A causal-model theory of conceptual representation and categorization.

By B. Rehder. 2003

212 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Cognitive Science & Psychology

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “Providing a learner with different types of causal knowledge changes how they learn and generalize, as shown by changing the causal network that underlies the features of objects (Rehder, 2003; Rehder & Hastie, 2001) or the causal prescription for producing new examples of the concept (see Freyd, 19”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “(Andriessen, 2002; Bjork et al., 2013; Doumas and Hummel, 2005; Falkenhainer, 1990; Facione and Gittens, 2015; Lombrozo, 2016; Rehder, 2003; Selfridge, Daniell, and Simmons, 1985; Williams, Lombrozo, and Rehder, 2012; Williams and Lombrozo, 2013; Feltovich, Coulson, and Spiro, 2001).”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Rehder [153] looked specifically at categorical or formal explanations.”
Explaining Explanations in AI (2018, background): “Rather, the reviewed empirical evidence indicates that humans psychologically prefer contrastive explanations (Miller, 2017, p.18)(Rehder, 2003, 2006).”
Guilty by Association: Product-Level Category Stigma and Audience Expectations in the U.S. Craft Beer Industry (2018, background): “Furthermore, a line of research in cognitive psychology (Rehder, 2003a, 2003b) highlights the causal nature of how audiences interpret category membership through cause–effect associations related to certain features of a category.”
Towards more human-like concept learning in machines : compositionality, causality, and learning-to-learn (2014, background): “Experiments show that providing a learner with different types of causal knowledge changes how they learn and generalize, whether it is by manipulating the causal network that underlies the features of objects (Rehder & Hastie, 2001; Rehder, 2003) or the causal prescription for drawing a new type of”
Intuitive theories as grammars for causal inference (2007, background): “…B. Tenenbaum Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Thomas L. Griffiths Department of Cognitive and Linguistic Sciences Brown University Sourabh Niyogi Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology”
Explanation and Prior Knowledge Running Head: Explanation and Prior Knowledge Explanation and Prior Knowledge Interact to Guide Learning Explanation and Prior Knowledge Interact to Guide Learning (unknown): “Prominent theories of conceptual representation accord a central role to " explanatory beliefs " (Carey, 1985; Murphy & Medin, 1985), a phrase that is often used synonymously with a learner's prior knowledge (see also Ahn, 1998; Lombrozo, 2009; Rehder, 2003; Rips, 1989).”
Concepts in a Probabilistic Language of Thought (2014, background): “Starting around ten years ago, several authors began to consider Bayesian networks as formal models for intuitive theories (Goodman et al., 2006; Gopnik et al., 2004; Rehder, 2003; Tenenbaum & Griffiths, 2003), focusing on their causal aspects.”
Structure and strength in causal induction. (2005, unknown): “…beginning to be applied in cognitive science (Danks & McKenzie, under revision; Gopnik, Glymour, Sobel, Schulz, & Kushnir, 2004; Glymour, 1998; 2001; Lagnado & Sloman, 2002; Rehder, 2003; Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003; Tenenbaum & Griffiths, 2001; 2003; Waldmann & Martignon, 1998).”
Building Machines that Learn and Think Like People (2018, background): “Providing a learner with different types of causal knowledge changes how they learn and generalize, as shown by changing the causal network that underlies the features of objects (Rehder, 2003; Rehder & Hastie, 2001) or the causal prescription for producing new examples of the concept (see Freyd, 19”
When similarity and causality compete in category-based property generalization (2006, background): “For example, I have proposed a formal representation of causal knowledge in which category features are related by probabilistic causal mechanisms ( Rehder, 2003a, 2003b ).”
A Rational Analysis of Rule-Based Concept Learning (2008, methodology): “Indeed, if recent suggestions that concepts are fundamentally based on causal relations (Rehder, 2003) are correct, we might expect to see subtle traces of the INF representation even in categorization settings that are not overtly causal.”