Two proposals for causal grammars

By T. Griffiths, Josh Tenenbaum. 2007

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/a35b41c88601fc21515a7e63ac8ee8b8fb9e9804

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In Artificial Intelligence

In Developmental and Educational Psychology

In Language and Linguistics

Citations in the corpus

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory (2012, unknown): “Over the past 10 years, a number of researchers have explored whether children might have Bayes net–like representations of causal structure and whether they can learn causal structure from evidence in the way that the formalism suggests.”
Bayesian models of cognition (2008, methodology): “…acquisition (Chater & Manning, 2006; Xu & Tenenbaum, in press), symbolic reasoning (Oaksford & Chater, 2001), causal learning and inference (Steyvers, Tenenbaum, Wagenmakers, & Blum, 2003; Griﬃths & Tenenbaum, 2005, 2007a), and social cognition (Baker, Tenenbaum, & Saxe, 2007), among other topics.”
Theory-based causal induction. (2009, background): “Hagmayer and Waldmann (2002) presented an elegant series of studies that showed that different assumptions about the delay between cause and effect could lead to different interpretation of the same set of events, determining which events were assumed to be related.”
Using Causal Induction in Humans to Learn and Infer Causality from Video (2013, background): “Although Bayesian networks are commonly used to represent causality (Pearl, 2009), grammar models have the expressive power to represent a greater breadth of possibilities than a single instance of a Bayesian network (Griffiths & Tenenbaum, 2007).”
Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning (2016, background): “Such representations enable humans to exploit their physical and social environments in flexible and inherently model-based ways (Dolan & Dayan, 2013; Griffiths & Tenenbaum, 2007; Sloman, 2005).”
When Younger Learners Can Be Better (or at Least More Open-Minded) Than Older Ones (2015, methodology): “cognitive development, causal learning, Bayesian models, simulated annealing hypotheses about which kinds of more specific hypotheses are likely (Griffiths & Tenenbaum 2007; Kemp, Perfors, & Tenenbaum, 2007).”
Learning Perceptual Causality from Video (2013, background): “To represent causal knowledge, a grammar model is needed (Bayesian networks lack their expressive power) (Griffiths and Tenenbaum 2007).”