Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Authors: Shashi Kant Gupta
Mengmi Zhang
Chia-Chien Wu
Jeremy M. Wolfe
Gabriel Kreiman
Venue: NeurIPS 2021 Poster (2021)
Keywords: visual search
asymmetry
deep learning
recognition
neuroscience
eye movement
visual cortex
psychophyiscs
human behavior
visual cognition
inductive bias
natural statistics

Abstract: Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys at home or a friend in a crowd. An intriguing property of some classical search tasks is an asymmetry such that finding a target A among distractors B can be easier than finding B among A. To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and a search image as inputs and produces a sequence of eye movements until the target is found. The model integrates eccentricity-dependent visual recognition with target-dependent top-down cues. We compared the model against human behavior in six paradigmatic search tasks that show asymmetry in humans. Without prior exposure to the stimuli or task-specific training, the model provides a plausible mechanism for search asymmetry. We hypothesized that the polarity of search asymmetry arises from experience with the natural environment. We tested this hypothesis by training the model on augmented versions of ImageNet where the biases of natural images were either removed or reversed. The polarity of search asymmetry disappeared or was altered depending on the training protocol. This study highlights how classical perceptual properties can emerge in neural network models, without the need for task-specific training, but rather as a consequence of the statistical properties of the developmental diet fed to the model. All source code and data are publicly available at https://github.com/kreimanlab/VisualSearchAsymmetry.

Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases
Shashi Kant Gupta', Mengmi Zhang Chia-Chien Wu Jeremy M. Wolfe", and Gabriel Kreiman 2.3
Indian Institute of Technology Kanpur, India Children's Hospital, Harvard Medical School Centte for Brains, Minds and Machines
A Brigham and Women's 's Hospital, Harvard Medical School Address correspondence to gabriel.kreiman @tthhharvard.cdu
Abstract
Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys home or friend in crowd. An intriguing property of some classical search tasks is an asymmetry such that finding a target A among distractors B be easier than finding B among A. To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and search image as inputs and produces a sequence of eye movements until the target is found. The model integrates eccentricity -eeetricityydeeeddeett visual recognition with target-dependent top-down cues. We compared the model against human behavior in six paradigmatic search tasks that show asymmetry humans. Without prior exposure to the stimuli or task-specific training, the model provides a plausible mechanism for search asymmetry. We hypothesized that the polarity of search asymmetry arises from experience with the natural environment. We tested this hypothesis by training the model augmented versions of ImageNet where the biases natural images were either removed or reversed. The polarity of search asymmetry disappeared or was altered depending on the training protocol. This study highlights how classical perceptual properties emerge in neural network models, without the need for task task-specific training, but rather as a consequence diet fed to the model. All source code and data are publicly available at https; //github. com imwnlalaaa/aaaaaaaaaaaaaaaaabaaa wwwpanaaaaiiiilllleeaaalalllee wuw.lsearchhsymeet...
the statistical properties of the developmental
Introduction
Humans and other primates continuously move their eyes in search of objects, food, friends. Psychophysical studies have documented how visual search depends on the complex interplay between
the target objects, search images, and the subjects' memory and attention 44 29
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[8]
30).
There has also heen progress in describing the neurophysiologica steps involved visual processing
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and the neural circuits that orchestrate attention and eye movements 14
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paradigmatic and intriguing effect visual search asymmetry: searching for an object, A, amidst other objects, B, can be substantially easier than searching for object B amongst instances of A. For example, detecting a curved line among straight lines is faster than searching for a straight line among curved lines. Search asymmetry has been observed in wide range of tasks [48] 22 46
50 43
Despite extensive phenomenological characterization 34 13 8 the mechanisms underlying how neural representations guide visual search and lead to search asymmetry remain mysterious.
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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51 25 we developed an image- Inspired by prior works on visual search modelling 77 21 19 computable model (eccNET) to shed light on the fundamental inductive biases inherent to neural computations during visual search. The proposed model combines and top-down modulation through target-dependent attention. In contrast to deep convolutional neural networks (CNNs) that assume uniform resolution over all locations, we introduce eccentricity- dependent pooling layers in the visual recognition processor, mimicking the separation between fovea and periphery in the primate visual system. As the task-dependent target information is essential during visual search 14 3 49 5T, the model stores the target features and uses them in a top-down fashion to modulate unit activations in the search image, generating sequence of eye movements until the target is found.
sampling,
We examined six foundational psychophysics experiments showing visual search asymmetry and tested eccNET on the same stimuli. Importantly, the model was pre-trained for object classification on ImageNet and was not trained with the target or search images, or with human visual search data. The model spontaneously revealed visual search asymmetry and qualitatively matched the polarity of human behavior. We tested whether asymmetry arises from the natural statistics seen during object classification tasks. When eccNET was trained from scratch on an augmented ImageNet with altered stimulus statistics. e.g., rotating the images by 90 degrees, the polarity of search asymmetry disappeared or was modified. This demonstrates that, in addition to the model's 's architecture, inductive biases in the developmental training set also govern complex visual behaviors. These observations are consistent with, and build bridges between, psychophysics observations in visual search studies
35 40 7 2 g and analyses of behavioral biases of deep networks 28 53 33
2
Psychophysics Experiments in Visual Search Asymmetry
We studied six visual search asymmetry psychophysics experiments 48 Subjects searched for target intermixed with distractors in search array (see Appendix A for stimulus details). There were target-present and target-absent trials; subjects had to press one of two keys to indicate whether the target was present or not.
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(Figure [].
Experiment (Curvature) involved two conditions (Figure IA) [48]: searching for (a) a straight line among curved lines, and (b) curved line among straight lines. The target and distractors were presented in any of the four orientations: -45, 0, 45, and 90 degrees.
Experiment 2 (Lighting Direction) involved two conditions (Figure T BB) [22]: searching for (a) left-right luminance changes among right-left luminance changes, and (b) top-down luminance changes among down-top luminance changes. There were 17 intensity levels.
Experiments 3-4 (Intersection) involved four conditions (Figure IC-D) [4] searching for (a)
cross among non-crosses, (b) a non-cross among crosses, (c) rotated L among rotated Ts, and (d) rotated T among rorated Ls. The objects were presented any of the four orientations: 0, 90, 180, and 270 degrees.
searching for (a) vertical line among 20-degrees-tileer lines, (b) a 20-degree-tilees tilte line among vertical straight lines, (c) 20-degree tilted line among tilted lines from -80 to 80 degrees, and (d) vertical straight line among tilted lines from -80 to 80 degrees.
Experiments 5-6 (Orientation) involved four conditions (Figure TE-F) 477:
3 Eccentricit-deeendeent network (eccNET) model
schematic of the proposed visual search model is shown in Figure eccNET takes two inputs: a target image (4e, object to search) and search image (4,, where the target object is embedded amidst distractors). eccNET starts fixating on the center of 4, and produces a sequence of fixations. eccNET uses pre-trained 2D-CNN as proxy for the ventral visual cortex, to extract eccentricityddependee visual features from I and I,. At each fixation n, these features are used to calculate top-down attention map (A,,). A winner-take-all mechanism selects the maximum of the attention map A, as the location for the n + 1-th fixation. This process iterates until eccNET finds the target with a total of N fixations. eccNET has infinite inhibition of return and therefore does not revisit previous locations. Humans do not have perfect memory and re-visit previously fixated locations (52). However, in the 6 experiments considered here N is small; therefore, the probability of return fixations is small

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Figure Schematic illustration of the experiments for asymmetry in visual search. Each experiment has two conditions; in each condition, an example search image (left) and target image (right) are shown. The target image is smaller than the search image (see scale bars below each image). A. Experiment Searching for curve among lines and vice versa ((48). B. Experiment Searching for vertical luminance changes among horizontal luminance changes and vice versa ((22)) C. Experiment 3. Searching for shapes with no intersection among crosses and vice versa Searching for rotated Ts among rotated Ls and vice versa ((46)). E. Experiment 5. Searching for oblique lines with fixed angles among vertical lines and vice versa (. F. Experiment Similar Experiment 5 but using oblique lines of different orientations
). D. Experiment
[47
In all cases, subjects find the target faster in the condition on the right.
and the effect of limited working memory capacity negligible. eccNET needs verify whether the target is present at each fixation. Since we focus here on visual search (target localization instead of recognition), we simplify the problem by bypassing the target verification step and using an "oracle" recognition system (26). The oracle checks whether the selected fixation falls within the ground truth target location, defined as bounding box of the target object. We only consider target-present trials for the model and therefore eccNET will always find the target.
Compared with the invariant visual search network (IVSN, [T highlight novel components in eccNET, which we show to be critical in Section
have uniform receptive field sizes (pooling window sizes) within each layer. In stark contrast, visual cortex shows strong eccentricity- dependent receptive field sizes. Here we introduced eccentricity-depente replacing all the max-pooling layers in VGG16.
Standard 2D-CNNs, such as VGG16
pooling layers,
2. Visual search models compute an attention map to decide where to fixate next. The attention map in IVSN did not change from one fixation the next. Because the visual cortex module manner, here the
in eccNET changes with the fixation location in an attentional maps (A,) are updated at each fixation n.
3. In contrast IVSN where the top-down modulation happens only in single layer, eccNET combines top-down modulated features across multiple layers.
Receptive field sizes in visual cortex increase from one brain area to the next (Figure3B. right). This increase is captured by current visual recognition models through pooling operations. addition, receptive field sizes also increase with eccentricity within given visual area [16] (Figure 3 right). Current 2D-CNNs assume uniform sampling within a layer and do not reflect this eccentricity dependence. contrast, we introduced eccentricity deentricity-dependen pooling layers in eCcNET. Several psychophysics observations g 35 and
Recentricity-deeendeer pooling visual cortex in eccNET.

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Figure 2: Schematic of the computational model of visual search. The model takes input larget image (4,) and search image (I,), both of which are processed through the same 2D-CNN with shared weights. At each fixation n, the model produces a top-down modulation map (A,) that directs the next eye movement (Section The color bar on the right denotes attention values. Instead of using a typical 2D-CNN with uniform pooling window sizes, here we introduce an accentricity-dependene sampling within each pooling layer of the network by modifying the VGG16 ((4T) architecture. The upper right inset illustrates accentricity-dependeer pooling layer shows the receptive field sizes (ri,n) for each unit j with distance from the centre (the color bar denotes the size of the pooling window in pixels). See Figure S19 for example eye movement patterns from eceNet in each experiment.
works neuro- -automanatomicall connectivity 42 45
6 are consistent with the enhancement of search asymmetry by virtue of eecentiieit-dependeed sampling. We first define notations used in standard average pooling layers [1]. For simplicity, we describe the model components using pixels (Appendix D shows scaling factors used to convert pixels to degrees of visual angles (dva) to compare with human behavior in Section ( The model does not aim for perfect quantitative match with the macaque neurophysiologies receptive field sizes (see Appendix for further discussion).
data, but rather the goal is to preserve the trend of eccentricity
Traditionally, unit j in layer ++ VGG16 takes the average of all input units i the previous layer | within local receptive field size r1+1 and its activation value is given by: v F141 -1+1 vi
In the enrentricityddependee operation, the receptive field size r/41, of input unit j in layer + linear function of the Fuclidean distance di d'+1. between input unit j and the current fixation location (n) on layer 1.
- 2,
- 6)+ 2.5].
if df /RT41 18+1 < 6
11,
if d 1. /144
(I)
The floor function rounds down the window sizes. The positive scaling factor 7++1 for layer I ++ defines how fast the receptive field size of unit j expands with respect to its distance from the fixation layer receptive field size (Figure BB: in this figure, the fixation location is at the image center). Therefore, the resolution highest in the fixation location and decreases peripheral regions. Based on the slope of eccentricity versus receptive field size in the macaque visual cortex [16] we experimentally set 73 = 0.00, 26 0.00, 110 0.14, 214 = 0.32, and 7i8 0.64. See Figure 3 for the slopes of eccentricity versus receptive field sizes over pooling layers. We define For those units within the fovea, we set a constant receptive field size of 2 pixels. Constant +/++1 is positive scaling factor which converts the dva of the input image to the pixel units at the layer / (see Appendix for specific values of 1)))). 1. As in the stride size the original pooling layers of VGG16, empirically set constant stride of 2 pixels for all eccentricity-depene pooling layers.
The further away the unit
from the current fixation, the larger the
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Figure 3: Eccentricity dependence in the model matches that in macaque monkey visual cortex. A. Visualization of one example image and its corresponding eccentricity-dependee
sampling at in to assess the small amount of blurring in the center. The figures are best viewed after zooming
layers 10, 14, and 18 (see Appendix E for implementation).
sampling leads to increasing receptive field sizes as function of eccentricity, in addition to increased receptive field sizes across layers for the model (left) and also for the macaque visual cortex ([16), see Appendix for implementation).
A visualization for ,i,, is shown in Figure where different colors denote how the receptive field sizes expand within a pooling layer. Receptive field sizes r/,n. versus eccentricity df,,, in pixels at each pooling layer are reported in Table S1. Figure 3 illustrates the change in acuity at different pooling layers. These customized eccentricity-deeenddee other object recognition deep neural networks. All the computational steps are differentiable and can be trained end-to-end with other layers. However, since our goal is to test the generalization of eccNET from object recognition to visual search, we did not retrain eccNET and instead, we used the pre-trained weights of VGG16 on the ImageNet classification task.
pooling layers can be easily integrated into
Top-down attention modulation across multiple layers of visual cortex in eccNET. Previous works have described top-down modulation in simplified models for search asymmetry [1 Here we focus on developing a top-down modulation mechanism in deep networks. Given the current th fixation location, the visual cortex of eccNET with eccentricity -eeeeddettyydeeeddeet pooling layers extracts feature maps of4 at layer + for the target image 4. Correspondingly, the model extracts of.,, in response to the search image I,. Inspired by the neural circuitry of visual search the top-down modulation map Ar+1 1-,,n as:
5I
we define
A++1-1, m( Di.,)
where m(-) is the target modulation function defined as 2D convolution with of41 as convolution kernel operating of.,, and Φ441 is the feature map after pooling operation ø1- Note that the layer + modulates the activity of layer I. Following the layer conventions in TensorFlow Keras 1. we empirically selected 1 9, 13, 17 as the layers where top-down modulation performed (see Figure S23 for exact layer numbering).
To compute the overall top-down modulation map An, we first resize A10-9.n and 13, be of the same size as A18-177,0 eccNET then takes the weighted linear combination of normalized
top-down modulation maps across all three layers: A.
Σ
Wi,n (max A141 min 1-11-t..))
(At+1-11, -min A4
1=9,13,17
where Wi,n are weight factors governing how strong top-down modulation the Ith layer contributes to the overall attention map A₃- Each of the top-down attention maps contributes different sets of unique features. Since these features are specific to the target image type, the weights are not necessarily equal and they depend on the demands of the given task. One way to obtain these weights would by parameter fitting. Instead, to avoid parameter fitting specific to the given visual search experiment, these weights Wi,n were calculated during the individual search trials using the maximum activation value from each individual top-down modulation map: wt, = (max (max ( 9,133117 max A()1-inn) ). A higher activation value will mean high similarity
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between the target and search image at the corresponding feature level. Thus, a higher weight to the attention map produced using that feature layer ensures that the model assigns higher importance to the attention maps at those feature layers that are more prominent in the target image (see Figure S22 and Appendix for examples on how the attention map is computed).
Comparison with human performance and baseline models. The psychophysies experiments in Section 2 did not measure eye movements and instead report key press reaction time (RT) in milliseconds when subjects detected the target. To compare fixation sequences predicted by eccNET with reaction times measured in human experiments, we conducted a similar experiment as Experiment 4 in Section using eye tracking (Figure S1, Appendix C). Since RT results from a combination of time taken by eye movements plus finger motor response time, we performed a linear least-squares regression (Appendix B) on the eye tracking and RT data collected from this experiment. We fit RT as function of number of fixations N until the target was found: RT = a * N + B. The slope a 252.36 ms/fixation approximates the duration of a single saccade and subsequent fixation, and the intercept/ 3 376.27 can be interpreted as a finger motor response time. We assumed that and B are approximately independent of the task. We used the same fixed values of and / to convert the number of fixations predicted by eccNET to RT throughout all experiments.
We introduced two evaluation metrics. First, we evaluated the model and human performance showing key press reaction times (RT) as function of number of items on the stimulus, as commonly used in psychophysies 48] [2 46 47 (Section 4). We compute the slope of the RT versus number of items plots for the hard (H, larger search slopes) and easy (E, lower search slopes) conditions within each experiment. We define the Asymmetry Index, as (H-
(H + E)) for each experiment. If model follows the human asymmetry patterns for a given experiment, it will have a positive Asymmetry Index. The Asymmetry Index takes a value of 0 if there is no asymmetry, and negative value indicates that the model shows the opposite behavior to humans.
We included four baselines for comparison with eccNET:
Chance: sequence of fixations is generated by uniform random sampling.
pixelMatching: the attention map is generated by sliding the raw pixels of Le over I, (stride 1 x 1).
GBVS 19 : we used bottom-up saliency as the attention map.
IVSN 5i
the top-down attention map is based on the features from the top layer of VGG16.
Results
eccNET predicts human fixations in visual search: The model produces a sequence eye move- ments in every trial (see Figure S19 for example eye movement patterns by eccNet in each experi- ment). The original asymmetry search experiments (FigurqT) did not measure eye movements. Thus repeated the L vs. T search experiment (Experiment 4) to measure eye movements. We also used data from three other search tasks to compare the fixation patterns (51). We compared the fixation patterns between humans and EccNet in terms of three metrics [5:: 1. number of fixations required to find the target in each trial (the cumulative probability distribution p(n)) that the subject or model finds the target within fixations); 2. scanpath similarity score, which compares the spatiotemporal similarity in fixation sequences [5) 55:: and 3. the distribution of saccade sizes. The results show that the model approximates the fixations made by humans on trial-by-triae trial basis both in terms of the number of fixations and scanpath similarity. The model also presents higher consistency with humans in the saccade distributions than any of the previous models in [51] (Figures S15-18)
eccNET qualitatively captures visual search asymmetry. Figure 4 (left, red line) shows the results of Experiment 1 (Figure IA, left), where subjects looked for a straight line amongst curved lines. Increasing the number of distractors led to longer reaction times (RTs), as commonly observed in classical visual search studies. When the target and distractors were reversed and subjects had to search for a curved line in the midst of straight lines, the RTs were lower and showed minimal dependence on the number of objects (Figure 4A, left, blue line). In other words, it is easier to search for a curved line amidst straight lines than the reverse.
The same target and search images were presented to eccNET. Example fixation sequences from the model for Experiment I are shown in Figure S19A. Figure (right) shows eceNET's RT as function of the number of objects for Experiment 1. eccNET had not seen any of these types of

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The model shows visual search asymmetry and qualitatively captures human behav- ior. Reaction time as a function of the number of items in the display for each of the six experiments in Figure for for humans (left, solid lines) and for the model (right, dashed lines). The results for humans were obtained from [48] 22, (46 [47]. The line colors denote the different experimental conditions. Error bars for the model denote standard error (SE). Error bars were not available in the original publications for the human data.
Figure 4:
images before and the model was never trained to search for curves or straight lines. Critically, there was no explicit training to show an asymmetry between the two experimental conditions. Remarkably, eccNET qualitatively captured the key observations from the psychophysics experiment (48). When searching for curves among straight lines (blue), the RT' were largely independent of the number of distractors. In contrast, when searching for straight lines among curves (red), the RTs increased substantially with the number of distractors. The model's absolute RTs were not identical to human RTs (more discussions in Section 5 Of note, there was no training to quantitatively fit the human data, In sum, without any explicit training, eccNET qualitatively captures this fundamental asymmetry in visual search behavior.
We considered five additional experiments (Section 2 that reveal similar asymmetries on a wide range of distinct features (Figure (Figure[B-F)), BB-F). In Experiment 2. it is easier to search for top-down luminance changes than left-right luminance changes (Figure LB: (221).
l) In Experiments 3-4, it is easier to find
non-intersections among crosses than the reverse (Figure 1 C: [46] ), and it is easier to find a a rotated
letter T among rotated letters I. than the reverse (Figure ID; 446 ). In Experiments 5-6, it is easier
to find tilted bar amongst vertical distractors than the reverse when distractors are homogencous (Figure TE; (47) but the situation reverses when the distractors are heterogenous (Figure IE-F; (7)). In all of these cases, the psychophysies results reveal lower RTs and only weak increase in RTs with increasing numbers of distractors for the easier search condition and a more pronounced increase in RT with more distractors for the harder condition (Figure 44-F. left panels).
Without any image-specific or task tasc-spe training, eccNET qualitatively captured these asymmetries (Figure 4 ADD F, right panels). As noted in Experiment 1, eccNET did not necessarily match the human RT' at quantitative level. While in some cases the RT's for eccNET were comparable to humans (e.g., Figure FC), in other cases, there were differences (e.g., Fi Despite the shifts along the y-axis in terms of the absolute RTs, eccNET qualitatively reproduced the visual search asymmetries in 5 out of 6 experiments. However, in Experiment 5 where humans showed minimal search asymmetry effect, eccNET showed the opposite behavior (more discussions in Section 55)
The slope of RT versus number of object plots is commonly used to evaluate human search efficiency. We computed the Asymmetry Index (Section 3 to compare the human and model results. Positive values for the asymmetry index for eccNET indicate that the model matches human behavior. There was general agreement the Asymmetry Indices between eccNET and humans, except for Experiment 5 (Figure 5)) Since the Asymmetry Index is calculated using the search slopes (Section 3 is independent of shifts along the y-axis in Figure Thus, the eccNET Asymmetry Indices can match the human indices regardless of the agreement in the absolute RT values.
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Figure 5: EccNet outperforms other baselines and ablation demonstrates the relevance of its components. A. Asymmetry Index (Section3) for eceNet (y-axis) versus humans (x-axis) for each experiment. Points in the 1st quadrant indicate that eccNet follows the human asymmetry patterns. See Figure S14 for Asymmetry Index plots of other models. B. Comparison to ablated and alternative models. Average Asymmetry Index for humans (dark green), eccNET (light green), and alternative models (light gray) defined in Section 3 experiments.
The error bars show the standard error (SH) over all 6
Baseline models fail to capture search asymmetry: We compared the performance of eccNET with several baseline models (Figure see also Figures S2-S5 for the corresponding RT versus number of objects plots). All the bascline models also had infinite inhibition of return, oracle recognition, and the same method to convert number of fixations into RT values.
As a null hypothesis, we considered a model where fixations landed on random locations. This model consistently required longer reaction times and yielded an Asymmetry Index value close to zero, showing no correlation with human behavior (Figure 5B. Figure S3, chance). A simple template- matching algorithm also failed to capture human behavior (Figure [B, Figure S5 pixelMatch), suggesting that mere pixel-level comparisons are insufficient to explain human visual search. Next, we considered purely bottom-up algorithm that relied exclusively on saliency (Figure Figure 84, GBVS); the failure of this model shows that it is important to take into account the top-down target features to compute the overall attention map.
None of these baseline models contain complex features from natural images. We reasoned that the previous IVSN architecture [51], which was exposed to natural images through ImageNet, would yield better performance. Indeed, IVSN showed higher Asymmetry Index than the other baselines, yet its performance was below that of eccNET (Figure 5B. Figure S2, /VSN). Thus, the components introduced in eccNET play a critical role; next, we investigated each of these components.
Model ablations reveal essential components contributing to search asymmetry: We systemati- cally ablated two essential components of eccNET (Figure 5B. In eccNET, top-down modulation occurs at three levels: layer 10 to layer 9 (A10-9), 14 to 13 (A14-11), and 18 to 17 (A18-17). We considered eccNET 18-17. where top-down modulation only happened at layer 18 to 17 (A18-117). eccNET 18-117 yielded lower average Asymmetry Index (0.084), suggesting that visual search benefits from the combination of layers for top-down attention modulation (see also Figure S7).
To model the distinction between foveal and peripheral vision, we introduced pooling layers. The resulting increase in receptive field size qualitatively consistent with the receptive field sizes of neurons in the macaque visual cortex (Figure 3B.. To assess the impact of this step. we replaced all eccentricity-depeeee (cccNET,oecd) ). The lower average Asymmetry Index implics that max- ppoling layers of VGG16 an essential component in eccNET for asymmetry in visual search (see also Figure S6). Moreover, to evaluate whether asymmetry depends on the architecture of the recognition backbone, we tested ResNet152 200 on the six experiments (Figure S24). Though the ResNet152 backbone does not approximate human behaviors as well eccNet with VGG16, it still shows similar asymmetry behavior (positive asymmetry search index) in four out of the six experiments implying search asymmetry is general effect for deep networks.
pooling layers with the max
pooling is
The statistics of training data biases polarity search asymmetry. In addition to the ablation experiments, image-computable model enables to further dissect the mechanisms responsible
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for visual search asymmetry by examining the effect of training. Given that the model was not designed, or explicitly trained, to achieve asymmetric behavior, we were intrigued by how asymmetry could emerge. We hypothesized that asymmetry can be acquired from the features learnt through the natural statistics of the training images. To test this hypothesis, as a proof-of-princije we first focused on Experiment 2 (Figure IB).
We trained the visual cortex of eccNET from scratch using training set with completely different image statistics, Instead of using ImageNet containing millions of natural images, we trained eccNET on MNIST [3], which contains grayscale images of in Experiment 2 (Appendix G2). The asymmetry effect of eccNET MNIST disappeared and the absolute reaction times required to find the target increased substantially (Figure(6C) The average Asymmetry Index was also significantly reduced (Figure 5B, Figure S8).
digits. We tested eccNET cocNET_MNIST MNIST
To better understand which aspects of natural image statistics are critical for asymmetry emerge, we focused on the distinction between different lighting directions. We conjectured that humans are more used to lights coming from a vertical than a horizontal direction and that this bias would be present in the ImageNet dataset. To test this idea, we trained eccNET from scratch using an altered version of ImageNet where the training images were rotated by 90 degrees counter-clockwise. and tested eccNET on the same stimuli in Experiment 2 (see Appendix G1 for details). Note that random image rotation is not a data augmentation step for the VGG16 model pre-trained on ImageNet 4I] thus, the VGG16 pre-trained on ImageNet was only exposed to standard lighting conditions. Interestingly, eceNET trained on the 90-degree-rotated ImageNet (eccNET Ro190) shows a polarity reversal in asymmetry (Figure 6 Figure S9), while the absolute RT difference between the two search conditions remains roughly the same as eccNET.
We further evaluated the role of the training regime in other experiments. First, we trained the model on Imagenet images after applying a "fisheye" transform to reduce the proportion of straight lines and increase the proportion of curves. Interestingly we observed reversal in the polarity of asymmetry for Experiment 1 (curves among straight lines search) while the polarity for other experiments remained unchanged (Figure S10). Second, we introduced extra vertical and horizontal lines in the training data, thus increasing the proportion straight lines. Since reducing the proportion of straight lines reverses the polarity for "Curve vs Lines", we expected that increasing the proportion of straight lines might increase the Asymmetry Index. However, the polarity for asymmetry did not change for Experiment 1 (Figure S11). Third, to test whether dataset statistics other than Imagenet would alter the polarity, we also trained the model on the Places 365 dataset and rotated Place 365 dataset. We found this manipulation altered some of the asymmetry polarities but not others, similar to the experiment done on MNIST dataset. Unlike the results using the MNIST case, the absolute reaction times required to find the target did not increase significantly (Figures S12-13). In sum, both the architecture and the training regime play an important role in visual search behavior. In most cases, but not in all manipulations, the features learnt during object recognition useful for guiding visual search and the statistics from the training images contribute to visual search asymmetry.
5 Discussion
We examined six classical experiments demonstrating asymmetry in visual search, whereby humans find target, A, amidst distractors, B, much faster than in the reverse search condition of B among A.

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Given the similarity between the target and distractors, it is not clear why one condition should be easier than the other. Thus, these asymmetries reflect strong priors in how the visual system guides search behavior. We propose eccNET, a visual search model with accentricttyddeeeneen sampling and top-down attention modulation. At the heart of the model is "ventral visual cortex" module pre-trained on ImageNet for classification [36]. The model had no previous exposure to the images in the current study, which are not in ImageNet. Moreover, the model was not trained in any of these tasks, did not have any tuning parameters dependent eye movement or reaction time (RT) data, and was not designed to reveal asymmetry. Strikingly, despite this lack of tuning or training, asymmetric search behavior emerged in the model. Furthermore, eccNET captures observations in five out of six human psychophysics experiments in terms of the search costs and Asymmetry Index.
image-computable models allow us to examine potential mechanisms underlying asymmetries in visual search. Even though the target and search images are different from those in ImageNet, natural images do contain edges of different size, color, and orientation. To the extent that the distribution of image statistics in ImageNet reflects the natural world, a question that has been contested and deserves further scrutiny, one might expect that the training set could capture some priors inherent to human perception. We considered this conjecture further, especially in the case of Experiment 2. The asymmetry in vertical versus horizontal illumination changes has been attributed to the fact that animals are used to seeing light coming from the top (the sun), a bias likely to be reflected in ImageNet. Consistent with this idea, changing the training diet for eccNET alters its visual search behavior. Specifically, rotating the images by 90 degrees, altering the illumination direction used to train eccNET, led to reversal in the polarity of search asymmetry. Modifications in search asymmetry in other tasks were sometimes also evident upon introducing other changes in the training data such as modifying the proportion of straight lines, using the places365 or MNIST datasets (Figure 66 S8, S10-13). However, the training regime is not the only factor that governs search asymmetry. For example, the network architecture also plays critical role (Figure|5B)
Although eccNET qualitatively captures the critical observations in the psychophysics experiments, the model does not always yield accurate estimates of the absolute RTs. Several factors might contribute towards the discrepancy between the model and humans. First and foremost, eccNET lacks multiple critical components of human visual search abilities, as also suggested by [27]. These include finite working memory, object recognition, and contextual reasoning, among others. Furthermore, the VGG16 backbone of eccNET constitutes only first-order approximation to the intricacies of ventral visual cortex. Second, to convert fixations from eccNET to key press reaction time, we assumed that the relationship between number of fixations and RT's is independent of the experimental conditions. This assumption probably constitutes an oversimplification. While the approximate frequency of saccades tends be similar across tasks, there can still be differences in the saccade frequency and fixation duration depending on the nature of the stimuli, on the difficulty of the task, on some of the implementation aspects such as monitor size and contrast, and even on the subjects themselves [52] Third, the human psychophysics tasks involved both target present and target absent trials. Deciding whether an image contains target or not (human psychophysics experiments) is not identical to generating a sequence of saccades until the target is fixated upon (model). It is likely that humans located the target when they pressed a key to indicate target presence, but this was not measured in the experiments and it is conceivable that a key was pressed while the subject was still fixating on a distractor. Fourth, humans tend to make "return fixations", whereby they fixate on the target, move the eyes away and come back to the target [52] while eccNET would stop at the first time of fixating on the target. Fifth, it is worth nothing that it is possible to obtain tighter quantitative fits to the RTs by incorporating bottom-up saliency contribution to the attention map and fitting the parameters which corresponds to the relative contribution of top-down and bottom-up up attention maps (see Appendix H and Figures S20-21 for more details).
We introduce biologically plausible visual search model that can qualitatively approximate the asym- metry of human visual search behaviors. The success of the model encourages further investigation of improved computational models of visual search, emphasizes the importance of directly comparing models with biological architectures and behavioral outputs, and demonstrates that complex human biases can be derived from model's 's architecture and the statisties of training images.
10

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Acknowledgements
This work was supported by NIH RO1EY026025 and the Center for Brains, Minds and Machines, funded by NSF STC award CCF-1231216. Mengmi Zhang was supported by postdoctoral fellowship of the Agency for Science, Technology and Research. Shashi Kant Gupta was supported by The Department of Biotechnology (DBT), Govt of India, Indo-US Science and Technology Forum (IUSSTF) and WINStep Forward under "Khorana Program for Scholars".
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Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] See Sec. 5
(c) Did you discuss any potential negative societal impacts of your work? [N/A]
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full set of assumptions all theoretical results? [N/A]
(b) Did you include complete proofs of all theoretical results? [N/A]
3. If you ran experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or as a URL)? [Yes] URL included
in the paper
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Appendix G.
(c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [Yes] For the applicable figures, we provided SE (standard errors) errorbars.
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(d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See Appendix
4. If you using existing assets (e.g., code, data, models) curating/releasing new assets...
(a) If your work uses existing assets, did you cite the creators? [Yes]
(b) Did you mention the license of the assets? [N/A]
(c) Did you include any new assets either the supplemental material or as URL? [Yes]
Together with source code, we uploaded all the stimuli in zip file.
(d) Did/ you discuss whether and how consent was obtained from people whose data you'le rre're using/curating? [N/A]
(e) Did you discuss whether the data you are using/curating contains personally identifiable information offensive content? [N/A]
5. If you used crowdsourcing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, if applicable? [Yes] See Appendix C and FigS1.
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14


Reviews:
[{"review_id": "_O_k-mBvE9T", "reviewer": "Reviewer_iR66", "summary": "This paper presents a paradigm in which a deep neural network searches for a target image. This is accomplished by 2 streams, one of which represents the search object and the other representing the complete visual input. A winner-take-all mechanism along with inhibition of return is performed to elicit a search, and this is compared with human visual performance insofar as different stimuli give rise to asymmetries in difficulty of the search task.", "questions": "", "limitations": "", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Allow me to preface the comments that follow by a general statement that I found this work to be interesting and generally well done. However, I also have a number of concerns and these are reflected in the statements that follow:\n\n\n1. References for visual search asymmetries are given in [10][23-25]. In my view these are inadequate. These should include at least references to:\n\nTreisman, Anne, and Stephen Gormican. \"Feature analysis in early vision: evidence from search asymmetries.\" Psychological review 95.1 (1988): 15.\n\nCarrasco, Marisa, et al. \"Feature asymmetries in visual search: Effects of display duration, target eccentricity, orientation and spatial frequency.\" Vision research 38.3 (1998): 347-374.\n\nRosenholtz, R. (2001). Search asymmetries? What search asymmetries?. Perception & Psychophysics, 63(3), 476-489.\n\nYamani, Y., & McCarley, J. S. (2010). Visual search asymmetries within color-coded and intensity-coded displays. Journal of Experimental Psychology: Applied, 16(2), 124.\n\nDosher, B. A., Han, S., & Lu, Z. L. (2004). Parallel processing in visual search asymmetry. Journal of Experimental Psychology: Human Perception and Performance, 30(1), 3.\n\n\n2. Given the central hypothesis of asymmetries as a consequence of statistical properties, one might also consider:\n\nRosenholtz, R., Huang, J., Raj, A., Balas, B. J., & Ilie, L. (2012). A summary statistic representation in peripheral vision explains visual search. Journal of vision, 12(4), 14-14.\n\nShen, J., & Reingold, E. M. Visual search asymmetry: The influence of stimulus familiarity and low-level features.  Perception & Psychophysics.\n\nVisual representation determines search difficulty: explaining visual search asymmetries\nNeil D. B. Bruce and John K. Tsotsos, Front. Comput. Neurosci., 13 July 2011\n\nStatistical templates for visual search , John F. Ackermann; Michael S. Landy, Journal of Vision. 2014; 14(3):18.\n\n\n3. Eccentricity dependent layers are a crucial contribution in this type of study and it is nice to see these included.\n\n4. In the introduction, the paper mentions that \"When eccNET was trained from scratch on an augmented ImageNet with altered stimulus statistics, the polarity of search asymmetry disappeared or was modified, demonstrating that inductive biases in the developmental training set can govern complex visual behaviors.\" - without knowledge at this point of the \"altered stimulus statistics\", this statement stands out. If there were a summary indication of how they are altered in a general sense this would improve readability.\n\n5. The 6 visual search paradigms considered are reasonable, albeit, I wonder why some alternatives in the literature mentioned above were not considered - e.g. chroma, novelty etc.\n\n6. The overall model is sensible and it is nice to see this type of work as an alternative to traditional deep learning models for computer vision.\n\n7. A winner-take-all mechanism is reasonable given the representation it works on, however, it might be interesting to consider alternatives that take into account spatial distribution of saccades etc., or e.g. an approach that is optimal in reducing uncertainty about the target (akin to Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387-391.)\n\n8. The eccentricity mechanism seems more explicit than implicit. What I mean by this is that the loss of resolution at early layers should propagate upward by virtue of connectivity and yield highly blurred, but nevertheless rich featured representations at the top. In this work the eccentricity seems to be forced on the model not by the sensor (the eye), but by construction of the network.\n\n9. While the model shows general agreement with conditions that give rise to asymmetries, this is more in a rough qualitative sense than a strong quantitative sense. This is not a deal breaker, but it would be nice to see how conditions of training impact these curves. Moreover, experiment E is unconvincing. The human data for this case also looks counterintuitive to me - but I could be wrong.\n\n10. Figure 5 reveals that the model outperforms alternatives, but some of these seem like a strawman having no real strong mechanism for producing asymmetries by construction. (or rather, not relying on features that are driven by image statistics in contrast to models that do)\n\n11. The training of the model on MNIST, and focused on experiment 2 provides only weak evidence that the underlying representation learned by the model is what gives rise to any asymmetries observed (although from a personal point of view, that this assertion is true may not need further proof)\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "I see no significant limitations or potential negative societal impact.", "review_text": "", "extra_scores": {"ethical_concerns": "I don't view the work as having any ethical concerns associated with it.", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}, {"review_id": "ZDKP9yYq8PO", "reviewer": "Reviewer_cDnL", "summary": "The paper proposes a neural network model of target-based visual search. The network is a VGG-16 with two important modifications: eccentricity-dependent pooling and multi-layer target matching that guides the simulated fixations. The model qualitatively reproduces five classical findings of visual search asymmetry without task-driven parameter tuning (but fails to reproduce a sixth effect and does not quantitatively capture the human visual search slopes).", "questions": "", "limitations": "", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Overall, this is a solid contribution that further bridges the gap between neural network modeling and cognitive psychology. In particular, the effect of training on rotated ImageNet is quite striking. It demonstrates how neural network modeling can test evolutionary or developmental hypotheses that are otherwise extremely difficult to assess.\n\nMajor comments:\n1. My main concern is equation (2). It requires considerable clarification. The target representation in layer $l+1$ is defined using a different set of channels than the search representation in layer $l$. How can $\\phi^t_{l+1,n}$ serve as a valid and meaningful convolutional kernel for $\\phi^s_{l,n}$? Intuitively, I would expect that $\\phi^t_{l,n}$ would be used to define the attentional gain of features in $\\phi^s_{l,n}$ (so the channel representation is consistent). Since the model seems to work well, I am assuming I misunderstood. This point requires both a verbal explanation and greater mathematical explicitness.\n\n2. Why does the target representation $\\phi^t_{l+1,n}$ take the fixation location as an argument? As a template used for spatial convolution, I would expect it to be invariant to the fixation location.\n\n3. Training the network with the modified pooling layers on ImageNet from scratch is more ecologically valid than performing \"network surgery\" (i.e., replacing the pooling layers in the already trained model). Furthermore, such a trained-from-scratch model would be a better control for the models trained from scratch on MNIST and rotated ImageNet. Last, training from scratch will also support the claim that \"these customized eccentricity-dependent pooling layers can be easily integrated into other object recognition deep neural networks\".\n\nMinor:\nLine 105: VGG16 \"traditionally\" uses max pooling and not average pooling (Simonyan & Zisserman, 2014).\n\nReference:\nSimonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Limitations are adequately discussed. There is no apparent negative societal impact.", "review_text": "", "extra_scores": {"ethical_concerns": "N/A", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}, {"review_id": "Z0vxG8WPHC", "reviewer": "Reviewer_U5qd", "summary": "The authors address a fascinating phenomenon of asymmetries in visual search. Having been observed over half a century ago it has eluded many attempts at explanation.\nThe authors argue that such asymmetries are induced by biases in statistics of the learning experience (or the training data set). To test this hypothesis they designed a neural network architecture, eccNET, and trained on Imaginet dataset of natural images and on an augmented Imagenet that compensates for the said biases. Extensive computational experiment results support their hypothesis, and ablation studies support the importance of the eccentricity-dependent pooling.", "questions": "", "limitations": "", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "This elegant paper addresses a much studied problem of asymmetries in visual search. The proposed eccNET succinctly captures visual search mechanisms thought to operate in primate brain, and the presented empirical study of \"reaction times\" corroborate the plausibility of the approach. \nThe most interesting and impactful claim of the paper, that search asymmetries are introduced by biases in training datasets, is addressed by manipulating Imagenet dataset. A 90 deg rotation of the dataset reverses the search time pattern for illumination-dependent features, in agreement of the hypothesis. \n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Given the importance of the hypothesis that asymmetries are caused by biases in training datasets, it would be desirable to see results of manipulating the training datasets to affect the remaining 5 search experiments (Fig 1).  A demonstration in a single domain lends the hypothesis only anecdotal support.\n\nSuggestions:\nExtend training data manipulations to address sources of asymmetry for other 5 types of features that exhibit search  asymmetry.\n\nSocietal impact: \n1) paving the way for more efficient artificial vision systems can have sizeable impact on the many industries that are using CV;\n2) better understanding of primate (including human) vision system may lead to better cures for vision pathologies.\n", "review_text": "", "extra_scores": {"ethical_concerns": "none", "needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper describes a new computational model of search asymmetry. The paper received two positive reviews and initially one negative review but the rebuttal satisfied this reviewer who increased their score to a marginal accept. All reviewers commented on the many strengths of this paper. Most of the limitations brought up by the reviewers were addressed in the rebuttal and the remaining ones should be stated in the discussion.
Decision: Accept (Poster)