Categorizing perceived causal events

Nicolás Marchant, Bonan Zhao, Neil R. Bramley, Diego Morales, Sergio E. Chaigneau

Producción científica: Contribución a una conferenciaArtículorevisión exhaustiva


Over the last few decades, Causal Model Theory (CMT) has become a dominant framework for human causal-based reasoning, including categorization and inference. CMT prescribes how people should reason about probabilistic events in terms of causal models. In typical causal-based categorization experiments, subjects are provided with verbal descriptions of causally linked features, generally including probabilistic information. Another line of research focuses on perceived or experienced causal events, rather than on verbal descriptions. In this work we asked whether effects which are consistent with CMT, and that have been obtained with verbal descriptions, generalize to visually perceived events. In two experiments, we presented subjects with videos of a 3D A→B causal event rather than verbal descriptions. In Exp. 1, we found that subjects who saw the causal event did not show the coherence effect in categorization (i.e., subjects tend to rate the null ¬A¬B event as a category member). However, subjects who did see the null event during training did show the effect. In Exp. 2, we ruled out the possibility that Exp. 1's results were simply an effect of how frequently events were experienced during training. We conclude that a one-shot perceived causal event is not sufficient for people to show causal-based reasoning as CMT predicts.

Idioma originalInglés
Número de páginas8
EstadoPublicada - 2022
Publicado de forma externa
Evento44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canadá
Duración: 27 jul. 202230 jul. 2022


Conferencia44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022


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