Causal-based cognition is thought to be relevant for human beings because it allows inferring the unfolding of events. Theories of causal-based cognition offer researchers a way to understand inter-feature relations, above and beyond the purely associative relations posited by similarity theories. In the causal-model theory (a.k.a. the Generative Model), people are thought to categorize an exemplar depending on how likely its particular feature combination is, given the category's causal model. This mechanism predicts the coherence effect (i.e., when people categorize, features interact). This effect has been widely reported in the literature. In the current experiment, we sought to specify conditions that modulate the coherence effect. To that end, we implemented a between-subjects manipulation where participants had to judge either category membership or category consistency. Our results show that subjects exhibit a larger coherence effect in consistency condition. We discuss our results' relevance for causal-model theory and for the possibility of distinguishing causal-based from similarity-based processing.
|Número de páginas||5|
|Estado||Publicada - 2020|
|Publicado de forma externa||Sí|
|Evento||42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online|
Duración: 29 jul. 2020 → 1 ago. 2020
|Conferencia||42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020|
|Período||29/07/20 → 1/08/20|