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Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena
Author(s) -
Rottman Benjamin M.,
Gentner Dedre,
Goldwater Micah B.
Publication year - 2012
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/j.1551-6709.2012.01253.x
Subject(s) - categorization , causal structure , causal model , domain (mathematical analysis) , sort , cognitive psychology , sorting , causal inference , psychology , causal analysis , causal reasoning , cognitive science , computer science , artificial intelligence , natural language processing , cognition , mathematics , information retrieval , econometrics , statistics , algorithm , quantum mechanics , neuroscience , mathematical analysis , physics
We investigated the understanding of causal systems categories—categories defined by common causal structure rather than by common domain content—among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real‐world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain‐based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: The novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.