Explanation and categorization: how "why?" informs "what?".

By Tania Lombrozo. 2009

103 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, History and Philosophy of Science, Molecular Biology, Cognitive Science & Psychology

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In History and Philosophy of Science

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, background): “cepts support prediction (Murphy & Ross, 1994; Rips, 1975), action (Barsalou, 1983), communication (A. B. Markman & Makin, 1998), imagination (Jern & Kemp, 2013; Ward, 1994), explanation (Lombrozo, 2009; Williams 22 & Lombrozo, 2010), and composition (Murphy, 1988; Osherson & Smi”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Similarly, Murphy and Medin (1985) showed that a conceptual coherence is often governed by an underlying theory—a causal mechanism of understanding; a point echoed by Lombrozo (2009b), in which she argued that explanations and concept formation are closely related.”
Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Lombrozo [106] studied at the same issue of functional vs.”
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].”
Explaining Explanations in AI (2018, background): “Lombrozo (2009) for example demonstrated that the type of explanation provided (e.g. mechanistic, functional) can influence the recipient’s view of the importance of features in categorising a phenomenon (Poulin et al., 2006).”
Contrastive Constraints Guide Explanation-Based Category Learning (2017, background): “The form of the explanation, whether it is causal or functional, has been linked to the structure of the relevant category knowledge (Lombrozo, 2009), presenting another potential way that context is able to impact the acquisition of category knowledge by means of explanation.”
Background shifts affect explanatory style: how a pragmatic theory of explanation accounts for background effects in the generation of explanations (2010, background): “…explanatory power it contains, affects how we reason about people (Malle 2004) and events in the physical world (Salmon 1998a), how we learn and represent concepts (Lombrozo 2009; Murphy and Medin 1985) and make category-based inferences based upon that knowledge (Medin et al. 2003; Sloman 1994).”
Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda (2018, background): “Researchers from Psychology have developed a rich theory of different types of explanations (functional, mechanistic, causal [123, 124]) and explored their role in learning, reasoning, categorization of knowledge [122] and scientific understanding [168].”
The role of explanation in discovery and generalization: evidence from category learning (2010, background): “…they support explanations (Patalano, Chin-Parker, & Ross, 2006), that different explanations differentially influence conceptual representations (Lombrozo, 2009), and that background beliefs that explain feature combinations facilitate category learning (Murphy & Allopenna, 1994) and influence…”
Building Machines that Learn and Think Like People (2018, unknown): “Markman & Makin, 1998), imagination (Jern & Kemp, 2013; Ward, 1994), explanation (Lombrozo, 2009; Williams & Lombrozo, 2010), and composition (Murphy, 1988; Osherson & Smith, 1981).”
Towards more human-like concept learning in machines : compositionality, causality, and learning-to-learn (2014, background): “Feldman, 1997), ∙ parse objects into parts and relations (Biederman, 1987), ∙ provide explanations (Lombrozo, 2009; Williams & Lombrozo, 2010), ∙ communicate with others (A.”
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).”
Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions. (2010, background): “Moreover, recent findings attest to the cognitive significance of the distinction, with explanation type having a reliable impact on explanatory preferences (e.g. Kelemen, 1999a; Lombrozo, Kelemen, & Zaitchik, 2007) and on judgments about category membership (Lombrozo, 2009).”
The scope of formal explanation (2017, background): “In fact, our conceptual systems naturally make use of multiple modes of explanation (e.g., Keil, 1994; Kelemen, 1999; Kelemen & Rosset, 2009; Lombrozo, 2009; Lombrozo & Gwynne, 2014; Prasada & Dillingham, 2006).”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “However, research shows users may also be able to develop a mental model of the algorithm based on a collection of explanations from multiple individual instances [133].”