Original: Benchmarking human visual search computational models in natural scenes: models comparison and reference datasets
Revised: Benchmarking human visual search computational models in natural scenes: models comparison and reference datasets

Original: Abstract
Revised: Abstract

Original: Visual search is an essential part of almost any everyday human goal-directedinteraction with the environment.
Revised: Visual search is an essential part of almost any everyday human goal-directed interaction with the environment.

Original: Nowadays, several algorithms are able to predictgaze positions during simple observation, but few models attempt to simulatehuman behavior during visual search in natural scenes.
Revised: Nowadays, several algorithms are able to predict gaze positions during simple observation, but few models attempt to simulate human behavior during visual search in natural scenes.

Original: Furthermore, these modelsvary widely in their design and exhibit differences in the datasets and metricswith which they were evaluated.
Revised: Furthermore, these models vary widely in their design and exhibit differences in the datasets and metrics with which they were evaluated.

Original: Thus, there is a need for a reference point, onwhich each model can be tested and from where potential improvements can bederived.
Revised: Thus, there is a need for a reference point, on which each model can be tested and from where potential improvements can be derived.

Original: In the present work, we select publicly available state-of-the-art visualsearch models in natural scenes and evaluate them on different datasets, employingthe same metrics to estimate their efﬁciency and similarity with human subjects.
Revised: In the present work, we select publicly available state-of-the-art visual search models in natural scenes and evaluate them on different datasets, employing the same metrics to estimate their efﬁciency and similarity with human subjects.

Original: In particular, we propose an improvement to the Ideal Bayesian Searcher through a combination with a neural network-based visual search model, enabling it togeneralize to other datasets.
Revised: In particular, we propose an improvement to the Ideal Bayesian Searcher through a combination with a neural network-based visual search model, enabling it to generalize to other datasets.

Original: The present work sheds light on the limitations of current models and how potential improvements can be accomplished by combiningapproaches.
Revised: The present work sheds light on the limitations of current models and how potential improvements can be accomplished by combining approaches.

Original: Moreover, it moves forward on providing a solution for the urgent needfor benchmarking data and metrics to support the development of more generalhuman visual search computational models.
Revised: Moreover, it moves forward on providing a solution for the urgent need for benchmarking data and metrics to support the development of more general human visual search computational models.

Original:  Introduction
Revised:  Introduction

Original: Nowadays, in the ﬁeld of Neuroscience, the mechanisms behind the visual processing of an image inthe center of the visual ﬁeld with no eye movements are well understood.
Revised: Nowadays, in the ﬁeld of Neuroscience, the mechanisms behind the visual processing of an image in the center of the visual ﬁeld with no eye movements have been thoroughly studied [1, 2].

Original: At the same time, the ﬁeldof Artiﬁcial Intelligence has developed precise models of image processing and feature extraction(both objective and subjective), including artiﬁcial neural networks.
Revised: At the same time, the ﬁeld of Artiﬁcial Intelligence has developed precise models of image processing and feature extraction (both objective and subjective), including artiﬁcial neural networks.

Original: These were inspired by and, inmany cases, resemble visual processing in the brain.
Revised: These were inspired by and, in many cases, resemble visual processing in the brain.

Original: Among their recent successes, they have beencapable of providing highly effective models which can predict gaze positions at an image when it isbeing freely explored [1, 2, 3, 4].
Revised: Among their recent successes, they have been capable of providing highly effective models which can predict gaze positions at an image when it is being freely explored [3, 4, 5, 6].

Original: Nonetheless, there are two fundamental aspects where current computational models are yet toprovide answers.
Revised: Nonetheless, there are two fundamental aspects where current computational models are yet to provide answers.

Original: Firstly, when a biological system (mainly humans) observes an image, it is generallydone with a goal in mind or to fulﬁll a task, such as memorizing the image or searching for anobject.
Revised: Firstly, when a biological system (mainly humans) observes an image, it is generally done with a goal in mind or to fulﬁll a task, such as memorizing the image or searching for an object.

Original: Secondly, it is not only relevant where the eye ﬁxates upon, but also in what order it scans theimage: eye movements do not follow random behavior, but rather different strategies which intend tominimize the number of ﬁxations necessary to reach a goal [5, 6, 7, 8].
Revised: Secondly, it is not only relevant where the eye ﬁxates upon, but also in what order it scans the image: eye movements do not follow random behavior, but rather different strategies which intend to minimize the number of ﬁxations necessary to reach a goal [7, 8, 9, 10].

Original: Currently, there are diverse algorithms that attempt to predict the sequence of eye movements (calledscanpath ) when looking for an object in a natural scene [9, 10, 11, 12].
Revised: Currently, there are diverse algorithms that attempt to predict the sequence of eye movements (called scanpath ) when looking for an object in a natural scene [11, 12, 13, 14].

Original: However, the theoreticalframeworks behind these models vary widely from each other.
Revised: However, the theoretical frameworks behind these models vary widely from each other.

Original: For instance, there are models which employ convolutional or recurrent neural networks [10, 11], others utilize reinforcement learning[12], and a third category applies a Bayesian approach [9, 13, 14].
Revised: For instance, there are models which employ convolutional or recurrent neural networks [12, 13], others utilize reinforcement learning [14], and a third category applies a Bayesian approach [11, 15, 16].

Original: Likewise, the datasets in whichthese models were evaluated can be categorized by the difﬁculty of the task (measured by the numberof ﬁxations made to ﬁnd the target), the content of the images (absence or presence of text, humanfaces, and other distractors), how the target is presented, and so on.
Revised: Likewise, the datasets in which these models were evaluated can be categorized by the difﬁculty of the task (measured by the number of ﬁxations made to ﬁnd the target), the content of the images (absence or presence of text, human faces, and other distractors), how the target is presented, and so on.

Original: These differences can also befound in the methodologies employed to evaluate the results obtained.
Revised: These differences can also be found in the methodologies employed to evaluate the results obtained.

Original: All of this hinders the task ofconcluding what works best and what needs further study.
Revised: All of this hinders the task of concluding what works best and what needs further study.

Original: Exploration models greatly beneﬁted fromthe construction of a saliency benchmark [15, 16] 1 , and the ﬁeld of visual search is in need of one.
Revised: Exploration models greatly beneﬁted from the construction of a saliency benchmark [17, 18] 2 , and the ﬁeld of visual search is in need of one.

Original: In the present work, we aimed to compare state-of-the-art visual search models in natural scenes.
Revised: In the present work, we aimed to compare state-of-the-art visual search models in natural scenes.

Original: We selected three publicly available visual search in natural scenes algorithms [9, 10, 12], alongsidethe datasets they were tested in, with the addition of another published dataset [11], and we deﬁnecommon metrics for their due comparison.
Revised: We selected three publicly available visual search in natural scenes algorithms [11, 12, 14], alongside the datasets they were tested in, with the addition of another published dataset [13], and we deﬁne common metrics for their due comparison.

Original: Going further, we discuss their differences and make thenecessary adjustments to bring them together into a single pipeline.
Revised: Going further, we discuss their differences and make the necessary adjustments to bring them together into a single pipeline.

Original:  Methods2.1 Datasets
Revised:  Methods 2.1 Datasets

Original: Each dataset comprises a set of search images, target objects for each image, and human subjects’scanpaths on those images (Table S1).
Revised: Each dataset comprises a set of search images, target objects for each image, and human subjects’ scanpaths on those images (Table S1).

Original: First, there are some differences between them in regard toexperiment design.
Revised: First, there are some differences between them in regard to experiment design.

Original: The Interiors dataset (which corresponds to the IBS family models) uses templatesas targets and observers have to look for a speciﬁc object [9].
Revised: The Interiors dataset (which corresponds to the IBS family models) uses templates as targets and observers have to look for a speciﬁc object [11].

Original: Both MCS and COCOSearch18 state theobject category (“microwave” and “clock” for the ﬁrst, “car”, “bottle” and others for the latter) andparticipants have to look for a broad object category (which could be in the image or not) [10, 12], andin the Unrestricted dataset (the one used for the IVSN model in natural scenes) a generalized versionof the object is presented, so participants have to look for an object in a narrower category deﬁned bythe template (for example, a shoe or a stroller) [10].
Revised: Both MCS and COCOSearch18 state the object category (“microwave” and “clock” for the ﬁrst, “car”, “bottle” and others for the latter) and participants have to look for a broad object category (which could be in the image or not) [12, 14], and in the Unrestricted dataset (the one used for the IVSN model in natural scenes) a generalized version of the object is presented, so participants have to look for an object in a narrower category deﬁned by the template (for example, a shoe or a stroller) [12].

Original: Second, not all of the human scanpaths ﬁnishjust as soon as a ﬁxation from the participant lands on the target.
Revised: Second, not all of the human scanpaths ﬁnish just as soon as a ﬁxation from the participant lands on the target.

Original: In MCS , COCOSearch18 and the Unrestricted dataset, participants have to press a button to ﬁnish the trial, so they can ﬁxate on thetarget many times before they decide it is there.
Revised: In MCS , COCOSearch18 and the Unrestricted dataset, participants have to press a button to ﬁnish the trial, so they can ﬁxate on the target many times before they decide it is there.

Original: In the Interiors dataset, the trial ﬁnishes when theparticipants ﬁxate on the target or after a varying upper limit of saccades is reached.
Revised: In the Interiors dataset, the trial ﬁnishes when the participants ﬁxate on the target or after a varying upper limit of saccades is reached.

Original: Third, the criteriaof when the target was found also varies across datasets.
Revised: Third, the criteria of when the target was found also varies across datasets.

Original: When compared to their correspondingmodels, the authors of MCS and COCOSearch18 decide that the target was found if the averageposition during a ﬁxation lands inside the target’s bounding box (with no regard of the ﬁxationaleye movements or the size of the fovea), while Sclar et al. reduce the ﬁxations to a grid using cellswhose size is estimated from the fovea, and Zhang et al.
Revised: When compared to their corresponding models, the authors of MCS and COCOSearch18 decide that the target was found if the average position during a ﬁxation lands inside the target’s bounding box (with no regard of the ﬁxational eye movements or the size of the fovea), while Sclar et al. reduce the ﬁxations to a grid using cells whose size is estimated from the fovea, and Zhang et al.

Original: use a square window, centered on the currenttarget, whose size corresponds to two times the mean width and height of all the targets’ boundingboxes.
Revised: use a square window, centered on the current target, whose size corresponds to two times the mean width and height of all the targets’ bounding boxes.

Original: Fourth, the content of the images vary across datasets.
Revised: Fourth, the content of the images vary across datasets.

Original: For instance, the Unrestricted datasetis completely unconstrained and the only one to include human ﬁgures (faces or bodies) in them, the Interiors dataset only includes images of crowded interiors which could have several objects similarto the target (distractors), and both MCS and COCOSearch18 are the only ones with colored images,resulting in shorter scanpaths.
Revised: For instance, the Unrestricted dataset is completely unconstrained and the only one to include human ﬁgures (faces or bodies) in them, the Interiors dataset only includes images of crowded interiors which could have several objects similar to the target (distractors), and both MCS and COCOSearch18 are the only ones with colored images, resulting in shorter scanpaths.

Original: And, ﬁfth, there was a huge variability in the number of participants(10 to 57) and images (134 to 2489).
Revised: And, ﬁfth, there was a huge variability in the number of participants (10 to 57) and images (134 to 2489).

Original: Thus, we carried out a preprocessing of these datasets in order to ﬁt them to a common format,equalized them as much as possible, and set a common criteria for all of them (see Appendix A.2).
Revised: Thus, we carried out a preprocessing of these datasets in order to ﬁt them to a common format, equalized them as much as possible, and set a common criteria for all of them (see Appendix A.2).

Original: 2.2 Visual Search Models
Revised: 2.2 Visual Search Models

Original: We analyzed three models: the ﬁrst one is the Invariant Visual Search Network (IVSN) [10] 2 .
Revised: We analyzed three models: the ﬁrst one is the Invariant Visual Search Network (IVSN) [12] 3 .

Original: IVSNis based on convolutional neural networks, due to their resemblance to humans’ visual cortex, andperforms zero-shot invariant visual search.
Revised: IVSN is based on convolutional neural networks, due to their resemblance to humans’ visual cortex, and performs zero-shot invariant visual search.

Original: It makes use of two different versions of a pretrained
Revised: It makes use of two different versions of a pretrained

Original: VGG16: one for the search image, which is used to extract bottom-up features (visual ventral cortexnetwork), and a second one for the target image, which is used to extract top-down features (prefrontalcortex network).
Revised: VGG16: one for the search image, which is used to extract bottom-up features (visual ventral cortex network), and a second one for the target image, which is used to extract top-down features (prefrontal cortex network).

Original: Once these extractions have been done, the feature representation of the targetimage is used to modulate the representation of the search image through a convolution, generating what Zhang et.
Revised: Once these extractions have been done, the feature representation of the target image is used to modulate the representation of the search image through a convolution, generating what Zhang et.

Original: al. called an attention map.
Revised: al. called an attention map.

Original: A greedy search is then performed on this attention map,which means the whole image is processed at once (contrary to how human vision works), there is noaccumulation of information across saccades, and the inhibition of return is forced.
Revised: A greedy search is then performed on this attention map, which means the whole image is processed at once (contrary to how human vision works), there is no accumulation of information across saccades, and the inhibition of return is forced.

Original: The second model is the correlation-based Ideal Bayesian Searcher (cIBS) [9] 3 . cIBS
Revised: The second model is the correlation-based Ideal Bayesian Searcher (cIBS) [11] 4 . cIBS

Original: attempts tominimize the number of ﬁxations needed to ﬁnd the target.
Revised: attempts to minimize the number of ﬁxations needed to ﬁnd the target.

Original: First, it uses a saliency map of the searchimage computed with DeepGaze II [17] as prior.
Revised: First, it uses a saliency map of the search image computed with DeepGaze II [19] as prior.

Original: Second, at each step, the next ﬁxation is calculatedas the one that maximizes the likelihood of ﬁnding the target, taking into account all the informationgathered in previous steps [18].
Revised: Second, at each step, the next ﬁxation is calculated as the one that maximizes the likelihood of ﬁnding the target, taking into account all the information gathered in previous steps [20].

Original: The likelihood is calculated through a visibility map (estimatedas a 2D Gaussian centered on the current ﬁxation) and a target similarity map, which is computedwith the search image and the target image via cross-correlation [9].
Revised: The likelihood is calculated through a visibility map (estimated as a 2D Gaussian centered on the current ﬁxation) and a target similarity map, which is computed with the search image and the target image via cross-correlation [11].

Original: Given the opportunity to testthis model in several different datasets, we evaluated different versions of it, modifying its targetsimilarity map.
Revised: Given the opportunity to test this model in several different datasets, we evaluated different versions of it, modifying its target similarity map.

Original: In particular, we replaced cross-correlation with SSIM (sIBS) and a convolutionalneural network (nnIBS) .
Revised: In particular, we replaced cross-correlation with SSIM (sIBS) and a convolutional neural network (nnIBS) .

Original: The latter is based on the attention map computed by IVSN.
Revised: The latter is based on the attention map computed by IVSN.

Original: By performingthis substitution, the resulting model is able to capture object invariance while maintaining a goodperformance on template matching.
Revised: By performing this substitution, the resulting model is able to capture object invariance while maintaining a good performance on template matching.

Original: This variation was used against the other models.
Revised: This variation was used against the other models.

Original: The last model evaluated is based on Inverse Reinforcement Learning (IRL)[12] 4 .
Revised: The last model evaluated is based on Inverse Reinforcement Learning (IRL)[14] 5 .

Original: It was trainedon COCOSearch18 , performing categorical visual search, and it is only capable of searching for anobject that belongs to one of those 18 categories.
Revised: It was trained on COCOSearch18 , performing categorical visual search, and it is only capable of searching for an object that belongs to one of those 18 categories.

Original: In a similar manner to cIBS, a Gaussian blur isapplied to the search image to account for loss of visibility in the periphery.
Revised: In a similar manner to cIBS, a Gaussian blur is applied to the search image to account for loss of visibility in the periphery.

Original: Both, the unaltered searchimage and the blurred image, are preprocessed with a neural network pretrained on COCO2017which performs a panoptic segmentation (Detectron2 [19]) and this is used as the model’s input.
Revised: Both, the unaltered search image and the blurred image, are preprocessed with a neural network pretrained on COCO which performs a panoptic segmentation (Detectron2 [21]) and this is used as the model’s input.

Original: Ontop of this, a one-hot encoding of the target object category is concatenated with the input of eachlayer in the network.
Revised: On top of this, a one-hot encoding of the target object category is concatenated with the input of each layer in the network.

Original: This way, the authors aim to capture both the image and the target object’scontext.
Revised: This way, the authors aim to capture both the image and the target object’s context.

Original: The code for the preprocessing of the images was not available, so we reproduced it fromthe references in the publication (see Appendix A.6 for details).
Revised: The code for the preprocessing of the images was not available, so we reproduced it from the references in the publication (see Appendix A.7 for details).

Original: Models were ported to Python (whenever necessary) in order to engulf them in a common framework.
Revised: All of the models described were ported to Python (whenever necessary) in order to engulf them in a common framework.

Original: Additionally, the necessary changes to run them on the previously established dataset’s format weremade (Appendix A.4).
Revised: Additionally, the necessary changes to run them on the previously established dataset’s format were made (Appendix A.5).

Original: Therefore, each model is now able to receive any of the four preprocesseddatasets as input and the output follows the same criteria as that of human subjects’, allowing for acorrect comparison.
Revised: Therefore, each model is now able to receive any of the four preprocessed datasets as input and the output follows the same criteria as that of human subjects’, allowing for a correct comparison.

Original: 2.3 Metrics
Revised: 2.3 Metrics

Original: We are interested in how the computational models compare against humansboth in terms of efﬁciencyand scanpath similarity.
Revised: We are interested in how the computational models compare against humans in terms of both efﬁciency and scanpath similarity.

Original: To analyze efﬁciency, we measured the proportion of targets found for a givennumber of ﬁxations and the area under this curve (AUC) is reported.
Revised: To analyze efﬁciency, we measured the proportion of targets found for a given number of ﬁxations and the area under this curve (AUC) is reported.

Original: To analyze similarity, we use 
Revised: To analyze similarity, we use Multi-Match (MM) [22].

Revised: In short, Multi-Match represents scanpaths as geometrical vectors in a two-dimensional space (any scanpath is build up of a vector sequence in which the vectors represent saccades, and the start and end position of saccade vectors represent ﬁxations).

Revised: Two such sequences (which can differ in length) are compared on the four dimensions vector shape, vector length (saccadic amplitude), vector position, and vector direction for a multidimensional similarity evaluation.

Original: Multi-Match (MM) [20] in all of its dimensions, excluding the temporal one as we are not consideringﬁxation durations.
Revised: The temporal dimension is excluded as we are not considering ﬁxation durations.

Original: For each model, its scanpaths were compared to each subject’s scanpaths andthe result was averaged across subjects for each image (hmMM).
Revised: For each model, its scanpaths were compared to each subject’s scanpaths and the result was averaged across subjects for each image (hmMM).

Original: Also, a human ground truth wasestimated by comparing human subjects’ scanpaths to each other and averaging the results acrosssubjects for each image (whMM).
Revised: Also, a human ground truth was estimated by comparing human subjects’ scanpaths to each other and averaging the results across subjects for each image (whMM).

Original: This was done for every scanpath with length greater than twoand in which the target was found (Tables S3 and S4).
Revised: This was done for every scanpath with length greater than two and in which the target was found (Tables S3 and S4).

Original: Difﬁculty varies across images, and so doeswhMM.
Revised: Difﬁculty varies across images, and so does whMM.

Original: To investigate if this variability is also present in the models, whMM and hmMM wereplotted in two axes (Fig.
Revised: To investigate if this variability is also present in the models, whMM and hmMM were plotted in two axes (Fig.

Original: 1D-E, 2E-H) and their correlation was calculated.
Revised: 1D-E, 2E-H) and their correlation was calculated.

Original: These plots provide aneasy overview of how well the model has captured human behavior: the closer the points are to thediagonal, the more exchangeable the model’s scanpaths are with those of human subjects, whereasthe points further to the left represent those images in which within human similarity was greatest,but human-model similarity was not as high [9].
Revised: These plots provide an easy overview of how well the model has captured human behavior: the closer the points are to the diagonal, the more exchangeable the model’s scanpaths are with those of human subjects, whereas the points further to the left represent those images in which within human similarity was greatest, but human-model similarity was not as high [11].

Original:  Results
Revised:  Results

Original: Different sets of images, targets and tasks present different challenges to the models.
Revised: Different sets of images, targets and tasks present different challenges to the models.

Original: These datasetsall have crowded images, full of distractors (with different degrees of similarity), and with a strongcontext.
Revised: These datasets all have crowded images, full of distractors (with different degrees of similarity), and with a strong context.

Original: Moreover, the task was slightly different (looking for an exact object or for a category) and, thus, the presentation of the target example that guides the search was different.
Revised: Moreover, the task was slightly different (looking for an exact object or for a category) and, thus, the presentation of the target example that guides the search was different.

Original: The cIBS modelwas previously evaluated in looking for an exact object [9].
Revised: The cIBS model was previously evaluated in looking for an exact object [11].

Original: Brieﬂy, this model consists of two parts:the prior that gives it a ﬁrst gist of the image and an initial context, and the IBS rule to update theprobabilities of ﬁnding the target, which is based on a similarity map that compares each image patchwith the target.
Revised: Brieﬂy, this model consists of two parts: the prior that gives it a ﬁrst gist of the image and an initial context, and the IBS rule to update the probabilities of ﬁnding the target, which is based on a similarity map that compares each image patch with the target.

Original: In order to allow the model to both generalize the target template and also to getconfused/attracted by distractors (as humans are), we evaluated three different similarity maps: thecorrelation and the SSIM between each image patch and the target (cIBS and sIBS, respectively), andthe attention map from the IVSN model (nnIBS).
Revised: In order to allow the model to both generalize the target template and also to get confused/attracted by distractors (as humans are), we evaluated three different similarity maps: the correlation and the SSIM between each image patch and the target (cIBS and sIBS, respectively), and the attention map from the IVSN model (nnIBS).

Original: Performance was greatest with SSIM (Fig.
Revised: Performance was greatest with SSIM (Fig.

Original: 1A-C),albeit at a greater computational expense.
Revised: 1A-C), albeit at a greater computational expense.

Original: Crucially, nnIBS was the only one capable of ﬁnding thegeneralized targets used in the Unrestricted dataset, whereas the other two (cIBS and sIBS) had touse cropped versions of the targets in the images (templates).
Revised: Crucially, nnIBS was the only one capable of ﬁnding the generalized targets used in the Unrestricted dataset. This means it is able to perform categorical visual search and correctly generalize to the other datasets. Conversely, both cIBS and sIBS had to use cropped versions of the targets in the images (templates).

Original: Nonetheless, this modiﬁcation did notalter scanpath similarity with human subjects (Table S3 and Fig.
Revised: Nonetheless, this modiﬁcation did not alter scanpath similarity with human subjects (Table S3 and Fig.

Original: 1D-F).
Revised: 1D-F).

Original:  trials where 
Revised: In A), human performance is displayed as box plots due to the upper limits set on the number of saccades for human subjects (varying between 2, 4, 8 and 12). D-F) Mean MM values within humans (whMM) as function of the mean MM values between each human observer and a model (hmMM) (Table S3). Only trials where the target was found are considered.

Original: Another modiﬁcation that we performed to the models in order to adapt them to different datasetsinvolved changing the patch size used for inhibition-of-return and target identiﬁcation in IVSN.
Revised: Another modiﬁcation that we performed to the models in order to adapt them to different datasets involved changing the patch size used for inhibition-of-return and target identiﬁcation in IVSN.

Original: Thismodel performs greedy search through an attention map built with neural networks.
Revised: This model performs greedy search through an attention map built with neural networks.

Original: After an imagepatch is explored, the probabilities in that position are zeroed, forcing an inhibition-of-return effect[10].
Revised: After an image patch is explored, the probabilities in that position are zeroed, forcing an inhibition-of-return effect [12].

Original: Thus, it is strongly dependent on this patch size.
Revised: Thus, it is strongly dependent on this patch size.

Original: We evaluated different values for it relative tothe mean target size of each dataset, including the one proposed by the authors, and kept the bestworking version (Appendix B.2).
Revised: We evaluated different values for it relative to the mean target size of each dataset, including the one proposed by the authors, and kept the best working version (Appendix B.2).

Original: When comparing all of the visual search models, not surprisingly, each one performed best in its owndataset.
Revised: When comparing all of the visual search models, not surprisingly, each one performed best in its own dataset.

Original: As IRL depends on the categories of the COCOSearch18 dataset, it could only be evaluatedon a small portion of the Unrestricted and Interiors datasets.
Revised: As IRL depends on the categories of the COCOSearch18 dataset, it could only be evaluated on a small portion of the Unrestricted and Interiors datasets.

Original: In both datasets, IRL’s performancewas particularly low, achieving under 50% accuracy (19 of 46 and 17 of 42 successful trials in the
Revised: In both datasets, IRL’s performance was particularly low, achieving under 50% accuracy (19 of 46 and 17 of 42 successful trials in the

Original: Interiors and Unrestricted datasets, respectively).
Revised: Interiors and Unrestricted datasets, respectively).

Original: Additionally, even though it could run on the entire
Revised: Additionally, even though it could run on the entire

Original: MCS dataset (for it only contains microwaves and clocks as targets), its performance plummeted incomparison to the other models and human subjects (Fig.
Revised: MCS dataset (for it only contains microwaves and clocks as targets), its performance plummeted in comparison to the other models and human subjects (Fig.

Original: 2D).
Revised: 2D).

Original: In all datasets, IVSN showed great search efﬁciency, mainly in the ﬁrst few ﬁxations, likely as aconsequence of its wider view of the scene –i.e. the model doesn’t include a decay due to the foveatedstructure of the eye–.
Revised: In all datasets, IVSN showed great search efﬁciency, mainly in the ﬁrst few ﬁxations, likely as a consequence of its wider view of the scene –i.e. the model doesn’t include a decay due to the foveated structure of the eye–.

Original: This, alongside its independence between ﬁxations (there is no accumulation ofinformation), is probably why its similarity with human subjects never was the highest (Table S4).
Revised: This, alongside its independence between ﬁxations (there is no accumulation of information), is probably why its similarity with human subjects never was the highest (Table S4).

Original: Finally, nnIBS was the most consistent with human behavior in regard to scanpath similarity (Table
Revised: Finally, nnIBS was the most consistent with human behavior in regard to scanpath similarity (Table

Original: S4), although it performed poorly in COCOSearch18 (Fig.
Revised: S4), although it performed poorly in COCOSearch18 (Fig.

Original: 2C).
Revised: 2C).

Original: Remarkably, the distractors in the
Revised: Remarkably, the distractors in the

Original: Interiors dataset seemed to be capable of fooling IVSN in some cases; that did not happen for nnIBS,even though it used the same attention map (Fig. S4).
Revised: Interiors dataset seemed to be capable of fooling IVSN in some cases; that did not happen for nnIBS, even though it used the same attention map (Fig. S4).

Original: This could be due to its greedy behavior, whichsimply goes to the point that most closely resembles the target, regardless of distance or other factors(see also Fig.
Revised: This could be due to its greedy behavior, which simply goes to the point that most closely resembles the target, regardless of distance or other factors (see also Fig.

Original: S3).
Revised: S3).

Original: In order to guide future improvements of visual search algorithms, we explored other trials where themodels’ scanpath dissimilarity with human subjects was greatest.
Revised: In order to guide future improvements of visual search algorithms, we explored other trials where the models’ scanpath dissimilarity with human subjects was greatest.

Original: Onthe one hand, humans were able to rapidly understand the context of the image they were looking at and performed their searchaccordingly [21, 22, 23].
Revised: On one hand, humans were able to rapidly understand the context of the image they were looking at and performed their search accordingly [23, 24, 25].

Original: For instance, while looking for a car, they immediately understood it wouldbe on the road and not in the sky or buildings (Fig.
Revised: For instance, while looking for a car, they immediately understood it would be on the road and not in the sky or buildings (Fig.

Original: S5A, see also Fig.
Revised: S5A, see also Fig.

Original: S5B-C for other examples).
Revised: S5B-C for other examples).

Original: Meanwhile, none of the visual search models analyzed here were able to capture this behavior.
Revised: Meanwhile, none of the visual search models analyzed here were able to capture this behavior.

Original: Onthe other hand, it is also important to note that human subjects did not follow salient distractors (suchas human faces while looking for something else), whereas nnIBS did, mainly in the ﬁrst ﬁxations(Fig. S4).
Revised: On the other hand, it is also important to note that human subjects did not follow salient distractors (such as human faces while looking for something else), whereas nnIBS did, mainly in the ﬁrst ﬁxations (Fig. S6).

Original: This is because of its prior, which is a saliency map computed with DeepGaze II [17], aneural network trained on human subject data on free viewing.
Revised: This is because of its prior, which is a saliency map computed with DeepGaze II [19], a neural network trained on human subject data on free viewing.

Original:  Conclusions
Revised:  Conclusions

Original: Even though all of the models and datasets evaluated were designed on the task of visual searchin natural scenes, the stark differences in the models’ performance in different datasets show howmuch work still needs to be done in terms of providing a clear deﬁnition of the problem at handand how to assess the different solutions.
Revised: Even though all of the models and datasets evaluated were designed on the task of visual search in natural scenes, the stark differences in the models’ performance in different datasets show how much work still needs to be done in terms of providing a clear deﬁnition of the problem at hand and how to assess the different solutions.

Revised: While categorical visual search provides the advantage of evaluating the ability to capture object invariance, searching for a speciﬁc target allows to evaluate the ability of identifying the target among several distractors belonging to the same category.

Revised: The criteria used for data collection is also of relevance, where automatically stopping when ﬁxating at the target may fail to provide an account of when human subjects actively recognize the target.

Revised: Lastly, although colored images may be more realistic, they often result in shorter scanpaths than grayscale images (Table S1), due to the added information they possess.

Original: Our work shows some beneﬁts and drawbacks of eachparadigm considered.
Revised: With regard to the solutions themselves, our work shows some beneﬁts and drawbacks of each paradigm considered.

Original: On the one hand, the IRL approach seems to be limited at present, as it is ableto incorporate context information, but is not generalizable outside of its own dataset.
Revised: On the one hand, the IRL approach seems to be limited at present, as it is able to incorporate context information, but it does not generalize outside of its own dataset.

Original: On the otherhand, while DNNs based models like IVSN appear to have better efﬁciency, the Bayesian approachproduced more human-like scanpaths.
Revised: On the other hand, while DNNs based models like IVSN appear to have better efﬁciency, the Bayesian approach produced the most human-like scanpaths.

Original: This is consistent with the idea of modeling human visualsearch as an active sampling process where successive steps attempt to reduce uncertainty about thelocalization of the target [24, 25].
Revised: This is consistent with the idea of modeling human visual search as an active sampling process where successive steps attempt to reduce uncertainty about the localization of the target [26, 27].

Original: Here, we suggest that combining these paradigms could potentiallygive rise to more precise and interpretable models.
Revised: Here, we suggest that combining these paradigms could potentially give rise to more precise and interpretable models.

Original: In particular, by modeling the visual system withina DNN framework [26], while applying a Bayesian approach for the central decision making [27, 28].
Revised: In particular, by modeling the visual system within a DNN framework [28], while applying a Bayesian approach for the central decision making [29, 30].

Original: Notwithstanding, combining scene content with the target’s
Revised: Notwithstanding, combining scene content with the target’s

Original: meaning remained elusive for these stateof-the-art visual search computational models and proved to be particularly important for simulatinghuman subjects’ behavior.
Revised: meaning remained elusive for these stateof-the-art visual search computational models and proved to be particularly important for simulating human subjects’ behavior.

Original: Further work should be carried out in order to develop general visualsearch algorithms capable of capturing context.
Revised: Further work should be carried out in order to develop general visual search algorithms capable of capturing context.

Original: nnIBS could beneﬁt from modifying its prior bycreating a map that correlates the target with the image’s context.
Revised: nnIBS could beneﬁt from modifying its prior by creating a map that correlates the target with the image’s context.

Revised: Recently, Levi and Ullman [31] have proposed an efﬁcient manner of using relational reasoning to build context for small object detection, aggregating information across the entire image.

Revised: This approach, named the Efﬁcient Non

Revised: Local module or ENL, could prove to be useful for this task.

Original: The present work sheds light on both the limitations of current visual search algorithms and howpotential improvements can be accomplished by combining approaches.
Revised: The present work sheds light on both the limitations of current visual search algorithms and how potential improvements can be accomplished by combining approaches.

Original: Moreover, it is a ﬁrst stepinto solving the urgency for the deﬁnition of a common set of metrics and data for the developmentof more general human visual search computational models. 
Revised: Moreover, it is a ﬁrst step into solving the urgency for the deﬁnition of a common set of metrics and data for the development of more general human visual search computational models. Author contributions

Revised: F.T. and G.R. prepared the datasets, coded the IBS models and adapted the other models, including numerical optimizations and speed-ups.

Original: Appendix
Revised: F.T., G.R., G.B. and J.K. performed the analysis and wrote the manuscript. Acknowledgments The authors were supported by the CONICET and the University of Buenos Aires (UBA). The research was supported by the CONICET (PIP 11220150100787CO), the ARL (Award W911NF1920240) and the National Agency of Promotion of Science and Technology (PICT 2018-2699).

Original:  A Methods and MaterialsA.1 Datasets description
Revised: Appendix A Methods and Materials A.1 Datasets description

Original: Each dataset comprises a different set of search images, target objects for each image, and humansubjects’ scanpaths on those images (Table S1).
Revised: Each dataset comprises a different set of search images, target objects for each image, and human subjects’ scanpaths on those images (Table S1).

Original: [Table]
Revised: [Table]

Original: A.2 Equalizing datasets
Revised: A.2 Equalizing datasets

Original: To bring each dataset to a common format, a series of steps were carried out.
Revised: To bring each dataset to a common format [Table] a series of steps were carried out.

Original: To begin with, all of thehuman subjects’ scanpaths were cut as soon as a ﬁxation had landed on the target and only trials inwhich the target was found were considered.
Revised: To begin with, all of the human subjects’ scanpaths were cut as soon as a ﬁxation had landed on the target and only trials in which the target was found were considered.

Original: The same criteria was applied to the models.
Revised: The same criteria was applied to the models.

Original: In order todo this, an estimation of the size of the fovea (i.e. the region around the ﬁxation point in which thetarget could be identiﬁed) had to be done for each dataset (Table S1).
Revised: In order to do this, an estimation of the size of the fovea (i [Table] e. the region around the ﬁxation point in which the target could be identiﬁed) had to be done for each dataset (Table S1).

Original: With respect to the images,those in the Unrestricted dataset had to be transformed to grayscale, and the ones in the MCS datasetwere rescaled to their average size.
Revised: With respect to the images, those in the Unrestricted dataset had to be transformed to grayscale, and the ones in the MCS dataset were rescaled to their average size.

Original: The targets’ bounding boxes were not available for the latter, sothey had to be retrieved from the COCO dataset’s annotations, and target absent trials were excludedfrom the present analysis.
Revised: The targets’ bounding boxes were not available for the latter, so they had to be retrieved from the COCO dataset’s annotations, and target absent trials were excluded from the present analysis.

Original: Additionally, since only training trials of COCOSearch18 are available,every image corresponding to testing was discarded, and a random subset was selected for evaluationto avoid overﬁtting.
Revised: Additionally, since only training trials of COCOSearch18 are available, every image corresponding to testing was discarded, and a random subset was selected for evaluation to avoid overﬁtting.

Original: Given the difference in the experiments’ design, target images had to be cropped from the searchimages in both the MCS and COCOSearch18 datasets, since two of the models evaluated need animage of the target to search.
Revised: Given the differences in the experiments’ design, target images had to be cropped from the search images in both the MCS and COCOSearch18 datasets, since two of the models evaluated need an image of the target to search.

Original: Conversely, images from the Unrestricted and Interiors datasets had tobe categorized by hand in one of the COCO  categories depending on their target object, so they couldbe processed by the visual search model trained on the COCOSearch18 dataset. 
Revised: Conversely, images from the Unrestricted and Interiors datasets had to be categorized by hand as one of the COCO object categories depending on their target, so they could be processed by the visual search model trained on the COCOSearch18 dataset. These differences were also present in the scanpaths’ length of human subjects.

Revised: Since every visual search model possesses a maximum number of ﬁxations, this upper limit was set for each dataset according to the saturation of human performance.

Original: Trivial search images (that is to say, those where the initial ﬁxation lands on the target) were discardedin the MCS and Unrestricted datasets.
Revised: Trivial search images (that is to say, those where the initial ﬁxation lands on the target) were discarded in the MCS and Unrestricted datasets.

Original: In the case of the Unrestricted dataset, images were showntwice to the participants, and here only the ﬁrst trials were kept.
Revised: In the case of the Unrestricted dataset, images were shown twice to the participants, and here only the ﬁrst trials were kept.

Original: COCOSearch18 used some of theimages for more than one trial, changing the target; these are considered as different images.
Revised: COCOSearch18 used some of the images for more than one trial, changing the target; these are regarded as different images.

Original: Finally, a common format for human subjects’ scanpaths and the input images and targets was deﬁned,based on JSON ﬁles.
Revised: Finally, a common format for human subjects’ scanpaths and the input images and targets was deﬁned, based on JSON ﬁles.

Original: The models’ output follow the same format as those of human subjects.
Revised: The models’ output follow the same format as those of human subjects.

Original: Thisway, a concordance in terms of nomenclature across all datasets and models is also achieved.
Revised: This way, a concordance in terms of nomenclature across all datasets and models is also achieved.

Original: A.3 Models’ scanpath parameters
Revised: A.3 Models’ selection criteria

Original: [Table]
Revised: Additional visual search models were considered, such as the ones described in [13], [15], and [16], but they were discarded for either their code not being available or not admitting natural scenes images as input. However, we do intend to facilitate the incorporation of new models by publishing all of the code used here. A.4 Models’ scanpath parameters

Original: Despite the number of differences between models, we attempt to set a common criteria for decidingwhen the target was found.
Revised: Despite the number of differences between models, we attempt to set a common criteria for deciding when the target was found.

Revised: All of them use an oracle to decide if the target was found, and the search is carried out until the target is found or a maximum number of ﬁxations is reached.

Original: It is worth noticing that it is not possible to apply precisely the same ruleto all of them, since IVSN runs over the original image and the IBS family and IRL run over differentgrids (Table S2).
Revised: It is worth noticing that it is not possible to apply precisely the same rule to every one of them, since IVSN runs over the original image and the IBS family and IRL run over different grids (Table S2).

Original: [Table]
Revised: [Table]

Original: A.4 IBS Python Implementation
Revised: A.5 IBS Python Implementation

Original: [Table]

Original: The original code, written in MATLAB, was ported to Python, an open-source programming languageavailable to everyone.
Revised: The original code, written in MATLAB, was ported to Python, an open-source programming language available to everyone.

Original: From the start, the goal in mind was to be as clear as possible when it came towriting the code, including a precise deﬁnition of the input and output of each method.
Revised: From the start, the goal in mind was to be as clear as possible when it came to writing the code, including a precise deﬁnition of the input and output of each method.

Original: Additionally,we focused on an object-oriented approach, creating classes for each important concept withinthe model (prior, visibility map, target similarity map, and so on).
Revised: Additionally, we focused on an object-oriented approach, creating classes for each important concept within the model (prior, visibility map, target similarity map, and so on).

Original: All of this allows for differenthypotheses to be tested with ease, including the resulting models in the present work, and futureimprovements (such as modifying the prior, in light of the results displayed here) can be implementedwithout much trouble.
Revised: All of this allows for different hypotheses to be tested with ease, including the resulting models in the present work, and future improvements (such as modifying the prior, in light of the results displayed here) can be implemented without much trouble.

Original: We believe well-written code is central for reproducible research.
Revised: We believe well-written code is central for reproducible research.

Original: On the other hand, since the model’s hyper-parameters are tuned for images whose resolution is768x1024, its input is automatically resized to this size, where the visibility and target similaritymaps are computed, before downscaling it to a grid.
Revised: On the other hand, since the model’s hyper-parameters are tuned for images whose resolution is 768x1024, its input is automatically resized to this size, where the visibility and target similarity maps are computed, before downscaling it to a grid.

Original: A.4.1 Prior estimation
Revised: A.5.1 Prior estimation

Original: The estimation of the prior is done by computing saliency maps using the center bias over the inputimages, which in this case  done via DeepGaze II [17].
Revised: The estimation of the prior is done by computing saliency maps using the center bias over the input images, which in this case is done via DeepGaze II [19].

Original: In order to run the model on several differentdatasets, the estimation of the prior is now incorporated in the model through a TensorFlow adaptationof a Jupyter Notebook provided by the BETHGE LAB 5 .
Revised: In order to run the model on several different datasets, the estimation of the prior is now incorporated in the model through a TensorFlow adaptation of a Jupyter Notebook provided by the BETHGE LAB 6 .

Original: A.4.2 Cross-correlation in colored images
Revised: A.5.2 Cross-correlation in colored images

Original: Given that the input of the original model was grayscale images, an adaptation had to be done inorder to run the model on datasets where images are colored.
Revised: Given that the input of the original model was grayscale images, an adaptation had to be done in order to run the model on datasets where images are colored.

Original: Cross-correlation returns a differentvalue for each color channel, so a weighted-average was calculated from common RGB to grayscaleformulas.
Revised: Cross-correlation returns a different value for each color channel, so a weighted-average was calculated from common RGB to grayscale formulas.

Original: In particular, we chose the one used by the scikit-image library:
Revised: In particular, we chose the one used by the scikit-image library:

Original: [Equation]
Revised: [Equation]

Original: A.4.3 SSIM
Revised: A.5.3 SSIM

Original: Due to the extensive use of SSIM in the video industry (to quantify for image quality, for instance), itarose as a natural replacement for cross-correlation when it came to computing the similarity betweenthe search image and target.
Revised: Due to the extensive use of SSIM in the video industry (to quantify for image quality, for instance), it arose as a natural replacement for cross-correlation when it came to computing the similarity between the search image and target.

Original: Moreover, it is capable of working in colored images.
Revised: Moreover, it is capable of working in colored images.

Original: SSIM is computed on the target and each possible region of the image.
Revised: SSIM is computed on the target and each possible region of the image.

Original: Then, these results areaveraged and stored in the center pixel of the corresponding region.
Revised: Then, these results are averaged and stored in the center pixel of the corresponding region.

Original: With this approach, black barswould remain at the edges of the images, so they are managed differently: we trim from the targetthose pixels that would overﬂow the image’s bounds and then we compute SSIM between the target’sleftovers and the corresponding region of the image.
Revised: With this approach, black bars would remain at the edges of the images, so they are managed differently: we trim from the target those pixels that would overﬂow the image’s bounds and then we compute SSIM between the target’s leftovers and the corresponding region of the image.

Original: Since these calculations are made pixel by pixel,the computation behind this method is very expensive, but they have to be done only once (for theresult of each target and search image is stored and can be reutilized).
Revised: Since these calculations are made pixel by pixel, the computation behind this method is very expensive, but they have to be done only once (for the result of each target and search image is stored and can be reutilized).

Original: A.4.4 IVSN
Revised: A.5.4 IVSN

Original: By making use of our PyTorch implementation of the IVSN model, it became possible to adapt theattention map computed by IVSN[10].
Revised: By making use of our PyTorch implementation of the IVSN model, it became possible to adapt the attention map computed by IVSN[12].

Original: Since its values range from zero to one, this adaptation waspretty straightforward: it simply serves as a replacement for cross-correlation.
Revised: Since its values range from zero to one, this adaptation was pretty straightforward: it simply serves as a replacement for cross-correlation.

Original: The advantages ofcapturing object invariance while retaining a good performance in searching for templates with littlecost in computation made it our best choice for this benchmark.
Revised: The advantages of capturing object invariance while retaining a good performance in searching for templates with little cost in computation made it our best choice for this benchmark.

Original: A.5 IVSN Python Implementation
Revised: A.6 IVSN Python Implementation

Original: The code available 6 is written both in MATLAB and LUA, using the ﬁrst for pre and postprocessingand the latter for computations with the Caffe VGG16 model.
Revised: The code available 7 is written both in MATLAB and LUA, using the ﬁrst for pre and postprocessing and the latter for computations with the Caffe VGG16 model.

Original: Besides making the necessaryadjustments to read the input and write the output in the previously deﬁned JSON format, our Pythonimplementation uses the PyTorch library alongside its implementation of VGG16.
Revised: Besides making the necessary adjustments to read the input and write the output in the previously deﬁned JSON format, our Python implementation uses the PyTorch library alongside its implementation of VGG16.

Original: Given the greedy nature of the algorithm, changes on the scanpaths produced were observed dependingon small factors, such as using a different formula for the grayscale conversion of the input imagesor a different interpolation method for upscaling the neural network’s output.
Revised: Given the greedy nature of the algorithm, changes on the scanpaths produced were observed depending on small factors, such as using a different formula for the grayscale conversion of the input images or a different interpolation method for upscaling the neural network’s output.

Original: Nonetheless, itsperformance remained unaffected (Fig. S1).
Revised: Nonetheless, its performance remained unaffected (Fig. S1).

Original: A.6 Adapting the IRL model
Revised: A.7 Adapting the IRL model

Original: This model is already written in Python 7 , but its execution is not straightforward, since only thecode for training the neural network is available.
Revised: This model is already written in Python 8 , but its execution is not straightforward, since only the code for training the neural network is available.

Original: Moreover, the preprocessing of the search imagesdescribed in [12] is also missing.
Revised: Moreover, the preprocessing of the search images described in [14] is also missing.

Original: Following their description, in our implementation, input images are resized to 320x512 and agaussian blur with σ = 2 is applied to them, producing two images for each search image.
Revised: Following their description, in our implementation, input images are resized to 320x512 and a gaussian blur with σ = 2 is applied to them, producing two images for each search image.

Original: Both ofthese are fed forward through the Panoptic-FPN in Detectron2 [19] with Resnet-50 3X as backbone 8and the output is resized to 20x32.
Revised: Both of these are fed forward through the Panoptic-FPN in Detectron2 [21] with Resnet-50 3X as backboneand the output is resized to 20x32.

Original: We found differences in IRL’s performance depending on how thisdownscaling was done.
Revised: We found differences in IRL’s performance depending on how this downscaling was done.

Original: Detectron2 itself provides an output size as a parameter, but the performancewas meager when compared to downscaling using common image interpolation techniques (such asnearest-neighbor), so the latter was used in our implementation.
Revised: Detectron2 itself provides an output size as a parameter, but the performance was meager when compared to downscaling using common image interpolation techniques (such as nearest-neighbor), so the latter was used in our implementation.

Original: In all cases, we adopt the criteria ofusing the best performing model when some speciﬁcations are missing.
Revised: In all cases, we adopt the criteria of using the best performing model when some speciﬁcations are missing.

Original: Once this has been done, masks are created from these semantic segmentations for each of the[-
-]
Revised: Once this has been done, masks are created from these semantic segmentations for each of the[-
-]

Original: COCO categories (if a category was not present in the search image, then its corresponding mask issimply the null matrix).
Revised: COCO categories (if a category was not present in the search image, then its corresponding mask is simply the null matrix).

Original: These masks are the model’s input.
Revised: These masks are the model’s input.

Original: All of this preprocessing has been integrated to the model itself, so our implementation is able tocreate the so-called Dynamic Context Belief maps on any given image.
Revised: All of this preprocessing has been integrated to the model itself, so our implementation is able to create the so-called Dynamic Context Belief maps on any given image.

Original: However, the categorizationof the target object has to be done by hand.
Revised: However, the categorization of the target object has to be done by hand.

Original: B Results
Revised: B Results

Original:  different similarity for
Revised: B.1 Comparison of different similarity maps within the IBS family

Original: Table S3) [Table] COCOSearch [Table]
Revised: Results for the different methods used in the target similarity map computation (Table S3): crosscorrelation (cIBS), SSIM (sIBS), and neural network-based (nnIBS).

Original: [Table]
Revised: [Table]

Original: B.2 Comparison of different patch sizes for the IVSN model
Revised: B.2 Comparison of different patch sizes for the IVSN model

Original: The behavior of IVSN is deeply ingrained with the patch size it uses for target identiﬁcation and toapply the inhibition-of-return effect while searching in its attention map (Fig. S2).
Revised: The behavior of IVSN is deeply ingrained with the patch size it uses for target identiﬁcation and to apply the inhibition-of-return effect while searching in its attention map (Fig. S2).

Original: B.3 Comparison between modeling approaches
Revised: B.3 Comparison between modeling approaches

Original: [Table]
Revised: [Table]

Original: B.4 Comparison between modeling approaches: some failure examples
Revised: B.4 Comparison between modeling approaches: some failure examples

Original: Here, we present examples which illustrate some failures of the models.
Revised: Here, we present examples which illustrate some failures of the models.

Original: In every ﬁgure, initialﬁxations are displayed in red, while participants scanpaths are superimposed with different colors.
Revised: In every ﬁgure, initial ﬁxations are displayed in red, while participants’ scanpaths are superimposed with different colors.

Original: Figure S3 depicts a case where there is a sheer difference between IVSN and human subjects in termsof saccade amplitude, highlighting the relevance of modeling visual decay in the periphery.
Revised: Figure S3 depicts a case where there is a sheer difference between IVSN and human subjects in terms of saccade amplitude, highlighting the relevance of modeling visual decay in the periphery.

Original: In Figure S4, two different examples are shown where IVSN fell for distractors similar to the target,whereas nnIBS proved to be more robust.
Revised: In Figure S4, two different examples are shown where IVSN fell for distractors similar to the target, whereas nnIBS proved to be more robust.

Original: Finally, Figure S5 shows how human subjects are capable of incorporating both the context of theimage and the target’s meaning to guide their search accordingly, a behavior that remains elusive forcurrent visual search models. 
Revised: Finally, Figure S5 shows how human subjects are capable of incorporating both the context of the image and the target’s meaning to guide their search accordingly, a behavior that remains elusive for current visual search models. The target is a potted plant, so humans only looked at the kitchen counter level Similar to B , where the target is a bottle placed in a bathroom.

Original: C Data and Code Availability
Revised: C Data and Code Availability

Original: All of the code used for this benchmark, including metrics, plots, and visual search models, alongsidethe preprocessed datasets, will be available at [GitHub link will be inserted here] .
Revised: All of the code used for this benchmark, including metrics, plots, and visual search models, alongside the preprocessed datasets, are available at the SVRHM branch in the github.com/FerminT/VisualSearchBenchmark repository.