Biology subjects

Op de Beeck, H. P.

Publications and source records attributed to Op de Beeck, H. P..

5 recordsLinked to original sources

bioRxiv2023

The representational dynamics of the animal appearance bias in human visual cortex are indicative of fast feedforward processing

The human visual system has a seemingly unique tendency to interpret zoomorphic objects as animals, not as objects. This animal appearance bias is very strong in the ventral visual pathway as measured through functional magnetic resonance imaging (fMRI), but it is absent in feedforward deep convolutional neural networks. Here we investigate how this bias emerges over time by probing its representational dynamics through multivariate electroencephalography (EEG). The initially activated representations to lookalike zoomorphic objects are very similar to the representations activated by animal pictures and very different from the neural responses to regular objects. Neural responses that reflect the true identity of the zoomorphic objects as inanimate objects are weaker and appear later, as do effects of task context. The strong early emergence of an animal appearance bias strongly supports a feedforward explanation, indicating that lack of recurrence in deep neural networks is not an explanation for their failure to show this bias.

neuroscience↗

bioRxiv2020

Untangling the animacy organization of occipitotemporal cortex

Some of the most impressive functional specialization in the human brain is found in occipitotemporal cortex (OTC), where several areas exhibit selectivity for a small number of visual categories, such as faces and bodies, and spatially cluster based on stimulus animacy. Previous studies suggest this animacy organization reflects the representation of an intuitive taxonomic hierarchy, distinct from the presence of face- and body-selective areas in OTC. Using human fMRI, we investigated the independent contribution of these two factors - the face-body division and taxonomic hierarchy - in accounting for the animacy organization of OTC, and whether they might also be reflected in the architecture of several deep neural networks. We found that graded selectivity based on animal resemblance to human faces and bodies masquerades as an apparent animacy continuum, which suggests that taxonomy is not a separate factor underlying the organization of the ventral visual pathway.

neuroscience↗

bioRxiv2020

Extensive individual differences of category information in ventral temporal cortex in the congenitally blind

Human ventral temporal cortex (VTC) is a cortical expanse that performs different functions and computations, but is especially critical for visual categorization. Nevertheless, accumulating evidence shows that category-selective regions persist in VTC in the absence of visual experience - for example, in congenitally blind (CB) participants. Despite this evidence, a large body of previous work comparing functional representations in VTC between sighted and CB participants performed univariate analyses at the group level, which assume a homogeneous population - an assumption that has not been formally tested until the present study. Specifically, using fMRI in CB and sighted participants (male and female), we empirically show that at the group level, distributed category representations in VTC are more reliable in the sighted (when viewing visual stimuli) compared to the CB (when hearing auditorily-substituted visual stimuli). Despite these group differences, there is extensive heterogeneity in VTC category representations in the CB to the point that VTC category representations in a subset of CB participants (some who were born without eyes, but not all) are more similar to sighted individuals compared to other CB participants. Together, our findings support a novel idea that driving factors contributing to the formation of VTC category representations in the blind are subject-specific, which complements factors that may generalize across group members. More broadly, the present findings caution conclusions of homogeneity across subjects within a group when performing group neuroimaging analyses without explicitly quantifying individual differences.

neuroscience↗

bioRxiv2019

Generalization asymmetry in multivariate cross-classification: When representation A generalizes better to representation B than B to A

In recent years, the use of multivariate cross-classification (MVCC) has grown in popularity as a way to test for the consistency of information in neural patterns of activation across cognitive states. In this approach, a classification algorithm is trained on dataset A and then tested on a different dataset B in order to test for commonalities in the information representation in both datasets. Interestingly, several papers report an asymmetry in the generalization direction: training on A and testing on B returns significantly better decoding results than training on B and testing on A. Whereas several neurocognitive hypotheses have been put forward as an explanation for this phenomenon, none of them has been demonstrated directly. Through simple simulations, we show that asymmetry can arise as soon as two datasets with identical ground truths have a different signal-to-noise ratio (SNR) - generalization is best from the lower SNR to the higher SNR dataset. The extent of the asymmetry is further modulated by the overlap in informative voxels and whether the two datasets have an equal number of informative voxels. These findings demonstrate that the observation of decoding direction asymmetry in MVCC can be explained by simple SNR differences and not necessarily implies complex neurocognitive mechanisms.

neuroscience↗

bioRxiv2017

Multi-method brain imaging reveals impaired representations as well as altered connectivity in adults with dyscalculia

Two hypotheses have been proposed about the etiology of neurodevelopmental disorders: representation impairments versus disrupted access to representations. We implemented a multi-method brain imaging approach to directly compare the representation vs. access hypotheses in dyscalculia, a highly prevalent but understudied neurodevelopmental disorder in learning to calculate. We combined several magnetic resonance imaging methods and analyses, including multivariate analyses, functional and structural connectivity, and voxel-based morphometry analysis, in a sample of 24 adults with dyscalculia and 24 carefully matched controls. Results showed a clear deficit in the non-symbolic magnitude representations in parietal, temporal, and frontal regions in dyscalculia. We also observed hyper-connectivity in visual brain regions and increased grey matter volume in the default mode network in adults with dyscalculia. Hence, dyscalculia is related to a combination of diverse neural markers which are altogether distributed across a substantial portion of cerebral cortex, supporting a multifactorial model of this neurodevelopmental disorder.

neuroscience↗