Expectation modulates learning emotional words: Evidence from a hierarchical Bayesian model

In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanism underlying emotional language processing, but have failed to recognize that languages are acquired in a changing environment. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Our computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals’ expectations can modulate the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increases, individuals respond faster and exhibit higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and the preference for processing negative information.