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http://dx.doi.org/10.14695/KJSOS.2021.24.1.59

Category-based Feature Inference in Causal Chain  

Choi, InBeom (광운대학교 산업심리학과)
Li, Hyung-Chul O. (광운대학교 산업심리학과)
Kim, ShinWoo (광운대학교 산업심리학과)
Publication Information
Science of Emotion and Sensibility / v.24, no.1, 2021 , pp. 59-72 More about this Journal
Abstract
Concepts and categories offer the basis for inference pertaining to unobserved features. Prior research on category-based induction that used blank properties has suggested that similarity between categories and features explains feature inference (Rips, 1975; Osherson et al., 1990). However, it was shown by later research that prior knowledge had a large influence on category-based inference and cases were reported where similarity effects completely disappeared. Thus, this study tested category-based feature inference when features are connected in a causal chain and proposed a feature inference model that predicts participants' inference ratings. Each participant learned a category with four features connected in a causal chain and then performed feature inference tasks for an unobserved feature in various exemplars of the category. The results revealed nonindependence, that is, the features not only linked directly to the target feature but also to those screened-off by other feature nodes and affected feature inference (a violation of the causal Markov condition). Feature inference model of causal model theory (Sloman, 2005) explained nonindependence by predicting the effects of directly linked features and indirectly related features. Indirect features equally affected participants' inference regardless of causal distance, and the model predicted smaller effects regarding causally distant features.
Keywords
Causal Reasoning; Category-Based Feature Inference; Causal Markov Condition; Causal Model Theory; Causal Chain;
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