Abstract
Research on the interplay between language and emotion has shown evidence that the affective content of words influences their recognition. However, the direction of the effects is not clear, as there are mixed findings regarding the role of positive and, especially, negative valence. We conducted a Bayesian multi-level meta-analysis to examine the role of valence in visual word recognition, focusing on the lexical decision task. The results revealed a facilitative effect of positive valence on lexical decision times. That is, positive words led to faster responses than both negative and neutral words. In contrast, negative valence did not have any effect, although the analysis of several moderator variables suggested that there might be a facilitative effect in some cases, specifically, when negative words elicit very strong and intense emotions. These results shed light on the complexities of emotional word processing. They also point to the need for psycholinguistic models to take affective information into account, and thus provide a complete view of visual word recognition.