The neural origins of visual crowding as revealed by event-related potentials and oscillatory dynamics

Accepted Manuscript The neural origins of visual crowding as revealed by event-related potentials and oscillatory dynamics Luca Ronconi, PhD, Sara Bertoni, Rosilari Bellacosa Marotti, PhD PII:

S0010-9452(16)30034-X

DOI:

10.1016/j.cortex.2016.03.005

Reference:

CORTEX 1700

To appear in:

Cortex

Received Date: 7 July 2015 Revised Date:

20 November 2015

Accepted Date: 2 March 2016

Please cite this article as: Ronconi L, Bertoni S, Bellacosa Marotti R, The neural origins of visual crowding as revealed by event-related potentials and oscillatory dynamics, CORTEX (2016), doi: 10.1016/j.cortex.2016.03.005. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT The neural origins of visual crowding as revealed by event-related potentials and oscillatory dynamics

Luca Ronconi1,2,3*, Sara Bertoni1,2, Rosilari Bellacosa Marotti1* Department of General Psychology and 2 Developmental and Cognitive Neuroscience Lab, University of Padua, Italy

Scientific Institute IRCCS “E. Medea”, Bosisio Parini, Lecco, Italy.

Correspondence must be sent to:

M AN U

*These authors contributed equally to this article.

SC

3

RI PT

1

Luca Ronconi, PhD ([email protected]) Department of General Psychology, University of Padua, Via Venezia 8, 35131 Padova, Italy, Phone: +39 049 827 6149. Rosilari Bellacosa Marotti, PhD ([email protected]), new address: International School

AC C

EP

3787 628.

TE D

for Advanced Studies (SISSA), via Bonomea 265, 34136, Trieste, Italy, Phone: +39 040

Abbreviated title: Neural origins of visual crowding

Keywords: visual cortex; letters processing; visual perception; VEP; MEG.

1

ACCEPTED MANUSCRIPT Abstract Visual crowding is the difficulty in perceiving a target in the presence of nearby flankers. Most neurophysiological studies of crowding employed functional neuroimaging, but because of its low temporal resolution, no definitive answer can be given to the question: is crowding arising at the earliest or at later stages of visual processing? Here, we used a

RI PT

classic letters crowding paradigm in combination with electroencephalography (EEG). We manipulated the critical space between peripheral target and flankers, while ensuring a proper control of basic stimulus characteristics. Analyses were focused on event-related potentials (ERPs) and oscillatory activity in the alpha (8-12 Hz), beta (15-30 Hz) and

SC

gamma (30-80 Hz) bands. At the ERP level, we found that the first sign of a crowdinginduced modulation of EEG activity was a suppression of the N1 component. Oscillatory analysis revealed an early stimulus-evoked gamma enhancement and a later alpha

M AN U

reduction that, however, were not influenced by the amount of crowding. Importantly, reduction in the beta band reflected the amount of crowding (i.e., stronger reduction for strong relative to mid crowding condition) and correlated with individual behavioral performance. Collectively, these findings show that crowding for complex objects emerges

AC C

EP

TE D

at later stages of visual processing, possibly as a result of large-scale network interaction.

2

ACCEPTED MANUSCRIPT 1. Introduction Visual crowding is the difficulty in perceiving a target in the presence of nearby flankers. Crowding limits recognition, not detection, is typically observed in peripheral vision and can occur with simple objects, such as oriented gratings, and also with complex objects, such as letters and faces (Pelli & Tillman, 2008; Whitney & Levi, 2011).

RI PT

Studying visual crowding may lead to a better understanding of conscious object perception and has also clinical implications, in particular in some neurodevelopmental disorders: crowding is larger in dyslexia (Bouma & Legein, 1977; Martelli, Di Filippo, Spinelli, & Zoccolotti, 2009; Zorzi et al., 2012; see Gori & Facoetti, 2015 for a review),

SC

whereas it is reduced in autism (Baldassi et al., 2009; Keita, Mottron, & Bertone, 2010). Crowding emerges both in identification and discrimination paradigms. For example, crowding affects the ability to discriminate phase (Chakravarti & Pelli, 2011) and

M AN U

orientation (Greenwood, Bex, & Dakin, 2010) of single gabor patches. However, it is also visible with more complex objects: when close flankers are present, the accuracy in reporting the overall orientation of a letter is reduced (e.g. Tripathy & Cavanagh, 2002). Among other factors, the distance between the target and flankers is well known to make the target more difficult to see. Indeed, the amount of perceived crowding depends on how densely spaced the surrounding objects are (Bouma, 1970; Whitney & Levi, 2011). The

the flankers increases.

TE D

identification of a cluttered object improves as the critical spacing between the target and

Although there are several psychophysical studies on factors influencing crowding, its

EP

neural substrate is still unclear. Crowding may emerge at an early stage of visual perception (e.g., striate visual area, V1), where local features are integrated in more complex percepts (Pelli, 2008). Disrupting this phase would result in a compromised

AC C

representation also at later stages of the visual hierarchy. On the other hand, higher areas (e.g., V4) may have a predominant role. For example, receptive fields in V4 are larger and an incorrect integration of target and flankers features may take place (Liu, Jiang, Sun, & Heet, 2009; Motter, 2006). Identifying the neural underpinnings of visual crowding would help clarifying the mechanisms responsible of crowding. Recent attempts to identify the neural locus of visual crowding have been mainly carried out with functional magnetic resonance imaging (fMRI) (Anderson, Dakin, Schwarzkopf, Rees, & Greenwood, 2012; Bi, Cai, Zhou, & Fang, 2009; Fang & He, 2008; Freeman, 3

ACCEPTED MANUSCRIPT Donner, & Heeger, 2011; Kwon, Bao, Millin, & Tjan, 2014; Millin, Arman, Chung, & Tjan, 2014). Results from these studies are far from being exhaustive, suggesting either a lowlevel (Millin et al., 2014) or a high-level (Anderson et al., 2012) locus for crowding. However, because of the very low temporal resolution of the fMRI technique, it is hard to clarify whether modulations of early visual areas (e.g. V1, V2) induced by crowding arise

RI PT

effectively in V1 or whether they are the results of recurrent connections between higher (such as V4, infero-temporal area - IT) and lower visual areas (Jensen, Bonnefond, Marshall, & Tiesinga, 2015; Tong, 2003). Moreover, since crowding is not necessarily a single homogeneous phenomenon, it is also possible that its neural underpinnings are

SC

tightly related to stimulus features, with crowding for simple objects emerging at an earlier stage of visual processing while crowding for complex objects emerging in later stages. Electroencephalography (EEG) has a temporal resolution in the order of milliseconds and

M AN U

allows researchers to precisely map the time course of activation in the visual hierarchy while measuring visual crowding. The few previous studies evaluating crowding with EEG methodology observed inconsistent results.

Chicherov, Plomp, and Herzog (2014) found that crowding occurs when target and flanker elements are grouped into wholes and cannot be fully attributed to lower cortical areas such as V1. The authors presented participants with a foveal vernier stimulus flanked by

TE D

arrays of vertical lines. The idea is that crowding is strong when the flankers can be grouped with the target because they have equal length. Crowding is weaker, instead, when flankers are longer and the target can be segregated from them (Malania, Herzog, &

EP

Westheimer, 2007; Manassi, Sayim, & Herzog, 2012). Indeed, Chicherov et al. (2014), using dense-array EEG and a data-driven point-by-point analysis of the neural activity, found crowding to lower the amplitude of a late visual ERP component (i.e., the N1),

AC C

peaking around 200 ms post-stimulus. Source analysis revealed the lateral occipital complex as the neural locus of this modulation. Contrarily, an earlier component (i.e., the P1; peaking around 120 ms post-stimulus) merely mirrored basic physical stimulus characteristics. Following investigation (Chicherov & Herzog, 2015) supports that crowding is due to target-flanker grouping rather than to low-level mutual inhibitions between the features. Moreover it has revealed that flankers suppress target in crowded displays, whereas flanker response is constant across conditions (Chicherov & Herzog, 2015).

4

ACCEPTED MANUSCRIPT This approach, however, fails to address the effect of critical spacing, one of the fundamental variable when studying visual crowding (Bouma, 1970; Whitney & Levi, 2011; Rosen, Chakravarthi, & Pelli, 2014). Spacing was instead varied by Chen et al. (2014). The authors – by using simple grating stimuli – found that crowding was reflected in the C1 component of the event-related

RI PT

potentials (ERPs). The C1 usually peaks 60-100 ms post-stimulus and is the earliest cortical potential elicited by a visual stimulus, which cortical generator is located in the primary visual area (V1) (Di Russo, Martinez, Sereno, Pitzalis, & Hillyard, 2002). Chen et al. (2014) performed also a fMRI experiment, showing that the only area exhibiting

SC

significant BOLD signal modulation relative to different crowding level was V1 (i.e., increased BOLD suppression under crowded condition). Thus, the authors concluded that crowding emerges at the earliest stage of the visual processing hierarchy. However, they

M AN U

did not test the possible effect of crowding in later ERP components. In their study, moreover, the arrangement of flankers position was not tightly controlled to ensure a balanced perceptual stimulation, as for example a previous fMRI study by Anderson et al. (2012) that used similar grating stimuli did. One possibility is that different configurations elicited different neural activation patterns in all retinotopic visual areas (V1-V4), consequently affecting early visual ERP components, well known to be sensible to

TE D

changes in basic features of visual stimuli, such as local variations of luminance and spatial frequency (e.g. Busch, Debener, Kranczioch, Engel, & Herrmann, 2004; Johannes, Munte, Heinze, & Mangun., 1995; Kenemans, Baas, Mangun, Lijffijt, & Verbaten, 2000;

EP

Regan, 1973). Finally, Anderson, Ester, Klee, Vogel, & Awh (2014) used EEG to explore spatial attention and substitution errors in crowding. However they did not provide direct investigation of the neural basis of the phenomenon.

AC C

The picture emerging from these neurophysiological studies is still unclear. In the present study we manipulated visual crowding for complex objects (i.e., letters) in peripheral visual field by varying the critical space between target and flankers. At the same time, we carefully controlled for changes in physical properties of the stimulus array. By employing dense-array EEG and with robust data-driven analysis, we aim to clarify if visual crowding for complex objects such as letters emerges at an early or late stage of visual processing. Moreover, we analyze event-related oscillatory response in the alpha, beta and gamma frequency range (alpha: 8-12 Hz, beta: 15-30 Hz, gamma: 30-80 Hz), which relationship 5

ACCEPTED MANUSCRIPT with visual crowding has never been investigated. Oscillatory activity at all these frequency bands have been previously associated to perceptual processes (Engel & Fries, 2010; Fries, 2009; Martinovic & Busch, 2011; Womelsdorf, Fries, Mitra, & Desimone, 2006; Tan, Lana, & Uhlhaas, 2013; Tallon-Baudry & Bertrand, 1999). Although their precise role is still matter of intense scientific investigation (see Jensen et al., 2015), the current general idea

RI PT

is that oscillatory activity at higher frequency (i.e., gamma) reflects feed-forward stimulus processing restricted to local neural ensembles, while activity modulation at lower frequency (i.e. alpha, alpha, beta) reflects feedback loops and large-scale cortical interactions (Bastos et al. 2015; Hipp, Engel, & Siegel, 2011; Kerkoerle et al.

2014;

SC

Schmiedt et al. 2014; van Zaretskaya & Bartels, 2015; for reviews see Donner & Siegel, 2011; Jensen et al., 2015). Thus, analyzing crowding-induced modulation in the oscillatory dynamics at different frequency bands can further help to clarify the neural mechanisms

M AN U

underlying crowding for complex objects.

2. Method 2.1 Participants

Twenty-five adult participants (11 male, mean age=26.16, age range=22-34) recruited at

TE D

the University of Padua, Italy, took part in the present study. Participants provided informed consent, had normal or corrected-to-normal vision and normal hearing. They reported no history of neurological disorders. The study was approved by the Ethics

EP

Committee of the Department of General Psychology at the University of Padua and conforms to the principles elucidated in the Declaration of Helsinki of 2013.

AC C

2.2 Stimuli

Participants sat in a dark room and viewed stimuli binocularly on a 19” LCD monitor with 60 Hz refresh rate. The viewing distance was set to 57 cm. Stimuli were displayed on a mid-level gray background, with 40 cd/m2 luminance. They were generated via Psychtoolbox for Matlab (Brainard, 1997) and consisted of 1.5 x 1.5 deg gaborized H-like, T-like or random configurations. Gabors that constituted each stimulus were built as the product of an oriented sinewave grating and a circular Gaussian window: 6

ACCEPTED MANUSCRIPT G(x,y) = e− (x2 + y2)/2σ2*cos [2π*(cosθ*x+sinθ*y)/s+p], Eq.1

In Eq. 1 the orientation θ could be either 0 deg for vertical and 90 deg for horizontal gabors. The phase of the sinusoid (p) was set on 90 deg. The spatial frequency (s) of the

deg. Stimuli were presented at full contrast (Michelson).

RI PT

elements was 2 c/deg and the standard deviation of the Gaussian envelope (σ) was 0.12

The use of gabor patches ensured control over local properties of the configurations, such as orientation, spatial frequency and contrast.

SC

On each trial, stimuli were built as follows. We first designed a matrix of the size of the stimulus. We divided it into a 5 x 5 grid of equally spaced x, y locations. For letter stimuli (Hs and Ts), gabors were placed along the path of the letter. We used 9 patches to form

M AN U

Ts and 13 to form Hs (see Figure 1B). Centre-to-centre distance between adjacent patches was kept constant at 0.3 deg. Patches could be both horizontal and vertical. In the random configurations thirteen x, y coordinates were randomly chosen within the grid. Gabors were then dropped on these selected locations. We refer to these configurations as “random gaps”. Random gaps were used in this study as “fillers” to ensure that variations in the critical spacing between target and flankers did not lead to

TE D

changes in physical properties of the stimulus configuration. Low-level properties might indeed confound modulations in EEG that are otherwise interpretable as crowding correlates. In other words, a variation in spacing or the addition of flankers induce changes

EP

in stimulation of the visual field (see Anderson et al., 2012, for similar considerations). Early ERPs reflect activity of areas that are affected by these changes (e.g. Busch et al., 2004; Johannes et al., 1995; Kenemans et al., 2000; Regan, 1973). Our fillers were

AC C

created to ensure a constant low-level stimulation across the different crowding conditions, even in the baseline where the target is usually presented in isolation (see Procedure). We created 6 random gaps, which were then rotated on plane of 90, 180 and 270 deg. This way we obtained 24 different random configurations to display through the experiment (see Procedure). Gabors in random gaps were either vertical or horizontal (see Figure 1A, 1B). We preferred iso-oriented to randomly oriented gabors for two reasons. First, we expected fixed orientation not to introduce variation in the local stimulus configuration and thus not to modulate early visual components. Second, it is known that a target embedded 7

ACCEPTED MANUSCRIPT in an iso-oriented background pops out (Kastner, Nothdurft, & Pigarevet, 1997; Knierim & Van Essen, 1992). Seven configurations (letters and fillers) were simultaneously presented at each trial. They were vertically arranged at 1.9 deg centre-to-centre distance (see Figure 1A). Overall the

RI PT

stimulus covered an area of 1.5 x 13.3 deg on the display.

2.3 Procedure

Testing occurred individually in a sound-attenuating and dimly lit room. We used E-Prime 2 (Psychology Software Tools, www.pstnet.com) to generate a rotation discrimination task,

SC

where participants had to indicate the rotation of a central target (T) presented at 11 deg of eccentricity and vertically flanked by irrelevant configurations. The T could have one of four possible rotations (0 to 270 deg in step of 90 deg). Rotation was randomly chosen at

M AN U

each trial. This paradigm is widely employed in crowding studies (e.g., Chakravarthi & Cavanagh, 2007, 2009; Tripathy & Cavanagh, 2002; Yeshurun & Rashal, 2010). Flankers were six (three on top and three on bottom) and were classified as “nearby”, “intermediate” and “far” according to their relative positions relative to the target. Flankers could be either Hs or random gaps (see Stimuli section). Also flankers were rotated (0 to 270 deg in step

trial.

TE D

of 90 deg) and rotations were randomly selected independently for each flanker on each

We tested three conditions that varied according to the order of the flankers. Note that the top and bottom far positions were always occupied by random gaps. In the ‘strong

EP

crowding’ condition, we placed H patterns as nearby top and bottom flankers and the remaining four positions were occupied by random gaps. We did the opposite in the ‘mid crowding’ condition, so that nearby top and bottom flankers were random gaps and

AC C

intermediate positions were occupied by Hs. In the ‘no crowding’ conditions, random gaps occupied nearby, intermediate and far positions (see Figure 1A). Procedure was as follows. A black fixation cross was centrally displayed for 2 sec. The stimulus configuration was then briefly flashed (54 ms) at 11 deg eccentricity either on the left or on the right of the fixation cross with equal probability with equal probability. A blank screen was then shown for 2 sec. Finally, we presented a response display showing the four possible T rotations and the corresponding response keys. One sec after response, a new trial started. The interval between stimulus presentation and collection of response 8

ACCEPTED MANUSCRIPT prevented motor artifacts to interfere within the temporal window of interest for EEG analysis. The use of a response display reduced participants’ memory load. Participants were asked to keep fixation on the central cross for the entire trial duration and report as accurately as possible the orientation of the target, without time constraints. A briefly presented picture of an eye reminded participants when blinking was allowed, i.e. during

RI PT

response display and inter-trial interval. We tested 140 trials per condition, so that each session contained 840 trials (420 trials for left and 420 trials for right stimulus presentation). The entire session was divided in several blocks in order to prevent fatigue.

SC

Left and right presentation and crowding conditions were randomized across trials.

2.4 EEG recording and pre-processing

M AN U

[Insert Figure 1 about here]

EEG was recorded using 58 tin electrodes (Fp1, FPz, Fp2, AF3, AF4, F7, F5, F3, F1, Fz, F2, F4, F6, F8, FC5, FC3, FC1, FCz, FC2, FC4, FC6, T7, C5, C3, C1, Cz, C2, C4, C6, T8, TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz, PO4, PO6, PO8, O1, Oz, O2) mounted on an elastic cap (Electro-

TE D

Cap International, Inc.; Eaton, OH), and from the two mastoids (M1, M2) and the nasion (Nz). All electrodes were online referenced to Cz. Moreover, vertical and horizontal electro-oculograms were recorded from electrodes placed below each eye and on the two

EP

external canthi. EEG was continuously recorded in the DC mode and stored for later analysis using NeuroScan software (NeuroScan Labs, Sterling, Virginia, USA). The sampling rate was 500 Hz and impedance was kept below 10 KΩ. The bandwidth of input

AC C

data ranged from DC to 100 Hz. Offline, data were down-sampled at 250 Hz, notch-filtered at 50 Hz, band-pass filtered between 0.1 and 80 Hz and recomputed to an average reference.

Data analysis was performed using Matlab (MathWorks, Inc., Natick, MA) and EEGLAB (Delorme & Makeig, 2004; http://www.sccnucsd.edu/eeglab). Epochs were initially segmented in 2.5 sec, 1 sec before and 1.5 sec after the stimulus onset. Spherical interpolation was carried out on individual bad channels if required (average number of interpolated channels=1.6, range=1-4). 9

ACCEPTED MANUSCRIPT 2.4.1 Data analysis - Event-related potential For event-related potentials (ERPs) analyses, epochs were low-pass filtered at 30 Hz and reduced to a time window of 700 ms, from -200 to 500 ms relative to the stimulus onset. Ocular artifacts were detected and removed using the Independent Component Analysis

RI PT

(ICA). Moreover, epochs containing voltage deviation that exceeds ±75 µV were also removed. Following this procedure, 4.41% of trials (range: 1-11.9%) were removed after artifact rejection.

In accordance to the procedure elucidated by Maris and Oostenveld (2007) and Groppe,

SC

Urbach, and Kutas (2011), we applied a robust non-parametric cluster-based permutation statistical approach that allowed us to detect reliable differences between the ERPs of the three experimental conditions (strong crowding, mid crowding, and no crowding) at all time

M AN U

points. This approach is data-driven and does not require to focus a priori on specific ERPs components (e.g., C1, P1, N1, etc.) and/or channels, but rather it allowed us to test point-by-point the significant differences between experimental conditions in the entire time range of interest (i.e., 0-500 ms after the target onset) and in all channels at the same time, while ensuring a proper control of multiple testing (Groppe et al., 2011; Maris & Oostenveld, 2007). To this aim, the ERPs from these conditions were submitted to a

TE D

repeated measures, two-tailed cluster mass permutation tests (Bullmore et al., 1999) using a family-wise alpha level of 0.05. All time points between 0 and 500 ms after the target onset at all 58 scalp electrodes were included in the test (i.e., 7250 total comparisons) and

EP

any electrodes within approximately 5.44 cm of one another were considered spatial neighbours. Repeated measures t-tests were performed for each comparison using the original data and 2500 random within-participant permutations of the data. 2500

AC C

permutations were used to estimate the distribution of the null hypothesis as it is over twice the number recommended by Manly (1997) for a family-wise alpha level of 0.05. For each permutation, all t-scores corresponding to uncorrected p-values of 0.05 or less were formed into clusters. The sum of the t-scores in each cluster is the "mass" of that cluster and the most extreme cluster mass in each of the 2501 sets of tests was recorded and used to estimate the distribution of the null hypothesis. Such permutation test analysis was used instead of more conventional mean amplitude ANOVAs because it provides much better spatial and temporal resolution while 10

ACCEPTED MANUSCRIPT maintaining sufficient control of the family-wise alpha level (i.e., it corrects for the large number of comparisons) (for details see Groppe et al., 2011; Maris & Oostenveld, 2007).

2.4.2 Data-analysis - Event-related oscillatory activity For the analysis of event-related oscillatory activity, the entire 2.5 sec epoch was used and

RI PT

data were collapsed across trials for left and right visual hemifield. Ocular artifacts were detected and removed with the same ICA-based procedure described above. Moreover, epochs containing voltage deviation that exceeds ±150 µV and epochs contaminated by muscular artefact were removed. Following this procedure 9.13% of trials (range: 1.54-

SC

20.83%) were removed after artifact rejection.

Then, time-frequency decompositions were performed for all 58 cortical channels with a complex Morlet wavelet analysis computed on the entire epoch using the newtimef()

M AN U

function (Delorme & Makeig, 2004). Baseline comprised all time points before the target onset. We used 3 cycles at the lowest frequency and 16 cycles at the highest frequency. These parameters provide estimates of the event-related spectrum perturbation (ERSP; i.e., changes in spectral power over time in the frequency range of interest) in 100 logspaced frequencies from 3 Hz up to 80 Hz in a 200 points time interval from -442 to 938 ms relative to the target onset.

TE D

We aimed to test ERSP in the mid and high frequency spectrum, specifically alpha (8-12 Hz), beta (15-30 Hz) and gamma (30-80 Hz). To detect reliable differences in these two frequency bands, we applied the same cluster-based permutation tests described above

EP

for the ERP analysis. Differences in power values among conditions were not analyzed point-by-point in this case as we did in the ERPs analyses. Rather, we analyzed ERSP data in the time range from 0 to 700-750 ms on all 58 channels by means of cluster-based

AC C

permutation tests in subsequent temporal windows of 250 ms for alpha and beta, and of 100 ms for gamma. Different time windows lengths for alpha and beta vs. gamma frequency bands were used because of the different ERSP profile, showing a fast response in the gamma band and a more long-lasting response in the alpha and beta band (see Figure 3B).

3. Results 3.1 Behavioral results 11

ACCEPTED MANUSCRIPT Response accuracies were submitted to a repeated-measure ANOVA with the following within-subject factors: Condition (strong crowding, mid crowding, no crowding,) and Visual Hemifield (left vs. right). The ANOVA revealed a significant main effect of Condition (F(2,48)= 217.47, p<.001; see Figure 1C). Mean accuracy rate in the strong, mid and no crowding conditions were 0.52 (SD=.08), 0.74 (SD=.14) and 0.92 (SD=.06), respectively.

RI PT

Post-hoc comparisons revealed that mean accuracies were significantly different between strong and mid crowding conditions (t(24)=9.95, p<.001), between strong and no crowding conditions (t(24)= 30.21, p<0.001) and between mid and no crowding conditions (t(24)=8.57, p<0.001). These results show that the distance between target and flankers effectively

SC

modulated crowding effect, impacting on target discrimination accuracy.

The main effect of the Visual Hemifield and the interaction were not significant.

M AN U

3.2 ERP results

One participant was excluded from ERP/ERSP analysis because of excessive alpha (8-12 Hz) activity.

Figure 2 shows the results of the point-by-point cluster-based permutation tests for each comparison (strong vs. mid crowding, strong vs. no crowding and mid vs. no crowding), as a function of the visual hemifield where the target appeared. Cluster-based correction were

TE D

applied for all the 58 channels, but for plotting purpose the averaged ERPs waveforms were calculated considering only parietal and occipital channels (TP7, CP5, CP3, CP1, CPz, CP2, CP4, CP6, TP8, P7, P5, P3, P1, Pz, P2, P4, P6, P8, PO7, PO5, PO3, POz,

EP

PO4, PO6, PO8, O1, Oz, O2), where ERP in response to a visual stimulus are expected to emerge. The complete set of channels showing cluster-corrected significant differences for each comparison can be seen in Figure 1C and 1F, where the topographical scalp maps

AC C

of each comparison are plotted at different time points in the 0-500 ms post-stimulus period. Furthermore, Supplementary Figures 1 (for left hemifield target) and 2 (for right hemifield target) show the ERPs of the three experimental conditions for all parietal and occipital channels.

While comparisons between strong and mid crowding for stimuli presented in the left visual hemifield and between mid and no crowding for stimuli presented in the right visual hemifield did not reveal any significant cluster-corrected difference in parietal and occipital

12

ACCEPTED MANUSCRIPT channels, all the other comparison are coherently showing a significant modulation of crowding that starts in the N1 time range (~240 ms) (see Figure 2).

RI PT

[Insert Figure 2 about here]

3.3 Oscillatory power of alpha, beta and gamma bands

Initially, variations of oscillatory activity evoked by the target were tested for left and right hemifield target separately. This analysis did not reveal any cluster-corrected significant

SC

difference as a function of the crowding condition. For this reason, we collapsed the data of the two hemifields.

In the strong vs. mid crowding comparison, which is the most relevant to elucidate the

M AN U

oscillatory correlates of visual crowding, an enhancement in gamma power was observed shortly after stimulus presentation (~100-200 ms post-stimulus; see Figure 3B). However, no significant cluster-corrected difference in gamma activity (30-80 Hz) emerged in any 100-ms time window from 0 to 700 ms after the stimulus onset. Thus, early gamma band variation was not influenced by the level of visual crowding in our paradigm. Alpha and beta activity variation was evident after the gamma enhancement (after ~200

TE D

ms), and was reflected in an overall power reduction relative to the baseline period. For alpha power reduction, however, no significant cluster-corrected difference emerged in any of the 250-ms time window from 0 to 750 ms after the stimulus onset. Importantly, a

EP

significant cluster-corrected difference between the strong and the mid crowding conditions emerged in the beta range in the time window that ranged from 250 to 500 ms. Specifically, in this time window, a cluster of central-right occipito-parietal channels (Oz,

AC C

O2, PO4, PO6, PO8, P6, P8) showed a greater beta power reduction in the strong relative to the mid crowding condition (see Figure 3A, 3B). In the strong vs. no crowding and in the mid vs. no crowding comparisons, no significant cluster-corrected differences emerged in any frequency band.

3.4 Relationship between oscillatory beta activity and performance on visual crowding We tested the relationship between modulation of beta band activity and behavioral performance in our paradigm. The individual beta power difference (∆-beta) in the strong13

ACCEPTED MANUSCRIPT mid crowding conditions (averaged in the time window from 250 to 500 ms) was extracted for each channel of the significant cluster. Moreover, an index of the individual behavioral performance was calculated as the Spearman's rank correlation coefficient (Spearman's rho) obtained from individual accuracies by using the single trial data of the three experimental conditions. Larger correlation coefficients (rho) correspond to behavioral

RI PT

performances that are more affected by visual crowding. A significant Pearson correlation between individual rho and ∆-beta values emerged for the channel O2 (r(24)=-.427, p=.037; see Figure 3C). This correlation shows that stronger beta power suppression (i.e., higher ∆-beta values) was related to higher rho values. In

SC

other words, larger difference of beta activity after stimulus onset corresponded to a behavioral performance largely affected by visual crowding.

M AN U

[Insert Figure 3 about here]

4. Discussion

Despite the large number of studies that investigated crowding, only recently efforts have directly attempted at finding its neural correlates. Investigation has been mainly carried out

TE D

with fMRI, but because of its low temporal resolution, no definitive conclusions can be drawn about evidence of visual crowding arising at the earliest levels of visual cortical processing (e.g., V1).

EP

In this debate, the high-temporal resolution of EEG can help to clarify if visual crowding emerges at early or later stages. The few EEG studies that were conducted on visual crowding to date (Chen et al., 2014; Chicherov et al. 2014, Chicherov & Herzog, 2015)

AC C

reported contradictory findings. Moreover, they focused only on ERPs and did not evaluate oscillatory activity, which can be an important measure to understand the crowding phenomenon and the mechanistic properties of its generation in the visual hierarchy. In the present study we used a classic letter crowding paradigm, manipulating the critical space between peripheral target and flankers. Nonetheless, we carefully assured a proper control of basic stimulus characteristics (i.e., local spatial frequency, contrast, luminance) so that the critical comparison between strong crowding (i.e., flankers positioned nearby the target) and mid crowding (i.e., flankers positioned far from the target) was not 14

ACCEPTED MANUSCRIPT confounded by changes in basic stimuli characteristics. Our behavioural results are perfectly fitting what predicted by visual crowding, with the strong crowding condition characterized by a lower accuracy relative to both the mid crowding and the no crowding conditions. Also, the mid crowding had lower accuracy relative to the no crowding condition.

RI PT

One may argue that participants used the orientation of the central stem of the T to guide their performance. However, letter stimuli strongly similar to those employed in the present study have been used in several previous works, with comparable accuracy data (e.g., Chakravarthi & Cavanagh, 2007, 2009; Tripathy & Cavanagh, 2002; Yeshurun & Rashal,

SC

2010). Moreover, the orientations of the target and of each flanker were randomly selected at every trial and were independent one from the other. Consequently, also possible configurational effects created by a specific target-flanker orientation were random. In

M AN U

sum, even though we cannot exclude that participants used some kind of strategy for the target discrimination, this should not impact on the neurophysiological response for the different crowding conditions.

Analyses of ERPs and oscillatory activity were conducted with a data-driven approach, where no (or minimum) a priori assumptions were made. ERPs showed that the first sign of a crowding-induced modulation of EEG activity was a suppression of the N1

TE D

component. For the right hemifield target, in particular, the N1 elicited by the stimulus presentation in parieto-occipital channels was significantly reduced in the strong crowding relative to both the mid and the no crowding condition. This result is consistent with the

EP

evidence of Chicherov et al. (2014) that, though with a different behavioral paradigm, equally demonstrated that the earliest signature of visual crowding was a suppression of the N1 component. Previous studies on texture segmentation (Bach & Meigen, 1992,

AC C

1997; Caputo & Casco, 1999; Fahle, Quenzer, Braun, & Spang, 2003) and contour detection (Machilsen & Wagemans, 2011; Mathes, Trenner, & Fahle, 2006; Shpaner, Molholm, Forde, & Foxe, 2013) typically found N1 suppression to be associated with the inability to segment a stimulus target from the background. Conversely, N1 amplitude is enhanced when a structure emerges (Kanizsa figures, Murray et al., 2002; Glass patterns, Pei, Pettet, Vildavski, & Norcia, 2005). Chicherov et al. (2014) conclude that the N1 modulation reflects grouping of target and flankers in crowded conditions. Finally, in these studies N1 was found to reflect activation in high level areas (Murray et al., 2002; Pei et 15

ACCEPTED MANUSCRIPT al., 2005; Shpaner et al., 2013). This is further in agreement with the idea of crowding being a late process. It is well known that N1 also shows facilitation by cued spatial attention (Hillyard & AnlloVento, 1998). However, in this manuscript we did not specifically manipulate attention, for example by giving observers endogenous or exogenous spatial cues. We also randomized

RI PT

the presentation of the stimuli in the left and right visual field, to prevent observers from actively pre-allocate spatial attention. Another question that our study did not address and that consequently may represent a possible limitation was the effect of the congruency strength between target and flankers, which is known to influence target letter identification

SC

and discrimination (Eriksen & Eriksen, 1974; vanLeeuwen & Lachmann, 2004; Plomp, van Leeuwen, & Ioannides, 2010). However, a previous magnetoencephalography (MEG) study by Plomp et al. (2010) asked their participants to categorize letters and

M AN U

pseudoletters presented along with surrounding information that differed in term of congruency. A negative component at N1 latency (localized in left cuneus) was found to reflect the presence of the context, regardless of congruency, and the authors attributed this to crowding.

Our ERPs data suggest that the neural processing of the target letter in crowded condition differed between the two hemifields. In particular, even for a left hemifield target

TE D

presentation, significant cluster of electrodes reflecting the level of crowding was found to be more lateralized on the posterior left scalp channels. We suggest that this evidence reflects the higher specialization of the ventral stream of the left hemisphere for processing

et al., 2007).

EP

small group of letters (i.e. even bigrams) as well as words (Dehaene et al., 2005; Vinckier

It is also interesting to observe that the different crowding levels impacted on the ERPs

AC C

until ~400 ms relative to the stimulus onset, with stronger crowding leading to increased amplitude for the second positive component (i.e., P2) visible at posterior electrodes. Previous studies showed that the visual P2 component is involved in visual short-term memory and working memory (Wolach & Prat, 2001; Lefebvre, Marchand, Eskes, & Connolly, 2005; Freunberger, Klimesch, Doppelmayr, & Höller, 2007). The generation of the P2 is related to theta-phase locking (Freunberger et al., 2007), which is largely recognized to have a fundamental role in memory processes (e.g. for reviews see Sauseng, Griesmayr, Freunberger, & Klimesch, 2010). Thus, our data suggest that an 16

ACCEPTED MANUSCRIPT increased memory demand was elicited for conditions where crowding was stronger. P2 with different amplitude was also found in perceptual paradigms. Machilsen, Novitskiy, Vancleef, & Wagemans (2011) found lower P2 amplitude when a target contour poppedout from the background. Amplitude increased instead with noise-only display. Similarly, Straube and Fahle (2010) found smaller amplitude with detectable figures embedded in

RI PT

noise. The authors agree that the posterior P2 is inversely correlated to perceptual saliency. Consistently, this component seems to be enhanced when the target is relatively infrequent (Luck & Hillyard, 1994). These considerations are in agreement with the typical crowding-induced drop in target discrimination accuracy that we also observed in our

SC

behavioural data.

On the contrary, our findings are inconsistent with results from Chen et al. (2014), which highlighted the emergence of crowding in a very early stages of cortical stimulus

M AN U

processing reflected in the C1 component (~80 ms). We suggest that discrepancy between Chen et al. and the present results can be attributed to two different reasons. The first possibility is that their evidence could be due to the fact that the authors did not control for basic changes in stimuli characteristics that are known to affect early ERP components, as a previous fMRI study using Gabor stimuli by Anderson et al. (2012) did. The second possibility is that crowding is not a unitary phenomenon and thus occurs at

TE D

multiple levels in the visual system depending on the nature of stimuli employed. According to this hypothesis crowding would affect Gabor stimuli at early stages, while crowding of complex objects (such as the letters used in the present study) occurs at

EP

higher stages. The question remains still open for the former category of visual input. Conversely, results of the present study – where crowding for letters was measured while controlling for potential low-level confounding factors – clearly suggest a form of crowding

AC C

for complex objects that arises at later stages in the visual processing hierarchy. Analysis of oscillatory activity also confirmed that visual crowding was reflected in neural processing at a later stage in the visual hierarchy. While we observed an initial enhancement in gamma activity shortly after stimulus presentation, this fast response did not differ between the strong and the mid crowding conditions. Interestingly, we observed a general suppression in the alpha and beta band activity after the stimulus presentation. However, only power reduction in the beta band, contrarily to the alpha, reflected the amount of visual crowding. Specifically, the strong crowding condition was characterized 17

ACCEPTED MANUSCRIPT by a stronger reduction in the beta band relative to the mid crowding condition starting from ~200 ms post-stimulus. This is the first result – to the best of our knowledge – that unveils the oscillatory correlates of visual crowding. Evidence of beta activity suppression as an oscillatory index of visual crowding is also confirmed by the correlation that we found between the difference in beta power (i.e., ∆-beta) and the overall individual influence in

RI PT

performance caused by crowding (i.e. individual rho values of the correlation between response accuracies and crowding condition). Specifically, larger suppression of beta activity after stimulus onset (i.e., larger ∆-beta indexes) was related to behavioral performances more affected by visual crowding (i.e., higher rho values).

SC

It is worth noticing that the estimation of gamma activity during EEG recording can be compromised by non-cortical activity such as muscular artefacts and micro-saccades (Fries, Scheeringa, & Oostenveld, 2008). However, according to Hipp and Siegel (2013),

M AN U

the combination of rejection of epochs containing muscular activity and correction of ocular artefacts by ICA that we used in the present study has been shown to be useful in removing such non-cortical gamma activity.

Although the precise role of alpha, beta and gamma band modulation in visual processing is still unclear and it is an active area of research at present (see Jensen et al., 2015), high-frequency oscillatory activity (i.e., gamma) may reflect feed-forward stimulus

TE D

processing restricted to local neural ensembles, while oscillatory activity modulation at lower frequency (i.e. alpha and beta) may reflect feedback signals and large-scale cortical interaction (Bastos et al. 2015; Hipp et al. 2011; Schmiedt et al. 2014; van Kerkoerle et al.

EP

2014; Zaretskaya & Bartels 2015; for reviews see Donner & Siegel, 2011; Jensen et al., 2015). It is thus reasonable to speculate that our evidence of a crowding-related suppression of beta oscillations is reflecting the interaction of large-scale cortical networks,

AC C

possibly through feedback signals from higher order visual cortical areas or top-down fronto-parietal attentional network. Interestingly, a recent study by Zaretskaya and Bartels (2015) that used bistable motion stimuli found a reduction of power in the beta-band during global as compared to local perception. Source localization methods localized this modulation to the posterior parietal cortex, suggesting that part of the mechanisms of spatial binding and visual attention are shared, as both involve neural activity of the posterior parietal cortex.

18

ACCEPTED MANUSCRIPT Future studies are required to characterized the precise mechanistic role of beta power suppression. One possibility is that this power suppression is reflecting increased synchronization in cortical nodes internal, and possibly also external, to the visual processing stages, as demonstrated by previous studies (e.g. Hipp et al., 2011). In conclusion, in the present study we modulated visual crowding of complex objects (i.e.,

RI PT

letters) and at the same time we avoided confounders caused by manipulation of visual stimuli. We found that differences related to crowding started to be evident in a suppression of the N1 ERP component and in a beta power reduction, both occurring in comparable time windows. These findings are congruent with the idea that crowding – at

SC

least for complex visual stimuli – emerges at later stages of the visual processing hierarchy, possibly reflecting large-scale cortical interaction from higher- to lower- level visual areas or top-down modulation from the fronto-parietal attentional network. It remains

M AN U

to be clarified whether this is a common mechanism that can be generalized to stimuli of different levels of complexity or whether multiple neural mechanisms of crowding exist. It remains to be clarified whether this is a common mechanism that can be generalized to stimuli of different levels of complexity or whether multiple neural mechanisms of crowding

AC C

EP

TE D

exist.

19

ACCEPTED MANUSCRIPT Acknowledgements We are deeply thankful to Andrea Facoetti for helpful discussion on the topic and to Chiara

AC C

EP

TE D

M AN U

SC

RI PT

Spironelli for her technical assistance with EEG.

20

ACCEPTED MANUSCRIPT

Figure legends Figure 1. Representation of the experimental procedure and behavioral results. Panel A shows three examples of stimuli arrays for the strong, mid and no crowding conditions. These array could appear on left or right side of the fixation at 11 deg of eccentricity from a

RI PT

central fixation point and subjects had to report the orientation of the T-like target stimulus. Panel B shows three examples of the stimuli employed. Panel C represents the behavioral results, where accuracy rate are plotted as a function of crowding condition (*=p<.001,

SC

bars represent SEM).

Figure 2. N1 ERP component is the first that shows a significant crowding related

M AN U

modulation. Event-related potentials (ERPs) results for right (Panels A, B, C) and left hemifield target (Panels D, E, F). Panels A, B, D and E represent ERP waveforms obtained by averaging the signal from all the posterior (parietal and occipital, see Results) channels that show a significant difference between conditions. The gray box below the xaxis shows the time points for each posterior channel where the cluster-corrected permutation tests revealed a significant difference between conditions. Panels C and F

TE D

show the time course of the differential topographical maps at different time points, with the channels showing a significant difference highlighted in white.

Figure 3. Beta band (15-30 Hz) oscillatory power reflects the amount of visual crowding.

EP

Panel A shows the differential maps in each 250 ms time window for beta power, with significant channels highlighted in white. Panel B shows the results of the time-frequency

AC C

analysis for the strong and mid crowding condition for one representative channel of the cluster (O2), where the mid- and high-frequency (alpha, beta and gamma) spectrum variation in oscillatory power relative to the baseline period (before target onset) can be observed. Panel C represents the scatter plot of the correlation between the individual beta power difference (∆-beta, y-axis) in the strong-mid crowding conditions (250-500 ms, channel O2) and the individual rho values obtained from the correlation of the single-trial individual accuracy data with the three crowding conditions (x-axis).

21

ACCEPTED MANUSCRIPT References Anderson, D. E., Ester, E. F., Klee, D., Vogel, E. K., & Awh, E. (2014). Electrophysiological evidence for failures of item individuation in crowded visual displays. Journal of Cognitive Neuroscience, 26(10), 2298-2309.

RI PT

Anderson, E. J., Dakin, S. C., Schwarzkopf, D. S., Rees, G., & Greenwood, J. A. (2012). The neural correlates of crowding-induced changes in appearance. Current Biology, 22(13), 1199-1206.

SC

Bach, M., & Meigen, T. (1992). Electrophysiological correlates of texture segregation in the human visual evoked potential. Vision Research, 32(3), 417-424.

M AN U

Bach, M., & Meigen, T. (1997). Similar electrophysiological correlates of texture segregation induced by luminance, orientation, motion and stereo. Vision Research, 37(11), 1409-1414.

Baldassi, S., Pei, F., Megna, N., Recupero, G., Viespoli, M., Igliozzi, R., . . . Cioni, G.

TE D

(2009). Search superiority in autism within, but not outside the crowding regime. Vision Research, 49(16), 2151-2156.

Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J. M., Oostenveld, R., Dowdall, J. R.,

EP

. . . Fries, P. (2015). Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron, 85(2), 390-401.

AC C

Bi, T., Cai, P., Zhou, T., & Fang, F. (2009). The effect of crowding on orientation-selective adaptation in human early visual cortex. Journal of Vision, 9(11), 13.1-1310. Bouma, H. (1970). Interaction effects in parafoveal letter recognition. Nature, 226, 177178.

Bouma, H., & Legein, C. P. (1977). Foveal and parafoveal recognition of letters and words by dyslexics and by average readers. Neuropsychologia, 15(1), 69-80. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433-436. 23

ACCEPTED MANUSCRIPT Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999). Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging, 18(1), 32-42.

RI PT

Busch, N. A., Debener, S., Kranczioch, C., Engel, A. K., & Herrmann, C. S. (2004). Size matters: Effects of stimulus size, duration and eccentricity on the visual gamma-band response. Clinical Neurophysiology, 115(8), 1810-1820.

SC

Caputo, G., & Casco, C., (1999). A visual evoked potential correlate of global figureground segmentation. Vision Research, 39, 1597–1610.

M AN U

Chakravarthi, R., & Cavanagh, P. (2007). Temporal properties of the polarity advantage effect in crowding. Journal of Vision, 7(2), 11.1-13.

Chakravarthi, R., & Cavanagh, P. (2009). Bilateral field advantage in visual crowding. Vision Research, 49(13), 1638-1646.

TE D

Chakravarthi R., & Pelli, D. G. (2011). The same binding in contour integration and crowding. Journal of Vision , 11(8):10, 1–12. Chen, J., He, Y., Zhu, Z., Zhou, T., Peng, Y., Zhang, X., & Fang, F. (2014). Attention-

EP

dependent early cortical suppression contributes to crowding. The Journal of Neuroscience, 34(32), 10465-10474.

AC C

Chicherov, V., & Herzog, M. H. (2015). Targets but not flankers are suppressed in crowding as revealed by EEG frequency tagging. Neuroimage, in press. doi: 10.1016/j.neuroimage.2015.06.047 Chicherov, V., Plomp, G., & Herzog, M. H. (2014). Neural correlates of visual crowding. Neuroimage, 93(1), 23-31.

24

ACCEPTED MANUSCRIPT Dehaene, S., Cohen, L., Sigman, M., & Vinckier, F. (2005). The neural code for written words: a proposal. Trends in Cognitive Sciences, 9(7), 335-41. Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of singletrial EEG dynamics including independent component analysis. Journal of

RI PT

Neuroscience Methods, 134(1), 9-21.

Di Russo, F., Martinez, A., Sereno, M. I., Pitzalis, S., & Hillyard, S. A. (2002). Cortical sources of the early components of the visual evoked potential. Human Brain

SC

Mapping, 15(2), 95-111.

Donner, T. H., & Siegel, M. (2011). A framework for local cortical oscillation patterns.

M AN U

Trends in Cognitive Sciences, 15(5), 191-199.

Engel, A. K., & Fries, P. (2010). Beta-band oscillations--signalling the status quo? Current Opinion in Neurobiology, 20(2), 156-165.

Eriksen, B., & Eriksen, C. (1974). Effects of noise letters upon the identification of a target

Fahle,

M.,

Quenzer,

TE D

letter in a nonsearch task. Perception & Psychophysics, 16, 143–149. T.,

Braun,

C.,

&

Spang,

K.

(2003).

Feature-specific

electrophysiological correlates of texture segregation. Vision Research, 43(1), 7-19.

EP

Fang, F., & He, S. (2008). Crowding alters the spatial distribution of attention modulation in human primary visual cortex. Journal of Vision, 8(9), 6.1-9.

AC C

Freeman, J., Donner, T. H., & Heeger, D. J. (2011). Inter-area correlations in the ventral visual pathway reflect feature integration. Journal of Vision, 11(4), 10. Freunberger, R., Klimesch, W., Doppelmayr, M., & Höller, Y. (2007). Visual P2 component is related to theta phase-locking. Neuroscience Letters, 426(3), 181-186. Fries, P., Scheeringa, R., & Oostenveld, R. (2008). Finding gamma. Neuron, 58(3), 303305. 25

ACCEPTED MANUSCRIPT Fries, P. (2009). Neuronal gamma-band synchronization as a fundamental process in cortical computation. Annual Review of Neuroscience, 32, 209-224. Gori, S., & Facoetti, A. (2015). How the visual aspects can be crucial in reading acquisition? The intriguing case of crowding and developmental dyslexia. Journal of

RI PT

Vision, 15(1), 8.

Greenwood, J. A., Bex, P. J., & Dakin S. C. (2010). Crowding changes appearance. Current Biology, 20, 496–501.

SC

Groppe, D. M., Urbach, T. P., & Kutas, M. (2011). Mass univariate analysis of eventrelated brain potentials/fields I: A critical tutorial review. Psychophysiology, 48(12),

M AN U

1711-1725.

Hillyard, S. A., & Anllo-Vento, L. (1998). Event-related brain potentials in the study of visual selective attention. Proceedings of the National Academy of Sciences of the United States of America, 95(3), 781–787.

TE D

Hipp, J. F., Engel, A. K., & Siegel, M. (2011). Oscillatory synchronization in large-scale cortical networks predicts perception. Neuron, 69(2), 387-396. Hipp, J. F., & Siegel, M. (2013). Dissociating neuronal gamma-band activity from cranial

EP

and ocular muscle activity in EEG. Frontiers in human neuroscience, 7. Jensen, O., Bonnefond, M., Marshall, T. R., & Tiesinga, P. (2015). Oscillatory mechanisms

AC C

of feedforward and feedback visual processing. Trends in Neurosciences, 38(4), 192194.

Johannes, S., Munte, T. F., Heinze, H. J., & Mangun, G. R. (1995). Luminance and spatial attention effects on early visual processing. Brain Research.Cognitive Brain Research, 2(3), 189-205.

26

ACCEPTED MANUSCRIPT Kastner, S., Nothdurft, H. C., & Pigarev, I. N. (1997). Neuronal correlates of pop-out in cat striate cortex. Vision Research, 37(4), 371-376. Keita, L., Mottron, L., & Bertone, A. (2010). Far visual acuity is unremarkable in autism: Do we need to focus on crowding? Autism Research, 3(6), 333-341.

RI PT

Kenemans, J. L., Baas, J. M., Mangun, G. R., Lijffijt, M., & Verbaten, M. N. (2000). On the processing of spatial frequencies as revealed by evoked-potential source modeling. Clinical Neurophysiology, 111(6), 1113-1123.

SC

Knierim, J. J., & Van Essen, D. C. (1992). Neuronal responses to static texture patterns in area V1 of the alert macaque monkey. Journal of Neurophysiology, 67(4), 961-980.

M AN U

Kwon, M., Bao, P., Millin, R., & Tjan, B. S. (2014). Radial-tangential anisotropy of crowding in the early visual areas. Journal of Neurophysiology, 112(10), 2413-2422. Lefebrve, C. D., Marchand, Y., Eskes, G.A., & Connolly, J. A. (2005). Assessment of working memory abilities using an event-related brain potential (ERP)-compatible digit

TE D

span backward task. Clinical Neurophysiology, 116(7), 1665-1680. Liu, T., Jiang, Y., Sun, X., & He, S. (2009). Reduction of the crowding effect in spatially adjacent but cortically remote visual stimuli. Current Biology, 19(2), 127-132.

EP

Luck, S. J., & Hillyard, S. A. (1994). Electrophysiological correlates of feature analysis during visual search. Psychophysiology, 31, 291–308.

AC C

Machilsen, B., Novitskiy, N., Vancleef, K., & Wagemans, J. (2011). Context modulates the ERP signature of contour integration. PLoS One, 6, e25151. Machilsen, B., & Wagemans, J. (2011). Integration of contour and surface information in shape detection. Vision Research, 51(1), 179-186. Malania, M., Herzog, M. H., & Westheimer, G. (2007). Grouping of contextual elements that affect vernier thresholds. Journal of Vision, 7(2), 1.1-7. 27

ACCEPTED MANUSCRIPT Manassi, M., Sayim, B., & Herzog, M. H. (2012). Grouping, pooling, and when bigger is better in visual crowding. Journal of Vision, 12(10), 13. Manly B.F.J. (1997). Randomization, bootstrap, and Monte Carlo methods in Biology. (2th ed.). London:Chapman & Hall.

data. Journal of Neuroscience Methods, 164(1), 177-190.

RI PT

Maris, E., & Oostenveld, R. (2007). Nonparametric statistical testing of EEG- and MEG-

Martelli, M., Di Filippo, G., Spinelli, D., & Zoccolotti, P. (2009). Crowding, reading, and

SC

developmental dyslexia. Journal of Vision, 9(4), 14.1-18.

Martinovic, J., & Busch, N. A. (2011). High frequency oscillations as a correlate of visual

M AN U

perception. International Journal of Psychophysiology, 79(1), 32-38. Mathes, B., Trenner, D., & Fahle, M. (2006). The electrophysiological correlate of contour integration is modulated by task demands. Brain Research, 1114(1), 98-112. Millin, R., Arman, A. C., Chung, S. T., & Tjan, B. S. (2014). Visual crowding in V1.

TE D

Cerebral Cortex, 24(12), 3107-3115.

Motter, B. C. (2006). Modulation of transient and sustained response components of V4 neurons by temporal crowding in flashed stimulus sequences. The Journal of

EP

Neuroscience, 26(38), 9683-9694.

Murray, M. M., Wylie, G. R., Higgins, B. A., Javitt, D. C., Schroeder, C. E., & Foxe, J. J.

AC C

(2002). The spatiotemporal dynamics of illusory contour processing: Combined highdensity electrical mapping, source analysis, and functional magnetic resonance imaging. The Journal of Neuroscience, 22(12), 5055-5073. Pei, F., Pettet, M. W., Vildavski, V. Y., & Norcia, A. M. (2005). Event-related potentials show configural specificity of global form processing. Neuroreport, 16(13), 1427-1430.

28

ACCEPTED MANUSCRIPT Pelli, D. G., & Tillman, K. A. (2008). The uncrowded window of object recognition. Nature Neuroscience, 11(10), 1129-1135. Pelli, D. G. (2008). Crowding: A cortical constraint on object recognition. Current Opinion in Neurobiology, 18(4), 445-451.

RI PT

Plomp, G., van Leeuwen, C., & Ioannides, A. A. (2010). Functional specialization and dynamic resource allocation in visual cortex. Human Brain Mapping, 31(1): 1-13. Regan, D. (1973). Evoked potentials specific to spatial patterns of luminance and colour.

SC

Vision Research, 13(12), 2381-2402.

Rosen, S., Chakravarthi, R., & Pelli, D. G. (2014). The Bouma law of crowding, revised

M AN U

Critical spacing is equal across parts, not objects. Journal of Vision, 14(6):10, 1–15. Sauseng, P., Griesmayr, B., Freunberger, R., & Klimesch, W. (2010). Control mechanisms in working memory: a possible function of EEG theta oscillations. Neuroscience & Biobehavioral Reviews, 34(7), 1015-1022.

TE D

Sayim, B., & Cavanagh, P. (2013). Grouping and crowding affect target appearance over different spatial scales. PLoS ONE, 8(8), e71188. Schmiedt, J. T., Maier, A., Fries, P., Saunders, R. C., Leopold, D. A., & Schmid, M. C.

EP

(2014). Beta oscillation dynamics in extrastriate cortex after removal of primary visual cortex. The Journal of Neuroscience, 34(35), 11857-11864.

AC C

Shpaner, M., Molholm, S., Forde, E., & Foxe, J. J. (2013). Disambiguating the roles of area V1 and the lateral occipital complex (LOC) in contour integration. Neuroimage, 69, 146-156.

Straube S., & Fahle, M. (2010). The electrophysiological correlate of saliency: Evidence from a figure-detection task. Brain Research, 1307, 89–102.

29

ACCEPTED MANUSCRIPT Tallon-Baudry, C., & Bertrand, O. (1999). Oscillatory gamma activity in humans and its role in object representation. Trends in Cognitive Sciences, 3(4), 151-162. Tan, H. M., Lana, L., & Uhlhaas, P. J. (2013). High-frequency neural oscillations and visual processing deficits in schizophrenia. Frontiers in Psychology, 4-621. F.

(2003).

Primary

visual

cortex

Reviews.Neuroscience, 4(3), 219-229.

and

visual

awareness.

RI PT

Tong,

Nature

Tripathy, S. P., & Cavanagh, P. (2002). The extent of crowding in peripheral vision does

SC

not scale with target size. Vision Research, 42(20), 2357-2369.

van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M. A., Poort, J., van der Togt,

M AN U

C., & Roelfsema, P. R. (2014). Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 111(40), 14332-14341. van Leeuwen, C., & Lachmann, T. (2004). Negative and positive congruence effects in

TE D

letters and shapes. Perception & Psychophysics 6, 908–925. Vinckier, F., Dehaene, S., Jobert, A., Dubus, J. P., Sigman, M., & Cohen, L. (2007). Hierarchical coding of letter strings in the ventral stream: dissecting the inner

EP

organization of the visual word-form system. Neuron, 55(1), 143-56. Whitney, D., & Levi, D. M. (2011). Visual crowding: A fundamental limit on conscious

AC C

perception and object recognition. Trends in Cognitive Sciences, 15(4), 160-168. Wolach, I., & Pratt, H. (2001). The mode of short-term memory encoding as indicated by event-related potentials in a memory scanning task with distractions. Clinical Neurophysiology, 112(1), 186-197.

30

ACCEPTED MANUSCRIPT Womelsdorf, T., Fries, P., Mitra, P. P., & Desimone, R. (2006). Gamma-band synchronization in visual cortex predicts speed of change detection. Nature, 439(7077), 733-736. Yeshurun, Y., & Rashal, E. (2010). Precueing attention to the target location diminishes

RI PT

crowding and reduces the critical distance. Journal of Vision, 10(10), 16.

Zaretskaya, N., & Bartels, A. (2015). Gestalt perception is associated with reduced parietal beta oscillations. Neuroimage, 112, 61-69.

SC

Zorzi, M., Barbiero, C., Facoetti, A., Lonciari, I., Carrozzi, M., Montico, M., . . . Ziegler, J. C. (2012). Extra-large letter spacing improves reading in dyslexia. Proceedings of the

M AN U

National Academy of Sciences of the United States of America, 109(28), 11455-

AC C

EP

TE D

11459.

31

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT

AC C

EP

TE D

M AN U

SC

RI PT

ACCEPTED MANUSCRIPT