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인과적 사슬구조에서의 범주기반 속성추론

Category-based Feature Inference in Causal Chain

최인범|이형철|김신우
한국감성과학회 (The Korean Society For Emotion & Sensibility)|2021.03|감성과학 (Science of Emotion & Sensibility)|제24권 제1호|pp. 59-72|DOI https://doi.org/10.14695/KJSOS.2021.24.1.59

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참고문헌

키워드

인과적 마코프 조건범주기반 속성추론인과적 추론Causal ReasoningCategory-Based Feature Inference인과모형 이론인과적 사슬Causal Markov ConditionCausal Model TheoryCausal Chain
주제분류감성과학

초록

개념과 범주는 관찰하지 못한 속성을 추론할 수 있는 기반을 제공한다. 무의미 속성을 사용한 범주기반 속성추론연구들은 범주 및 속성의 유사성이 추론을 설명하는 핵심 요인이라는 것을 제안했다(Rips, 1975; Osherson et al., 1990). 이후 연구들은 사람들의 사전지식이 범주기반 추론에 막대한 영향을 미치며 심지어 유사성 효과가 완전히사라지는 경우도 있음을 보고했다. 본 연구는 범주 속성들이 사전지식의 한 종류인 인과적 지식에 의해 사슬구조로연결되었을 때의 범주기반 속성추론을 검증했으며 그 결과를 예측하는 속성추론모형을 제안했다. 참가자들은 네 개의 속성들이 사슬구조를 이루는 인과적 범주를 학습한 뒤 해당 범주의 다양한 범주 예시들의 숨겨진 속성에 대한추론을 실시했다. 그 결과 인과적으로 직접 연결된 속성뿐만 아니라 다른 속성 노드에 의해 차폐된 속성들도 추론에영향을 미치는 비독립성이 나타났다(인과적 마코프 조건의 위배). 인과모형이론(Sloman, 2005)에 기반한 속성추론모형을 적용하여 참가자들의 추론을 모델링한 결과 인과적 연결의 직접 효과뿐만 아니라 간접 효과 즉 인과추론의 비독립성도 예측하는 것으로 나타났다. 다만 간접적으로 연결된 속성들은 인과적 거리와 무관하게 참가자들의 추론 평정에 동일하게 영향을 미쳤지만 모형은 거리가 멀어짐에 따라 추론에 미치는 영향이 작아짐을 예측했다.

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.

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