Biology subjects

Ruff, C. C.

Publications and source records attributed to Ruff, C. C..

At least 19 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.

neuroscience↗

bioRxiv2026

Goal-dependent resource-rational compression of attribute differences explains nonlinearities in multi-attribute decision making

Why do multi-attribute choices so often depart from classical weighted-additive decision rules? Rather than attributing such deviations solely to biases or heuristics, we propose a resource-rational account in which value differences are encoded via capacity-limited information channels. Under resource-rational compression, these difference representations are systematically distorted, such that behavior deviates from weighted-additive predictions because value differences are not represented veridically. This theoretical account makes testable predictions about power-law relationships between true and internally represented differences. The amount of power-law-like compression is determined by information-processing capacity, emergent long-tailed prior distributions over attribute differences, and, in choice contexts, goal-dependent subjective weights that govern the allocation of limited capacity across attribute channels. We test and find support for these predictions in an attribute difference-estimation task and by reanalyzing existing food- and social-choice datasets. These results provide converging evidence for a normative, information-theoretic account of systematic nonlinearities in multi-attribute decision making. Together they show how goals can interact with cognitive capacity and priors to shape representational precision in ways that may facilitate or impair decision making.

neuroscience↗

bioRxiv2026

Multi-stage efficient coding of perception and value in goal-directed behavior

To act effectively, the brain must transform information through a chain of processing stages, from sensing the environment, to evaluating options, to selecting actions. Because neural resources are limited, each stage should represent information efficiently. Yet efficient coding has been studied almost exclusively in perception, and always one stage at a time. Whether perception and valuation are each governed by their own efficient code, and how these codes interact along the pathway from sensation to action, remains an open question. We developed a formal framework, comparing models in which efficient coding and Bayesian decoding shape perception only, valuation only, or both. To tease apart contributions from each stage, we designed an experiment that independently varied how stimuli map onto values. In a preregistered study, behavior was best explained by efficient coding operating at both stages, with each stage tracking its own objective prior. Crucially, changing the value distribution reversed the classic repulsion biases seen in orientation perception, revealing separable efficient codes in perception and valuation. The two stages also operated on different timescales: perceptual representations reflected stable, long-term environmental structure, while value representations updated rapidly with changing context. Together, these findings show that separable efficient codes at successive processing stages combine to shape behavior. This principle likely extends well beyond perception and valuation: whenever behavior depends on abstract, constructed representations rather than raw sensory signals, the brain may solve the efficiency problem anew at each stage.

neuroscience↗

bioRxiv2026

Neural Dynamics of Belief and Value Computations Guiding Strategic Social Decisions

Successful strategic behavior must be grounded in beliefs about the opponent and her intentions. While many potential models have been proposed to explain choices in such situations, the neural mechanisms that govern learning and choice in complex strategic contexts remain poorly understood. Here, we use a computational model that combines dynamic learning and choice mechanisms to explain both choices and response times of human participants engaged in a competitive strategic task. Using electroencephalography (EEG), we identify temporally structured stages of neural processing that support an evolving value-based decision process, corresponding to first- and second-order belief updates and evidence accumulation related to the comparison of action values. Gamma-band phase coupling between central and parietal EEG signals varied with individual winning rate, suggesting that strategic behavior involves coordinated information transfer across spatially remote areas. Together, our data characterize the temporally evolving neural dynamics of belief and valuation processes that underlie strategic choice and provide neural validation for assumptions embedded in computational models of this behavior.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

Subliminal beauty engages the brain's valuation circuits

Neuroeconomic models propose that the anterior ventral striatum (aVS) and ventromedial prefrontal cortex (vmPFC) are key regions for computing subjective value (SV) signals that guide choice. However, the role of conscious awareness in this process remains debated. Here we examined whether SV can be automatically computed in these regions without conscious awareness. In an fMRI experiment, participants viewed faces that varied in attractiveness under three conditions: (i) suppressed from awareness via continuous flash suppression (CFS), (ii) clearly visible without suppression, or (iii) absent (background only) with CFS. Participants reported trial-wise facial identity (objective) and visibility (subjective) measures of facial awareness. In a post-fMRI session, they rated the attractiveness of each face as a measure of SV. When faces were seen, task performance (d) exceeded chance and responses were faster than in absent trials. When faces were unseen, performance was at chance but responses were slower than absent trials. Neurally, seen and unseen faces elicited greater neural signal than absent trials and showed similar neural patterns in the fusiform face area (FFA). Critically, neural signal in vmPFC correlated with SV for both seen and unseen faces, with similar neural patterns. In aVS, SV-related signal was only observed for unseen faces. Furthermore, mean face-related signal in FFA correlated with SV-related signals in aVS and vmPFC for unseen faces. These findings demonstrate that SV can be automatically computed in aVS and vmPFC without conscious awareness, suggesting a neural pathway by which subliminal information can influence value-based choice.

neuroscience↗

bioRxiv2025

War Trauma Impairs Prenatal Brain Development: Evidence from Genetically-Informed Brain Imaging

The global rise in armed conflicts has raised concerns about their long-term consequences. While the immediate mental and physical impacts of war are well documented, the effects on unborn children of expecting mothers remain poorly understood. Here, we leverage a large dataset (N=37,856) combining historical World War II bombing records with brain imaging and genetic data to examine whether in-utero exposure to air raids is associated with lasting differences in adult brain structure, cognition, and physical health. Using a pre-registered quasi-experimental design controlling for genetic and environmental factors, we find that exposure during the last trimester within 2 km of bombings is associated with lower gray matter volume, which partially mediates adverse effects on intelligence and obesity. These findings suggest a plausible pathway linking prenatal trauma to long-term differences in neurodevelopmental and functional outcomes, underscoring the vulnerability of the fetal brain to acute stress and highlighting the importance of protecting pregnant women during armed conflicts.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

Growing Minds, Integrating Senses: Neural and Computational Insights into Age-related Changes in Audio-Visual and Tactile-Visual Learning in Children

Multisensory processing and learning shape cognitive and language development, influencing how we perceive and interact with the world from an early age. While multisensory processes mature into adolescence, it remains poorly understood how age influences multisensory associative learning. This study investigated age-related effects on multisensory processing and learning during audio-visual and tactile-visual learning in 67 children (5.7-13 years) by integrating behavioural and neuroimaging data with computational methods. A reinforcement-learning drift diffusion model revealed that older children processed information faster and made more efficient decisions on multisensory associations. These age-related increases coincided with higher activity in brain regions associated with cognitive control, multisensory integration, and memory retrieval, specifically during audio-visual learning. Notably, the bilateral anterior insula exhibited heightened activation in response to lower reward prediction errors, indicative of increased sensitivity to negative feedback with development. Finally, reward prediction errors modulated activation in reward processing and cognitive control regions, with this modulation remaining modality-independent and largely stable across age. In conclusion, while children employ similar learning strategies, older children make decisions more efficiently and engage neural resources more strongly. Our findings reflect ongoing maturation of neural networks supporting multisensory learning in middle childhood, enabling more adaptive learning in later childhood. Highlights- faster information processing in older children during a multisensory learning task - increasing brain activation with age in visual, parietal, and frontal regions - reward prediction error processing are independent of age and modality - heightened response to negative reward prediction errors with increasing age

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Enhancing altruism by electrically augmenting frontoparietal gamma-band phase coupling

Cooperation, productivity, and cohesion in human societies depend on altruism, the tendency to share resources with others even though this is costly. While altruism is a widely shared social norm, people vary strongly in their inclination to behave altruistically, in particular across situations with different types of inequality in resource distribution. What neurobiological factors underlie this variability? And can these be targeted by interventions to enhance altruistic behavior? Here, we build on recent EEG findings that altruistic choices during disadvantageous inequality correlate with oscillatory gamma-band coherence between frontal regions (representing others interest) and parietal regions (representing neural evidence accumulation). We apply a transcranial alternating current stimulation (tACS) protocol designed to exogenously enhance this fronto-parietal coherence and find that this leads to increased altruism, specifically during disadvantageous inequality as hypothesized based on the EGG findings. Computational modeling reveals that this transcranial entrainment does not just add noise to the decision process but specifically increases the weight individuals assign to other-regarding concerns during choices. Our findings show that altruism can be enhanced by neurostimulation designed to enhance oscillatory synchronization between frontal and parietal areas. This establishes a neural basis for altruism and identifies a neural target for interventions aimed at improving prosocial behavior.

neuroscience↗

bioRxiv2023

Salience-based information integration: An overarching function of the "social brain"?

Behavior in social contexts is routinely accompanied by neural activity in a brain network comprising the bilateral temporoparietal junction (TPJ), dorsomedial and dorsolateral prefrontal cortex (dmPFC and dlPFC), and precuneus. This network - often referred to as the "social brain network" (SBN) - is thought to have evolved in response to the information processing demands of life in social groups. However, its precise functional contributions to behavior are unclear, since many of its areas are also activated in non-social contexts requiring, for example, attentional orienting or context updating. Here we argue that these results may reflect a basic neural mechanism implemented by areas in this network that is commonly required in both social and non-social contexts: Integrating multiple sensory and memory inputs into salient configurations, such as social constellations or perceptual Gestalts. We tested this hypothesis using a numeracy paradigm that orthogonally varied the salience of sensory target configurations and the required motor responses. Even in this non-social task, several regions of the SBN (TPJ, dmPFC, and precuneus) showed higher activity when the goal required the brain to attend to more versus less salient perceptual configurations. This activation pattern was specific to configuration salience and did not reflect general task demand or switching to new contexts. Taken together, these results suggest that the integration of information into salient configurations may be a key function of SBN regions, thus offering a new perspective on the widespread recruitment of these areas across social and non-social contexts.

neuroscience↗

bioRxiv2021

Causal evidence for a domain-specific role of left superior frontal sulcus in human perceptual decision-making

Humans and animals can flexibly choose their actions based on different information, ranging from objective states of the environment (e.g., apples are bigger than cherries) to subjective preferences (e.g., cherries are tastier than apples). Whether the brain instantiates these different choices by recruiting either specialised or shared neural circuitry remains debated. Specifically, domain-general accounts of prefrontal cortex (PFC) function propose that prefrontal areas flexibly process either perceptual or value-based evidence depending on what is required for the present choice, whereas domain-specific theories posit that PFC sub-areas, such as the left superior frontal sulcus (SFS), selectively integrate evidence relevant for perceptual decisions. Here we comprehensively test the functional role of the left SFS for choices based on perceptual and value-based evidence, by combining fMRI with a behavioural paradigm, computational modelling, and transcranial magnetic stimulation. Confirming predictions by a sequential sampling model, we show that TMS-induced excitability reduction of the left SFS selectively changes the processing of decision-relevant perceptual information and associated neural processes. In contrast, value-based decision making and associated neural processes remain unaffected. This specificity of SFS function is evident at all levels of analysis (behavioural, computational, and neural, including functional connectivity), demonstrating that the left SFS causally contributes to evidence integration for perceptual but not value-based decisions.

neuroscience↗

bioRxiv2021

Human brain anatomy reflects separable genetic and environmental components of socioeconomic status

Recent studies report that socioeconomic status (SES) correlates with brain structure. Yet, such findings are variable and little is known about underlying causes. We present a well-powered voxel-based analysis of grey matter volume (GMV) across levels of SES, finding many small SES effects widely distributed across the brain, including cortical, subcortical and cerebellar regions. We also construct a polygenic index of SES to control for the additive effects of common genetic variation related to SES, which attenuates observed SES-GMV relations, to different degrees in different areas. Remaining variance, which may be attributable to environmental factors, is substantially accounted for by body mass index, a marker for lifestyle related to SES. In sum, SES affects multiple brain regions through measurable genetic and environmental effects. One-sentence SummarySocioeconomic status is linked with brain anatomy through a varying balance of genetic and environmental influences.

neuroscience↗

bioRxiv2021

Transcranial theta-burst stimulation of primary sensory cortex attenuates somatosensory threat memory in humans

Sensory cortices are required for learning to discriminate complex stimuli that predict threat from those that predict safety in rodents. Yet, sensory cortices may not be needed to learn threat associations to simple stimuli. It is unknown whether these findings apply in humans. Here, we investigated the role of primary sensory cortex in discriminative threat conditioning with simple and complex somatosensory conditioned stimuli (CS) in healthy humans. Immediately before conditioning, participants received continuous theta-burst transcranial magnetic stimulation (cTBS) to primary somatosensory cortex either in the CS-contralateral or CS-ipsilateral hemisphere. After overnight consolidation, threat memory was attenuated in the contralateral compared to the ipsilateral group, as indicated by reduced startle eye-blink potentiation. There was no evidence for a difference between simple and complex stimuli, or that CS identification or conditioning was affected, suggesting a stronger effect of cTBS on consolidation than on initial stimulus processing. We propose that non-invasive stimulation of sensory cortex may provide a new avenue for interfering with threat memories in humans.

neuroscience↗

bioRxiv2020

A pupil-linked arousal mechanism for deciding to engage in future physical effort

An organisms fitness is determined by how it chooses to adapt effort in response to challenges. Actual effort exertion correlates with activity in dorsomedial prefrontal cortex (dmPFC) and noradrenergic pupil dilation, but little is known about how these neurophysiological processes guide decisions about future efforts: They may either provide anticipatory energization helping to accept the challenge, or a cost representation weighted against expected rewards. Here we provide evidence for the former, by measuring pupil and fMRI brain responses while humans chose whether to exert efforts to obtain rewards. Pupil-dilation rate and dMPFC fMRI activity related to anticipated effort level, with stronger correlations when participants chose to accept the challenge. These choice-dependent effort representations were stronger in participants whose behavioral choices were more sensitive to effort. Our results identify a process involving the peripheral and central human nervous system that guides decisions to exert effort by simulating the required energization.

neuroscience↗

bioRxiv2019

Genetic Underpinnings of Risky Behavior Relate to Altered Neuroanatomy

Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically-informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving, and sexual behaviour, in a European sample from the UK Biobank (N= 12,675). We find negative associations between risky behaviour and grey matter volume (GMV) in distinct brain regions, including amygdala, ventral striatum, hypothalamus, and dorsolateral prefrontal cortex (dlPFC). These effects replicate in an independent sample recruited from the same population (N=13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N=297,025), are inversely associated with GMV in dlPFC, putamen, and hypothalamus. This relation mediates ~2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking. One Sentence SummaryRisky behaviour and its genetic associations are linked to less grey matter volume in distinct brain regions.

neuroscience↗