Biology subjects

Dobs, K.

Publications and source records attributed to Dobs, K..

10 recordsLinked to original sources

bioRxiv2026

Cortical alpha rhythms predictively track occluded motion trajectories

Objects in the real world frequently move along complex, non-linear trajectories shaped by their environment. As they do, they often pass temporarily out of sight, occluded by the surrounding environment. Yet humans can maintain representations of object position and motion even when objects are no longer visible. Here, we investigate the neural basis of tracking objects undergoing non-linear, environmentally constrained motion during occlusion. We recorded EEG while participants mentally tracked the position of a ball falling through differently curved pipes (Pipe condition). The pipe condition was compared to conditions in which only the ball trajectory was visible (Ball-only condition) or in which the pipe was completely covered by an occluder (Occluder condition). On occasional probe trials, the video was terminated at randomized time points and participants had to indicate the ball's last position using the mouse cursor. Behaviorally, participants systematically reported the ball ahead of its true position, with the strongest forward displacement in the occluded conditions. Neurally, multivariate decoding analysis on the EEG data revealed that alpha-band activity carried direction-specific motion information within and across conditions. Critically, cross-condition decoding revealed shared representations between perceived (Ball-only) and mentally tracked (Occluder) motion with representations in the occluder condition anticipating later representations in the ball-only condition. Together, our behavioral and neural findings provide convergent evidence that during non-linear motion tracking under occlusion, the brain codes object motion ahead of its current position, an 'ahead of time' strategy that may underlie temporal precision when tracking objects in a dynamically changing world.

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bioRxiv2026

Prior scene context reshapes feature reliance during rapid perception

Human perception is shaped by both sensory input and prior knowledge or expectations. But how does prior contextual information influence rapid visual processing? Here, we combined eye tracking with feature-based encoding models across two experiments to predict detection latencies in a core visual task: rapid face detection in natural scenes (N = 38 per experiment). In the first experiment, we manipulated the presence of faceless scene previews. In the second experiment, we additionally restricted peripheral visual input using a moving-window paradigm, thereby increasing reliance on prior information. Across both experiments, prior context facilitated face detection, particularly for challenging images. This facilitation was already evident in the very first eye movement, suggesting that previews shape perceptual strategies from the outset. To quantify what information guided behavior, we modeled detection latencies using a set of image-based predictors capturing (i) sensory information and (ii) a scene-derived spatial prior: the expected face location. Both predictor classes explained latency variation across images. Among sensory predictors, the difference in deep neural network responses induced by the presence of the face provided the strongest out-of-sample prediction of detection latency. Critically, when scene previews were available, the contribution of the spatial prior increased, while reliance on sensory-driven features was generally reduced. Together, these findings indicate that prior scene context shifts the balance of information used for rapid face detection from sensory-driven to expectation-based spatial guidance.

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bioRxiv2026

Face and body representations converge along the visual hierarchy in models and cortex

Human visual cortex contains regions specialized for faces and bodies, yet we perceive people as a whole. Why does the brain appear to segregate faces and bodies, and how are they integrated to support person perception? Here, we test whether deep neural network models optimized for visual recognition develop segregated or integrated face and body processing, and how this compares to fMRI activity in visual cortex during natural image viewing. We find that models contain face- and body-selective units but also mixed-selective units that are tuned to both faces and bodies. While face- and body-selective units explain unique variance in their corresponding cortical regions, mixed-selective units best explain activity across regions, and shared variance increases from posterior to anterior cortex. Together, our findings suggest that face and body processing reflects a balance of segregation and integration along the visual hierarchy in humans and models, supporting specialized yet flexible person perception.

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bioRxiv2026

Prior Scene Context Shapes the Neural Dynamics of Face Detection

Detecting faces in our surroundings typically only takes a fraction of a second. How can such a rapid perceptual process still be influenced by prior expectations? Here, we used electroencephalography (EEG) to investigate how prior scene context modulates the temporal dynamics of neural face representations. Participants viewed natural scenes containing a single face (left or right), each preceded by either a faceless version of the same scene (preview condition) or a gray screen (no-preview condition), while performing a face detection task. Using multivariate decoding, we found that face location could be decoded shortly after target onset. Critically, decoding accuracy was higher in the preview condition at early stages, whereas the no-preview condition showed higher decoding at later time points, suggesting rapid facilitation by prior context followed by compensatory processing when contextual information was absent. In contrast, time-frequency decoding revealed a sustained preview advantage across alpha, beta, and gamma bands, even during time periods when evoked responses favored the no-preview condition. This dissociation between evoked and induced neural signals indicates that prior scene context engages distinct neural processes: early evoked activity reflects rapid contextual facilitation of sensory representations, while induced oscillatory activity may support prolonged context-dependent modulation. Together, these results show how prior scene context and sensory-driven processing jointly shape rapid face perception through temporally and spectrally distinct neural dynamics. Significance StatementScientists studying perception have long investigated how the brain combines incoming sensory input with prior knowledge and expectations. Very fast perceptual decisions, however, are often assumed to rely primarily on sensory input alone. Here, we show that prior scene context shapes the neural processing of faces even during rapid face detection. When observers had prior information about a scene, neural representations of face location emerged earlier and followed a different temporal profile than when no contextual information was available. These findings demonstrate that even rapid face detection reflects an interaction between sensory-driven and context-dependent processes, and that prior knowledge influences perception through multiple, temporally distinct neural mechanisms.

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bioRxiv2025

Diverse Visual Experience Promotes Integrated Representations and Mitigates Bias in Deep Neural Networks for Face Perception

Humans are experts at recognizing faces, yet this expertise is not uniform: people perceive faces from familiar facial groups more accurately than those from unfamiliar ones--a phenomenon known as the Other-Race Effect (ORE). Diverse facial exposure mitigates this bias, but how it reorganizes face representations to support cross-group recognition remains unclear. To address this question, we combined controlled training variation in deep convolutional neural networks (CNNs) with analyses of representational geometry, lesioning, and human behavioral data. CNNs trained exclusively on either Asian or White faces reproduced ORE-like biases, whereas a CNN trained on both groups showed reduced bias and balanced recognition performance. Critically, representational similarity analyses and lesioning revealed largely overlapping feature spaces across groups, indicating an integrated representational organization. Trial-by-trial comparisons with human choices further showed that the dual-trained CNN best captured human face-matching behavior across both Asian and White participants, outperforming single-trained networks on cross-group trials. Together, these findings show that diverse visual experience promotes an integrated representational geometry that supports cross-group generalization, providing a computational account of how exposure diversity may shape human face representations.

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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

Spatiotemporal Dynamics of Invariant Face Representations in the Human Brain

The human brain can effortlessly extract a familiar faces age, gender, and identity despite dramatic changes in appearance, such as head orientation, lighting, or expression. Yet, the spatiotemporal dynamics underlying this ability, and how they depend on task demands, remain unclear. Here, we used multivariate decoding of magnetoencephalography (MEG) responses and source localization to characterize the emergence of invariant face representations. Human participants viewed natural images of highly familiar celebrities that systematically varied in viewpoint, gender, and age, while performing a one-back task on the identity or the image. Time-resolved decoding revealed that identity information emerged rapidly and became increasingly invariant to viewpoint over time. We observed a temporal hierarchy: view-specific identity information appeared at 64 ms, followed by mirror-invariant representations at 75 ms and fully view-invariant identity at 89 ms. Identity-invariant age and gender information emerged around the same time as view-invariant identity. Task demands modulated only late-stage identity and gender representations, suggesting that early face processing is predominantly feedforward. Source localization at peak decoding times showed consistent involvement of the occipital face area (OFA) and fusiform face area (FFA), with stronger identity and age signals than gender. Our findings reveal the spatiotemporal dynamics by which the brain extracts view-invariant identity from familiar faces, suggest that age and gender are processed in parallel, and show that task demands modulate later processing stages. Together, these results offer new constraints on computational models of face perception.

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bioRxiv2022

Using deep convolutional neural networks to test why human face recognition works the way it does

Human face recognition is highly accurate, and exhibits a number of distinctive and well documented behavioral "signatures" such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is "special". But why does human face perception exhibit these properties in the first place? Here we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As for face perception, the car-trained network showed a drop in performance for inverted versus upright cars. Similarly, CNNs trained only on inverted faces produce an inverted inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so "special" after all. Significance StatementFor decades, cognitive scientists have collected and characterized behavioral signatures of face recognition. Here we move beyond the mere curation of behavioral phenomena to asking why the human face system works the way it does. We find that many classic signatures of human face perception emerge spontaneously in CNNs trained on face discrimination, but not in CNNs trained on object classification (or on both object classification and face detection), suggesting that these long-documented properties of the human face perception system reflect optimizations for face recognition per se, not by-products of a generic visual categorization system. This work further illustrates how CNN models can be synergistically linked to classic behavioral findings in vision research, thereby providing psychological insights into human perception.

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bioRxiv2021

Brain-like functional specialization emerges spontaneously in deep neural networks

The last quarter century of cognitive neuroscience has revealed numerous cortical regions in humans with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what other people are thinking. But it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception, using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects the computational requirements of the task. We find that networks trained on generic object recognition perform poorly on face recognition and vice versa, and further that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. Thus, generic visual features that suffice for object recognition are apparently suboptimal for face recognition and vice versa. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.

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bioRxiv2018

How face perception unfolds over time

Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. To this end, we used magnetoencephalography to measure the time course of neural responses to faces, thereby addressing two fundamental questions about how face processing unfolds over time. First, using representational similarity analysis, we found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. Second, identity and gender representations of familiar faces were enhanced very early on, indicating that the previously-reported behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms. These findings start to reveal the time course of face perception in humans, and provide powerful new constraints on computational theories of face perception.

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