Much research (e.g., Keil, 1989; Murphy & Medin, 1985; Rips, 1989) has emphasized the critical role that domain knowledge plays in categorization judgments. Recent instantiations of this view (e.g., Ahn, et al., 2000; Rehder & Hastie, 2001) have focused on characterizing how causal knowledge supports categorization decisions. We suggest that a more satisfactory account of categorization can be gained by considering the broader role that causal information plays in processes of explanation. An explanation-based perspective treats categorization as an inference to the best explanation (Murphy & Medin, 1985; Rips, 1989). This suggests that a critical source of constraints on categorization may come from a direct investigation of explanatory goodness. We present evidence that the causal status effect (e.g., Ahn, et al., 2000)—i.e., the phenomenon in which causes tend to be more heavily weighted than effects in categorization judgments—depends on the goodness of the explanation in which the causal information is embedded.
|Original language||English (US)|
|Title of host publication||Proceedings of the 30th Annual Conference of the Cognitive Science Society|
|Editors||Vladimir Sloutsky, Brad Love, Ken McRae|
|Publisher||Cognitive Science Society|
|Number of pages||6|
|State||Published - 2008|