Detecting blickets: how young children use information about novel causal powers in categorization and induction.

By Alison Gopnik, David M. Sobel. 2000

394 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Cognitive Science & Psychology

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Citations in the corpus

The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence (2020, background): “Children do this naturally, as Gopnik and Sobel (Gopnik & Sobel, 2000) have elegantly shown; we need machines that can do the same.”
A theory of causal learning in children: causal maps and Bayes nets. (2004, background): “Gopnik and colleagues (Gopnik & Esterly, 1999; Gopnik & Nazzi, 2003; Gopnik & Sobel, 2000; Nazzi & Gopnik, 2000) devised the “blicket detector” to explore children’s causal learning.”
The Innate Mind: Structure and Contents (2005, background): “Recent evidence provides some compelling demonstrations of how this might work in detail (Gopnik & Sobel, 2000).”
Learning to Understand the Forms of Causality Implicit in Scientifically Accepted Explanations (2003, background): “Gopnik and Sobel (2000) found that young children could use information about causal powers to categorize and make assumptions about novel objects and that even the youngest children (2½-years-old) were to some extent (though not as much as 3- and 4-year-olds) able to override perceptual…”
Theory-based causal induction. (2009, methodology): “structures for a two-ball machine are summarized in Griffiths (2005). Using this theory, we can compute a posterior distribution over Graphs 0–19 for any data D.”
Making classroom assessment more accountable to scientific reasoning: A case for attending to mechanistic thinking (2009, background): “Gopnik and her colleagues (e.g., Gopnik & Sobel, 2000; Gopnik, Sobel, Shulz, & Glymour, 2001) have carried out recognized work on children’s causal reasoning.”
Tell me why! Explanations support learning relational and causal structure (2021, background): “Causality: Humans focus on causal structure, even as children (Gopnik et al., 1999; Gopnik & Sobel, 2000), and our causal understanding is closely linked to explanations (Lombrozo & Vasilyeva, 2017).”
Recognizing mechanistic reasoning in student scientific inquiry: A framework for discourse analysis developed from philosophy of science (2008, background): “1 Although in the studies by Gopnik and Sobel (2000) and Gopnik”
Imagination and the generation of new ideas (2015, background): “Researchers have used machines that only appear to activate in response to stimuli for many studies of children’s sensitivity to patterns of covariation for causal reasoning (Gopnik & Sobel, 2000; Buchsbaum et al., 2012).”
Tell me why! - Explanations support learning of relational and causal structure (2021, background): “Causality: Humans focus on causal structure, even as children (Gopnik et al., 1999; Gopnik and Sobel, 2000), and our causal understanding is closely linked to explanations (Lombrozo and Vasilyeva, 2017).”
Précis of Semantic Cognition: A Parallel Distributed Processing Approach (2008, background): “In a very different series of studies, Gopnik and Sobel (2000) have shown that i) children make inferences about the causal properties of novel items and ii) use these inferences to govern their decisions about how names should generalize.”
On the nature and origin of intuitive theories : learning, physics and psychology (2015, background): “For my purpose, the takeaway is that Pearl’s research on causality had an important influence on developmental research beginning in the late 90’s [65, 68], when some researchers in computation and development began seeing Causal Bayes Nets as the formal nuts and bolts of the theory-theory.”
Two proposals for causal grammars (2007, background): “In Griffiths (2005) and Griffiths and Tenenbaum (in prep), we show how this approach can explain people’s judgments in identifying causal structure from contingency data (Griffiths & Tenenbaum, in press), reasoning about mechanical systems (Gopnik et al.”