The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology

By Clark Glymour. 2001

415 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/3f3a76a4337d3940099350d4baf9e0c17b1de9b2

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In Developmental and Educational Psychology

Citations in the corpus

A theory of causal learning in children: causal maps and Bayes nets. (2004, background): “Generalizations of Cheng’s rule have been shown to be able to correctly estimate her generative causal power parameters in certain cases in which the potential cause A and the effect E are both influenced by an unobserved common cause (Glymour, 2001).”
Connecting Scientific Reasoning and Causal Inference (2004, methodology): “Similarly, models of counterfactual reasoning (Roese & Olson, 1996) rely on comparison of probabilities under two conditions (in which an event does or does not occur), whereas Bayesian net mod- els (Glymour, 2001) also emphasize prior probabilities as constraining computations of causal power.”
Semantic Cognition: A Parallel Distributed Processing Approach (2004, methodology): “Bayes nets are graph structures that encode causal relationships and that provide a framework for a formal theory of causal inference (Pearl, 2000; Glymour, 2001).”
Learning the Structure of Linear Latent Variable Models (2006, methodology): “This is, by far, the most common method used in several applied sciences (Glymour, 2002).”
Explanation and understanding (2015, background): “Causal patterns may be discerned from merely correlational ones by construction of “causal Bayes nets” (Glymour 2001), which track patterns of nested contingencies in a manner that strongly rules out noncausal relations.”
Intuitive theories as grammars for causal inference (2007, methodology): “In particular, we will argue that one widely used framework for representing causal relationships, known as causal graphical models or causal Bayesian networks (Pearl, 2000; Glymour, 2001), is not sufficiently expressive to represent intuitive theories in their full generality and power.”
Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers (2004, background): “In their basic object A, but an undefined value for object B (see Glymour, 2001).”
The max-min hill-climbing Bayesian network structure learning algorithm (2006, methodology): “, 2000) and developmental and cognitive psychology (Glymour, 2001).”
Causal learning in rats and humans: A minimal rational model (2008, background): “In fact, some have argued that previously developed theories such as power PC theory can be modeled as a special case of causal Bayes nets (see Glymour, 2001).”
Must, knowledge, and (in)directness (2016, background): “Psychology: Glymour 2001; Sloman 2005; Gopnik and Schultz 2007; Danks 2014, and—with additional structure—Tenenbaum et al. 2011.”