Biology subjects

de Hollander, G.

Publications and source records attributed to de Hollander, G..

12 recordsLinked to original sources

bioRxiv2026

Domain-specific and domain-general signatures of dyscalculia in computational models of behavior and parietal cortex

Developmental dyscalculia is a learning disorder characterized by marked difficulties in numerical cognition, yet its cognitive and neural underpinnings remain debated. Competing accounts attribute dyscalculia either to domain-specific imprecise representations of number or to domain-general deficits in the maintenance and integration of numerical information. Here, we test both accounts in 33 adolescents and young adults with dyscalculia and 33 age- and gender-matched controls (15-23 years) who performed a sequential numerosity comparison task during fMRI. Joint computational modeling of choices and response times allowed us to dissociate perceptual and memory noise from decision variables governing the speed-accuracy tradeoff, while complementary neuroimaging analyses characterized neural number representations and their network embedding. Specifically, we quantified the precision of parietal numerosity tuning with numerical population receptive field (nPRF) modeling and derived individualized functional network maps of the same region using community detection algorithms applied to connectivity patterns. Dyscalculic participants showed greater perceptual and memory noise but also a more cautious response criterion, trading speed for accuracy to compensate for representational imprecision. Neurally, the numerosity model explained less variance in parietal cortex, suggesting less precise numerical representations, and individualized network maps assigned less of this cortex to the dorsal attention network and more to a visual network. Rather than supporting a dichotomy between the two accounts, our findings suggest that dyscalculia reflects both less precise numerical representations in parietal cortex and an altered allocation of this cortex to distributed functional systems.

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bioRxiv2026

A Neural Population Code for Value in Human Orbitofrontal Cortex.

Adaptive behavior depends on the ability to rapidly evaluate options and select those that promise the greatest benefit. Such decisions rely on neural representations of value distributed across multiple brain regions, including the orbitofrontal (OFC) and ventromedial prefrontal cortex (vmPFC), yet the neural code underlying these value representations remains unresolved. The dominant account proposes that OFC/vmPFC neurons encode value through a linear rate code, resulting in a single point estimate at the population level. However, this framework is difficult to reconcile with the heterogeneous tuning observed in individual OFC/vmPFC neurons, which can exhibit both positive and negative correlations with subjective value. Here, we test the alternative hypothesis--derived from theories of neural coding in perceptual systems-- that the OFC/vmPFC implements a probabilistic population code based on non-linear tuning functions. Such tuning allows population activity to represent not only subjective value but also the uncertainty surrounding it, in the form of a flexible posterior probability distribution. Using a population receptive field framework, we fitted non-linear value-tuning functions to functional magnetic resonance imaging data acquired during a value judgment task. Bayesian inversion of this encoding model enabled robust out-of-sample decoding of subjective value across several brain regions, including the OFC/vmPFC. Importantly, value uncertainty estimated from decoded medial OFC/vmPFC posteriors predicted within-subject preference instability, choice stochasticity, and confidence in option values, demonstrating its behavioral relevance and suggesting that participants had conscious access to this information. Complementary single-unit recordings from a subset of monkey OFC neurons similarly revealed nonlinear value-selective tuning. Together, these findings establish a probabilistic, non-linear population code for value in the OFC/vmPFC. This provides a neural foundation for the probabilistic code through which value, and uncertainty about value, can guide choice.

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bioRxiv2025

Separable neurocomputational mechanisms underlying multisensory learning

Efficient control of behavior requires multisensory learning from information distributed across senses. However, most neurocomputational studies have focused on unisensory signals. Here, we identify distinct but interacting neurocomputational mechanisms that support learning of multisensory associations. We designed a task in which behaviorally relevant information was available only from combinations of visual cues with either auditory or tactile cues. In 58 participants undergoing fMRI, we dissociated three processes: multisensory statistical learning (SL), modeled as stimulus-locked Shannon surprise; reinforcement learning (RL), modeled as feedback-locked signed reward prediction errors (RPEs); and feedback-locked unsigned RPEs (uRPEs), reflecting surprise about reward outcomes. Behaviorally, response times scaled with Shannon surprise (SL) while accuracy improved with feedback (RL). Model-based fMRI revealed dissociable but complementary networks: RPEs engaged ventral striatum, vmPFC, and left angular gyrus; surprise recruited bilateral angular gyrus, dlPFC, and precuneus; and uRPEs involved insula, dorsomedial prefrontal, and lateral frontoparietal cortices. Several of these regions extend beyond canonical learning circuits and may contribute to learning when behavior depends on information distributed across sensory modalities. All three networks were modality-general, showing comparable strength for audiovisual and visuotactile learning. Notably, left angular gyrus tracked both Shannon surprise and RPE, suggesting its potential role for integrating structural and value information. These findings indicate that the brain engages distinct but complementary systems for structure-based, reward-based, and outcomesurprise computations. By combining behavioral modeling and fMRI with a novel task design, we provide a framework for dissecting the neurocomputational architecture of multisensory learning.

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bioRxiv2025

Acute stress reduces risk-aversion by changing magnitude perception

Stress is thought to impair financial decision-making by influencing the willingness to take risks. This effect is commonly attributed to stress-related changes in affective evaluation of rewards and their associated uncertainty. However, existing research has yielded inconsistent findings, and the psychological and neural mechanisms at play remain poorly understood. Here we show that acute psychosocial stress can affect financial risk-taking by altering basic perception of payoff magnitudes, independent of subjective valuation processes. Psychophysical and fMRI modelling reveals that stress reduces risk-aversion without changing choice consistency. A Bayesian perceptual model attributes this effect to an upward shift in prior beliefs about payoff magnitude: Under stress, participants choices are guided by relatively more optimistic beliefs about the risky options. A convergent pattern emerges in parietal neural representations of the payoff magnitudes, which show a systematic upward shift in neural coding as a consequence of the stress manipulation. These findings provide converging neural and behavioral evidence that stress influences financial risk-taking by changing neurocomputational processes related to magnitude perception. This has significant implications for psychological and neuroscience theories of stress and risky behavior, with potential applications to policy and clinical interventions.

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bioRxiv2025

Distributed range adaptation in human parietal encoding of numbers

The brain's representations are encoded in the collective activity of neural populations, whose non-monotonic tuning properties define the population code. Adaptive behavior calls for flexible retuning of this code as stimuli statistics change across contexts. Yet how such adaptation occurs is unknown. Using fMRI during a numerosity-estimation task with variable number ranges, we show that number representations in human parietal cortex dynamically recalibrate to context, enabling context-sensitive behavioral accuracy. The tuning properties of neural populations collectively shift and rescale with the range. This distributed range adaptation achieves efficient coding in real time: neural precision scales with the range and predicts corresponding changes in behavioral precision. Individuals with stronger neural adaptation show larger behavioral adjustments. These findings extend static sensory efficient coding to adaptive representations of abstract magnitudes. Such flexible population tuning may constitute a canonical mechanism of encoding networks that enables the brain to sustain precise, adaptive behavior.

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bioRxiv2025

Psilocybin alters visual-contextual computations

Psilocybin alters perception and brain dynamics. Contextual computations are ubiquitous in the brain. Here, we investigate the effects of psilocybin using psychophysics, ultra-high field functional MRI, and computational modeling. We find that 1) psilocybin alters contextual perception in the Ebbinghaus illusion, 2) psilocybin alters contextual modulation in cortical responses to visual stimuli, and 3) we propose a computational model capable of capturing and linking these changes. Leveraging vision as a beachhead, our findings highlight the alteration of contextual computations as a potential general mechanism underlying psychedelic action. TeaserPsilocybin alters visual-contextual computations, a potential general computational mechanism for psychedelic effects in the human brain.

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bioRxiv2025

Risk preferences causally rely on parietal magnitude representations: Evidence from combined TMS-fMRI

Risk preferences have traditionally been considered as stable traits that reflect subjective-valuation processes in prefrontal areas. More recently, however, it has been suggested that risk preferences may also be shaped by how choice problems are perceived and processed in perceptual brain regions. Specifically, the acuity of the parietal approximate number system (ANS), which encodes payoff magnitudes for different choice options, has been shown to correlate with both risk preferences and choice consistency. However, this correlational relationship leaves open the question whether parietal magnitude representations in fact causally underlie choice. Here, we provide direct evidence for such a key causal role of parietal magnitude representations in economic choice, using continuous theta-burst transcranial magnetic stimulation (cTBS), combined with functional MRI (fMRI) and numerical population receptive field (nPRF) modeling. Our stimulation protocol targeted numerosity-tuned regions in the right parietal cortex, identified through nPRF modeling of individual fMRI data (n=35; within-subject design). The stimulation successfully perturbed neural processing, as evidenced by decreased amplitude of numerical magnitude-tuned responses and less accurate multivariate decoding of presented magnitudes from unseen data that were not used for model fitting. In line with a perceptual account of risky choice, the reduction in neural information capacity was also reflected in noisier behavioral responses. Moreover, a computational cognitive model fitted to choice behavior revealed that perturbing the ANS specifically increased the noisiness of small-magnitude representations. This perturbation made small magnitudes to be perceived as larger than they actually are, leading to more risk-seeking behavior. Finally, individual estimates of the cTBS effect on cognitive noise correlated with the corresponding decrease in amplitude of numerical magnitude-tuned fMRI responses, further solidifying the role of the parietal ANS in economic choice. In conclusion, our study demonstrates that the precision of parietal magnitude representations causally influences economic decision-making, with noisier encoding promoting biased risk-taking as formalized in recent perceptual models of risky choice.

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bioRxiv2024

Rapid Changes in Risk Preferences Originate from Bayesian Inference on Parietal Magnitude Representations

Risk attitudes - the willingness to accept greater uncertainty to achieve larger potential rewards - determine many aspects of our lives and are often interpreted as an individual trait that reflects a general taste for risk. However, this perspective cannot explain why the choices that people make concerning risky outcomes can change considerably across contexts and even across repetitions of the identical decisions. Here we provide modelling and neural evidence that such rapid changes in risk attitudes emerge from Bayesian inference on noisy magnitude representations in the parietal cortex. Our participants underwent fMRI while choosing between safe and risky options that were either held in working memory or present on the screen. Risky options that were held in working memory were less likely to be chosen (risk aversion) when they had relatively large payoffs but more likely to be chosen (risk-seeking) when they had relatively small payoffs. These counterintuitive effects can be explained mechanistically by a computational model of the Bayesian inference underlying payoff perception: Options kept in working memory are noisier and therefore more prone to central tendency biases, leading small (or large) payoffs to be overestimated (or underestimated) more. Congruent with the behavioural modelling, fMRI population-receptive field modelling showed that on trials where intraparietal payoff representations were noisier, choices were also less consistent and less risk-neutral, in line with participants resorting more to their prior beliefs about potential payoffs. Our results highlight that individual risk attitudes and their puzzling changes across contexts and choice repetitions can be rooted in perceptual inference on noisy parietal magnitude representations, with profound implications for economic, psychological, and neuroscience theories of risky behaviour.

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bioRxiv2024

A retinotopic reference frame for space throughout human visual cortex

We perceive the world as stable despite our rapid eye movements. To explain our sense of visual stability, it has been suggested that the brain encodes the location of attended visual stimuli in an external, or spatiotopic, reference frame. However, such spatiotopy is seemingly at odds with the fundamental retinotopic organization of visual inputs. Here, we probe the spatial reference frame of vision using ultra-high-field (7T) fMRI and voxel-level receptive field modeling, while manipulating both gaze direction and spatial attention. To manipulate spatial attention, participants performed an equally demanding visual task on either a bar stimulus that traversed the visual field, or a small stimulus at fixation. To dissociate retinal stimulus position from its real-world position the entire stimulus array was placed at one of three distinct horizontal screen positions in each run. We found that population receptive fields in all cortical visual field maps are pinioned to the retina, irrespective of how spatial attention is deployed. This pattern of results is strong evidence for a fully retinotopic reference frame for visual-spatial processing. Reasoning that a spatiotopic reference frame could independently be computed at the population level of entire visual areas rather than in individual voxels, we additionally used Bayesian decoding of stimulus location from the BOLD response patterns in visual areas. We found that decoded stimulus locations also adhere to the retinotopic frame of reference. Again, this result holds for all visual areas and irrespective of the deployment of spatial attention. Our findings reorient the search for visual stability mechanisms toward transient sensorimotor interactions rather than static spatiotopic maps.

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bioRxiv2022

Individual risk attitudes arise from noise in neurocognitive magnitude representations

Humans are generally risk averse: they prefer options with smaller certain outcomes over those with larger uncertain ones. This risk aversion is classically explained with a concave utility function, meaning that successive increases in monetary payoffs should increase subjective valuations by progressively smaller amounts. Here, we provide neural and behavioural evidence that risk aversion may also arise from a purely perceptual bias: The noisy logarithmic coding of numerical magnitudes can lead individuals to underestimate the size of larger monetary payoffs, leading to apparent risk aversion even when subjective valuation increases linearly with the estimated amount. A formal model of this process predicts that risk aversion should systematically increase when individuals represent numerical magnitudes more noisily. We confirmed this prediction by measuring both the mental and neural acuity of magnitude representations during a purely perceptual task and relating these measures to individual risk attitudes during separate financial decisions. Computational model fitting suggested that subjects based both types of choices on similar mental magnitude representations, with correlated precision across the separate perceptual and risky choices. Increased stimulus noise due to the presentation format of risky outcomes led to increased risk aversion, just as predicted by the model. The precision of the underlying neural magnitude representations was estimated with a numerical population receptive field model fitted to the fMRI data of the perceptual task. Subjects with more precise magnitude representations in parietal cortex indeed showed less variable behaviour and less risk-aversion in the separate financial choices. Our results highlight that individual patterns of economic behaviour may, at least partially, be determined by capacity limitations in perceptual processing rather than by processes that assign subjective values to monetary rewards.

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bioRxiv2021

No evidence for three functionally specialized subregions in the subthalamic nucleus: A model-based 7 T fMRI study

The subthalamic nucleus (STN) is a small, subcortical brain structure. It is a target for deep brain stimulation, an invasive treatment that reduces motor symptoms of Parkinsons disease. Side effects of DBS are commonly explained using the tripartite model of STN organization, which proposes three functionally distinct subregions in the STN specialized in cognitive, limbic, and motor processing. However, evidence for the tripartite model exclusively comes from anatomical studies and functional studies using clinical patients. Here, we provide the first experimental tests of the tripartite model in healthy volunteers using ultra-high field 7 Tesla (T) functional magnetic resonance imaging (fMRI). 34 participants performed a random-dot motion decision-making task with a difficulty manipulation and a choice payoff manipulation aimed to differentially affect cognitive and limbic networks. Moreover, participants responded with their left and right index finger, differentially affecting motor networks. We analysed BOLD signal in three subregions of equal volume of the STN along the dorsolateral-ventromedial axis, identified using manually delineated high resolution anatomical images. Our results indicate that all segments responded equally to the experimental manipulations, and did not support the tripartite model.

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bioRxiv2020

Ultra-high resolution fMRI reveals origins of feedforward and feedback activity within laminae of human ocular dominance columns

Ultra-high field MRI can functionally image the cerebral cortex of human subjects at the submillimeter scale of cortical columns and laminae. Here, we investigate both in concert, by, for the first time, imaging ocular dominance columns (ODCs) in primary visual cortex (V1) across different cortical depths. We ensured that putative ODC patterns in V1 (a) are stable across runs, sessions, and scanners located in different continents (b) have a width ([~]1.3 mm) expected from post-mortem and animal work and (c) are absent at the retinotopic location of the blind spot. We then dissociated the effects of bottom-up thalamo-cortical input and attentional feedback processes on activity in V1 across cortical depth. Importantly, the separation of bottom-up information flows into ODCs allowed us to validly compare attentional conditions while keeping the stimulus identical throughout the experiment. We find that, when correcting for draining vein effects and using both model-based and model-free approaches, the effect of monocular stimulation is largest at deep and middle cortical depths. Conversely, spatial attention influences BOLD activity exclusively near the pial surface. Our findings show that simultaneous interrogation of columnar and laminar dimensions of the cortical fold can dissociate thalamocortical inputs from top-down processing, and allow the investigation of their interactions without any stimulus manipulation. Significance StatementThe advent of ultra-high field fMRI allows for the study of the human brain non-invasively at submillimeter resolution, bringing the scale of cortical columns and laminae into focus. De Hollander et al imaged the ocular dominance columns and laminae of V1 in concert, while manipulating top-down attention. This allowed them to separate feedforward from feedback processes in the brain itself, without resorting to the manipulation of incoming information. Their results show how feedforward and feedback processes interact in the primary visual cortex, highlighting the different computational roles separate laminae play.

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