A risk-sensitive reinforcement learning model with anticorrelated valuation fit reversal learning behavior better than standard models¶
Claim · All claims
Evidence · 1 study · associationalr2·q2
Currency · newest study 2015 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2018: tool and model accuracy changes quickly
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 RSAV model clearly outperformed the Rescorla-Wagner and risk-sensitive models across all subjects and in both groups separately, with exceedance probability 1. → Hauser 2015
Evidence¶
Hauser 2015¶
Hauser, T. U., Iannaccone, R., Walitza, S., Brandeis, D., & Brem, S. (2015). Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development. Neuroimage, 104, 347-354. https://doi.org/10.1016/j.neuroimage.2014.09.018
q2 · i? · associational · r2
Computational model comparison using AIC and Bayesian model selection for groups on the behavioral data of all 36 participants. Table 1 reports RSAV exceedance probability of 1 in the whole sample and in each group, versus 0 for the Rescorla-Wagner and risk-sensitive models; the RSAV also updates the unchosen option.
"The RSAV model clearly outperformed the other models across all subjects, as well as in both groups separately (Table 1)."
Discussion¶
Related Claims¶
- Reward prediction errors are positively correlated with putamen, vmPFC, amygdala and posterior cingulate and anticorrelated with aIns, dmPFC and dlPFC — related
- Adolescents learn faster from negative reward prediction errors than adults in probabilistic reversal learning — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
rsav-model-outperformed-standard-rl-models |
title |
A risk-sensitive reinforcement learning model with anticorrelated valuation fit reversal learning behavior better than standard models |
description |
A risk-sensitive reinforcement learning model with anticorrelated valuation fit reversal learning behavior better than standard models |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2015 |
recheck_by |
2018 |
generated |
by process:wiki-ingest on 2026-10-17 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — hauser-2015 |
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