback for a correct answer. The observer initiated the first trial of each block by
pressing a spacebar. After the observer’s initiation, each trial was proceeded
automatically until the end of each block with inter-trial interval of 150 ms.
There were 2 target-flanker conditions per location: radial and tangential. Four
locations were tested in all experiments and 2 locations for the Experiment 2c. All
conditions were blocked. Each block consisted of 13 trials: 10 trials with flankers and 3
trials without flankers for measuring finger error. The total number of blocks was 80 for
all 4 locations in each experiment (except for 40 blocks for Exp.3.2c) and the order was
randomized across observers.
Analysis
For each condition and observer, 3 parameters (threshold, slope and lapse rate) of
a log-Weibull function were updated to maximize the posterior probabilities by
observer’s response in each trial. Threshold was a performance level of 63% of a
63

normalized log-Weibull function. Here, we used this threshold level as the crowding zone
for threshold, since this threshold point is an exact point to achieve a certain level of
performance in terms of the operational definition of crowding zone. The elbow was
determined by the intersection between slope and a high asymptote. Slope of the
psychometric function is determined at the point of inflection at the threshold. The high
asymptote was estimated by weighted average of lapse rate from the psi-marginal method
and finger-errors from no-flanker trials. We assumed the intersection of the two lines as
the elbow at where the identification performance started to drop. Figure 4.2 shows how
the crowding zones in elbow way and threshold way were determined. We set the guess
rate by the number of possible target stimuli.

Figure 4.2. Estimating the crowding zone for elbow and threshold from a psychometric
function. Accuracy of letter discrimination task as a function of target-flanker distance.
The threshold is estimated at the inflection point of a psychometric function. At the
intersection of two lines: the slope at the inflection point at the level of threshold on the
psychometric function and high asymptote line is determined as the elbow.
64

We presented a letter target with 2 tumbling E flankers (Exp.4.1a-d), a letter target
with 2 letter flankers (Exp.4.2a-c), and a tumbling T target and 2 tumbling T flankers
(Exp.4.3). In the Exp.4.1 and 4.2, there were some minor variations with stimuli locations
and target letters which were used for updating parameters.
Each condition was bootstrapped 500 times to acquire the SE of the elbow. SE of
the threshold was directly acquired from the psi-method function. Figure 4.3 shows a
scatter-plot of elbow vs. threshold: Figure 4.3a is for grouped by observers across
experiments and 4.3b is for grouped by experiments across observers. In each 2D scatter
plot, each dot indicates a crowding zone in elbow way(x-axis) and a crowding zone in
threshold way (y-axis) from one condition of one observer. Here, the crowding zone is
expressed in a log of ratio, which was calculated as log10(crowding zone/eccentricity). In
the relation of any pair of two dots, when a gradient of a pair is positive, it is called a
positive pair; when a gradient is negative, it is called a negative pair. For the within
observers analysis, we compared all observer’s pairs regardless the type of experiment.
For the within experiments analysis, we compared all pairs of each experiment regardless
of different observers.
To test the monotonicity, we examined whether all the pairs within observers and
within experiment would be one polarity (positive or negative) or mixed. In order to test
whether the polarity of pair is statistically positive or negative, we generated a normal
difference distribution of each pair and calculated p-value which the area of the
65

distribution sits in the second and fourth quadrant. The p-value indicated the probability
that the polarity of pair was positive.
Results
We found that there were no statistically significant negative pairs using a paired
t-test with either a Bonferroni corrected p-value (p<0.05 / number of negative pairs) and
FDR corrected p-value (see Table 4.2 and Figure 4.3). Our findings suggest that the
relation between crowding zones in elbow and threshold way preserved monotonicity. It
should be noted that most of the pairs were positive pair and many of them were
significant pairs, indicating that the change of the crowding zone size from two
measurements were the same direction in most cases. While a small number of pairs were
numerically negative pairs, they were not significantly negative within observers or
experiments.
All pairs Negative pair Significantly negative pair
Within experiments 11936 1241 0
Within observers 2336 259 0
Table 4.2. Number of pairs

66

Figure 4.3. Number of negatives as a function of p-value. The p-value was corrected with
Bonferroni correction which controls the family-wise error rate. The corrected p-value for
the within experiment analysis was 4.0290e-05, and for the within observer analysis was
1.9304e-04. For both analyses, none of negative pairs was significant.
Discussion
Our key finding is that the monotonicity between the elbow and threshold were
statistically preserved within observers and experiments. In other words, the elbow and
threshold are statistically linearly correlated. This finding provides the first evidence that
the two methods measure qualitatively the same crowding zone.
Our study was designed to directly compare two different measurements of
crowding: threshold and elbow (amplitude and spatial extent in Pelli & Tillman, 2008;
threshold and spatial extent in Sun, Chung, & Tjan, 2010). It has been argued that
threshold can be used to measure the performance of crowding effect, however, elbow
crowding zone can be used to measure the spatial extent of crowding. Based on our
67

findings, these two different metrics for crowding change overall in the same direction
across different experiments and observers, suggesting that two methods are measuring
qualitatively the same crowding zone.
Our results do not imply that two crowding zones always change in the same
direction. The elbow could be invariant while other could vary across stimuli or
observers. That might be the reason for contradictory results across studies. Several
studies showed that the elbow was invariant across different types of stimuli or tasks:
letter vs. objects (Wallace & Tjan, 2011), line drawing face vs. letter(Martelli et al.,
2005), first- vs. second-order letter (Chung, Li, & Levi, 2007), size, saturation, and hue of
stimuli (van den Berg et al., 2007), and attention (Scolari et al., 2007; Strasburger, 2005).
However, other studies showed that threshold was vary across types of stimuli; upright
face vs normal face (Louie et al., 2007), and target-flanker polarity (Chakravarthi &
Cavanagh, 2007b; Chung & Mansfield, 2009; Kooi et al., 1994). In terms of training, the
threshold crowding zone can be reduced by a training (Chung et al., 2012; Chung, 2007;
Hussain et al., 2012). Figuring out what causes these difference in two crowding zone
measurements is outside the scope of this study. The significance of our study is that
these two crowding zone measurements are linearly correlated. This will provide a basis
to study how the two crowding zones differ quantitatively.
Another contribution of our study is a novel method to measure these two types of
crowding zone at the same time within an observer. Several attempts have been done to
measure the crowding zone by fitting data with multiple methods to prevent any artifact
68

from different methods (Yashar et al., 2015; Yeshurun & Rashal, 2010). Here, we
constructed a psychometric function for each observer with a given stimulus to
simultaneously estimate the two types of measurements for crowding zone. It would have
advantage for comparing results from two different types of measurements, which came
from the same data set from an observer than fitting different ways from different
experiments and observers. Importantly, our method is relatively fast. It minimizes any
learning effect during the crowding zone measurements, and eliminates the contribution
of training that could lead to differences between two measurements made sequentially
rather than simultaneously.

69

Chapter 5
Conclusion
In my first study, we found that invisible flankers rendered by the interocular
suppression (IOS) still interfered with a target and prevented to the target identification.
This result was independent of the type of stimulus (Gabors or letters). However, the
same invisible flankers induced by the adaptation-induced blindness (AIB), the
performance of target identification was correlated with the number of perceived
flankers, rather than the number of presented flankers. Therefore, the method used to
manipulate visual awareness determines whether invisible flankers have any effect on
crowding. Also, our finding speculated that the neural locus of crowding would be
between the neural locus of IOS and AIB, and it would not be compatible with the
theories implying the high-level origin of crowding. In my second study, we found that
the radial size of the crowding zone in the PRL and Sideways locations were reduced by
an induced PRL through a perceptual learning. For a normally sighted observer, the
performance for a fixation stability and visual search were enhanced rapidly through
perceptual learning. Our finding suggested that the oculomotor system in normally
sighted observers was also malleable to rapidly adjust to an environment of a simulated
central vision loss. In my third study, we found that two types of crowding zone
measurements; elbow and threshold are linearly correlated. In other words, two methods
for measuring the crowding zone measure qualitatively the same crowding zone. Our
70

finding will provide a basis to study how the two crowding zones are different
quantitatively.
71

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Appendix

Figure S1. Frequency of the number of perceived flankers in Exp.2.1. Observer perceived
no flankers in a majority of the trials, despite of the fact that one or more flankers were
always present in a trial.

91

Figure S2. Reanalysis of Exp.2.1 using partial data sets as in Wallis & Bex (2011). a)
Accuracy of orientation discrimination as a function of the number of presented flankers,
for trials when observers did not report seeing any flankers. Accuracy decreases with the
increasing number of presented flankers (thick maroon line, slope: β = -0.22, SE = 0.06, z
= -4.024, p = 5.71e-05). b) Accuracy of orientation discrimination as a function of the
number of perceived flankers, for trials when the number of presented flankers was four.
Accuracy does not change significantly with the number of perceived flankers (thick blue
line, β = 0.21, SE = 0.11, z = 1.92, p = 0.055). The dotted lines indicate chance
performance. Error bars represent standard error. Size of the square symbols shows the
number of trials of a given condition for a given observer. Different observers’ data are
represented with different colors. Fitted lines were generated from the logistic models:
(a) Physical_model = glm(correct~presented, data= trials_with_0_perceived_flankers,
family=binomial(link="logit"))
(b) Perceptual_model = glm(correct~preceived, data=trials_with_4_flankers,
family=binomial(link="logit"))
92

Figure S3. Reanalysis of Exp.2.2 using partial data sets as in Wallis & Bex (2011). a)
Accuracy of orientation discrimination as a function of the number of presented flankers
using only trials when an observer did not report seeing any flankers. Accuracy does not
change significantly with the number of presented flankers (thick maroon line, slope: β =
-0.05, SE = 0.04, z = -1.358, p = 0.174). b) Accuracy of orientation discrimination as a
function of the number of perceived flankers using only trials where the number of
presented flankers was four. Accuracy decreases with increasing number of the perceived
flankers (thick blue line, β = -0.11, SE = 0.04, z = -2.51, p =0.012). The dotted lines
indicate chance performance. Error bars represent standard error. Size of the square
symbols shows the number of trials of a given condition for a given observer. Different
observers’ data are represented with different colors. Fitted lines were generated from the
logistic models:
93

(a) Physical_model = glm(correct~presented, data= trials_with_0_perceived_flankers,
family=binomial(link="logit"))
(b) Perceptual_model = glm(correct~preceived, data=trials_with_4_flankers,
family=binomial(link="logit"))

Figure S4. Reanalysis of Exp.2.3 using partial data sets as in Wallis & Bex (2011). a)
Accuracy of orientation discrimination as a function of the number of presented flankers,
using data from trials when an observer reported seeing no flankers. Accuracy decreases
with increasing number of the presented flankers (thick maroon line, slope: β = -0.43, SE
= 0.04, z = -9.671, p < 2e-16). b) Accuracy of orientation discrimination as a function of
the number of perceived flankers, using data from trials when the presented number of
flankers was four. Accuracy does not change significantly with the number of perceived
flankers (thick blue line, β = -0.11, SE = 0.32, z = -0.34, p =0.738). The dotted lines
indicate chance performance. Error bars represent standard error. Size of the square
94

symbols shows the number of trials of a given condition for a given observer. Different
observers’ data are represented with different colors. Fitted lines were generated from the
logistic models:
(a) Physical_model = glm(correct~presented, data= trials_with_0_perceived_flankers,
family=binomial(link="logit"))
(b) Perceptual_model = glm(correct~preceived, data=trials_with_4_flankers,
family=binomial(link="logit"))

Abstract (if available)

Abstract To study the mechanism of crowding, I address three critical questions through a series of psychophysical experiments: 1) we inform on the neural locus of crowding by investigating the relationship between awareness and crowding, 2) the degree to which peripheral vision can be changed through long-term training, and 3) the relationship between two methods for measuring the crowding zone. ❧ Where is the neural locus of crowding? ❧ In the first study, we studied the neural origin of crowding. By knowing the neural locus of crowding, we can constrain theories on mechanism of crowding. Determining whether crowding depends on subjective awareness of the flankers can provide information on the neural origin of crowding, when underlying mechanism of awareness manipulation is known. However, recent studies that manipulated flanker awareness have yielded conflicting results. To better understand the interaction between flanker awareness and crowding, we suppressed flanker awareness with two methods: interocular suppression (IOS) and adaptation-induced blindness (AIB). We tested two different types of stimuli: gratings and letters. With IOS, we found that the magnitude of crowding increased as the number of physical flankers increased, even when the observers did not report seeing any of the flankers. In contrast, when flanker awareness was manipulated with AIB, the magnitude of crowding increased with the number of perceived flankers. Our results show that whether crowding is contingent on awareness of the flankers depends on the method used to suppress awareness. In addition, our results imply that the locus of crowding is upstream from the neural locus of IOS and close to or downstream from that of AIB. Neurophysiology and neuroimaging studies jointly implicate mid-to-high level visual processing stages for IOS, while direct evidence regarding the neural locus of AIB is limited. The most consistent interpretation of our empirical findings is to place the neural locus of crowding at an early cortical site, such as V1 or V2. ❧ How is peripheral vision changed through perceptual learning? ❧ In the second study, we studied how much the crowding zone is malleable through perceptual learning. To identify the mechanism underlying crowding, understanding plasticity of the peripheral vision should be necessary. It is natural to assume that the visual system, including the peripheral vision, are developed to process visual information optimally by natural scene statistics. Would it be possible to measure any changes on the peripheral visual processing through changing the statistical properties? It would help to figure out the property of the mechanism. We trained normally sighted adults with occluded central vision to induce a preferred retinal locus (PRL), which becomes a new locus in the periphery for fixation and the saccade reference. Here we asked whether the development of a PRL in normally sighted people facilitates peripheral vision by changing the crowding effect. We found that PRL induction led to a shrinkage of the crowding zone along the radial axis at the PRL location. These findings suggest that human crowding zones dynamically reorganize relative to the reference point of saccade, which is consistent with the saccade-confounded image-encoding theory (Nandy & Tjan, 2012). This study reveals the residual plasticity in adult peripheral vision and sheds light on new mechanisms of rehabilitation for patients with central field loss. ❧ How should the crowding zone be measured? ❧ In the third study, we tested whether crowding zones from different methods are measuring the same crowding zone or not. A complication in identifying the mechanism underlying crowding is in deciding how crowding should be measured. Previous studies that have sought to inform on the mechanism using psychophysics have used different measurements, making conflicting results difficult to interpret. Crowding zone refers to an interaction zone between a peripheral target and nearby flankers. In literature, there are two different ways to measure the crowding zone, based on two different definition of crowding zone: 1) a typical crowding zone, which is defined the starting point of target-flanker distance to interact, and 2) an operational crowding zone, which is defined a certain target-flanker distance to yield a certain target identification performance. To compare the properties of the crowding zone across studies, it would be important to know whether crowding zones from different methods are comparable or not. In here, when accuracy in identifying target stimuli as a function of distance between target and flankers is a dependent variable, the crowding zone can be determined by two-line fit, the intersection between a slope of a psychometric function and high asymptote for no-flanker condition. The crowding zone is also measured as the spacing associated with a specific level of accuracy where at the slope of the psychometric function is maximum. We tested whether the crowding zone measured with different methods preserves monotonicity within experiments or observers. We found the monotonicity of crowding zone from two different methods was statistically preserved within experiments and observers. This suggests that previous studies using these two methods to measure the crowding zone can be compared, and that the crowding zone is robust across different measurements.

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Asset Metadata

Creator Shin, Kilho (author)

Core Title Crowding in peripheral vision

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Psychology

Publication Date 03/14/2017

Defense Date 12/01/2016

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

Tag OAI-PMH Harvest,preferred retinal locus,visual crowding

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Tjan, Bosco S. (committee chair), Biederman, Irving (committee member), Lopez, Steven (committee member), Mather, Mara (committee member)

Creator Email giro.shin@gmail.com,kilhoshi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-349539

Unique identifier UC11258398

Identifier etd-ShinKilho-5141.pdf (filename),usctheses-c40-349539 (legacy record id)

Legacy Identifier etd-ShinKilho-5141.pdf

Dmrecord 349539

Document Type Dissertation

Rights Shin, Kilho

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA