Trial-by-trial learning signatures in self-reported affect that require introspection and are orthogonal to social choice

Do people learn to predict their feelings over time, and do such learning signatures manifest in behavior? Feeling ratings track with what we do. Understanding their properties may thus elucidate behavior. Inspired by reinforcement learning, differences between expected and experienced feelings - affective prediction errors - have recently entered the toolkit of behavioral prediction. But the extent of the analogy between affective prediction errors and conventional prediction errors about outcomes in the environment is unknown. Across reanalyses of existing data (N = 4,607) and four preregistered experiments (N = 1,806; U.S. online samples), we probe affective prediction errors to document and dissect a core analogy: Learning reflected in decreasing (affective) prediction errors over time. We found that decreases in affective prediction errors depended on introspection, as prior experience with a task absent affective reports did not yield the same decreases (Experiment 1). A task manipulation forcing participants to alter their choices showed increased affective prediction errors, ruling out simple response alignment (i.e., to report feeling “as predicted”; Experiment 2). Decreases in affective prediction errors transferred across structurally similar tasks (i.e., stealing vs. giving money; Experiment 3) and affective measures (i.e., from pride or guilt to valence; Experiment 4). Although affective prediction errors often tracked with social choice behavior overall, their absolute decrease over time did not. In sum, we present evidence for convergence (i.e., learning and transfer) and divergence (i.e., introspection dependence and predictive epiphenomenality) between affective prediction errors and conventional prediction errors. Implications for affective measures as a proxy for subjective value are discussed.