ArticlePLoS computational biology2022
Tracking the contribution of inductive bias to individualised internal models.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Domain-specific contexts promote model-based decision making for basketball players.Scientific reports · 2026Article
- The complexity of measuring reliability in learning tasks: An illustration using the Alternating Serial Reaction Time Task.Behavior research methods · 2024Article
- Feeling our place in the world: an active inference account of self-esteem.Neuroscience of consciousness · 2024Article
- Identifying Transfer Learning in the Reshaping of Inductive Biases.Open mind : discoveries in cognitive science · 2024Article
- Tracking human skill learning with a hierarchical Bayesian sequence model.PLoS computational biology · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence.
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Registered trials
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