ArticleJournal of neuroscience methods2015
Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.
Article in Journal of neuroscience methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Active inference under visuo-proprioceptive conflict: Simulation and empirical results.Scientific reports · 2020Trial
- A Dual Prediction Error Populations Model of Schizophrenia Spectrum Disorders: Bridging NMDA Hypofunction and Gain Hyperfunction.Schizophrenia bulletin · 2026Article
- Active Inference and Functional Parametrisation: Differential Flatness and Smooth Random Realisation.Entropy (Basel, Switzerland) · 2026Article
- The phantastic organ: predictive processing in the brain.Frontiers in psychology · 2026Review
- High-resolution Bayesian Virtual Epileptic Patient using neural field models.Network neuroscience (Cambridge, Mass.) · 2026Article
- Belief embodiment through eye movements facilitates memory-guided navigation.Nature communications · 2025Article
- Quantifying tracking quality during occlusion with an integrated gaze metric anchored to task performance.Scientific reports · 2025Article
- SPM-30 years and beyond.Cerebral cortex (New York, N.Y. : 1991) · 2025Review
- The Functional Anatomy of Nociception: Effective Connectivity in Chronic Pain and Placebo Response.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Deep Hybrid Models: Infer and Plan in a Dynamic World.Entropy (Basel, Switzerland) · 2025Article
- Article
- The Relationship Between Environmental Statistics and Predictive Gaze Behaviour During a Manual Interception Task: Eye Movements as Active Inference.Computational brain & behavior · 2024Article
- Active Vision in Binocular Depth Estimation: A Top-Down Perspective.Biomimetics (Basel, Switzerland) · 2023Article
- Cognitive effort and active inference.Neuropsychologia · 2023Article
- A Bayesian computational model to investigate expert anticipation of a seemingly unpredictable ball bounce.Psychological research · 2023Article
- Multivariate EEG activity reflects the Bayesian integration and the integrated Galilean relative velocity of sensory motion during sensorimotor behavior.Communications biology · 2023Article
- Flexible intentions: An Active Inference theory.Frontiers in computational neuroscience · 2023Article
- Dynamic causal modelling of immune heterogeneity.Scientific reports · 2021Article
- Difficulties with Speech-in-Noise Perception Related to Fundamental Grouping Processes in Auditory Cortex.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Minds and Brains, Sleep and Psychiatry.Psychiatric research and clinical practice · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThis paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models - usually biophysical models of neuronal activity. NEW
methodOur key innovation is to use a generative model based on a normative (Bayes-optimal) model of active inference to model oculomotor pursuit in terms of subjects' beliefs about how visual targets move and how their oculomotor system responds. Our aim here is to establish the face validity of the approach, by manipulating the content and precision of sensory information - and examining the ensuing changes in the subjects' implicit beliefs. These beliefs are inferred from their eye movements using the normative model.
resultsWe show that on average, subjects respond to an increase in the 'noise' of target motion by increasing sensory precision in their models of the target trajectory. In other words, they attend more to the sensory attributes of a noisier stimulus. Conversely, subjects only change kinetic parameters in their model but not precision, in response to increased target speed.
conclusionsUsing this technique one can estimate the precisions of subjects' hierarchical Bayesian beliefs about target motion. We hope to apply this paradigm to subjects with schizophrenia, whose pursuit abnormalities may result from the abnormal encoding of precision.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.