Biology subjects

Hebart, M.

Publications and source records attributed to Hebart, M..

3 recordsLinked to original sources

bioRxiv2026

Two dominant axes structure high-dimensional object representations in the human ventral temporal cortex

The ventral temporal cortex is critical for visual object recognition, yet the principles organising its representations remain unclear. Using ultra-high-field 7T fMRI and dense sampling of 1,854 object concepts, we applied exploratory factor analysis to multivoxel response patterns to estimate the main axes of representational variation. Despite high dimensionality, over half of the explainable variance concentrated along two dominant axes: a continuum spanning objects of high biological salience, and a second integrating spatial context, physical scale, and manipulability. Importantly, neither reduced to a single semantic label, instead reflecting mixtures of visual and conceptual properties. Together they defined a triangular geometry, anchored by animal-, furniture- and food-related objects, stable across participants and two independent datasets, that mapped onto a lateral-to-medial cortical gradient, with category-selective regions emerging as local peaks within a broader continuum. These findings reveal that high-dimensional object representations in the ventral temporal cortex are structured by a small number of interpretable axes.

neuroscience↗

bioRxiv2026

Encoding models uncover fine-grained feature selectivity for bodies, hands and tools

Category-selective areas in the occipitotemporal cortex (OTC) are typically characterized by broad tuning, yet neuroimaging suggests a finer-grained organization reflecting distinct computational roles. We combined image-level fMRI with artificial neural network (ANN)-based encoding models to investigate the selectivity and feature sensitivity of category-selective areas in ventral and lateral OTC. Using densely sampled fMRI data in three participants across six sessions, we identified functional dissociations between body, hand, and tool responses at the individual image level. Area-specific encoding models accurately predicted responses to millions of novel images, maintaining clear category preferences. Importantly, comparisons between models trained on areas selective for the same category revealed distinct feature sensitivities consistent with the areas anatomical location and hemispheric lateralization. These findings provide evidence for fine-grained specialization within OTC and demonstrate how ANN-based encoding models can uncover the computational, feature-level basis of category selectivity.

neuroscience↗

bioRxiv2025

Numerosity Is Directly Sensed and Dynamically Transformed in the Human Brain: Evidence from MEG-MRI Fusion

Humans can estimate the number of objects in a scene within a fraction of a second, suggesting that numerosity is encoded rapidly and directly by the visual system. Yet how this encoding unfolds over time and interacts with other visual features remains unclear. Here, we combined magnetoencephalography (MEG) with time-resolved representational similarity analysis (RSA) and MEG-fMRI fusion to track how numerosity is represented in the brain over time. We also used multidimensional scaling (MDS) to visualize the evolving patterns of neural activity. Two main findings emerged. First, numerosity exhibited the hallmarks of a primary perceptual attribute: its neural signature appeared rapidly after stimulus onset, preceding the encoding of non-numeric features that could otherwise define number. Second, Visualization of the neural patterns using MDS suggested a temporal transformation in representational geometry, reflecting the engagement of two distinct coding schemes- an early, linear number line, consistent with a "summation code", dominating activity in occipital regions, and a later, curved number line, consistent with "numerosity-tuned code", emerging more strongly in associative areas along the dorsal stream. Together, these findings demonstrate that numerosity processing is encoded directly from the visual image and unfolds through a rapid hierarchical transformation, from a broad quantity signal to a finely tuned, number-specific code, linking perceptual encoding to higher-level numerical abstraction.

neuroscience↗