Background shifts affect explanatory style: how a pragmatic theory of explanation accounts for background effects in the generation of explanations

By Seth Chin-Parker, Alexandra Bradner. 2010

22 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, General Decision Sciences, History and Philosophy of Science

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “Key theoretical, review, and empirical articles include: (Byrne, 1992; Chin-Parker and Bradner, 2010; Craik, 1967; Ebel, 1974; Fernbach, Sloman, Louis, and Shube, 2012; Geldard, 1939; George, 1953; Giffin, Wilkenfeld and Lombrozo, 2017; Gopnik, 2000; Kass and Leake, 1987; Keil, 2006; Keil,…”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Cognitive process — The process of abductive inference for ‘filling the gaps’ [27] to determine an explanation for a given event, called the explanandum, in which the causes for the event are identified, perhaps in relation to a particular counterfactual cases, and a subset of these causes is select”
Contrastive Constraints Guide Explanation-Based Category Learning (2017, background): “We propose that this initial stage takes place via a contrastive process—the specification occurs by establishing a contrast class that represents the possible, but non-occurring, alternatives to the topic of the explanation (Chin-Parker & Bradner, 2010; Schaffer, 2005; Sinnott-Armstrong, 2008).”
Explanation and Abductive Inference (2012, background): “…occurs comes from the observation that explanations are typically contrastive: They account for one state of affairs in contrast to another (see e.g., Chin-Parker & Bradner, 2010; Garfinkel, 1990; Hart & Honore, 1985; Hilton, 1996; Hilton & Slugoski, 1986; Mackie, 1980; van Fraassen, 1980).”
Goals Affect the Perceived Quality of Explanations (2015, background): “Chin-Parker and Bradner (2010) also found that the frequency with which participants generated mechanistic and teleological explanations was influenced by changing background conditions, but they did not vary participants’ goals.”