Exploitation (inverse temperature) and information utilization (learning rate) correlate with reward acquisition under both WM loads¶

Claim · All claims
Evidence · 1 study · causal r2 · q3 · i3 large
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i3 Corrected reward rate correlated significantly with inverse temperature (r = 0.69 low load; r = 0.41 high load) and with learning rate and mixing weight under low load. → Chengyan Yang 2025

Evidence¶

Chengyan Yang 2025¶

Chengyan Yang, Tongran Liu, Mengxin Wen & Xun Liu. (2025). The distinct functions of working memory and intelligence in model-based and model-free reinforcement learning. npj Science of Learning 10:72. https://doi.org/10.1038/s41539-025-00363-w

q3 · i3 · causal · r2

Correlation analyses of RL parameters and performance for 50 participants. Under high load, corrected reward rate correlated with inverse temperature (r = 0.41, p = 0.003) and learning rate (r = 0.32, p = 0.026); the article suggests participants proficient at exploitation and information utilization achieved higher rewards.

"the corrected reward rate was significantly correlated with RL parameters, especially with the inverse temperature (r = 0.69, 95% CI [0.51, 0.81], p < 0.001) and the mixing weight (r = 0.44, 95% CI [0.19, 0.64], p = 0.001) in the low WM load condition"

Discussion¶

Related Claims¶

• High WM load shifts learners from model-based toward model-free reinforcement learning strategies — related
• Intelligence correlates with RL performance under low WM load but not under high WM load — related
• Model-based strategy use and intelligence positively predict RL learning performance, while WM load negatively predicts it — related