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People have been shown to link particular sounds with particular shapes. For instance, the round-sounding nonword bouba tends to be associated with curved shapes, whereas the sharp-sounding nonword kiki is deemed to be related to angular shapes. People's tendency to associate sounds and shapes has been observed across different languages. In the present study, we reexamined the claim by Hung, Styles, and Hsieh (2017) that such sound-shape mappings can occur before an individual becomes aware of the visual stimuli. More precisely, we replicated their first experiment, in which congruent and incongruent stimuli (e.g., bouba presented in a round shape or an angular shape, respectively) were rendered invisible through continuous flash suppression. The results showed that congruent combinations, on average, broke suppression faster than incongruent combinations, thus providing converging evidence for Hung and colleagues' assertions. Collectively, these findings now provide a solid basis from which to explore the boundary conditions of the effect. ispartof: Psychological Science vol:30 issue:11 ispartof: location:United States status: Published online

In two studies we compare a distributional semantic model derived from word co-occurrences and a word association based model in their ability to predict properties that affect lexical processing. We focus on age of acquisition, concreteness, and three affective variables, namely valence, arousal, and dominance, since all these variables have been shown to be fundamental in word meaning. In both studies we use a model based on data obtained in a continued free word association task to predict these variables. In Study 1 we directly compare this model to a word co-occurrence model based on syntactic dependency relations to see which model is better at predicting the variables under scrutiny in Dutch. In Study 2 we replicate our findings in English and compare our results to those reported in the literature. In both studies we find the word association-based model fit to predict diverse word properties. Especially in the case of predicting affective word properties, we show that the association model is superior to the distributional model. ispartof: Journal of Cognition vol:1 issue:1 pages:1-14 ispartof: location:England status: Published online