Biology subjects

Mathys, C.

Publications and source records attributed to Mathys, C..

13 recordsLinked to original sources

bioRxiv2026

Cardiac belief updating from volatile physiological afferents

Cardiac interoception is commonly assessed by comparing subjective estimates of heart rate with physiological measurements. But beliefs can easily obscure these measures, such that they cannot readily distinguish sensitivity to afferent signals from the influence of prior expectations; this ultimately challenges the notion that behaviours under these tasks could reflect interoceptive processes at all. Here, we develop a computational framework that uses naturally occurring heart rate variability to quantify how strongly perceptual beliefs are updated by physiological evidence. We formalise interoception as weighted Bayesian updating under the joint influence of expectations and afferents, and derive a new measure, cardiac interoceptive sensitivity, that quantifies the extent to which beliefs move with incoming signals. Applying this to the largest Heart Rate Discrimination dataset to date (n=549), a task providing robust estimates of cardiac beliefs, we find that sensitivity is weak in the healthy population, but shows large interindividual differences, with 43% of the participants exhibiting behaviours at least minimally compatible with interoceptive processing. These findings challenge the interpretation of conventional measures of cardiac interoception. They also introduce a new framework to relate bodily signals that cannot be controlled experimentally to external signals that can, laying a computational foundation for embodied psychophysics.

neuroscience↗

bioRxiv2026

Perceived Control Over Breathing Perturbations Depends on Interosensory Input and Prediction Error

Interoception and homeostatic/allostatic control are not only fundamental for survival but play a key role for maintaining somatic and mental health. Furthermore, metacognitive evaluations of these processes, such as perceived failure of allostatic regulation, have been proposed to underlie the experience of chronic symptoms, such as chronic fatigue. A central question in this regard is what mechanisms the brain uses to evaluate control over bodily states. A prominent computational proposal posits that this is achieved by monitoring of interoceptive prediction errors (PEs). However, to date, this proposal has not been tested experimentally. Here, we tested this hypothesis by applying computational process models of perceived control to data from a novel behavioural paradigm, the Respiratory Metacognition of Control Task (RMCT). The RMCT manipulates control over breathing by changing inspiratory resistive loads as a function of control achieved in a gamified prediction task. We developed and compared trial-by-trial generative models of perceived explicit control in the RMCT, using data from 50 volunteers in a pre-registered analysis. Bayesian model selection suggested that perceived control over breathing is best explained as a function of both trial-wise interoceptive outcomes (breathing with or without an inspiratory resistive load) and trial-wise PEs about respiratory resistance. These results support a longstanding computational proposal of how the brain detects failures of bodily regulation and provide a mechanistic model for understanding inter-individual differences in perceived control over bodily states.

neuroscience↗

bioRxiv2025

Neurochemical markers of uncertainty processing in humans

How individuals process and respond to uncertainty has important implications for cognition and mental health. Here we use computational phenotyping to examine individualised "uncertainty fingerprints" in relation to neurometabolites and trait anxiety in humans. We introduce a novel categorical state-transition extension of the Hierarchical Gaussian Filter (HGF) to capture implicit learning in a four-choice probabilistic sensorimotor reversal learning task by tracking beliefs about stimulus transitions. Using 7-Tesla Magnetic Resonance Spectroscopy, we measured baseline neurotransmitter levels in the primary motor cortex (M1). Model-based results revealed dynamic belief updating in response to environmental changes. We further found region-specific relationships between M1 glutamate+ glutamine levels and prediction errors and volatility beliefs, revealing an important neural marker of probabilistic reversal learning in humans. High trait anxiety was associated with faster post-reversal responses. By integrating computational modelling with neurochemical assessments, this study provides novel insights into the neurocomputations that drive individual differences in processing uncertainty.

neuroscience↗

bioRxiv2024

Uncertainty in Thermosensory Expectations Enhances an Illusion of Pain

The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty. We find that these expectations directly modulate the perception of pain in the thermal grill illusion. Quantitative microstructural brain imaging revealed that the myeloarchitecture and iron content of the somatosensory cortex, the posterior insula and the amygdala reflect inter-individual variability in computational parameters related to learning and the degree to which uncertainty modulates illusory pain perception. Our findings offer a new framework to explain how the brain infers pain from innocuous thermal inputs. Our model has important implications for understanding the etiology of thermosensory symptoms in chronic pain conditions.

neuroscience↗

bioRxiv2024

Bayesian Workflow for Generative Modeling in Computational Psychiatry

Computational (generative) modelling of behaviour has considerable potential for clinical applications. In order to unlock the potential of generative models, reliable statistical inference is crucial. For this, Bayesian workflow has been suggested which, however, has rarely been applied in Translational Neuromodeling and Computational Psychiatry (TN/CP) so far. Here, we present a worked example of Bayesian workflow in the context of a typical application scenario for TN/CP. This application example uses Hierarchical Gaussian Filter (HGF) models, a family of computational models for hierarchical Bayesian belief updating. When equipped with a suitable response model, HGF models can be fit to behavioural data from cognitive tasks; these data frequently consist of binary responses and are typically univariate. This poses challenges for statistical inference due to the limited information contained in such data. We present a novel set of response models that allow for simultaneous inference from multivariate (here: two) behavioural data types. Using both simulations and empirical data from a speed-incentivised associative reward learning (SPIRL) task, we show that harnessing information from two different data streams (binary responses and continuous response times) improves the accuracy of inference (specifically, identifiability of parameters and models). Moreover, we find a linear relationship between log-transformed response times in the SPIRL task and participants uncertainty about the outcome. Our analysis illustrates the benefits of Bayesian workflow for a typical use case in TN/CP. We argue that adopting Bayesian workflow for generative modelling helps increase the transparency and robustness of results, which in turn is of fundamental importance for the long-term success of TN/CP.

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bioRxiv2022

Blocking D2/D3 dopamine receptors increases volatility of beliefs when we learn to trust others

The ability to flexibly adjust beliefs about other people is crucial for human social functioning. Dopamine has been proposed to regulate the precision of beliefs, but direct behavioural evidence of this is lacking. We investigated how a relatively high dose of the selective D2/D3 dopamine receptor antagonist sulpiride impacts learning about other peoples prosocial attitudes in a repeated trust game. Using a Bayesian model of belief updating, we show that sulpiride increased the volatility of beliefs, which led to higher precision-weights on prediction errors. This effect was entirely driven by participants with genetically conferring higher dopamine availability (Taq1a polymorphism). Higher precision weights were reflected in higher reciprocal behaviour in the repeated trust game but not in single-round trust games. This finding suggests that antipsychotic medication might acutely reduce rigidity of pathological beliefs.

neuroscience↗

bioRxiv2022

Effects of dopamine D2 and opioid receptor antagonism on the trade-off between model-based and model-free behavior in healthy volunteers

Our daily behaviour requires a flexible arbitration between actions we do out of habit and actions that are directed towards a specific goal. Drugs that target opioid and dopamine receptors are notorious for inducing maladaptive habitual drug consumption, yet how the opioidergic and dopaminergic neurotransmitter systems contribute to the arbitration between habitual and goal-directed behaviour is poorly understood. By combining pharmacological challenges with a well-established decision-making task and a novel computational model, we show that the administration of the D2 dopamine receptor antagonist amisulpride led to an increase in goal-directed or model-based relative to habitual or model-free behaviour, whereas the non-selective opioid receptor antagonist naltrexone had no appreciable effect. These findings highlight the distinct functional contributions of dopamine and opioid receptors to goal-directed and habitual behaviour and support the notion that D2 receptor antagonists promote stabilisation of goal-relevant information.

neuroscience↗

bioRxiv2021

TAPAS: an open-source software package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

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bioRxiv2020

Expecting the unexpected: the paranoid style of belief updating across species

Paranoia is the belief that harm is intended by others. It may arise from selective pressures to infer and avoid social threats, particularly in ambiguous or changing circumstances. We propose that uncertainty may be sufficient to elicit learning differences in paranoid individuals, without social threat. We used reversal learning behaviour and computational modelling to estimate belief updating across individuals with and without mental illness, online participants, and rats exposed to chronic methamphetamine, an elicitor of paranoia in humans. Paranoia is associated with a strong but immutable prior on volatility, accompanied by elevated sensitivity to perceived changes in the task environment. Methamphetamine exposure in rats recapitulates this impaired uncertainty-driven belief updating and rigid anticipation of a volatile environment. Our work provides evidence of fundamental, domain-general learning differences in paranoid individuals. This paradigm enables further assessment of the interplay between uncertainty and belief-updating across individuals and species.

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bioRxiv2019

Neural Arbitration between Social and Individual Learning Systems

Decision making often requires integrating self-gathered information with information acquired from observing others. Depending on the situation, it may be beneficial to rely more on one than the other source, taking into account that either information may be imprecise or deceiving. The process by which one source is selected over the other based on perceived reliability, here defined as arbitration, has not been fully elucidated. In this study, we formalised arbitration as the relative reliability (precision) of predictions afforded by each learning system using hierarchical Bayesian models. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and self-sampled outcomes. The number of points participants wagered on their predictions reflected arbitration: The higher the relative precision of one learning system over the other and the lower the intention volatility, the more points participants wagered on a given trial. Functional neuroimaging demonstrated that the arbitration signal was independent of decision confidence and involved modalityspecific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain whereas arbitrating in favour of social information engaged ventromedial prefrontal cortex and the temporoparietal junction. These findings are in line with domain specificity and indicate that relative precision captures arbitration between social and individual learning systems at both the behavioural and neural level.

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bioRxiv2019

Ketamine Affects Prediction Errors About Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities. Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the worlds statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

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bioRxiv2018

Subjective estimates of uncertainty and volatility during gambling predict impulsivity after subthalamic deep brain stimulation for Parkinson’s disease

1Subthalamic deep brain stimulation (STN-DBS) for Parkinsons disease may modulate chronometric and instrumental aspects of choice behaviour, including motor inhibition, decisional slowing, and value sensitivity. However, it is not well known whether STN-DBS affects more complex aspects of decision-making, such as the influence of subjective estimates of uncertainty on choices. In this study, thirty-eight participants with Parkinsons disease played a virtual casino prior to subthalamic DBS (whilst on medication) and again, three-months postoperatively (whilst on stimulation). At the group level, there was a small but statistically significant postoperative decrease in impulsivity, as quantified by the Barratt Impulsiveness Scale (BIS). The gambling behaviour of participants (bet increases, slot machine switches and double or nothing gambles) was associated with this self-reported measure of impulsivity. However, there was a large variance in outcome amongst participants, and we were interested in whether individual differences in subjective estimates of uncertainty (specifically, volatility) were related to differences in pre- and postoperative impulsivity. To examine these individual differences, we fit a computational model (the Hierarchical Gaussian Filter, HGF), to choices made during slot machine game play as well as a simpler reinforcement learning model based on the Rescorla-Wagner formalism. The HGF was superior in accounting for the behaviour of our participants, suggesting that participants incorporated beliefs about environmental uncertainty when updating their beliefs about gambling outcome and translating these beliefs into action. A specific aspect of subjective uncertainty, the participants estimate of the tendency of the slot machines winning probability to change (volatility), increased subsequent to DBS. Additionally, the decision temperature of the response model decreased post-operatively, implying greater stochasticity in the belief-to-choice mapping of participants. Model parameter estimates were significantly associated with impulsivity; specifically, increased uncertainty was related to increased postoperative impulsivity. Moreover, changes in these parameter estimates were significantly associated with the maximum post-operative change in impulsivity over a six month follow up period. Our findings suggest that impulsivity in persons with Parkinsons disease may be influenced by subjective estimates of uncertainty (environmental volatility) and implicate a role for the subthalamic nucleus in the modulation of outcome certainty. Furthermore, our work outlines a possible approach to characterising those persons who become more impulsive after subthalamic DBS, an intervention in which non-motor outcomes can be highly variable.

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bioRxiv2017

Overestimating environmental volatility increases switching behavior and is linked to activation of dorsolateral prefrontal cortex in schizophrenia

BackgroundReward-based decision-making is impaired in patients with schizophrenia (PSZ) as reflected by increased choice switching. The underlying cognitive and motivational processes as well as associated neural signatures remain unknown. Reinforcement Learning (RL) and hierarchical Bayesian learning account for choice switching in different ways. We hypothesized that enhanced choice switching, as seen in PSZ during reward-based decision-making, relates to higher-order beliefs about environmental volatility and examined the associated neural activity.\n\nMethods46 medicated PSZ and 43 healthy controls (HC) performed a reward-based decision-making task requiring flexible responses to changing action-outcome contingencies during functional Magnetic Resonance Imaging (fMRI). Detailed computational modeling of choice data was performed, including RL and the hierarchical Gaussian filter (HGF). Trajectories of learning from computational modeling informed the analysis of fMRI data.\n\nResultsA three-level HGF accounted best for the observed choice data. This model revealed a heightened initial belief about environmental volatility and a stronger influence of volatility on lower-level learning of action-outcome contingencies in PSZ as compared to HC. This was replicated in an independent sample of non-medicated PSZ. Beliefs about environmental volatility were reflected by higher activity in dorsolateral prefrontal cortex of PSZ as compared to HC.\n\nConclusionsOur study suggests that PSZ inferred the environment as overly volatile, which may explain increased choice switching. In PSZ, activity in dorsolateral prefrontal cortex was more strongly related to beliefs about environmental volatility. Our computational phenotyping approach may provide useful information to dissect clinical heterogeneity and could improve prediction of outcome.

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