Estimation-uncertainty affects decisions with and without learning opportunities

Kristoffer C. Aberg (), Levi Antle and Rony Paz ()
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Kristoffer C. Aberg: Weizmann Institute of Science
Levi Antle: Weizmann Institute of Science
Rony Paz: Weizmann Institute of Science

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Motivated behavior during reinforcement learning is determined by outcome expectations and their estimation-uncertainty (how frequently an option has been sampled), with the latter modulating exploration rates. However, although differences in sampling-rates are inherent to most types of reinforcement learning paradigms that confront highly rewarded options with less rewarded ones, it is unclear whether and how estimation-uncertainty lingers to affect long-term decisions without opportunities to learn or to explore. Here, we show that sampling-rates acquired during a reinforcement learning phase (with feedback) correlate with decision biases in a subsequent test phase (without feedback), independently from outcome expectations. Further, computational model-fits to behavior are improved by estimation-uncertainty, and specifically so for options with smaller sampling-rates/larger estimation-uncertainties. These results are replicated in two additional independent datasets. Our findings highlight that estimation-uncertainty is an important factor to consider when trying to understand human decision making.

Date: 2025
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DOI: 10.1038/s41467-025-61960-2

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