Intelligence correlates with RL performance under low WM load but not under high WM load¶

Claim · All claims
Evidence · 1 study · causal r? · q3 · i2 medium
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q3 i2 Intelligence showed a significant correlation with corrected reward rate in the low WM load condition (r = 0.35), but no significant correlation in the high WM load condition. → 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 · i2 · causal · r?

Correlation analyses among RL parameters, intelligence (Cattell's Culture Fair Intelligence Test), and corrected reward rate for 50 participants under each WM load condition. The article attributes the attenuation under high load to task-specific cognitive resources becoming more critical than general intellectual ability.

"intelligence demonstrated a significant correlation with the corrected reward rate in the low WM load condition (r = 0.35, 95% CI [0.07, 0.57], p = 0.013). However, no significant correlation was observed between learning performance and intelligence in the high WM load condition"

Discussion¶

Learner Variables¶

• General Cognitive Ability — moderator: an instructional effect differs with it

Related Claims¶

• High WM load shifts learners from model-based toward model-free reinforcement learning strategies — related
• Model-based strategy use and intelligence positively predict RL learning performance, while WM load negatively predicts it — related
• Model-based strategy use partially mediates the effect of WM load on RL learning performance — related
• Exploitation (inverse temperature) and information utilization (learning rate) correlate with reward acquisition under both WM loads — related