Biology subjects

Op de Beeck, H.

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

At least 19 recordsLinked to original sources

bioRxiv2026

The ViRA-L (Visual Recognition Assessment-Leuven): Development, Validation, and Age Stratified Normative Characterization of a Multi-Category Visual Recognition Battery

Objective: To develop and validate the Visual Recognition Assessment-Leuven (ViRA-L), a recognition-memory battery assessing ten visual categories within a matched task structure, and provide age-stratified normative data. Method: Healthy Dutch-speaking adults completed a visual recognition task for ten categories. Each category included six targets, 18 Learning trials, and 30 Novel trials requiring recognition across appearance changes. After exclusions, 417 participants remained. We evaluated reliability, score distributions, task effects, demographic associations, and category-specific norms. Results: Battery-level internal consistency was good (Cronbach's = .879; omega total = .901), with lower category-level estimates, broad score dispersion, and minimal floor and ceiling effects. Novel and difficult trials produced lower accuracy and slower responses. Age was the clearest demographic correlate of accuracy; gender and education showed no independent overall associations. Age effects varied by category and were larger in Novel than Learning trials. Model-based differences between ages 50 and 85 were largest for Pseudowords and Hands and smallest for Houses. Conclusions: The ViRA-L supports comparison of recognition-memory profiles under matched procedures. Category-dependent age associations support age-sensitive, category-specific interpretation. The battery provides a foundation for neuropsychological research; further validation is required for clinical diagnostic use.

neuroscience↗

bioRxiv2026

Dissociating representations of object shape, real-world size, and mobility in human visual cortex

Object representations in the human ventral occipitotemporal cortex (VOTC) are organized along multiple dimensions, including shape (rectilinear vs. curvilinear), real-world size (large vs. small), and mobility (stationary vs. mobile). However, these dimensions are strongly correlated in naturalistic vision, making their separate contributions to VOTC organization unclear. For example, large objects (e.g., a wardrobe, a house) are typically rectilinear and stationary, while small objects (e.g., a ball, a cup) are more curvilinear and mobile. Here, we used fMRI, together with a new stimulus set that orthogonally manipulates shape, size, and mobility, to investigate the separate influences of these dimensions on VOTC organization. Example stimuli include air balloon (large, curvilinear, mobile), radar dish (large, curvilinear, stationary), and mailbox (small, rectilinear, stationary). Contrasts revealed that large (vs. small), rectilinear (vs. curvilinear), and stationary (vs. mobile) dimensions all independently evoked strong and overlapping activity in medio-anterior VOTC. This overlapping activity was at the intersection of the parahippocampal place area (PPA) and the ventral place-memory area (VPMA). Similar results were found at the intersection of the scene-selective occipital place area and the lateral place-memory area (LPMA). Finally, large (vs. small), but not rectilinear (vs. curvilinear) or stationary (vs. mobile) activity, was found in additional posterior ventral scene-selective regions, as well as in early visual cortex. Overall, these results indicate that object shape, real-world size, and mobility dimensions all independently activate scene-selective PPA and OPA, showing joint selectivity for distinct low- and high-level object properties that are highly correlated in naturalistic vision.

neuroscience↗

bioRxiv2026

Hard-to-beat animacy perception: EEG evidence of accurate animate-inanimate distinction for ambiguous objects

Human recognition of objects as animate or inanimate is fast and accurate. However, this task may be challenging for objects with animal-like properties (e.g., presence of eyes/face), albeit being inanimate (e.g., a cow-mug). Lookalike objects provide an opportunity to examine how visual perception resolves categorical ambiguities and whether it exhibits an intrinsic bias to see animacy. During electroencephalography (EEG), we presented healthy humans with images of objects at a regular, rapid frequency (6 Hz), where every five exemplars of a standard category (animate or inanimate), an exemplar of the other oddball category was shown (1.2 Hz). In some conditions, oddball-stimuli were replaced by lookalikes. Periodic visual stimulation should give rise to a distinctive EEG response at 6 Hz. Moreover, if the oddball category elicits a different neural response than the standard category, a distinct response should be observed at 1.2 Hz. This categorization response was found for animate-oddballs among inanimate objects, and vice versa. It was also found for lookalike-oddballs presented among animate or inanimate objects, but it was significantly higher in the first case, implying that lookalike objects were perceived as more similar to inanimate than animate objects. The degree of animal resemblance modulated the amplitude of neural response to lookalikes, without however changing the category boundary. These results--also replicated in an artificial model of human vision--demonstrate that animal resemblance of objects is registered in visual perception, but it does not alter the critical ability to distinguish between what is truly alive and what is not.

neuroscience↗

bioRxiv2026

Feedforward computational models of vision do not explain expert neural processing of visual Braille in the human visual system

The human visual system, and in particular the Visual Word Form Area (VWFA), adapts to process letters and words, even when the stimuli do not share canonical script features, like Braille. Here we set-up to compare the organization of typical orthographic and peculiar visual scripts such as Braille in computational models. In a first experiment, we looked at how Braille letters are represented in an illiterate Convolutional Neural Network (AlexNet) and compared them to Latin alphabet and to Line Braille, a custom line-based script. We observed a predisposition of the network, pre-trained to perform object recognition, for line-based scripts. This finding suggests an initial advantage of line junctions over Braille in processing scripts likely based on typical visual computations applied to the visual world. In a second experiment, we trained two benchmark neural network architectures (AlexNet, CORnet Z) to classify words in the Latin script (literacy acquisition) and then in the Braille script (expertise acquisition). We modelled the processing of reading visual Braille and explored the networks representations at different layers. We observed clustering of features based on the visual properties of the scripts and not by the networks expertise. Unlike human participants, the representations of linguistic categories do not converge to a model of the linguistic (orthographic, phonological, semantic) properties. Overall, the lack of alignment between the visual processing of the trained computational models and neural data recorded in expert humans suggests that the fundamental processing of reading cannot be fully explained by simple feed-forward visual processing of the script, but likely relies on additional mechanisms including interactive relations between the visual and linguistic systems.

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bioRxiv2026

Can category-selective cortex predict categorisation behaviour?

One of the distinctive features of the human visual system is the presence in occipito-temporal cortex (OTC) of regions that show preferential activation to specific categories of visual objects. To understand how this selectivity relates to categorisation behaviour, studies have employed a distance-to-bound approach (DTB), where multivariate brain activity is used to estimate a decision boundary, from which behavioural performance can be predicted. Using this approach, correlations have been found between activity in OTC, and behavioural performance when carrying out certain categorisation tasks. However, it remains unclear what determines where in OTC this correlations can be found, and with which categorisation tasks they can be found. Here, we bridged this gap by relating category-selective regions of OTC, to behavioural performance while participants categorised images as belonging or not to their preferred categories. We adopted a more basic approach and considered simple, univariate activity, rather than relying on decoding to build our DTB. Our results show that activation in regions selective to faces (FFA & OFA), bodies (EBA), and scenes (PPA), is sufficient to predict behavioural performance while categorising images as being faces, bodies, or scenes, respectively. These results are largely consistent across reaction time and motor movements, and generalise to animacy classification. Overall, our data adds to evidence that category-selective regions in OTC can serve to guide categorisation behaviour, and underlines the validity of the DTB approach to address this relationship.

neuroscience↗

bioRxiv2025

Low-Dimensional and Optimised Representations of High-Level Information in the Expert Brain

What transforms a novice into an expert? Decades of research show that expertise relies on domain-specific knowledge, but a neural account of this transformation has remained fragmentary: we lack an understanding of what information expert representations encode, how they are structured for efficient use, and where in the brain they reside. Using chess as a model system, we combine neuroimaging with multivariate pattern analysis to reveal three principles of the expert brain. Expertise drives a shift in representational content, from surface visual features to high-level, relational information. It is accompanied by a structural change to low-dimensional, optimised representation: codes become more compact and better organised, yet retain the detail needed for precise evaluation. Finally, the representational load shifts from sensory-specific cortices to domain-general frontoparietal networks. The expert brain packs more into less, concentrating richer knowledge into fewer, better-organised representations that support the rapid, flexible decisions of mastery.

neuroscience↗

bioRxiv2025

Investigating action topography in visual cortex and deep artificial neural networks

High-level visual cortex contains category-selective areas embedded within larger-scale topographic maps like animacy and real-world size. Here, we propose action as a key organizing factor shaping visual cortex topography and assess the ability of topographic deep artificial neural networks (DANNs) in capturing this organization. Using fMRI, we examined responses to images of body-parts and objects with different degrees of action properties. In left lateral occipitotemporal cortex, we identified a topographically-organized action gradient, with overlapping activations for bodies, hands, tools, and manipulable objects along a dorsal-posterior to ventral-anterior axis, culminating at the intersection of body parts and objects exhibiting higher action properties. Multivariate analyses confirmed action as a crucial organizing principle, while shape and animacy dominated ventral occipitotemporal cortex and DANNs, which exhibited no action-based organization. Our proposed action dimension serves as a further organizing principle of object categories, advancing understanding of visual cortex organization and its divergence from DANN-based models.

neuroscience↗

bioRxiv2025

Alignment of human behavior, brain, and AI models in the high-level valence processing of complex social scenes

Humans can evaluate the emotional meaning of complex social scenes in real-life settings. Recent evidence from the human brain and AI models pointed to visual processing as a core substrate for representing emotional valence of natural images, but this conclusion may not generalize to complex social scenes. We implemented experiments with social scenes in which emotional valence is partially dissociated from visual characteristics, objects, and scene settings. Human behavior, neuroimaging, and visual AI models confirm that visual processing captures basic emotional associations of objects and scene elements. However, when the valence of social scenes is incongruent with these basic properties, higher levels of processing are needed in the human association cortex and AI models. Our results show how and when valence processing demands advanced cognitive, neural, and computational processes that extend beyond the encoding of visual features.

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bioRxiv2024

Impact of a transient neonatal visual deprivation on the development of the ventral occipito-temporal cortex in humans

How does experience shape the development of visual brain regions? We demonstrate that a transient period of visual deprivation early in life in humans leads to permanent alteration in the function of the early visual cortex (EVC), while leaving the categorical coding of downstream ventral occipito-temporal cortex (VOTC) mostly unaffected. We used fMRI to characterize the brain response to five categories (faces, bodies, objects, buildings and words) in a rare group of cataract-reversal individuals who experienced a transient period of blindness early in life, and in matched participants with typical visual development. We show that the encoding of low-level visual properties is impaired in EVC in cataract-reversal participants, while the categorical response in the VOTC is preserved. When altering the visual properties of our stimuli to mimic in controls the deficit of EVC response of the cataract, we observe a cascading alteration of the categorical coding from EVC to VOTC that is not observed in the cataract-reversal group. Our results show that a typical visual categorical organization develops in VOTC despite a period of visual deprivation early in life and in the presence of impaired processing in EVC. A deep neural network exposed to a visual diet that emulates the experience of our participants reproduced our brain observations within an artificial system. These findings emphasize how specific visual regions are differently affected by a period of visual deprivation early in life, with information encoded in EVC being permanently affected, while the categorical coding in VOTC shows resilience to deprivation.

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bioRxiv2024

Widespread neural reorganization related to expertise in reading visual Braille

Learning to read assigns linguistic value to an abstract visual code. Whether regions of the reading network tune to visual properties common to most scripts or code for more abstracted units of language remains debated. Here we investigate this question using visual Braille, a script developed for touch that does not share the typical explicit shape information of other alphabets, yet maps onto the same phonology and lexicon as other more regular scripts. First, we compared univariate responses in visual Braille readers and a naive control group and found that individually localized Visual Word Form Area (VWFA) was selectively activated for visual Braille when compared to scrambled Braille only in expert Braille readers. Multivariate analyses showed that linguistic properties can be decoded from Latin script in both groups and from Braille script in expert readers in an extended network of brain regions including the early visual cortex (V1), the lateral occipital region (LO), the VWFA and the left Posterior Temporal area (l-PosTemp). These results suggest that the tuning of an extended reading network to orthography relies more on the linguistic content of the script rather than their specific visual features (e.g. line junctions). Nevertheless, cross-scripts generalization was significantly lower than within-script decoding and failed to reveal common representations across Latin and Braille in experts in all regions except the l-PosTemp. These results suggest that V1, LO and VWFA encode orthographic representations in a script-specific manner, whereas l-PosTemp encodes abstracted linguistic information.

neuroscience↗

bioRxiv2024

Recurrent issues with deep neural networks of visual recognition

Object recognition requires flexible and robust information processing, especially in view of the challenges posed by naturalistic visual settings. The ventral stream in visual cortex is provided with this robustness by its recurrent connectivity. Recurrent deep neural networks (DNNs) have recently emerged as promising models of the ventral stream, surpassing feedforward DNNs in the ability to account for brain representations. In this study, we asked whether recurrent DNNs could also better account for human behaviour during visual recognition. We assembled a stimulus set that included manipulations that are often associated with recurrent processing in the literature, like occlusion, partial viewing, clutter, and spatial phase scrambling. We obtained a benchmark dataset from human participants performing a categorisation task on this stimulus set. By applying a wide range of model architectures to the same task, we uncovered a nuanced relationship between recurrence, model size, and performance. While recurrent models reach higher performance than their feedforward counterpart, we could not dissociate this improvement from that obtained by increasing model size. We found consistency between humans and models patterns of difficulty across the visual manipulations, but this was not modulated in an obvious way by the specific type of recurrence or size added to the model. Finally, depth/size rather than recurrence makes model confusion patterns more human-like. Contrary to previous assumptions, our findings challenge the notion that recurrent models are better models of human recognition behaviour than feedforward models, and emphasise the complexity of incorporating recurrence into computational models.

neuroscience↗

bioRxiv2024

Fine-grained neural coding of bodies and body parts in human visual cortex

The visual image of a human body provides a valuable source of socially relevant information. However, our understanding of the neuronal mechanisms underlying body perception in humans remains limited given the spatiotemporal constraints of functional imaging. Here we recorded multi-unit spiking activity in two neurosurgical patients in or near the extrastriate body area (EBA), a critical region for body perception. Our recordings revealed a strong preference for human bodies over a large range of control stimuli. Notably, this preference was driven by a distinct selectivity for body parts. Moreover, the observed body selectivity generalized to non-photographic depictions of bodies such as silhouettes and stick figures. Overall, our study provides an unprecedented access into the representation of bodies in the human visual cortex to bridge the gap between human neuroimaging and macaque electrophysiology studies, and form a solid basis for computational models of human body processing.

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bioRxiv2023

Neuronal tuning and population representations of shape and category in human visual cortex.

Object recognition and categorization are essential cognitive processes which engage considerable neural resources in the human ventral visual stream. However, the tuning properties of human ventral stream neurons for object shape and category are virtually unknown. We performed the first large-scale recordings of spiking activity in human Lateral Occipital Complex in response to stimuli in which the shape dimension was dissociated from the category dimension. Consistent with studies in nonhuman primates, the neuronal representations were primarily shape-based, although we also observed category-like encoding for images of animals. Surprisingly, linear decoders could reliably classify stimulus category even in data sets that were entirely shape-based. In addition, many tuning curves showed an interaction between shape and category tuning. These results represent the first detailed study on shape and category coding at the neuronal level in the human ventral visual stream, furnishing essential evidence that reconciles human imaging and macaque single-cell studies.

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bioRxiv2023

A computationally informed comparison between the strategies of humans and rodents in visual object recognition

Many species are able to recognize objects, but it has been proven difficult to pinpoint and compare how different species solve this task. Recent research suggested to combine computational and animal modelling in order to obtain a more systematic understanding of task complexity and compare strategies between species. In the present study, we created a large multidimensional stimulus set and designed a visual discrimination task partially based upon modelling with a convolutional deep neural network (CNN). Experiments included rats (N = 11; 1115 daily sessions in total for all rats together) and humans (N = 45). Each species was able to master the task and generalize to a variety of new images. Nevertheless, rats and humans showed very little convergence in terms of which object pairs were associated with high and low performance, suggesting the use of different strategies. There was an interaction between species and whether stimulus pairs favoured early or late processing in a CNN. A direct comparison with CNN representations and visual feature analyses revealed that rat performance was best captured by late convolutional layers and partially by visual features such as brightness and pixel-level similarity, while human performance related more to the higher-up fully connected layers. These findings highlight the additional value of using a computational approach for the design of object recognition tasks. Overall, this computationally informed investigation of object recognition behaviour reveals a strong discrepancy in strategies between rodent and human vision.

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bioRxiv2022

Category trumps shape as an organizational principle of object space in the human occipitotemporal cortex

The organizational principles of the object space represented in human ventral visual cortex are debated. Here we contrast two prominent proposals that, in addition to an organization in terms of animacy, propose either a representation related to aspect ratio or to the distinction between faces and bodies. We designed a critical test that dissociates the latter two categories from aspect ratio and investigated responses from human fMRI and deep neural networks (BigBiGAN). Representational similarity and decoding analyses showed that the object space in occipitotemporal cortex (OTC) and BigBiGAN was partially explained by animacy but not by aspect ratio. Data-driven approaches showed clusters for face and body stimuli and animate-inanimate separation in the representational space of OTC and BigBiGAN, but no arrangement related to aspect ratio. In sum, the findings go in favor of a model in terms of an animacy representation combined with strong selectivity for faces and bodies.

neuroscience↗

bioRxiv2022

A computational understanding of zoomorphic perception in the human brain

It is common to find objects that resemble animals on purpose (e.g., toys). While the perception of such objects as animal-like seems obvious to humans, such "Animal bias" for zoomorphic objects turned out to be a striking discrepancy between the human brain and artificial visual systems known as deep neural networks (DNNs). We provide a computational understanding of the human Animal bias. We successfully induced this bias in DNNs trained explicitly with zoomorphic objects. Alternative training schedules, focusing on previously identified differences between the brain and DNNs, failed to cause an Animal bias. Specifically, we considered the superordinate distinction between animate and inanimate classes, the sensitivity for faces and bodies, the bias for shape over texture, and the role of ecologically valid categories. These findings provide computational support that the Animal bias for zoomorphic objects is a unique property of human perception yet can be explained by human learning history.

neuroscience↗

bioRxiv2022

Mooney Face Image Processing in Deep Convolutional Neural Networks Compared to Humans

Deep Convolutional Neural Networks (CNNs) are criticised for their reliance on local shape features and texture rather than global shape. We test whether CNNs are able to process global shape information in the absence of local shape cues and texture by testing their performance on Mooney stimuli, which are face images thresholded to binary values. More specifically, we assess whether CNNs classify these abstract stimuli as face-like, and whether they exhibit the face inversion effect (FIE), where upright stimuli are classified positively at a higher rate compared to inverted. We tested two standard networks, one (CaffeNet) trained for general object recognition and another trained specifically for facial recognition (DeepFace). We found that both networks perform perceptual completion and exhibit the FIE, which is present over all levels of specificity. By matching the false positive rate of CNNs to humans, we found that both networks performed closer to the human average (85.73% for upright, 57.25% for inverted) for both conditions (61.31% and 62.70% for upright, 48.61% and 42.26% for inverted, for CaffeNet and DeepFace respectively). Rank order correlation between CNNs and humans across individual stimuli shows a significant correlation in upright and inverted conditions, indicating a relationship in image difficulty between observers and the model. We conclude that in spite of the texture and local shape bias of CNNs, which makes their performance distinct from humans, they are still able to process object images holistically.

bioinformatics↗

bioRxiv2021

Object-scene conceptual regularities reveal fundamental differences between biological and artificial object vision

Human vision is still largely unexplained. Computer vision made impressive progress on this front, but it is still unclear to which extent artificial neural networks approximate human object vision at the behavioral and neural levels. Here, we investigated whether machine object vision mimics the representational hierarchy of human object vision with an experimental design that allows testing within-domain representations for animals and scenes, as well as across-domain representations reflecting their real-world contextual regularities such as animal-scene pairs that often co-occur in the visual environment. We found that DCNNs trained in object recognition acquire representations, in their late processing stage, that closely capture human conceptual judgements about the co-occurrence of animals and their typical scenes. Likewise, the DCNNs representational hierarchy shows surprising similarities with the representational transformations emerging in domain-specific ventrotemporal areas up to domain-general frontoparietal areas. Despite these remarkable similarities, the underlying information processing differs. The ability of neural networks to learn a human-like high-level conceptual representation of object-scene co-occurrence depends upon the amount of object-scene co-occurrence present in the image set thus highlighting the fundamental role of training history. Further, although mid/high-level DCNN layers represent the category division for animals and scenes as observed in VTC, its information content shows reduced domain-specific representational richness. To conclude, by testing within- and between-domain selectivity while manipulating contextual regularities we reveal unknown similarities and differences in the information processing strategies employed by human and artificial visual systems. Author SummaryComputational object vision represents the new frontier of brain models, but do current artificial visual systems known as deep convolutional neural networks (DCNNs) represent the world as humans do? Our results reveal that DCNNs are able to capture important representational aspects of human vision both at the behavioral and neural levels. At the behavioral level, DCNNs are able to pick up contextual regularities of objects and scenes thus mimicking human high-level semantic knowledge such as learning that a polar bear "lives" in ice landscapes. At the neural representational level, DCNNs capture the representational hierarchy observed in the visual cortex all the way up to frontoparietal areas. Despite these remarkable correspondences, the information processing strategies implemented differ. In order to aim for future DCNNs to perceive the world as humans do, we suggest the need to consider aspects of training and tasks that more closely match the wide computational role of human object vision over and above object recognition.

neuroscience↗