Noa L. Hedrich

Nov 25, 2024 | journals.plos.org | Noa L. Hedrich |Universität Hamburg |Charité Universitätsmedizin Berlin |Eric Schulz

Loading metrics Open Access Peer-reviewedResearch Article ? This is an uncorrected proof. Citation: Hedrich NL, Schulz E, Hall-McMaster S, Schuck NW (2024) An inductive bias for slowly changing features in human reinforcement learning. PLoS Comput Biol 20(11): e1012568. https://doi.org/10.1371/journal.pcbi.1012568Editor: Stefano Palminteri, Ecole Normale Superieure, FRANCEReceived: January 21, 2024; Accepted: October 17, 2024; Published: November 25, 2024Copyright: © 2024 Hedrich et al.

Jan 24, 2024 | biorxiv.org | Eric Schulz |Nicolas W. Schuck |Noa L. Hedrich

AbstractIdentifying goal-relevant features in novel environments is a central challenge for efficient behaviour. We asked whether humans address this challenge by relying on prior knowledge about common properties of reward-predicting features. One such property is the rate of change of features, given that behaviourally relevant processes tend to change on a slower timescale than noise. Hence, we asked whether humans are biased to learn more when task-relevant features are slow rather than fast.