Claims

A reinforcement learning model with separate surprise and uncertainty learning-rate parameters per condition best fits gambling behavior, though individual parameter estimates did not reach significance

Claim · All claims
Evidence · 1 study (2 entries) · causal r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? The best-fitting RL model separated learning rates for experiential and observational trials, with significantly lower AIC than all alternatives. → Maria A. Woitow 2026 q2 i? Median surprise and uncertainty estimates trended above zero but none reached statistical significance and did not differ between conditions. → Maria A. Woitow 2026 (2)

Evidence

Maria A. Woitow 2026

Maria A. Woitow, Anthony I. Jang, Ben Eppinger, Matthew R. Nassar, Marcel Brass & Julia M. Rodriguez Buritica. (2026). Shared effects of one's own and others' experiences during reinforcement learning on episodic memory. npj Science of Learning 11:16. https://doi.org/10.1038/s41539-026-00409-7

q2 · i? · causal · r2

RL models fitted to choices of 79 participants; the model with separate surprise and uncertainty parameters for EL and OL trials had "significantly lower AIC" than all other models (p < 0.001 across pairwise comparisons), replicating Jang et al.'s modeling results.

"The model that best accounted for participants’behavior (Fig.2b, c) sepa- rated the learning rate for experiential (EL) and observational learning (OL) trials"

Maria A. Woitow 2026 (2)

Maria A. Woitow, Anthony I. Jang, Ben Eppinger, Matthew R. Nassar, Marcel Brass & Julia M. Rodriguez Buritica. (2026). Shared effects of one's own and others' experiences during reinforcement learning on episodic memory. npj Science of Learning 11:16. https://doi.org/10.1038/s41539-026-00409-7

q2 · i? · causal · r2

Wilcoxon signed-rank tests of maximum likelihood parameter estimates against zero: Surp EL median 0.11 (p = 0.87), Surp OL 0.30 (p = 0.19), Unc EL 0.49 (p = 0.06), Unc OL 0.29 (p = 0.06); estimates did not differ between conditions (Surp p = 0.34, Unc p = 0.97).

"While all four parameters trended above zero, none reached statistical significance (Surp EL: median = 0.11, p = 0.87; Surp OL: median = 0.30, p = 0.19; Unc EL: median = 0.49, p = 0.06; Unc OL: median = 0.29, p = 0.06; Wilcoxon signed-rank t-tests against 0)"

Discussion

Page metadata
Field Value
type claim
id rl-model-separate-surprise-uncertainty-el-ol
title A reinforcement learning model with separate surprise and uncertainty learning-rate parameters per condition best fits gambling behavior, though individual parameter estimates did not reach significance
description A reinforcement learning model with separate surprise and uncertainty learning-rate parameters per condition best fits gambling behavior, though individual parameter estimates did not reach significance
status review
evidence_strength moderate
newest_evidence 2026
generated by process:wiki-ingest on 2026-10-26
trust unverified — no one has confirmed this page's content
sources 2 — maria-a-woitow-2026, maria-a-woitow-2026-2

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