Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models
Authors: Meenakshi Khosla
Keith Jamison
Amy Kuceyeski
Mert R. Sabuncu
Venue: NeurIPS 2022 Accept (2022)
Keywords: deep neural networks
computational neuroscience
ventral visual stream
fMRI

Abstract: Decades of experimental research based on simple, abstract stimuli has revealed the coding principles of the ventral visual processing hierarchy, from the presence of edge detectors in the primary visual cortex to the selectivity for complex visual categories in the anterior ventral stream. However, these studies are, by construction, constrained by their $\textit{a priori}$ hypotheses. Furthermore, beyond the early stages, precise neuronal tuning properties and representational transformations along the ventral visual pathway remain poorly understood. In this work, we propose to employ response-optimized encoding models trained solely to predict the functional MRI activation, in order to gain insights into the tuning properties and representational transformations in the series of areas along the ventral visual pathway. We demonstrate the strong generalization abilities of these models on artificial stimuli and novel datasets. Intriguingly, we find that response-optimized models trained towards the ventral-occipital and lateral-occipital areas, but not early visual areas, can recapitulate complex visual behaviors like object categorization and perceived image-similarity in humans. We further probe the trained networks to reveal representational biases in different visual areas and generate experimentally testable hypotheses. Our analyses suggest a shape-based processing along the ventral visual stream and provide a unified picture of multiple neural phenomena characterized over the last decades with controlled fMRI studies. 

Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models
Meenakshi Khosla!.", Keith Jamison Amy Kuceyeski and Mert R. Sabuncu' 1.2. 1,2.3
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Cambridge, MA, 02139
Nancy E. and Peter C. Meinig School of Biomedical Engineering, Coreell University, Ithaca, NY 14853
3 Radiology. Weill Cornell Medicine, New York, NY 10065 Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065
Correspondence: mkhhosla Aacholla@mi.edu
Abstract
Decades of experimental research based on simple, abstract stimuli has revealed the coding principles of the ventral visual processing hierarchy, from the pres- ence of edge detectors in the primary visual cortex to the selectivity for complex visual categories in the anterior ventral stream. However, these studies are, by construction, constrained by their priori hypotheses. Furthermore, beyond the early stages, precise neuronal tuning properties and representational transforma- tions along the ventral visual pathway remain poorly understood. In this work, we propose to employ response-optimized encoding models trained solely to predict the functional MRI activation, order gain insights into the tuning properties and representational transformations in the series of areas along the ventral visual pathway. We demonstrate the strong generalization abilities of these models on artificial stimuli and novel datasets. Intriguingly, we find that response-ope -optiiizep models trained towards the ventral-occipital and -ocippall areas, but not early visual areas, can recapitulate complex visual behaviors like object catego- rization and perceived mmag-siimilarity humans. We further probe the trained networks to reveal representational biases in different visual areas and generate experimentally testable hypotheses.
Introduction
Understanding the nature of representations and computations in the visual system has been longstanding goal neuroscience. Substantial progress been made by presenting model organisms with simple, abstract stimuli like noise or sine-wave gratings and studying the evoked responses. These carefully crafted experiments, for instance, revealed the presence of edge detectors in the primary visual cortex (1] and sparked the search for the 'optimal input' beyond early visual areas across the visual cortical hierarchy. Significant efforts have also been made to understand the feature complexity of tuning in V4 and IT neurons 2 3 4 5). Over the last two decades, through numerous experiments, recording and analysis techniques, a conceptual understanding has emerged that early visual areas extract low-level features like edges or curves, mid-level regions extract complex local shapes, and high-level regions encode semantic categories, like faces or scenes 6 7 g 10 While much of the details remain unknown, there is accumulating evidence that visual categorization occurs in the ventral temporal cortex in humans [II] This understanding was enabled by synthesizing findings across different hypothesis-drivee studies, most of them employing artificial stimuli atypical of real-world vision, thus lacking ethological validity.
36th Conference on Neural Information Processing Systems (NcurIPS 2022).

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Recently, deep convolutional neural networks (CNNs) optimized for computer vision (CV) tasks, have emerged as the most accurate models of the primate ventral visual stream [2] L [5]. Furthermore, these CV task-optimized models offer a hierarchical correspondence to visual areas along the ventral stream: early CNN layers best predict VI, while intermediate and late layers best predict V4 and IT [12 16, 17 18 19 20, 21 22]. They provide intelligibility in the form of the goal-driven perspective, highlighting the functional role of representations [23]. However, we are far from a complete understanding of the top-down computational objectives (the battery of ethologicalll--reeean tasks) that may have shaped neural representations through evolution or experience- bassed learning; in the absence of right hypotheses, the task-optimized approach can fall short. Studies have already revealed significant inconsistencies between these models and human behavior and gaps remain in their respective robustness to distortions, image-level consistency etc [24] Recently, the idea of using neural data in the model development process has gained some traction, and demonstrated success in increasing both model-human behavior 25 26 27].
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and model-brain alignment, while revealing novel insights about information processing in neural systems 28 29
30 31 32).
33 to train hypothesis- In this study, we leverage recent advances in large-scale fMRI data collection agnostic computational models directly on neural data with novel predictive precision. Importantly, the stimulus set employed in this study contains crowded images of multiple objects in their nat- ural contexts, thus being more typical of everyday scenes and allowing to characterize neural representations and computations in rich, naturalistic conditions. We demonstrate the remarkable ability of these "response-optimized" neural encoding models in reproducing the stimulus- response relationship in voxels along the ventral visual stream for arbitrary images, equaling or surpassing the unprecedented prediction performance of task-optimized models. We show that these models generalize well to contexts that are vastly different from their training conditions (artificial stimuli, novel subjects, etc.), providing strong quantitative validation for the response-optimizatio approach of building encoding models for neurobiological visual systems beyond the retina [34]. Our approach further goes beyond prediction accuracy and aims to gain insights about neuronal tuning properties. Specifically, we implement neural encoding models that disentangle the processing of the "what" and the "where." " This allows us to reveal the selectivity of individual brain voxels along both location and appearance dimensions. We first demonstrate that the estimated spatial topographic organization from our neural encoding models exhibits remarkable agreement with the fine-scaled retinotopic organization of the cortex, which is typically measured with dedicated, painstaking experimental paradigms. We analyze the features that emerge in the neural encoding models to understand the tun- ing properties of cortical voxels. This analysis suggests shape- biased tuning across the ventral visual stream, increasing in complexity from Gabor-like features V1, curves/angles in V2 to concentric and complex shape processing in V4 and higher-order lateral-occipital and ventral-occipital ROIs respectively. We also go beyond single-voxel analysis to characterize how representations evolve along the ventral visual pathway. Probing the information encoded in activity reveals the monotonically increasing separability of category information along the ventral stream hierarchy, strongly supporting the computational goal of the ventral visual stream visual categorization system. We further demonstrate the correspondence between model representations (of high-level visual areas) and mental representations (as assessed with human similarity judgements), suggesting that these ooptiize models inherently capture human psychological judgements, without any explicit supervision do Furthermore, principle components analysis reveals that models of high-level visual ROIs capture complex stimulus dimensions like faces, bodies and shapes. Our paper illustrates how neural encoding models trained on hypothesis-agnostic, naturalistic datasets can be used to confirm neural phenomena characterized painstakingly using controlled experimental paradigms that might be hard to replicate in individual subjects. More importantly, however, we show how in silico experiments on neural encoding models can generate novel hypotheses and provide a powerful framework for building broad, generalizable theories about neural information processing.
patterns of
2 Materials and Methods
Natural Scenes Dataset A detailed description of the Natural Scenes Dataset [ is provided elsewhere [33] (see also the Appendix). The dataset contains measurements of fMRI responses from 8 participants who each viewed 9,000-10,000 natural scenes. A special set of 1,000 images were
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A Model schematic
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A Schematic of the neural encoding model architecture. B Zoomed-in view of the visual cortex on flatmap, showing the 5 visual ROIs.
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shared across subjects; the remaining were mutually exclusive. We focused on modeling responses within 5 visual cortical ROIs in the study. Three ROIs belonging to the retinoptic early visual cortex, namely, VIv, V2v and h44 were defined using a population receptive field (pRF) localizer scan session 133] Two higher order ROIs, namely ventral-occipital areas (VO1-2) and lateral-occipital areas (LO1-2) were delineated using a visual probabilistic atlas B3 (Figure IB).
Response-optimized neural encoding model We trained separate voxel-level neural encoding models for each of the above regions with the same backbone architecture. Keeping the architecture constant across visual areas ensured that differences in emergent representations are driven mainly by the nature response patterns. The predictive model comprises a convolutional neural network (CNN) feature feaNufe extractor shared across all subjects, but unique to specific visual areas. We employ linear readout model on top of the feature space to predict the responses of individual voxels in a specific region. The linear readout is factorized into spatial and feature dimensions following (36] (Figure TA). This allows us to separate spatial tuning (the "where") from feature tuning (the "what"). The shared CNN feature extractor consists of four convolutional blocks, with each block comprising the following feedforward computations: two x 3 convolutional layers, each followed by an inner batch norm, and a ReLU; and an anti-aliased AvgPool operation (stride 2) the end. We employ E(2)-steerable convolutions in all our models to extract features invariant to orientation [37] 38). This modeling choice is inspired by neural computations in early visual areas where groups of neurons are known to perform similar computations, e.g., edge or curve detection, at different orientations. Each convolutional layer contains 48 feature sets extracted at orientations. Weights of the readout are computed as outer products between spatial filter and a feature vector. The spatial filter further has a positivity constraint (enforced using rectification) and is cength-normalicn for each voxel.
Training and testing response-optimized encoding models We used 4 NSD subjects for training and reserved the other 4 subjects for studying generalization. The first group collectively saw 37,000 natural scene images, including the 1,000 shared images. We used the 1,000 shared images for testing our models and split the remaining stimulus set into 35,000 training and 2,000 validation images. All parameters of the neural encoding model were optimized jointly to minimize the masked mean squared error between the predicted and measured IMRI response. This loss allows us to propagate errors through the shared CNN even if the subjects are not exposed to common stimuli since we can exclude the subjects/voxel. for which the target response is not present from the loss calculation. We quantify 'predictive accuracy' on the test images as the Pearson's 's correlation coefficient between the predicted and measured fMRI response at each voxel. We also compare this against the noise-ceiling, expressed as the signal-to-noie ratio when considering the response variability of each voxel across 3 repetitions per image (see Appendix A.5). Table A.1 provides a summary of all analyses methods and baselines used for evaluating and interpreting the proposed response-optimizeed models.
Baseline models (a) Category ideal observer model: We fit a non-image-computaby model using human--enerated annotations for NSD images (obtained from the MS-COCO database). The input to the categorical model is a k-hot encoding vector corresponding to the 80 object categories annotated in the database, where each element indicates the presence/absence of the corresponding category in the image. We fitted la regularized linear regression models on this space to predict voxel responses for held-out stimuli (Appendix A.8). (b) Task-optimized Models: We investigated several DNN models, including AlexNet (39), ResNet-50 [40,, DenseNet [1] and CORnet-s [42). optimized for object recognition on the large-scale ImageNet dataset 43 (Appendix A.3, Table A.2). Since these models can demonstrate lower prediction performance due to the domain shift between ImageNet and NSD stimuli, we also employed a ResNet-50 architecture pre-trained for object detection on the MS- IS-COCO dataset [44 an additional baseline (since NSD stimuli were
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Figure 2: Quantitative analysis. Prediction performance of competing models across the 5 visual arcas. B Boxplot depicting the noise-normalizing pre- dictivity of the proposed response-optinized model. C Generalization of com- peting models novel sub- jects as a function of sam- ple size used for fitting their respective response functions (or readouts). Er- bars in harplots de- pict the 95% confidence- interval (CI) around esti- mated mean.
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drawn from the MS-COCO dataset). For CORnet-S, we pre-select the layer homologous to each visual region and employ a spatial x feature factorized readout (same as the response-optimized model) on top of the fixed feature backbone, allowing a more direct comparison to the proposed model. For the remaining models, we followed the more standard procedure for fitting task-optimized models to voxel responses based on full linear readout. We extracted features from pre-trained layers of each network and fitted l2 regularized linear regression models separately for each layer. The regularization parameter was optimized independently for each subject, each layer. each model and for voxels in each visual area by testing among 8 log-spaced values in [lc-4, le4]. For each visual area and model, we identified the layer that best predicts responses on the validation set. These model layers were used to quantify test performance. (c) Response-optimized (no rotation symmetries): Here, we train esponse-optimizze models with the same architecture as the proposed models but without sharing weights across filter orientations.
Characterizing neural representations by behavioral comparisons: (a) Assessing separabilityy of category and semantic information along the visual hicrarchy To probe the learned featural tuning in the response-optimized model for each visual area, we used representational similarity analysis (RSA) [45). Here, we correlated the patterns of predicted activity between multiple exemplars from different categories of stimuli to obtain a representational similarity matrix (RSM) for each visual area. Further, quantified the separability of categorical information within these similarity matrices by computing a correlation coefficient (Kendall's 's T) between model RSMs against ground truth adjacency matrix defined by object category labels. Specifically, the elements of this matrix are 1 if the corresponding two images belong to different categories and 0 if the images belong the same category. This analysis the THINGS database [46] that belong to a pre-defined set of categories for ease of visualization. These categories were chosen in accordance with previous fMRI studies employing RSA [47 48) and include the following: [face, hand, elephant, cat, plant, fruit, car, tool]. Subsequently, we also validated the obtained results using another large-scale vision dataset, namely 1m (4) We sampled 500 stimuli from each ImageNet-16 category, resulting in a total of 8,000 images and computed the similarity between model RSMs and the categorical adjacency matrix following the same approach described for the THINGS dataset. To assess visual information flow in relation to high-level semantic understanding, we computed pairwise similarity between predicted responses for held-out NSD images to construct model -redicced RSMs for each visual area. We constructed a semantic RSM by correlating the 80-D binary category vectors (which indicate whether each category was present in the image not) for every pair of these NSD images and quantified agreement with model-predicceed RSMs using Kendall's T (henceforth, termed the 'NSD: Semantic' analysis). We computed this agreement using measured RSMs for all visual areas as well. In RSA described here and anywhere else, error bars (95% CI) are computed by performing bootstrap sampling over voxels.
first performed using a subset of 104 images from

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(b) Assessing alignment with human perception We compared model representations against behavioral data from a Multiple Arrangement task where participants interpreted 100 NSD images and arranged them according to the similarity of their content (this dataset called 'nsdmeadows" was released with NSD B. Critically, these images were not seen by any of the response-optimizg models. We constructed a model Representational Dissimilarity Matrix (RDM) for each visual ROI using the correlation distance metric for quantifying pairwise dissimilarities between model-predicted voxel responses. We compared model RDMs against human pairwise dissimilarity judgements using the Kendall's T coefficient and call this the 'NSD: perception' analysis.
3 Results
Response-optimized models approach the noise ceiling for prediction performance and gen- eralize to novel subjects with low sample complexity We observed a remarkably high noise- response- normalized prediction accuracy (~70-85% using the proposed rotationally-symmen optimized model in all 5 visual ROIs, including the higher-order ROIs (L01-2 and VO1-2) (Figure ( Appendix Figure A.2, A.6). Next, we investigated the possible quantitative advantages of learning a rotationally-symeend architecture with no weight sharing across orientations. Importantly, observe that the proposed onationally-symment convolutional architecture performs on par with a standard CNN (Figure[AA), even outperforming the latter in certain visual ROIs, despite containing fewer parameters. This suggests that sharing weights across filter orientations provides strong inductive bias, allowing us to fit expressive models efficiently. We further compared these models against ideal observer and lask-optimized models, and found that response-optimized models consistently outperform these baselines in early visual areas. In high-level visual areas, the response-optinize model signifi- cantly outperforms the ideal observer model, while performing on par with or even surpassing some task-optimized models.
feature space against the representations from a standard convolutional
Next, we assessed how each of these predictive models generalizes the remaining set of 4 test subjects. This analysis intended to compare the statistical efficiency afforded by different repre- sentations [50]. As such, we examined the trend of how much trimulus-response data the mod- els need to achieve a certain level of prediction performance [50]. Here, we freeze the shared feature extractor network and vary the amount of ciimulus-responae
NSD synthetic
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where the linear prediction model is fit on new subjects. As shown in Figure 2 the response- optimized models can achieve significantly high accuracy even when only just 5% of the training examples from novel subjects (~~ 500 samples) are used to train the readout. This re- markable generalization of response-optimize networks, exceeding that of task-optimized net- works, novel subjects suggests models contain appropriate "inductive biases" " that im- prove sample efficiency.
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Figure 3: Generalization to synthetic stimuli. OOD generalization of competing models to synthetic (non- naturalistic) stimuli from nsdsynthetic experiment. Sam- ple stimuli are shown on the top. Error bars depict the 95% CI around estimated mean.
Computational models generalize to OOD datasets We next investigated out-of-distributtin (OOD) test settings to understand the generalization behavior of response-optimize models. The
first OOD setting we consider is the NSD-syntie DSD-synthetic experiment, comprising fMRI recordings to synthetic stimuli (samples shown in F from the same NSD subjects. As shown in Figure 3 response-optimized models significantly outperform task-optimized models (except in region VO) in this OOD domain ('artificial' stimuli), suggesting that the mapping from task-optimized DNN features to voxel responses, was domain/lataset-speeffff hand, allow for flexible OOD generalization. Generalization other novel fMRI datasets, including
Response-optimizz models, on the other
51 52 533 is shown and discussed in the Appendix B.2.
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Figure 4: pRF estimation with response-optmmized encoding models. A Scatter plots showing predicted and localizer-estimated retinotopic parameters for all voxels in all 4 subjects (see Suppl. Methods for polar angle metries shown scatter plots). Inset correlations are computed using voxels from all 4 subjects. B Predicted and localizer-estimated retinotopic parameters visualized on cortical flatmaps. Bolded values on top show subject-specific agreement. C Agreement between estimated and measured retinotopic maps as a function of training examples stimulus-responts pairs) from novel subjects used to train their linear readout. Error bars depict the 95% CI around estimated mean.
Learned spatial masks reproduce the fine-grained retinotopic organization of early visual cortex The factorized readout in the proposed model allows us to separate spatial and feature selectivity. We characterized the spatial tuning of early visual areas by estimating the population receptive field (pRF) from the learned spatial masks of the proposed response-optimizer model; we compare the estimated parameters against measurements from the independent NSD pRF localizer experiment that uses artificial stimuli. The pRF is defined as the region of the visual field within which a stimulus results in increased aggregated activity across a population of neurons, as reflected in IMRI. Based on the analysis of these data, each brain voxel's retinotopic organization can be described with three parameters: the pRF location in polar coordinates (polar angle and eccentricity), and size. Using the voxel-wise spatial mask of the readout in our encoding model, we estimated the pRF location as the average of the coordinates weighted by the learned spatial mask value at each grid location. To compare the estimated eccentricity against the corresponding measurements from pRF localizers, henceforth called 'eccentricity agreement' we calculated the Pearson's 's correlation coefficient between the measured and predicted eccentricity arrays in each ROI. To compare the estimated polar angle values against corresponding measurements from pRF localizer scans, henceforth called 'polar angle agreement', we adopted measure from circular statistics called circular correlation coefficient (54), due to the circular nature of angular data (Appendix A.4). It is important to note that the encoding models do not account for the spatial locations of target voxels. Despite this, the estimated spatial topographic organization from the encoding models exhibits remarkable levels of agreement with the fine-scaled retinotopic organization of the cortex without any explicit supervision to do so (Figure AAB)) eccentricity agreement E [0.48-0.79] for all 4 subjects whereas the polar angle agreement E [0.70-0.82]. We further compared this agreement on the estimated pRF parameters for the held-out set of NSD subjects by varying the quantity of data used to estimate their linear readouts from the fixed convolutional core of the response-optimizer model (pre-trained initial set of 4 subjects). We find that while the agreement increases with the sample size used to fit readouts, even with much fewer samples (e.g. 100), we can still characterize the retinotopic organization (Figure AC.. This is better than the retinotopic agreement observed for the CORnet- S model (which also employed a factorized readout, enabling characterization of spatial tuning (Appendix Figure A.)) Previously, retinotopic organization of the human visual cortex has been dominantly studied with spatially modulated stimuli. While encoding models have been shown to be broadly useful for characterizing retinotopy [35), B1], precise quantification of the agreement and localizer-estimated retinotopic parameters, as well as the effect of sample sizes on this agreement, has been lacking. Here, we present an alternate approach based on naturalistic stimulation and response-optimizes encoding models to delineate subject-specife retinotopic maps and precisely quantify the agreement. Remarkably, the retintopic organization estimated from the proposed encoding models is even more spatially smooth than the localizer-based organization, despite not being informed by anatomy.
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Response-optimized models characterize transformations of image representations along the visual cortical hierarchy

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1-SNE model predieted RDM
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Figure 5: A highlights that models of high-level visual areas (LO and VO) are more aligned with human percep- tion and image semantics than early visual ROI (V1-V4) models. B and C highlight the separability of cat- egory information along the ventral visual stream. B Results of the Repre- sentational Similarity Analysis (RSA) framework applied to the THINGS and ImageNet- 16 datasets containing different categories of objects. C Matrices of all pairwise similarities between model-predicted responses for sampled images in the THINGS dataset. [[.ftt] Top images for the most and least activating concepts of the first two PCs of preeeepredictedd VO responses. [Upper right] Total ex- plained variance number of PCs. [Lower right] All im- ages from the THINGS dataset pro- jected onto the first two principal di- mensions of the response and colored by their labels.
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(a) Monotonically increasing alignment with human perception along the ventral visual stream We evaluated models behavioral data from Multiple Arrangement task where participants arranged NSD images according to the similarity of their content. This behavioral analysis of neural network models (Figure 55A suggests that higher visual areas (LO and VO) more strongly capture human perceptual patterns and image semantics than early visual areas and this quantitative agreement increases monotonically along the ventral visual hierarchy ('NSD perception' and 'NSD semantics' analysis discussed in Methods). Importantly, responses patterns in their respective models group semantically similar categories together, as shown in the t-SNE plot for model-predictes RDM (region VO). For instance, faces are all clustered in the bottom right of the t-SNE plot and scenes group together in the left (5)g despite wide variations in the visual appearance of these categories. This is remarkable, given that the models received no supervision from perceptual/semanti measures. Human behavioral data ("nsdmeadows') appears very similar and is shown Appendix Figure A. This suggests that these models, solely optimized for high-level visual cortical response prediction without any semantic supervision - can even serve as powerful models for uncovering the structure of mental representations. Intriguingly, the representational structure of predicted responses exhibits stronger agreement with perceived similarity than the representational structure of measured responses. This suggests that both the perceptual and semantic information are likely more explicit in predicted responses (even for held-out data) than measured responses. One possible explanation is that fMRI measures of brain activity have undesired noise components and predictive models, in particular, response opttonize models as shown here, can denoise this fMRI activity (see also Appendix Figure A.3).
(b) Monotonically increasing separability of category information along the ventral visual stream Task-optimized computational models have suggested that the ventral stream hicrarchy "untangles' representations through a sequence of processing steps, so that representations of objects and categories that are inseparable the initial processing stage (c.g.. VI) become untangled at the last stage of the hierarchy [56] 23 Here, we present a strong evidence for this increased separability through appothesis-annostiic response-optimized models. Previous studies have shown that the representational geometry for the same set of stimuli varies systematically across different cortical areas, providing a useful signature for how information transformed across different cortical processing streams [56). Here, we employ the representational similarity analysis (RSA) framework to relate emergent representations responee-optimized models of difference brain regions against ground truth adjacency matrix defined by object category labels. Keeping model
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architecture the same, simply by changing the response targets for an encoding model from voxels in visual areas VI through LO-VO, we observe a drastic change in the geometry of the extracted representation. The increasingly prominent block-diagonal structure in the RSMs along the ventral visual stream (Figure(5p), consistent with categorical distinctions in the THINGS dataset, highlights that the distributed activity patterns to exemplars of the same category become increasingly more similar; and patterns to exemplars of different categories become progressively dissimilar along the processing stream, strongly supporting the computational goal of the ventral visual stream as a categorization system. We also quantified the separability of categorical information in different visual areas (Figure (5.. Here again, we see that this finding regarding increasing separabilitty of categorical information holds across both datasets containing different visual categories.
Response-optimized models reveal high-level visual information within central-ccipitall cortex Next, we wanted to discover what kinds of features emerge in the ventral-occipital region model and whether they are capable of discriminating between complex categories. Instead of looking at all model neurons emulating voxels, we built a compact neural representational space by passing large stimulus set (~22,0000 images from the THINGS database [46]) through the predictive models and then performing Principal Components Analysis (PCA) on the predicted responses of this network (Appendix A.6). Visualizing the images from top concept categories that elicit highest and lowest activation of individual principal components (PCs) reveals complex stimulus dimensions: the first PC, which explained ~ 49% of the variance in responses strongly separates faces, animals and bodies from inanimate objects, despite vast variations in the visual appearance of images within sub-categories. The second PC, accounting for ~ 16% variance, distinguished objects based on their shape (circular vs, rectilinear), suggesting that such shape features are important for VO. Importantly, these features do not emerge when the same analysis is applied to early visual ROIS (V1-V4).
Response-optimized models exhibit shape bias A striking known dis- crepancy between human and ma- chine vision is the shape vs. tex- ture bias: human classification deci- sions are shape-biased whereas CNNs trained for CV tasks tend to classify images by texture (57 58 We study whether the shape bias of humans is reflected in response-optimizer mod- els of cortical regions that subserve visual categorization in humans. We first train linear layer on top of the predicted responses of response- optimized models to map these re- sponses to visual categories in the ImageNet-16 16 database [57] using small subset of this database. We then assess the transfer performance of response-optin -pppose-optimized models for clas- sification held-out stimuli from the ImageNet-16 16 dataset as well as the Silhouette dataset described in (57) (Appendix A. 10). We find that the classification performance is signif- icantly better than what would be expected from the same network ar- chitecture with a random initializa- tion (Figure (B), suggesting that the brain popponse-opiimizatinn objective yields effective representations. Fur- ther, this classification performance is better using models of intermediate and high-level visual areas (V4, L.O and VO) than early visual ar- eas, even approaching the transfer performance of an MSSCOCO--triine object classification network
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with the same architecture (Figure 6 ttop, see Appendix A. for model details), providing further support for the separability of categorical information in high-level, but not early visual cortex (... The I.O model has the highest classification performance on the Silhouette dataset (which solely contain shape information), consistent with and providing further evidence for previous hypotheses that LO tracks object shape information [59]. We then evaluate the classification performance of this mapped response-optimized model on the diagnostic texture-shape cue conflict dataset [57] to assess whether the model makes its categorization decisions based on shape texture (Appendix A. We follow [57] and define shape bias as the percentage of times the model classifies an image based on its shape over texture, provided it returned either the correct shape correct texture label for the image. A model is shape-bias biased if its shape bias> 50%. We observed that all response- -epponse-pptmmized models exhibit shape bias (Figure 6B [bottom]] Appendix Figure A.1), with this metric ranging from 56.7-69.44 4%, suggesting that representations within the ventral visual stream are likely biased towards shapes than textures. This is in stark contrast standard CV models which show a texture bias, for e.g., the same architecture trained for multi-label object classification on MS- MS-COCO shows a strong texture bias and performs significantly worse on the Silhouettes dataset (Figure 6B, gray bar), suggesting that the distribution of NSD stimuli (which are drawn from MS-COCO) is not sufficient to drive the shape bias and the training objective (matching neural responses rather than object category labels) matters. Further, as shown in Figure 6 (bottom), it is not the linear mapping to neural response but the way features are optimized that explains the differing shape/texture bias of lask-optimized and -otronsepptmmeee encoding models. We next performed a feature visualization analysis to find visual patterns that maximally activate individual model neurons emulating brain voxels in response-optimized encoding models. Following [60], we start with a random noise input To for each model neuron and iteratively update the input along the gradient Ba, to synthesize inputs that would result in higher and higher predicted activation a; for that neuron (Appendix A.9). Visual inspection shows that the features increase in complexity as we go from early to high- visual ROI models: simple edge-like features or high-low frequency detectors are seen in V1, curves and edges are seen in V2, more complex curvatures in V4 and finally, fully-formed complex shapes like concentric hexagons are seen I.O and VO (Fig. 6 Appendix Fig. A.7). We also validated this increased complexity using quantitative measures based on compression ratios (Appendix Table A.3).
de
4 Discussion
In this study, we leveraged the large sample size and natural variation represented in the Natural Scenes Dataset train deep neural networks directly on the brain response. Concurrently, recent papers B1 30 have also demonstrated that when trained with large amounts of data such as NSD, the response-optimizzaion approach can perform competitively with ofte-offthheart task-optimized models (discussed in more detail in Appendix C.I). We studied model generalization in various settings: from synthetic stimuli to novel fMRI datasets with different preprocessing. Our results recapitulated key features of early visual areas (e.g. their precise retinotopic organization) and representational properties in higher level areas. We also demonstrated that response-optimizer models can capture human behavior, including not only object categorization, but also other fine- grained measures such as perceived image similarity. The response- optimization approach, in principle, allows exploring infinite paths in the model space, and when optimized properly. converging towards one that explains maximum possible variance in the data. Since any features that emerge in these optimized networks are result of optimization to match neural targets, unconfounded by any other supervision signal, these models can provide stronger tests about the visual representations within different visual areas.
In terms of human object categorization behavior, our models lack significantly compared to ImageNettsuppervised DNN models. While part of this may be due to the domain shift, the re- stricted transfer learning setting (a single linear layer mapping), small size of the transfer set or simply the coarse spatial resolution of IMRI, this gap is worth exploring. Further, using neural data to build better models for other communities (e.g. computer vision) is another scientifically fascinating direction and can develop reinforcing cycles. Studies have shown that explicitly encouraging neural networks to build representations similar V1 representations can improve their robustness under noise and adversarial attacks 66 (6]. In a similar vein, regularization with high-level visual cortical activity might help grant models brain-like biases (e.g. shape bias) and close the gap between human and machine vision. A Alternatively, probing the connections in response-optimizzed models, which already exhibit shape bias, using model pruning and attribution methods can yield a mechanistic understanding of the shape bias in neural activity and reveal principles that may enable CV models
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to attain similar representations. Recent studies have inspired optimism that studying connection weights reveal underlying computational principles may be tractable [4 63
Acknowledgements
This work was funded by the following grants: NIH RFI MH123232, NIH ROI LM012719, NIH R01 AG053949, NSF CAREER 1748377, and NSF NeuroNex Grant 1707312 (MS).
References
[1] David H Hubel and Torsten N Wiesel. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. The Journal of physiology, 160(1):106-154, 1962.
[2] Robert Desimone, Thomas D Albright, Charles G Gross, and Charles Bruce. Stimulus-selective properties of inferior temporal neurons in the macaque. Journal of Neuroscience, 4(8):2051- 2062, 1984.
[3] Keiji Tanaka, Hide-aki Saito, Yoshiro Fukada, and Madoka Moriya. Coding visual images of objects in the inferotemporal cortex of the macaque monkey. Journal of neurophysiology. 66(1):170-189, 1991.
[4] Anitha Pasupathy and Charles E Connor. Responses to contour features in macaque area v4. Journal of neurophysiology, 8265):2400-2502, 1999.
[5] Kazushige Tsunoda, Yukako Yamane, Makoto Nishizaki, and Manabu Tanifuji. Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns. Nature neuroscience, 4(8):832 80088888888, 2001.
[6] Anitha Pasupathy and Charles E Connor. Population coding of shape in area v4. Nature neuroscience, 5(12): 1332- 2002.
[7] Xiaomin Yue, Sophia Robert, and Leslic G Ungerleider. Curvature processing in human visual cortical areas. Neurolmage, 222:117299 117295, 2020.
[8] Scott L Brincat and Charles E Connor. Underlying principles of visual shape selectivity in posterior inferotemporal cortex. Nature neuroscience, 7(8):880-888 1888800886,400 2004.
[9] Nicole C Rust and James DiCarlo. Selectivity and tolerance ("invariance") both increase as visual information propagates from cortical area v4 to it. Journal of Neuroscience, 30(39):22978 12978- 12995, 2010.
[10] Paul E Downing, AW-Y Chan, MV Peelen, CM Dodds, and N Kanwisher. Domain specificity in visual cortex. Cerebral cortex, 16(10):1453-1461 2006.
[11] Kalanit Grill-Spector and Kevin S Weiner. The functional architecture of the ventral temporal cortex and its role categorization. Nature Reviews Neuroxcience, 15(8)533-554, 2014.
[12] D. Yamins, H. Hong. C. F. Cadieu, E. A. Solomon, Seibert, and J. DiCarlo. Performance- optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl. Acad. Sci. U.S.A., 11 111(23).819-8624 Jun 2014.
[13] Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J Majaj. Rishi Rajalingham, Elias B Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy. Franziska Geiger, et al. Brain-score: Which artificial neural network for object recognition most brain-like? BioRxiv, page 407007, 2020.
[14) Martin Schrimpf, Jonas Kubilius, Michael J Lee, N Apurva Ratan Murty, Robert Ajemian, and James DiCarlo. Integrative benchmarking to advance neurally mechanistic models of human intelligence. Neuron, 108(3):413-423 2020.
[15] Katherine R Storrs, Tim C Kietzmann, Alexander Walther, Johannes Mehrer, and Nikolaus Kriegeskorte. Diverse deep neural networks all predict human inferior temporal cortex well, after training and fitting. Journal of Cognitive Neuroscience, 23(10))2044-206 2021.
10

---

[16] Seyed-Mahdi Shaligh-Razave and Nikolaus Kriegeskorte. Deep supervised, but not un- supervised. models may explain cortical representation. PLoS computational biology. 10(11)=e1003115 2014.
[17] Umut Güçlü and Marcel AJ van Gerven. Deep neural networks reveal gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience, 35(27):10005-00014 2015.
[18] Radoslaw Martin Cichy, Aditya Khosla, Dimitrios Pantazis, Antonio Torralba, and Aude Oliva. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Scientific reports, 6(1):1-13, 2016.
[19] Michael Eickenberg, Alexandre Gramfort, Gaël Varoquaux, and Bertrand Thirion. Seeing it all: Convolutional network layers map the function of the human visual system. Neurolmage, 152:184-194 2017.
[20] Tomoyasu Horikawa and Yukiyasu Kamitani. Generic decoding of seen and imagined objects using hicrarchical visual features. Nature communications, 8(1):1-15, 2017.
[21] Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, and Zhongming Liu. Neural encoding and decoding with deep learning for dynamic natural vision. Cerebral cortex. (8(12):4136-411 2018.
[22] Mohamed Abdelhack and Yukiyasu Kamitani. Sharpening of hierarchical visual feature repre- sentations of blurred images. Eneuro, 5(3), 2018.
[23] D. L. Yamins and J. DiCarlo. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci., ((33)356-365, Mar 2016.
[24] Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A Wichmann, and Wieland Brendel. Partial success in closing the gap between human and machine vision. Advances in Neural Information Processing Systems, 34:23885- 23899, 2021.
[25] Callie Federer, Haoyan Xu, Alona Fyshe, and Joel Zylberberg. Improved object recognition using neural networks trained mimic the brain's statistical properties. Neural Networks, 131:103-114, 2020.
[26] Shahd Safarani, Arne Nix, Konstantin Willeke, Santiago Cadena, Kelli Restivo, George Denfield, Andreas Tolias, and Fabian Sinz. Towards robust vision by multi-task learning on monkey visual cortex. Advances in Neural Information Processing Systems, 34:739 751, 2021.
[27] Joel Dapello, Kohitij Kar, Martin Schrimpf, Robert Geary, Michael Ferguson, David D Cox, and James DiCarlo. Aligning model and macaque inferior temporal cortex representations improves model-to-human behavioral alignment and adversarial robustness. bioRxiv, 2022.
[28] Dan Schwartz, Mariya Toneva, and Leila Wehbe. Inducing brain-relevant bias in natural language processing models. Advances in neural information processing systems, 32, 2019.
[29] Katja Seeliger, Luca Ambrogioni, Yagmur Güęlüüüük, Leonieke M van den Bulk, Umut Güşlüü, and Marcel AJ van Gerven. End-to-end neural system identification with neural information flow. PLOS Computational Biology, 17(2): 1008558, 2021.
[30] Meenakshi Khosla and Leila Wehbe. High-level visual areas act like domain-general filters with strong selectivity and functional specialization. bioRxiv, 2022.
[31] Ghislain St-Y Yve,, Emily J Allen, Yihan Wu, Kendrick Kay, and Thomas Naselaris. Brain- optimized neural networks learn non non-hicrarchical models of representation in human visual cortex. bioRxiv, 2022.
[32]
Lurz, Mohammad Bashiri, Konstantin Willeke, Akshay Jagadish, Eric Wang, Edgar Y Walker, Santiago A Cadena, Taliah Muhammad, Erick Cobos, Andreas S Tolias, et al. Generalization in data-driven models of primary visual cortex, BioRxiv, pages 2020-10, 2021.
11

---

[33] Emily J Allen, Ghislain St-Yves, Yihan Wu, Jesse L Breedlove, Jacob S Prince, Logan T Dowdle, Matthias Nau, Brad Caron, Franco Pestilli, Ian Charest, al. massive fmni dataset to bridge cognitive neuroscience and artificial intelligence. Nature neuroscience, 25(1):116-126, 2022.
[34] Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen A Baccus, and Surya Ganguli. From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction. arXiv preprint arXiv:1912.06200 2019.
[35] Liang Wang, Ryan EB Mruezek, Michael J Arcaro, and Sabine Kastner. Probabilistic maps of visual topography in human cortex. Cerebral cortex, (5(10):3311-39331 2015.
[36] David A Klindt, Alexander $ Ecker, Thomas Euler, and Matthias Bethge. Neural system identification for large populations separating what and where. In Proceedings of the 31st International Conference on Neural Information Processing Systems, pages 3509-3519, 2017.
[37] Maurice Weiler and Gabriele Cesa. General -quieiquvari steerable enns. arXiv preprint htt:::911.002555 2019.
[38] Alexander S Ecker, Fabian H Sinz, Emmanouil Froudarakis, Paul G Fahey, Santiago A Cadena, Edgar Y Walker, Erick Cobos, Jacob Reimer, Andreas S Tolias, and Matthias Bethge. A rotation-equivariant convolutional neural network model of primary visual cortex. arXiv preprint arXiv: 1809, 2018.
[39] Alex Krizhevsky, lla Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 2012.
[40] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.
[41] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700-4708, 2017.
[42] Jonas Kubilius, Martin Schrimpf, Aran Nayebi, Daniel Bear, Daniel LK Yamins, and James J DiCarlo. Cornet: Modeling the neural mechanisms of core object recognition. BioRxiv, page 408385, 2018
[43] Jia Deng, R. Socher, Li Fei-Fei, Wei Dong, Kai Li, and Li-Jia Li. Imagenet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Pecoonition((CPPR) volume 00, pages 248-255, 06 2009.
[44] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015.
[45] Nikolaus Kriegeskorte, Marieke Mur, and Peter A Bandettini. Representational similarity analysis-connecting the branches of systems neuroscience. Frontiers systems neuroscience, 2:4, 2008
[46] Martin N Hebart, Adam H Dickter, Alexis Kidder, Wan Y Kwok, Anna Corriveau, Caitlin Van Wicklin, and Chris Baker. Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PloS one, 14(10):c0233792, 2019.
[47] Nikolaus Kriegeskorte, Relating population-code representations between man, monkey, and computational models. Frontiers in Neuroscience, 3:35, 2009.
[48] Yaoda Xu and Maryam between convolutional neural networks and the human brain. Nature communications, 12(1):1- 16, 2021.
Limits to visual representational correspondence
[49] Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann. Generalisation in humans and deep neural networks. Advances in neural information processing systems, 31, 2018.
12

---

[50] Anna A Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, and Leyla Isik. Is it that simple? linear mapping models in cognitive neuroscience. bioRxiv, 2021.
[51] Radoslaw Martin Cichy, Gemma Roig, and Aude Oliva. The algonauts project. Nature Machine Intelligence, (12):613-611) 2019.
[52] Nadine Chang, John A Pyles, Austin Marcus, Abhinav Gupta, Michael J Tarr, and Elissa M Aminoff. Bold5000, public fmri dataset while viewing 5000 visual images. Scientific data, (():1-18,2015
[53] Talia Konkle and George A Alvarez. A self-supervised domain-general learning framework for human ventral stream representation. Nature Commanications, 13(1):1-12, 2022.
[54] Rao Jammalamadaka and Ambar Sengupta. Topics circular statistics, volume 5. world scientific, 2001.
[55] Ghislain Yve and Thomas Naselaris. The weigure receptive field: an interpretable encoding model for complex feature spaces. Neurolmage, 180: 188-202, 2018.
[56] Seyed-Mahdi Khaligh- Razavi and Nikolaus Kriegeskorte. Deep supervised. but not unsu- pervised, models may explain it cortical representation. PLoS Computational Biology, 10, 2014.
[57] Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix Wichmann, and Wieland Brendel. Imagenet-trained cmns are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv preprint arXiv: 18/ 12231, 2018.
[58] Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip Kellman. Deep convolu- tional networks do not classify based global object shape. PLoS computational biology, 14(12)=e 1006613, 2018.
[59] Kalanit Grill-Spector, Zoe Kourtzi, and Nancy Kanwisher. The lateral occipital complex and its role in object recognition. Vision research, 41 2001.
[60] Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent. Visualizing higher-layer features of deep network. University of Montreal, 1341(3):1, 2009.
[61] Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David Cox, and James J DiCarlo. Simulating primary visual cortex at the front of cnns improves robustness image perturbations. Advances in Neural Information Processing Systems, 33:13073-13087 2020.
[62] Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge. Fabian Sinz. Zachary Pitkow, and Andreas Tolias. Learning from brains how to regularize machines. Advances in neural information processing systems, 32, 2019.
[63] Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter. Zoom in: An introduction to circuits. Distill, 5(3)e00024 001, 2020.
[64] Maurice Weiler and Gabriele Cesa. General Equivaann Steerable CNNs. Conference on Neural Information Processing Systems (NeurIPS), 2019.
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14


Reviews:
[{"review_id": "VQ6ApkpnLzE", "reviewer": "Reviewer_xc4V", "summary": "The paper proposed and encoding model using large fMRI datasets to predict the brain response to visual images in different regions of the ventral stream of the visual cortex. They test the model with different paradigms such as: generalization to out-of-distribution images, generalization to other subjects, spatial tuning in the early visual areas, separability of objects in higher visual areas, alignment with human perception, and shape bias. They then visualized the preferred images by different voxels predicted by the model. The main novelty here are the shape bias and the ability of the model to achieve these above things concurrently. Each one of these was observed already in previous models but not together. The paper is quite dense and the authors attempted their best to fit within the page limit.", "questions": "- Do you have any explanation for the aggregated distribution of points in figure 4A which seems to be relatively ordinal in comparison to the predicted?\n\n", "limitations": "- Encoding models are inherently limited by their dependence on the images solely unless other mental or prior information are added\n- Testing on a new dataset limits their ability to test the model against other models that focus on classification.\n- The appeal of encoding models is to understand how the model is able to achieve these feats that are similar to human visual cortex which is not addressed here.", "rating": 7, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- The paper succeeds in having a strong prediction accuracy for OOD images. While, many other models have managed to do that, it is not an easy task.\n- Shape bias is one novelty over the traditional DNN models for CV.\n- The model shows multiple similarities with the ventral stream properties instead of focusing on one or two.\n\nWeaknesses:\n- The paper is quite dense leading the authors to omit many necessary information and to have figures too small and cluttered.\n- Literature review in this field is lacking many important relevant references that discuss the idea of the similarity of hierarchy between humans and DNNs. To name a few:\n    - Horikawa, Tomoyasu, and Yukiyasu Kamitani. \"Generic decoding of seen and imagined objects using hierarchical visual features.\" Nature communications 8.1 (2017): 1-15.\n    - Abdelhack, Mohamed, and Yukiyasu Kamitani. \"Sharpening of hierarchical visual feature representations of blurred images.\" Eneuro 5.3 (2018).\n   - Tsunoda, Kazushige, et al. \"Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns.\" Nature neuroscience 4.8 (2001): 832-838.\n   - Schrimpf, Martin, et al. \"Brain-score: Which artificial neural network for object recognition is most brain-like?.\" BioRxiv (2020): 407007.\n   - Schrimpf, Martin, et al. \"Integrative benchmarking to advance neurally mechanistic models of human intelligence.\" Neuron 108.3 (2020): 413-423.\n   - Federer, Callie, et al. \"Improved object recognition using neural networks trained to mimic the brain’s statistical properties.\" Neural Networks 131 (2020): 103-114.\n\n\n- The paper tests many different conditions with their model leading the paper to be increasingly complex since each condition is done via a separate analysis. One suggestion is to have a summary table of test and how it is measured and baselines so one can understand the big picture.\n\n- Each of the baseline model is trained very differently which makes them not really good baselines\n\n- Viewing figure 6A does not support the statement in the text that preferred stimuli increase in complexity", "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "MW_4_T057G", "reviewer": "Reviewer_xZPs", "summary": "Here, the authors leverage the Natural Scenes Dataset to train up a “hypothesis-agnostic” encoding model. In contrast to recent task-optimized models, they train a series of CNNs (one per visual area) and linear readouts (one per voxel) to predict neural responses to a large set of images. Then, generally speaking, the authors test two things about these models: (1) whether they generalize in a non-trivial way to other subjects/datasets and (2) whether the model learns interesting features in the data that may not be present in task-optimized models.", "questions": "1. Why was only AlexNet used as a non-trivial baseline model, since there are other CNNs already known to be better fits to neural data?\n1. Why were pRFs not estimated for the task-optimized model?\n1. Is it wrong to expect the task-optimized model to look very similar to what is shown in Figure 5? Should this be unique to the proposed encoding model?\n1. Hopefully I didn't miss this, but did the authors measure shape bias in the neural data used to fit their encoding model? Presumably this bias is simply inherited by the encoding model? \n", "limitations": "One major limitation that I found missing in the discussion is that many of the important features found in their encoding model – e.g. correspondence to perceived image similarity – are also found in the neural data, which the models are fit to. The discussion would benefit from clarifying what unique contribution their encoding model brings to the table.", "rating": 7, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n\n1)\tThe writing is clear and concise in most places.  \n\n2)\tThe approach to the problem – using an encoding model with a shared convolutional core – is relatively novel.\n\n3)\tSome of the results impressive, especially when the authors focus on generalization and sample efficiency.\n\nWeaknesses\n\n1)\tIt is difficult to contextualize the encoding model, especially the results of Figure 1, without a more systematic set of baseline models. While it is not necessary to comprehensively survey the field a la CORnet (Kubulius et al. 2019), it is essential to know if the gains the authors’ see are unique to their proposed encoding model. Here, I would include top performing models for ImageNet (e.g. DenseNet and ResNet) along with the older AlexNet that they have already included.\n\n2)\tAs mentioned above, the authors argue that their encoding model (1) generalizes well and (2) learns non-trivial features and response properties. They present compelling evidence for (1), but (2) is less clear. For example, Figure 4 presents measured/predicted retinotopic parameters, but without the task-optimized network for comparison. Are these features unique to their encoding model? Even if the encoding model is a better match, it is explicitly optimized to better match neural data, so it hard to know whether this is truly surprising or not.\n\n    1.\tSame point holds for Figure 5 – I would expect this result in the task-optimized model as well. \n\n    1.\tMore broadly, I think the point about generalization is interesting – maybe their encoding model does learn a set of features that are generically useful in a visual system. However, the fact that the model learns certain features is potentially circular since the model is optimized to mirror neural responses. In other words, it would be more surprising for a task-optimized network to have neural-like responses, while here the model is explicitly optimized to have neural-like responses.\n\n3)\tRelated to the previous point, I’m a little confused about the shape bias analysis. Presumably the neural responses that the model is fit to have a shape bias – though I could not find this analysis – in which case the model should inherit a shape bias via the fitting procedure. Perhaps I am misunderstanding this, but it seems that the results in Figure 6 are potentially circular. \n\n\nMinor\n\n1)\tWhy is the prediction performance pooled across visual areas higher than the prediction performance for each visual area on its own?\n\n2)\tInclude units in all axes for convenience (e.g. RDM similarity is Kendall’s Tau, I would include in the y axis where relevant, e.g. Supp. Fig. 3A).\n\n3)\tShould the task-optimized network  also be in Figure 6B middle – Silhouettes transfer performance?\n", "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}, {"review_id": "LviymYQAZhJ", "reviewer": "Reviewer_3Hjp", "summary": "This work builds end-to-end voxel-level encoding models for fMRI recordings of a popular publicly available dataset of natural scene images (NSD). The authors perform extensive comparisons against networks optimized for image classification on different types of data, and show that their proposed response-optimized networks are better able to generalize to heldout subjects, are more sample-efficient, and better generalize to out of distribution images. The work also offers several interpretability analysis of the representations learned by the response-optimized networks and shows that they appear to align with human similarity judgments and have more shape bias than a task-optimized network.", "questions": "Major:\n1. I would like to hear the author’s response to the point I raised under Weaknesses about a fairer comparison to a task-optimized model that is actually trained on the NSD training images.\n2. Please discuss previous related work that has incorporated brain responses during training of models (see under Weaknesses for a few that I am aware of)\n3. The paper contains a mix of encoding model results and RSA. Can the authors comment on why this was done, as opposed to only using encoding/decoding analyses?\n4. Can the authors clarify how many images were used for train, validation, and test? L112 says that 1k were used for test and 37k for train and validation, but there are 37k images in total.\n\nMinor:\n- Related work to the finding that predictive models can denoise brain data: Ravishankar et al. 2021, Frontiers Comp Neuro showed that predictive models can help with improving both encoding and decoding performance (for single-trial MEG) https://www.frontiersin.org/articles/10.3389/fncom.2021.737324/full\n- Please include a 1 sentence description in the main paper of the type of noise ceiling computation that was done.\n- Lots of details relegated to supplementary with the text (see Suppl. Methods). The supplementary is long so please include specific pointers to where in the supplementary this information can be found.\n- Calling the object category vector “semantic” is strange because all of the model representations contain semantic information. I would change the name to something more descriptive. Also another term to describe this “binary vector” is a k-hot encoding.\n- Clarify whether the subjects were in a common space (e.g. MNI).\n- The supplementary can benefit from proofreading (I spotted several typos from a quick look)\n\nSuggestions for future/follow-up work:\n- The PC interpretation in Fig 5D is nice, but it would be good to do something a bit more principled. If you define a set of semantic norms for each THINGS image (those can be sampled from already available datasets), you may be able to actually show what semantic dimensions are highly associated with each ROI.\n", "limitations": "This work directly integrates brain recording measurements into the training of deep learning models. It should discuss any possible undesirable biases that can arise from this (for instance, any additional societal bias).", "rating": 7, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n\nThis work is very rich. It has a good technical contribution and the scientific interpretation is top-notch. I found the following highlights about the proposed method especially important:\n- Good generalization to held-out subjects \n- Good generalization to out of distribution images\n- Increased shape-bias\n- An improved sample efficiency\n\nWeakness:\n\n- No discussion of related work, this works needs to be addressed in order for this work to be published. Works that specifically integrate brain recordings during the training of models are important to mention and discuss with respect to the current work. Here are a few that I'm familiar with, and some of them even use the same image dataset:\n    - Seeliger et al. 2021, PLoS Comp Bio https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008558\n    - St-Yves et al. 2022, bioRxiv https://www.biorxiv.org/content/10.1101/2022.01.21.477293v1\n    - Khosla and Wehbe, 2022 bioRXiv https://www.biorxiv.org/content/10.1101/2022.03.16.484578v1\n    - In language: Schwartz et al. 2019 NeurIPS: https://proceedings.neurips.cc/paper/2019/hash/2b8501af7b64d1aaae7dd832805f0709-Abstract.html\n\n- Using an AlexNet that is trained on ImageNet as that task-optimized model baseline doesn’t seem like the best choice. The NDS images are from the Coco dataset which has a different distribution from ImageNet, and therefore the test NSD images are much more out of domain for the task-optimized model than they are for the response-optimized model. A more fair comparison as a task-optimized model is to either train the same CNN architecture (minus the linear layer on top) to classify the NDS images, or at least fine-tune AlexNet on the training NDS images. Similarly, in the generalization to ImageNet experiments, the authors compare to a baseline model that has been randomly initialized. A stronger (and fairer) baseline here would be a task-optimized model that has the same architecture as the response-optimized and was trained with the same NSD images, but with a different objective (image classification, rather than brain prediction).\n\n- The organization can be improved. Currently the Materials and Methods section is very long (ends on page 5) and details all experiments. By the time that the corresponding results come around, the reader has forgotten the necessary details from the experiment. I suggest describing the materials and main models in this section, and then moving the description of each analysis to the corresponding place in the results. \n", "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": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"ethics_review_area": ""}, "extra_text": {}}]

Author rebuttal:

Decision: Accept