The role of explanation in discovery and generalization: evidence from category learning

By Joseph Jay Williams, Tania Lombrozo. 2010

224 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, methodology): “The recent startling improvements in computer vision (Krizhevsky, Sutskever, & Hinton, 2012), machine translation (Johnson et al., 2016), and question answering (Weston et al, 2015) were possible, precisely because they avoided these limitations by foregoing symbolic compositionality altogether.”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Nevertheless, it is generally acknowledged that understanding and training need to promote the transfer of concepts and principles, and good explanations should facilitate that (Lombrozo and Gwynne, 2014b; Rittle-Johnson, 2006; Williams and Lombrozo, 2010; Williams, Lombrozo, and Rehder, 2013).”
Unmasking Clever Hans predictors and assessing what machines really learn (2019, background): “A crucial aspect of human behavior, and the main focus of this paper, is the ability to explain [71, 72].”
Dynamic inference and everyday conditional reasoning in the new paradigm (2013, background): “In the psychology of reasoning most work has involved the response to binary logical inconsistency (Elio & Pelletier, 1997; Markovits & Schmeltzer, 2007) where the role of explanation has also been explored (Khemlani & Johnson-Laird, 2011; Williams & Lombrozo, 2010).”
Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. (2011, background): “Williams and Lombrozo (2010) propose orienting students with an “explanation” directive. In their research, participants received artificial stimuli that comprised contrasting cases of category instances. Participants who were asked to explain category membership exhibited better learning and transf”
Tell me why! - Explanations support learning of relational and causal structure (2021, background): “Self-explanation: Asking humans to produce explanations for themselves, without providing feedback, can improve generalization (e.g. Chi et al., 1994; Rittle-Johnson, 2006; Williams and Lombrozo, 2010).”
Explanations in the wild. (2021, unknown): “…generalization and learning (both when people produce explanations and request them from others, Frazier et al., 2016; Gopnik, 2000; Liquin & Lombrozo, 2022; Mills et al., 2019; Walker et al., 2014; Williams & Lombrozo, 2010), satisfaction offers a window into the psychology of explanation.”
Human Learning in Atari (2017, background): “…2015) and build intuitive theories (Murphy and Medin 1985; Carey 1985; Gopnik, Meltzoff, and Bryant 1997), which they can use to explain (Lombrozo 2009; Williams and Lombrozo 2010), predict (Rips 1975; Murphy and Ross 1994), and imagine (Ward 1994; Jern and Copyright c 2017, Association for the…”
Tell me why! Explanations support learning relational and causal structure (2021, background): “Self-explanation: Asking humans to produce explanations for themselves, without providing feedback, can improve generalization (e.g. Chi et al., 1994; Rittle-Johnson, 2006; Williams & Lombrozo, 2010).”