This research examines how valence, arousal, and linguistic co-occurrence patterns influence shape-emotion associations. Across three studies, we tested whether positive and negative words are associated with rounded and sharp shapes, respectively, and whether a semantic analysis of language (Word2Vec) can predict these associations. Studies 1A and 1B focused primarily on valence, using a lexical decision task and a rating task. Both studies found that positive words were associated with rounded shapes and negative words with sharp shapes, with Word2Vec successfully predicting these mappings. Study 2 directly manipulated both valence and arousal, revealing that when arousal became a salient factor, it dominated shape-emotion associations: low-arousal words were reliably associated with rounded shapes, whereas high-arousal words showed no consistent shape association. Additionally, Word2Vec’s predictive power weakened when arousal was varied, suggesting that valence is more systematically encoded in linguistic representations, while arousal effects may depend more on the flexible, situated dynamics of emotional experience. Together, these findings suggest that while valence-based shape-emotion associations are stable under moderate arousal, salient arousal can shift conceptual mappings. These results highlight the joint contributions of linguistic and perceptual systems to emotional meaning and offer new insights into the grounding of emotion concepts.

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Shape-emotion associations,Valence,Arousal,Linguistic distributional models,Language and perception

• Psychology - Social Sciences
• Languages and Literature - Humanities