Transition dynamics shape mental state concepts

Autor:	Mark Allen Thornton, Milena Rmus, Amisha D. Vyas, Diana Tamir
Rok vydání:	2023
Předmět:	Cognitive science Predictive coding Artificial neural network Developmental Neuroscience Social cognition Statistical learning Dynamics (music) Theory of mind Mental state Similarity (psychology) Experimental and Cognitive Psychology Psychology General Psychology
Zdroj:	Journal of Experimental Psychology: General.
ISSN:	1939-2222 0096-3445
Popis:	People’s thoughts and feelings ebb and flow in predictable ways: surprise arises quickly, anticipation ramps up slowly, regret follows anger, love begets happiness, and so forth. Predicting these transitions between mental states can help people successfully navigate the social world. We hypothesize that the goal of predicting state dynamics shapes people’ mental state concepts. Across seven studies, when people observed more frequent transitions between a pair of novel mental states, they judged those states to be more conceptually similar to each other. In an eighth study, an artificial neural network trained to predict real human mental state dynamics spontaneously learned the same conceptual dimensions that people use to understand these states: the 3d Mind Model. Together these results suggest that mental state dynamics explain the origins of mental state concepts.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9edb92148b7fd659a921075eb633f1d2 https://doi.org/10.1037/xge0001405 Zobrazit plný text záznamu