How Past Experiences Shape New Beliefs: Generalisation and Adaptation in Human Reinforcement Learning
Abstract
Humans are adept at learning under uncertainty. However, the way in which we learn must adapt to different environments, and we must determine when to generalise learning strategies across different contexts, making use of prior experience to guide present learning. However, how learning adapts based on prior experience remains poorly understood. Here, we examine how prior exposure to environments with differing levels of uncertainty shapes subsequent learning. A large sample recruited online (N=330) completed a gamified two-armed bandit task comprising two blocks with varying uncertainty, manipulated through the volatility or changeability of outcome contingencies. Computational modelling enabled the extraction of learning rate parameters quantifying the speed with which participants update their expectations. Analyses revealed significant effects of both previously experienced and current block volatility learning rates, and that this combination of generalisation and adaptation was consistent across learning from positive and negative prediction errors. Exploratory regression and factor analyses did not identify any moderating effects of mental health symptoms. Our results demonstrate that prior experience biases belief updating strategies across environments, revealing a mechanism through which learning both adapts and generalises to facilitate effective behaviour.
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BibTeX
@misc{pavlik2026past,
title = {{How Past Experiences Shape New Beliefs: Generalisation and Adaptation in Human Reinforcement Learning}},
author = {Phoenix Pavlik and Tin Nok Isaac Choy and Marko Dimitrovski and Toby Wise},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/e7x63_v2},
url = {https://osf.io/preprints/psyarxiv/e7x63_v2/},
note = {CC0},
}
RIS
TY - UNPB
TI - How Past Experiences Shape New Beliefs: Generalisation and Adaptation in Human Reinforcement Learning
AU - Phoenix Pavlik
AU - Tin Nok Isaac Choy
AU - Marko Dimitrovski
AU - Toby Wise
PY - 2026
DA - 2026/10/27
PB - PsyArXiv
DO - 10.31234/osf.io/e7x63_v2
UR - https://osf.io/preprints/psyarxiv/e7x63_v2/
AB - Humans are adept at learning under uncertainty. However, the way in which we learn must adapt to different environments, and we must determine when to generalise learning strategies across different contexts, making use of prior experience to guide present learning. However, how learning adapts based on prior experience remains poorly understood. Here, we examine how prior exposure to environments with differing levels of uncertainty shapes subsequent learning. A large sample recruited online (N=330) completed a gamified two-armed bandit task comprising two blocks with varying uncertainty, manipulated through the volatility or changeability of outcome contingencies. Computational modelling enabled the extraction of learning rate parameters quantifying the speed with which participants update their expectations. Analyses revealed significant effects of both previously experienced and current block volatility learning rates, and that this combination of generalisation and adaptation was consistent across learning from positive and negative prediction errors. Exploratory regression and factor analyses did not identify any moderating effects of mental health symptoms. Our results demonstrate that prior experience biases belief updating strategies across environments, revealing a mechanism through which learning both adapts and generalises to facilitate effective behaviour.
ER -