ArticleScientific reports2022
Task-evoked pupillary responses track precision-weighted prediction errors and learning rate during interceptive visuomotor actions.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fluctuations in arousal reflect latent state transitions that facilitate behavioural optimization.Nature human behaviour · 2026Article
- A Dual Prediction Error Populations Model of Schizophrenia Spectrum Disorders: Bridging NMDA Hypofunction and Gain Hyperfunction.Schizophrenia bulletin · 2026Article
- Predictive Processing in Sport and Exercise Science: A Scoping Review and Theoretical Overview.Sports medicine - open · 2026Review
- Exploiting prior knowledge in continuous decision-making under uncertainty: the case of tennis experts.Experimental brain research · 2026Article
- Hidden State Inference or Continuous Belief Updating during a Dynamic Visuomotor Skill.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Article
- Prediction-based attention computing: a proof of concept study.Virtual reality · 2026Article
- Separating Subjective from Objective Food Value in the Human Insula: An Exploratory Study Using Intracranial EEG.Brain sciences · 2025Article
- The Relationship Between Environmental Statistics and Predictive Gaze Behaviour During a Manual Interception Task: Eye Movements as Active Inference.Computational brain & behavior · 2024Article
- Synthetic surprise as the foundation of the psychedelic experience.Neuroscience and biobehavioral reviews · 2024Review
- Arousal coherence, uncertainty, and well-being: an active inference account.Neuroscience of consciousness · 2024Review
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Authors and funding
7 authors.
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Abstract
In this study, we examined the relationship between physiological encoding of surprise and the learning of anticipatory eye movements. Active inference portrays perception and action as interconnected inference processes, driven by the imperative to minimise the surprise of sensory observations. To examine this characterisation of oculomotor learning during a hand-eye coordination task, we tested whether anticipatory eye movements were updated in accordance with Bayesian principles and whether trial-by-trial learning rates tracked pupil dilation as a marker of 'surprise'. Forty-four participants completed an interception task in immersive virtual reality that required them to hit bouncing balls that had either expected or unexpected bounce profiles. We recorded anticipatory eye movements known to index participants' beliefs about likely ball bounce trajectories. By fitting a hierarchical Bayesian inference model to the trial-wise trajectories of these predictive eye movements, we were able to estimate each individual's expectations about bounce trajectories, rates of belief updating, and precision-weighted prediction errors. We found that the task-evoked pupil response tracked prediction errors and learning rates but not beliefs about ball bounciness or environmental volatility. These findings are partially consistent with active inference accounts and shed light on how encoding of surprise may shape the control of action.
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