Limited validity of learning rates: Cognitive ability predicts learning performance, not learning rates

Computational phenotyping is a powerful approach for characterizing individual variability across cognitive domains, with significant applications in computational psychiatry. Within the reinforcement learning framework, the learning rate is widely used to classify individuals based on learning patterns and deviations from optimal behavior. However, its interpretation depends on psychometric properties, which are rarely examined. To assess construct validity, we collected single-trial learning data (N = 126) from an uncertainty-augmented reward learning task to obtain participants' performance and learning rates. We additionally collected psychometric data across a battery of executive functioning tasks to obtain measures of cognitive constructs with high relevance to decision-making and reward learning. Individual differences in learning rates were not systematically explained by cognitive abilities, suggesting limited construct validity, despite moderate validity in learning performance. We identify low-reliability scores and lack of generalizability for the learning rate as a possible explanation for its low validity. These findings highlight the need to improve the psychometric properties of learning rates for studying individual differences, despite their success in capturing group-level learning behavior.