Biology subjects

Vaziri-Pashkam, M.

Publications and source records attributed to Vaziri-Pashkam, M..

14 recordsLinked to original sources

bioRxiv2026

Divergent specializations for motion-driven representations in higher lateral and dorsal visual areas

The human visual system integrates both static and dynamic information to support form and shape perception, yet the computational principles underlying the integration of motion for object recognition remain unclear. Artificial neural networks (ANNs) offer a computational framework for developing and testing hypotheses about these principles: if ANNs trained on motion-related tasks develop representations that align with brain activity and support object categorization, this would suggest that the training objectives and architectural constraints of these networks may capture key aspects of motion processing in biological visual systems in general, and motion processing for object recognition, in particular. Here, we investigated this question using "object kinematograms", stimuli in which object form is conveyed solely through motion cues. We measured neural responses of two higher regions of the lateral and the dorsal visual pathways, respectively, with strong sensitivity to dynamic cues from objects: lateral occipitotemporal cortex (LOTbio), and left supramarginal gyrus (SMGlh), as well as primary visual cortex (V1). We compared brain responses to representations extracted from two neural networks: SlowFast, a dual-pathway architecture trained on action recognition that processes slow- and fast-varying visual information with cross-pathway integration, and DorsalNet, a model of the primate dorsal visual pathway trained on embodied self-motion estimation. Representational similarity analysis revealed distinct representational profiles across brain areas, demonstrating functional specialization in motion-based form processing. LOTbio was best characterized by the slow pathway of the SlowFast model, whereas SMGlh showed strong similarity to both models. Critically, we found that representations aligned with brain activity also better supported behavioral function: the full SlowFast model, incorporating both slow and fast pathways, outperformed other models in few-shot categorization of object kinematograms and showed the highest similarity to human perceptual judgments. These findings demonstrate that with appropriate inductive biases, specifically, dual-pathway architectures for multi-scale motion processing and training objectives focused on dynamic visual tasks, ANNs can develop functionally useful representations of motion-defined forms that exhibit better alignment with the visual regions involved in processing dynamic visual signals.

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bioRxiv2024

Graspable foods and tools elicit similar responses in visual cortex

Extrastriatal visual cortex is known to exhibit distinct response profiles to complex stimuli of varying ecological importance (e.g., faces, scenes, and tools). The dominant interpretation of these effects is that they reflect activation of distinct "category-selective" brain regions specialized to represent these and other stimulus categories. We sought to explore an alternative perspective: that the response to these stimuli is determined less by whether they form distinct categories, and more by their relevance to different forms of natural behavior. In this regard, food is an interesting test case, since it is primarily distinguished from other objects by its edibility, not its appearance, and there is evidence of food-selectivity in human visual cortex. Food is also associated with a common behavior, eating, and food consumption typically also involves the manipulation of food, often with the hands. In this context, food items share many properties in common with tools: they are graspable objects that we manipulate in self-directed and stereotyped forms of action. Thus, food items may be preferentially represented in extrastriatal visual cortex in part because of these shared affordance properties, rather than because they reflect a wholly distinct kind of category. We conducted fMRI and behavioral experiments to test this hypothesis. We found that behaviorally graspable food items and tools were judged to be similar in their action-related properties, and that the location, magnitude, and patterns of neural responses for images of graspable food items were similar in profile to the responses for tool stimuli. Our findings suggest that food-selectivity may reflect the behavioral affordances of food items rather than a distinct form of category-selectivity.

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bioRxiv2023

Time Course of Dual-task Interference in the Brain in Simulated Driving Environment

Due to the brains limited cognitive capacity, simultaneous execution of multiple tasks can lead to performance impairments, mainly when the tasks occur closely in time. This limitation is known as dual-task interference. We aimed to investigate the time course of this phenomenon in the brain, utilizing a combination of EEG, multivariate pattern analysis (MVPA), and drift-diffusion modeling (DDM). Here, participants first performed a tone discrimination task, followed by a lane-change task with either short or long onset time differences (Stimulus Onset Asynchrony, SOA), in a simulated driving environment. As expected, the dual-task interference increased the second tasks (lane-change) reaction time. The DDM analysis indicated that this increase was attributable to changes in both the decision time and the post-decision time. Our MVPA findings revealed a decrease in decoding accuracy for the lane-change task in short SOA compared to both long SOA and single-task conditions throughout the trial, highlighting the presence of interference. Moreover, the temporal generalization analysis identified a significant interference effect in short SOA compared to long SOA and single-task conditions after [~]250 ms relative to stimulus onset. Additionally, the conditional generalization analysis showed a delayed response after [~]450 ms. Searchlight analysis illustrated the progression of this information reduction, starting in occipital, parietal, and parieto-occipital leads responsible for perceptual and central processing and then transferring to the frontal leads for mapping decisions onto motor actions. Consistent with the hybrid dual-task interference theory, our results suggest that the processing of the two tasks occurs in a partial parallel manner for the first few hundred milliseconds and primarily in the perceptual and decision-processing stages. Subsequently, another competition arises between the two tasks to route information to motor areas for execution, resulting in the second tasks serial processing and delay or lengthening.

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bioRxiv2023

Attention Modulates Human Visual Responses to Objects by Tuning Sharpening

Visual stimuli compete with each other for cortical processing and attention biases this competition in favor of the attended stimulus. How does the relationship between the stimuli affect the strength of this attentional bias? Here, we used functional MRI to explore the effect of target-distractor similarity in neural representation on attentional modulation in the human visual cortex using univariate and multivariate pattern analyses. Using stimuli from four object categories (human bodies, cats, cars and houses), we investigated attentional effects in the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA. We demonstrated that the strength of the attentional bias towards the target is not fixed but decreases with increasing target-distractor similarity. Simulations provided evidence that this result pattern is explained by tuning sharpening rather than an increase in gain. Our findings provide a mechanistic explanation for behavioral effects of target-distractor similarity on attentional biases and suggest tuning sharpening as the underlying mechanism in object-based attention.

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bioRxiv2022

THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior

Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely-sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly-annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.

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bioRxiv2022

Disentangling object category representations driven by dynamic and static visual input

Humans can label and categorize objects in a visual scene with high accuracy and speed--a capacity well-characterized with neuroimaging studies using static images. However, motion is another cue that could be used by the visual system to classify objects. To determine how motion-defined object category information is processed in the brain, we created a novel stimulus set to isolate motion-defined signals from other sources of information. We extracted movement information from videos of 6 object categories and applied the motion to random dot patterns. Using these stimuli, we investigated whether fMRI responses elicited by motion cues could be decoded at the object category level in functionally defined regions of occipitotemporal and parietal cortex. Participants performed a one-back repetition detection task as they viewed motion-defined stimuli or static images from the original videos. Linear classifiers could decode object category for both stimulus formats in all higher order regions of interest. More posterior occipitotemporal and ventral regions showed higher accuracy in the static condition and more anterior occipitotemporal and dorsal regions showed higher accuracy in the dynamic condition. Significantly above chance classification accuracies were also observed in all regions when training and testing the SVM classifier across stimulus formats. These results demonstrate that motion-defined cues can elicit widespread robust category responses on par with those elicited by luminance cues in regions of object-selective visual cortex. The informational content of these responses overlapped with, but also demonstrated interesting distinctions from, those elicited by static cues. Significance StatementMuch research on visual object recognition has focused on recognizing objects in static images. However, motion cues are a rich source of information that humans might also use to categorize objects. Here, we present the first study to compare neural representations of several animate and inanimate objects when category information is presented in two formats: static cues or isolated dynamic cues. Our study shows that while higher order brain regions differentially process object categories depending on format, they also contain robust, abstract category representations that generalize across format. These results expand our previous understanding of motion-derived animate and inanimate object category processing and provide useful tools for future research on object category processing driven by multiple sources of visual information.

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bioRxiv2021

Two distinct networks containing position-tolerant representations of actions in the human brain

Humans can recognize other peoples actions in the social environment. This action recognition ability is rarely hindered by the movement of people in the environment. The neural basis of this tolerance to changes in the position of observed actions is not fully understood. Here, we aimed to identify brain regions capable of generalizing representations of actions across different positions and investigate the representational content of these regions. fMRI data were recorded from twenty-two subjects while they were watching video clips of ten different human actions in Point Light Display format. Each stimulus was presented in either the upper or the lower visual fields. Multivoxel pattern analysis and a searchlight technique were employed to identify brain regions that contain position-tolerant action representation: linear support vector machine classifiers were trained with fMRI patterns in response to stimuli presented in one position and tested with stimuli presented in another position. Results of this generalization test showed above-chance classification in the left and right lateral occipitotemporal cortex, right intraparietal sulcus, and right post-central gyrus. To explore the representational content of these regions, we constructed models based on the objective measures of movements and human subjective judgments about actions. We then evaluated the brain similarity matrix from the cross-position classification analysis based on these models. Results showed cross-position classifications in the lateral occipito-temporal ROIs were more strongly related to the subjective judgments, while those in the dorsal parietal ROIs were more strongly related to the objective movements. An ROI representational similarity analysis further confirmed the separation of the dorsal and lateral regions. These results provide evidence for two networks that contain abstract representations of human actions with distinct representational content.

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bioRxiv2021

The Normalization Model Captures the Effects of Object-based Attention in the Human Visual Cortex

Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we explored the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli.Focusing on the primary visual area V1, the object-selective regions LO and pFs, and the category-selective regions EBA and PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that the response to multiple stimuli could best be described by a model that takes normalization into account. We then explored the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide compelling evidence for normalization as a canonical computation operating in the human brain.

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bioRxiv2020

Predicting identity-preserving object transformations across the human ventral visual stream

In everyday life, we have no trouble recognizing and categorizing objects as they change in position, size, and orientation in our visual fields. This phenomenon is known as object invariance. Previous fMRI research suggests that higher-level object processing regions in the human lateral occipital cortex may link object responses from different affine states (i.e. size and viewpoint) through a general linear mapping function with the learned mapping capable of predicting responses of novel objects. In this study, we extended this approach to examine the mapping for both Euclidean (e.g. position and size) and non-Euclidean (e.g. image statistics and spatial frequency) transformations across the human ventral visual processing hierarchy, including areas V1, V2, V3, V4, ventral occipitotemporal cortex (VOT), and lateral occipitotemporal cortex (LOT). The predicted pattern generated from a linear mapping could capture a significant amount, but not all, of the variance of the true pattern across the ventral visual pathway. The derived linear mapping functions were not entirely category independent as performance was better for the categories included in the training. Moreover, prediction performance was not consistently better in higher than lower visual regions, nor were there notable differences between Euclidean and non-Euclidean transformations. Together, these findings demonstrate a near-orthogonal representation of object identity and non-identity features throughout the human ventral visual processing pathway, with the non-identity features largely untangled from the identity features early in the visual processing. Significance StatementPresently we still do not fully understand how object identity and non-identity (e.g. position, size) information are simultaneously represented in the primate ventral visual system to form invariant representations. Previous work suggests that the human lateral occipital cortex may be linking different affine states of object representations through general linear mapping functions. Here we show that across the entire human ventral processing pathway, we could link object responses in different states of non-identity transformations through linear mapping functions for both Euclidean and non-Euclidean transformations. These mapping functions are not identity-independent, suggesting that object identity and non-identity features are represented in a near, rather than a completely, orthogonal manner.

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bioRxiv2020

The development of transformation tolerant visual representations differs between the human brain and convolutional neural networks

Forming transformation-tolerant object representations is critical to high-level primate vision. Despite its significance, many details of tolerance in the human brain remain unknown. Likewise, despite the ability of convolutional neural networks (CNNs) to exhibit human-like object categorization performance, whether CNNs form tolerance similar to that of the human brain is unknown. Here we provide the first comprehensive documentation and comparison of three tolerance measures in the human brain and CNNs. We measured fMRI responses from human ventral visual areas to real-world objects across both Euclidean and non-Euclidean feature changes. In single fMRI voxels in higher visual areas, we observed robust object response rank-order preservation across feature changes. This is indicative of functional smoothness in tolerance at the fMRI meso-scale level that has never been reported before. At the voxel population level, we found highly consistent object representational structure across feature changes towards the end of ventral processing. Rank-order preservation, consistency, and a third tolerance measure, cross-decoding success (i.e., a linear classifiers ability to generalize performance across feature changes) showed an overall tight coupling. These tolerance measures were lower for Euclidean than non-Euclidean feature changes in lower visual areas, but increased over the course of ventral processing in most cases. These characteristics of tolerance, however, were absent in eight CNNs pretrained with ImageNet images with varying network architecture, depth, the presence/absence of recurrent processing, or whether a network was pretrained with the original or stylized ImageNet images that encouraged shape processing. Most notably, CNNs do not show increased representational consistency across feature changes at the higher layers. CNNs thus do not appear to develop the same kind of tolerance as the human brain over the course of visual processing. Significant StatementPerceiving object identity among changes in non-identity features and forming transformation-tolerant object representations is essential to high-level primate vision. Here we provide a comprehensive documentation and comparison of three tolerance measures between the human brain and CNNs pretrained for object classification. While all three measures show increased tolerance in the human brain across four types of feature changes towards the end of ventral visual processing, CNNs fail to develop the same kind of tolerance with visual processing.

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bioRxiv2020

The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks

Any given visual object input is characterized by multiple visual features, such as identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pathway, they have so far been studied relatively independently. Here we document the relative coding strength of object identity and nonidentity features in a brain region and how this may change across the human ventral visual pathway. We examined a total of four nonidentity features, including two Euclidean features (position and size) and two non-Euclidean features (image statistics and spatial frequency content of an image). Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with identity outweighed the non-Euclidean features, but not the Euclidean ones, in higher levels of visual processing. A similar analysis was performed in 14 convolutional neural networks (CNNs) pretrained to perform object categorization with varying architecture, depth, and with/without recurrent processing. While the relative coding strength of object identity and nonidentity features in lower CNN layers matched well with that in early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Similar results were obtained regardless of whether a CNN was trained with real-world or stylized object images that emphasized shape representation. Together, by measuring the relative coding strength of object identity and nonidentity features, our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs. SIGNIFICANCE STATEMENTThis study documented the relative coding strength of object identity compared to four types of nonidentity features along the human ventral visual processing pathway and compared brain responses with those of 14 CNNs pretrained to perform object categorization. Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with the coding strength of the different nonidentity features differed at higher levels of visual processing. While feature coding in lower CNN layers matched well with that of early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.

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bioRxiv2020

Disturbance of Information in Superior Parietal Lobe during Dual-task Interference in a Simulated Driving task

Performing a secondary task while driving causes a decline in driving performance. This phenomenon, called dual-task interference, can have lethal consequences. Previous fMRI studies have looked at the changes in the average brain activity to uncover the neural correlates of dual-task interference. From these results, it is unclear whether the overall modulations in brain activity result from general effects such as task difficulty, attentional modulations, and mental effort or whether it is caused by a change in the responses specific to each condition due to dual-task interference. To overcome this limitation, here, we used multi-voxel pattern analysis (MVPA) to interrogate the change in the information content in multiple brain regions during dual-task interference in simulated driving. Participants performed a lane change task in a simulated driving environment, along with a tone discrimination task with either short or long-time onset difference (Stimulus Onset Asynchrony, SOA) between the two tasks. Behavioral results indicated a robust dual-task effect on driving RT. MVPA revealed regions that carry information about the driving direction, including the superior parietal lobe (SPL), visual, and motor regions. Comparing the decoding accuracies across short and long SOA conditions, we showed lower accuracies in the SPL region in short than long SPA conditions. This change in accuracy was not observed in the visual and motor regions. In addition, the classification accuracy in the SPL was inversely correlated with participants reaction time in the driving task. These findings suggest that the dual-task interference in driving may be related to the disturbance of information processing in the SPL region. Significance StatementDuring real-world driving, when a driver wants to make a turn at an intersection and simultaneously respond to a cell phone call, his reaction time slows down. This effect is called dual-task interference. Here, we aimed to examine its neural mechanisms using a paradigm that consisted of a driving turn task and a tone discrimination task in a simulated environment. Results showed that the information for the driving turn was disturbed in the superior parietal lobe (SPL) during dual-task interference. We suggest that the driving performance decline in the presence of the secondary task might be related to the disturbance of information in the SPL.

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bioRxiv2020

Limited correspondence in visual representation between the human brain and convolutional neural networks

Convolutional neural networks (CNNs) have achieved very high object categorization performance recently. It has increasingly become a common practice in human fMRI research to regard CNNs as working model of the human visual system. Here we reevaluate this approach by comparing fMRI responses from the human brain in three experiments with those from 14 different CNNs. Our visual stimuli included original and filtered versions of real-world object images and images of artificial objects. Replicating previous findings, we found a brain-CNN correspondence in a number of CNNs with lower and higher levels of visual representations in the human brain better resembling those of lower and higher CNN layers, respectively. Moreover, the lower layers of some CNNs could fully capture the representational structure of human early visual areas for both the original and filtered real-world object images. Despite these successes, no CNN examined could fully capture the representational structure of higher human visual processing areas. They also failed to capture that of artificial object images in all levels of visual processing. The latter is particularly troublesome, as decades of vision research has demonstrated that the same algorithms used in the processing of natural images would support the processing of artificial visual stimuli in the primate brain. Similar results were obtained when a CNN was trained with stylized object images that emphasized shape representation. CNNs likely represent visual information in fundamentally different ways from the human brain. Current CNNs thus may not serve as sound working models of the human visual system. Significance StatementRecent CNNs have achieved very high object categorization performance, with some even exceeding human performance. It has become common practice in recent neuroscience research to regard CNNs as working models of the human visual system. Here we evaluate this approach by comparing fMRI responses from the human brain with those from 14 different CNNs. Despite CNNs ability to successfully perform visual object categorization like the human visual system, they appear to represent visual information in fundamentally different ways from the human brain. Current CNNs thus may not serve as sound working models of the human visual system. Given the current dominating trend of incorporating CNN modeling in visual neuroscience research, our results question the validity of such an approach.

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bioRxiv2019

Dual-task Interference in a Simulated Driving Environment: Serial or Parallel Processing?

When humans are required to perform two tasks concurrently, their performances decrease as the two tasks get closer together in time. This effect is known as dual-task interference. This limitation of the human brain could have lethal effects during demanding everyday tasks such as driving. Are the two tasks processed serially or in parallel during dual-task performance in naturalistic settings? Here, we investigated dual-task interference in a simulated driving environment and investigated the serial/parallel nature of processing during dual-task performance. Participants performed a lane change task on a desktop computer, along with an image discrimination task. We systematically varied the time difference between the onset of the two tasks (Stimulus Onset Asynchrony, SOA) and measured its effect on the amount of dual-task interference. Results showed that the reaction times (RTs) of two tasks in the dual-task condition were higher than those in the single-task condition. SOA influenced RTs of both tasks when they were presented second and the RTs of the image task when it was presented first. Manipulating the predictability of the order of the two tasks, we showed that unpredictability attenuated the effect of SOA by changing the order of the response to the two tasks. Next, using drift-diffusion modeling, we modeled the reaction time and choice of the subjects during dual-task performance in both predictable and unpredictable task order conditions. The modeling results indicated that performing two tasks concurrently, affects both the rate of evidence accumulation and the delays outside the evidence accumulation period, suggesting that the two tasks are performed in a partial-parallel manner. These results extend the findings of previous dual-task experiments to more naturalistic settings and deepen our understanding of the mechanisms of dual-task interference.

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