Biology subjects

Hebart, M. N.

Publications and source records attributed to Hebart, M. N..

At least 19 recordsLinked to original sources

bioRxiv2026

Primate visual cortex spontaneously computes the beauty of objects

During everyday vision, beauty is a spontaneous experience. Perceived beauty varies substantially across object categories: We find puppies more beautiful than moths and roses more beautiful than potatoes. Here, we provide evidence that early processing in human and non-human primate visual cortex already reflects perceived beauty, even without deliberate judgments of beauty. First, in a combined EEG-fMRI analysis, we show that beauty ratings for hundreds of object categories from the THINGS database are predicted from neural responses measured in separate groups of human participants viewing the same images without explicit beauty judgments. Beauty-related representations emerged in occipital visual cortex within 100ms and peaked around 130ms. Second, multi-unit activity in macaque visual cortex also predicted human beauty judgments, despite the animals highly limited knowledge of the objects. These results indicate a perceptual basis for the beauty of visual categories, which may underlie spontaneous preference formation in everyday situations. TeaserEarly visual areas in both human and non-human primates rapidly compute the beauty of real-world objects

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bioRxiv2026

Shared and Distinct Object Spaces in Human and Macaque Inferotemporal Cortex

Human and macaque studies of inferotemporal cortex (IT) have shaped our understanding of object vision, yet the extent of their representational alignment and the precise nature of this correspondence have remained unclear. To address these questions, we applied multivariate cross-species alignment to large-scale macaque multi-unit recordings and human fMRI responses to the same 8,640 naturalistic object images. The resulting shared space revealed a surprisingly rich, high-dimensional geometry that extended well beyond coarse correspondences such as animacy and shape and included a wide range of interpretable visual and conceptual dimensions, as well as specific conjunctions of the two. Unexpectedly, the space also uncovered a shared neural signature for orthography-related content, consistent with precursor mechanisms in macaques. Contrasting the within-species spaces revealed systematic differences in the expression of living and artificial categories, conspecific content, and visual versus conceptual structure. Together, these findings uncover a high-dimensional shared object space along with species-specific divergences, clarifying the scope and limits of cross-species comparability.

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bioRxiv2026

Principles of coarse-scale functional organization in occipitotemporal cortex

Occipitotemporal cortex is known to process visually perceived objects, yet identifying general principles underlying its coarse-scale functional organization remains challenging for two reasons. First, previous work leaves open whether proposed organizational dimensions, such as animacy or real-world size, are useful for characterizing high-level vision and whether they generalize to more diverse, naturalistic stimuli. Second, object properties are often highly intercorrelated, making it difficult to determine which dimensions, if any, dominate coarse-scale occipitotemporal cortex organization. We addressed these challenges through detailed analyses of the cortical topography underlying 15 object properties, using densely-sampled fMRI responses to thousands of natural images, and focusing on animacy and size given their established prominence. Although our results confirmed many characteristics of the purported animacy-size organization, they revealed distinctions that challenge dominant notions of this organization, highlighting the importance of generalizing to large-scale naturalistic data. Beyond animacy and size, we found that many of the 15 properties could alternatively serve as organizational dimensions of occipitotemporal cortex, yet none emerged as clearly dominant across all OTC. Together, our findings reveal a more complex animacy-size organization than previously assumed and highlight challenges of assigning individual principles to account for OTC organization.

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bioRxiv2025

Predicting functional topography of the human visual cortex from cortical anatomy at scale

Topographic organization, whereby neighboring cortical locations encode neighboring features in sensory or cognitive space, is a fundamental principle of brain function. Existing approaches for obtaining individual-specific topographic maps either require resource-intensive functional neuroimaging or, when relying on population atlases, lack precision for individual-level inference. Here, we introduce deepRetinotopy toolbox, a deep learning-based application for predicting the functional topographic organization of human visual cortex from cortical anatomy alone. DeepRetinotopy toolbox produces accurate retinotopic maps across diverse experimental conditions, imaging sites, and scanner types. We demonstrate how predicted maps can be utilized to automatically generate individual-specific visual area boundaries, overcoming common biases in manual annotations. Finally, we applied our method to 11,060 anatomical scans, which allowed us to quantify age-related changes in the functional organization of visual cortex predictable from anatomy alone, underscoring the methods broad utility for scalable, anatomy-based functional brain mapping.

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bioRxiv2025

Semantic Tuning of Single Neurons in the Human Medial Temporal Lobe

The Medial Temporal Lobe (MTL) is key to human cognition, supporting memory, emotional processing, navigation, and semantic coding. Rare direct human MTL recordings revealed concept cells, which were proposed as episodic memory building blocks due to their context- and modality-invariant response. However, there is a long-standing debate whether concept cells encode information in a discrete, all-or-none fashion, or as a graded, continuous function of semantic dimensions. To resolve this, we developed a closed-loop protocol that analyzes neuronal spiking in real time and adaptively presents new stimuli based on semantic similarity to response-eliciting previous ones. We found that human concept cells show graded responses, following semantic tuning curves. Furthermore, the tuning width and steepness of MTL neurons vary, with the hippocampus exhibiting the steepest tuning. Regional analysis of these tuning curves yields region-specific categorical response profiles (e.g., food responses in the amygdala). Finally, we show that at the population level, MTL activity is best captured by continuous semantic vector models, which outperform categorical and network-based metrics. Together, these findings establish that concept cells exhibit semantic tuning curves, providing new mechanistic insights into the neural organization of semantic information in the human brain.

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bioRxiv2025

Multidimensional feature tuning in category-selective areas of human visual cortex

Human high-level visual cortex has been described in two seemingly opposed ways. A categorical view emphasizes discrete category-selective areas, while a dimensional view highlights continuous feature maps spanning across these areas. Can these divergent perspectives on cortical organization be reconciled within a unifying framework? Using data-driven decomposition of fMRI responses in face-, body-, and scene-selective areas, we identified overlapping activity patterns shared across individuals. Each area encoded multiple interpretable dimensions tuned to both finer subcategory features and coarser cross-category distinctions beyond its preferred category, even in the most category-selective voxels. These dimensions formed distinct clusters within category-selective areas but were also sparsely distributed across the broader visual cortex, supporting both locally selective, category-specific, and globally distributed, feature-based coding. Together, these findings suggest multidimensional tuning as a fundamental organizing principle that integrates feature-selective clusters, category-selective areas, and large-scale tuning maps, providing a more comprehensive understanding of category representations in human visual cortex.

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bioRxiv2025

Determinants of visual ambiguity resolution

Visual inputs during natural perception are highly ambiguous: objects are frequently occluded, lighting conditions vary, and object identification depends significantly on prior experiences. However, why do certain images remain unidentifiable while others can be recognized immediately, and what visual features drive subjective clarification? To address these critical questions, we developed a unique dataset of 1,854 ambiguous images and collected more than 100,000 ratings (from a total of 947 participants) evaluating their identifiability before and after seeing undistorted versions of the images. Relating the representations of a brain-inspired neural network model in response to our images with human ratings, we show that subjective identification depends largely on the extent to which higher-level visual features from the original images are preserved in their ambiguous counterparts. In line with these results, an image-level regression analysis showed that the subjective identification of ambiguous images was best explained by high-level visual dimensions. Notably, the predominance of higher-level features over lower-level ones softens after participants disambiguate the images, suggesting that the visual system flexibly shifts between top-down guessing to bottom-up matching after disambiguation. Moreover, we found that the process of ambiguity resolution was accompanied by a notable decrease in semantic distance and a greater consistency in object naming among participants. However, the relationship between information gained after disambiguation and subjective identification was non-linear, indicating that acquiring more information does not necessarily enhance subjective clarity. Instead, we observed a U-shaped relationship, suggesting that subjective identification improves when the acquired information either strongly matches or mismatches prior predictions. Collectively, these findings advance our understanding on how we resolve ambiguity and extract meaning from incomplete visual information.

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bioRxiv2025

Decoding peripheral saccade targets from foveal retinotopic cortex

Human vision is characterized by frequent eye movements and constant shifts in visual input, yet our perception of the world remains remarkably stable. Here, we directly demonstrate image-specific foveal feedback to primary visual cortex in the context of saccadic eye movements. To this end, we used a gaze-contingent fMRI paradigm, in which peripheral saccade targets disappeared before they could be fixated. Despite no direct foveal stimulation, we were able to decode peripheral saccade targets from foveal retinotopic areas, demonstrating that image-specific feedback during saccade preparation may underlie this effect. Decoding was sensitive to shape but not semantic category of natural images, indicating feedback of only low-to-mid-level information. Cross-decoding to a control condition with foveal stimulus presentation indicates a shared representational format between foveal feedback and direct stimulation. Moreover, eccentricity-dependent analyses showed a u-shaped decoding curve, confirming that these results are not explained by spillover of peripheral activity or large receptive fields. Finally, fluctuations in foveal decodability covaried with activity in the intraparietal sulcus, thus providing a candidate region for driving foveal feedback. These findings suggest that foveal cortex predicts the features of incoming stimuli through feedback from higher cortical areas, which offers a candidate mechanism underlying stable perception.

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bioRxiv2024

Saccade onset, not fixation onset, best explains early sensory responses across the human visual cortex during naturalistic vision

Visual processing is traditionally studied using static viewing paradigms in which researchers analyse brain responses to the onsets of a sequence of randomly selected stimuli. Translating this "stimulus onset" approach to active vision paradigms that allow for free eye-movements, researchers often consider fixation onsets as the events that trigger visual activity across the visual system. Here, we test this assumption by analysing a large-scale magnetoencephalography (MEG) dataset with simultaneously recorded eye movements of 5 participants who freely explored thousands of natural images, yielding approximately 200,000 gaze events. We show that saccade-related events, particularly peak saccade curvature, rather than fixation onsets, explain most variance in latency of the early sensory component M100. Further, comparing the classic M100 elicited by stimulus onsets with the M100s elicited during active vision revealed stark differences, both in response to saccade-related and fixation-onset events. This indicates that phenomena discovered using gold standard stimulus onset paradigms do not necessarily translate to natural vision. Our findings challenge the prevailing approach to studying vision in static paradigms and highlight the importance of internally generated and action-driven signals in the dynamics of natural sensory processing.

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bioRxiv2024

The scope and limits of fine-grained category information in the ventral visual pathway

Humans can easily abstract incoming visual information into discrete semantic categories. Previous research employing functional MRI (fMRI) in humans has identified cortical organizing principles that allow not only for coarse-scale distinctions such as animate versus inanimate objects but also more fine-grained distinctions at the level of individual objects. This suggests that fMRI carries rather fine-grained information about individual objects. However, most previous work investigating fine-grained category representations either additionally included coarse-scale category comparisons of objects, which confounds fine-grained and coarse-scale distinctions, or only used a single exemplar of each object, which confounds visual and semantic information. To address these challenges, here we used multisession human fMRI (female and male) paired with a broad yet homogenous stimulus class of 48 terrestrial mammals, with 2 exemplars per mammal. Multivariate decoding and representational similarity analysis (RSA) revealed high image-specific reliability in low- and high-level visual regions, indicating stable representational patterns at the image level. In contrast, analyses across exemplars of the same animal yielded only small effects in the lateral occipital complex (LOC), indicating rather subtle category effects in this region. Variance partitioning with a deep neural network and shape model showed that across exemplar effects in EVC were largely explained by low-level visual appearance, while representations in LOC appeared to also contain higher category-specific information. These results suggest that representations typically measured with fMRI are dominated by image-specific visual or coarse-grained category information but indicate that commonly employed fMRI protocols may reveal subtle yet reliable distinctions between individual objects. Significance StatementWhile it has been suggested that functional MRI (fMRI) responses in ventral visual cortex carry fine-grained information about individual objects, much previous research has confounded fine-grained with coarse-scale category information or only used individual visual exemplars, which potentially confounds semantic and visual object information. Here we address these challenges in a multisession fMRI study where participants viewed a highly homogenous stimulus set of 48 land mammals with 2 exemplars per animal. Our results reveal a strong dominance of image-specific effects and additionally indicate subtle yet reliable category-specific effects in lateral occipital complex, underscoring the capacity of commonly employed fMRI protocols to uncover fine-grained visual information.

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bioRxiv2024

A high-throughput approach for the efficient prediction of perceived similarity of natural objects

Perceived similarity offers a window into the mental representations underlying our ability to make sense of our visual world, yet, the collection of similarity judgments quickly becomes infeasible for larger datasets, limiting their generality. To address this challenge, here we introduce a computational approach that predicts perceived similarity from neural network activations through a set of 49 interpretable dimensions learned on 1.46 million triplet odd-one-out judgments. The approach allowed us to predict separate, independently-sampled similarity scores with an accuracy of up to 0.898. Combining this approach with human ratings of the same dimensions led only to small improvements, indicating that the neural network used similar information as humans in this task. Predicting the similarity of highly homogeneous image classes revealed that performance critically depends on the granularity of the training data. Our approach allowed us to improve the brain-behavior correspondence in a large-scale neuroimaging dataset and visualize candidate image features humans use for making similarity judgments, thus highlighting which image parts may carry behaviorally-relevant information. Together, our results demonstrate that current neural networks carry information sufficient for capturing broadly-sampled similarity scores, offering a pathway towards the automated collection of similarity scores for natural images.

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bioRxiv2023

Multidimensional object properties are dynamically represented in the human brain

Our visual world consists of an immense number of unique objects and yet, we are easily able to identify, distinguish, interact, and reason about the things we see within a few hundred milliseconds. This requires that we integrate and focus on a wide array of object properties to support diverse behavioral goals. In the current study, we used a large-scale and comprehensively sampled stimulus set and developed an analysis approach to determine if we could capture how rich, multidimensional object representations unfold over time in the human brain. We modelled time-resolved MEG signals evoked by viewing single presentations of tens of thousands of object images based on millions of behavioral judgments. Extracting behavior-derived object dimensions from similarity judgments, we developed a data-driven approach to guide our understanding of the neural representation of the object space and found that every dimension is reflected in the neural signal. Studying the temporal profiles for different object dimensions we found that the time courses fell into two broad types, with either a distinct and early peak ([~]125 ms) or a slow rise to a late peak ([~]300 ms). Further, early effects were stable across participants, in contrast to later effects which showed more variability, suggesting that early peaks may carry stimulus-specific and later peaks more participant-specific information. Dimensions with early peaks appeared to be primarily visual dimensions and those with later peaks more conceptual, suggesting that conceptual representations are more variable across people. Together, these data provide a comprehensive account of how behavior-derived object properties unfold in the human brain and form the basis for the rich nature of object vision.

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bioRxiv2023

Distributed representations of behaviorally relevant object dimensions in the human visual system

Object vision is commonly thought to involve a hierarchy of brain regions processing increasingly complex image features, with high-level visual cortex supporting object recognition and categorization. However, object vision supports diverse behavioral goals, suggesting basic limitations of this category-centric framework. To address these limitations, we mapped a series of dimensions derived from a large-scale analysis of human similarity judgments directly onto the brain. Our results reveal broadly distributed representations of behaviorally-relevant information, demonstrating selectivity to a wide variety of novel dimensions while capturing known selectivities for visual features and categories. Behavior-derived dimensions were superior to categories at predicting brain responses, yielding mixed selectivity in much of visual cortex and sparse selectivity in category-selective clusters. This framework reconciles seemingly disparate findings regarding regional specialization, explaining category selectivity as a special case of sparse response profiles among representational dimensions, suggesting a more expansive view on visual processing in the human brain.

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bioRxiv2023

The link between visual representations and behavior in human scene perception

1.Scene recognition is a core sensory capacity that enables humans to adaptively interact with their environment. Despite substantial progress in the understanding of the neural representations underlying scene recognition, the relevance of these representations for behavior given varying task demands remains unknown. To address this, we aimed to identify behaviorally relevant scene representations, to characterize them in terms of their underlying visual features, and to reveal how they vary across different tasks. We recorded fMRI data while human participants viewed scenes and linked brain responses to behavior in three tasks acquired in separate sessions: manmade/natural categorization, basic-level categorization, and fixation color discrimination. We found correlations between categorization response times and scene-specific brain responses, quantified as the distance to a hyperplane derived from a multivariate classifier. Across tasks, these effects were found in largely distinct parts of the ventral visual stream. This suggests that different scene representations are relevant for behavior depending on the task. Next, using deep neural networks as a proxy for visual feature representations, we found that early/intermediate layers mediated the relationship between scene representations and behavior for both categorization tasks, indicating a contribution of low-/mid-level visual features to these representations. Finally, we observed opposite patterns of brain-behavior correlations in the manmade/natural and the fixation task, indicating interference of representations with behavior for task demands that do not align with the content of representations. Together, these results reveal the spatial extent, content, and task-dependence of the visual representations that mediate behavior in complex scenes.

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bioRxiv2022

A data-driven investigation of human action representations

Understanding actions performed by others requires us to integrate different types of information about people, scenes, objects, and their interactions. What organizing dimensions does the mind use to make sense of this complex action space? To address this question, we collected intuitive similarity judgments across two large-scale sets of naturalistic videos depicting everyday actions. We used cross-validated sparse non-negative matrix factorization (NMF) to identify the structure underlying action similarity judgments. A low-dimensional representation, consisting of nine to ten dimensions, was sufficient to accurately reconstruct human similarity judgments. The dimensions were robust to stimulus set perturbations and reproducible in a separate odd-one-out experiment. Human labels mapped these dimensions onto semantic axes relating to food, work, and home life; social axes relating to people and emotions; and one visual axis related to scene setting. While highly interpretable, these dimensions did not share a clear one-to-one correspondence with prior hypotheses of action-relevant dimensions. Together, our results reveal a low-dimensional set of robust and interpretable dimensions that organize intuitive action similarity judgments and highlight the importance of data-driven investigations of behavioral representations.

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bioRxiv2022

Parallel cognitive maps for short-term statistical and long-term semantic relationships in the hippocampal formation

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information, such as the transition probabilities between objects. However, objects can often be characterized in terms of different types of relations simultaneously, e.g. semantic similarities learned over the course of a lifetime as well as transitions experienced over a brief timeframe in an experimental setting. Here we ask how the hippocampal formation handles the embedding of stimuli in multiple relational structures that differ vastly in terms of their mode and timescale of acquisition: Does it integrate the different stimulus dimensions into one conjunctive map, or is each dimension represented in a parallel map? To this end, we reanalyzed functional magnetic resonance imaging (fMRI) data from Garvert et al. (2017) that had previously revealed an entorhinal map which coded for newly learnt statistical regularities. We used a triplet odd-one-out task to construct a semantic distance matrix for presented items and applied fMRI adaptation analysis to show that the degree of similarity of representations in bilateral hippocampus decreases as a function of semantic distance between presented objects. Importantly, while both maps localize to the hippocampal formation, this semantic map is anatomically distinct from the originally described entorhinal map. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps reflecting the embedding of objects in diverse relational structures.

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bioRxiv2022

The spatiotemporal neural dynamics of object recognition for natural images and line drawings

1.Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings. 2. Significance StatementWhen we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.

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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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