Torok, Balazs, Nagy, David, Kiss, Mariann, Janacsek, Karolina ORCID: 0000-0001-7829-8220, Nemeth, Dezso and Orban, Gergo (2022) Tracking the contribution of inductive bias to individualised internal models. PLoS Computational Biology, 18 (6):e1010182. ISSN 1553-734X (Print), 1553-7358 (Online) (doi:https://doi.org/10.1371/journal.pcbi.1010182)

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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.

Item Type:	Article
Uncontrolled Keywords:	probabilistic sequence learning; statistical learning; implicit learning; internal models; computational modelling; hidden markov models
Subjects:	B Philosophy. Psychology. Religion > BF Psychology R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Faculty / School / Research Centre / Research Group:	Faculty of Education, Health & Human Sciences Faculty of Education, Health & Human Sciences > Institute for Lifecourse Development Faculty of Education, Health & Human Sciences > Institute for Lifecourse Development > Centre for Thinking and Learning Faculty of Education, Health & Human Sciences > School of Human Sciences (HUM)
Last Modified:	06 Oct 2022 10:00
URI:	http://gala.gre.ac.uk/id/eprint/37708

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