Adaptive generalization and efficient learning under uncertainty

Abstract People often use recognizable features to infer the value of novel consumables. This “generalization” strategy is known to be beneficial in stable environments, such that individuals can use previously learned rules and values in efficiently exploring new situations. However, it remains unclear whether and how individuals adjust their generalization strategy in volatile environments where previously learned information becomes obsolete. We hypothesized that individuals adaptively use generalization by continuously updating their beliefs about the credibility of the feature-based reward generalization model at each state. Our data showed that participants used generalization more when the novel environment remained consistent with the previously learned monotonic association between feature and reward, suggesting efficient utilization of prior knowledge. Against other accounts, we found that individuals incorporated an arbitration mechanism between feature-based value generalization and model-based learning based on volatility tracking. Notably, our suggested model captured differential impacts of generalization dependent on the context-volatility, such that individuals who were biased the most toward generalization showed the lowest learning errors when the value of stimuli are generalized along the recognizable feature, but showed the highest errors in a volatile environment. This work provides novel insights into the adaptive usage of generalization, orchestrating two distinctive learning mechanisms through monitoring their credibility, and highlights the potential adverse effects of overgeneralization in volatile contexts.