Hans Op de Beeck

The paper investigates how object shape, real-world size, and mobility are represented in human visual cortex using fMRI and a novel stimulus set that manipulates these dimensions independently.

This study identifies three principles of expert brain representation using chess as a model, showing shifts in content, structure, and location of neural representations.

The paper uses EEG to show that humans accurately distinguish animate from inanimate objects, even when objects resemble animals, and that this ability is not affected by the degree of animal-like appearance.

The paper compares computational models' processing of visual Braille with human neural data, finding that feedforward models fail to replicate human expertise in visual Braille processing.

The paper shows that activation in category-selective regions of the occipito-temporal cortex predicts categorisation behaviour in face, body, and scene classification tasks.

The paper investigates action topography in visual cortex and DANNs, finding an action gradient in human brain regions that DANNs do not replicate.

This study shows that early visual deprivation in humans impairs the early visual cortex but leaves the categorical coding in the ventral occipito-temporal cortex largely unaffected.

This study compares recurrent and feedforward DNNs in modeling human visual recognition behavior, finding that model size, not recurrence, better aligns with human-like confusion patterns.

The paper investigates how human and AI models process emotional valence in complex social scenes, finding that higher-level processing is required when valence conflicts with visual features.

This study investigates how the brain processes visual Braille by comparing expert and naive readers, finding that linguistic content, not visual features, drives orthographic decoding in an extended reading network.

This study investigates the human bias toward perceiving zoomorphic objects as animal-like and shows that this bias can be induced in deep neural networks with specific training.

This study investigates neuronal tuning for shape and category in human visual cortex using spiking activity recordings, finding shape-based representations with category-like encoding and interactions between shape and category tuning.

This study records multi-unit spiking activity in human visual cortex to investigate body and body part representation, finding strong selectivity for bodies and generalization to non-photographic depictions.

The paper investigates the emergence of an animal appearance bias in human visual cortex using EEG, finding strong early neural responses to zoomorphic objects similar to animal pictures, supporting feedforward processing.

This study compares object recognition strategies between rats and humans using a computational framework, finding significant differences in how each species processes visual stimuli.

This paper compares the representational hierarchy of human and artificial visual systems, finding similarities in object-scene co-occurrence representations but differences in information processing strategies.

This study investigates how the hippocampus represents temporal order in motor sequences using fMRI, finding that it binds movements to their temporal position, unlike other motor regions.

This study investigates the organizational principles of object space in the human occipitotemporal cortex and finds that animacy, rather than aspect ratio, is the primary factor.

This study compares the ability of deep convolutional neural networks and humans to process Mooney face stimuli, finding that CNNs exhibit the face inversion effect and perform similarly to humans in some conditions.

This paper compares deep learning and human vision by training CNNs with increasing spatial frequency and finds improved classification of low-pass images.

The paper shows that visual representations become more stable along rodent visual processing hierarchies, with intrinsic dynamics contributing to this stability.

This study investigates how animacy organization in occipitotemporal cortex relates to face-body division and taxonomic hierarchy, finding that animal resemblance to faces and bodies creates an apparent animacy continuum.

This paper compares rodent object vision with convolutional deep neural networks, finding that mid-level layers suffice and challenging assumptions about complex representations.

The paper shows that category representations in VTC of congenitally blind individuals vary widely, with some showing similarity to sighted individuals despite group-level differences.

This study uses fMRI to test if temporal contiguity training affects size tolerance in human object-selective cortex, finding no such effect.

This study compares how deep CNNs and human visual cortex represent object shape and category, finding that CNNs and the brain encode these features separately, with CNNs peaking in final layers and the brain in anterior ventral temporal cortex.

The paper compares neural network structures from UNIVAR, RSA, and FCA in fMRI data, finding high similarity but distinct idiosyncratic features across methods.

The paper investigates whether neural distance predicts reaction times in categorization tasks involving animacy, shape, and crisscrossing categories, finding it correlates with RTs for animate stimuli.

The paper shows that ventral visual cortex represents object appearance over animacy, unlike neural networks, which prioritize animacy in categorization.

The paper shows that decoding asymmetry in multivariate cross-classification can arise from signal-to-noise ratio differences between datasets, not necessarily complex neurocognitive mechanisms.