Bayesian models of cognition

By T. Griffiths, Charles Kemp, Josh Tenenbaum. 2008

639 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, History and Philosophy of Science

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Citations in the corpus

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “Bayesian models have been shown to lead to great insights in assorted applications such as cognitive modeling [201,202] , ﬁshery [196,203] , gaming [204] , climate [205] , economet-rics [206] or robotics [207] .”
Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons (2011, background): “Therefore many researchers have suggested that information processing in the brain carries out probabilistic, rather than logical, inference for making decisions and choosing actions [9–22].”
Quantum Models of Cognition and Decision (2012, background): “, 1987); a second is neural network (Grossberg, 1982) and connectionist network (Rumelhart & McClelland, 1986) models; and a third is Bayesian models (Griffiths et al., 2008).”
How cognitive modeling can benefit from hierarchical Bayesian models. (2011, methodology): “sic theoretical level, the ‘objective Bayesian’ approach expounded by Jaynes (2003) encourages a different style of thinking about Bayesian analysis than the ‘subjective Bayesian’ approach of de Finetti (1974).”
Argumentation, Dialogue Theory, and Probability Modeling: Alternative Frameworks for Argumentation Research in Education (2011, background): “Griffiths, Kemp, and Tenenbaum (2008) developed hierarchical Bayesian techniques for analyzing causal inductive and scientific arguments that have both normative and descriptive applications (i.e., explanations of human cognition; see also J. R. Anderson & Lebiere, 1998).”
Bayesian Cognitive Modeling: A Practical Course (2014, background): “Start with the summary for cognitive scientists presented by Griffiths, Kemp, and Tenenbaum (2008).”
Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity (2013, background): “Following the ‘‘probabilistic turn’’ in cognitive science [3,4,69] and related hypotheses in computational neuroscience [1,2,5], probabilistic inference has become very successful in explaining behavioral data on human reasoning and other brain functions.”
The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology. (2016, background): “Its formulation as an intuitive theory was anticipated in some form by pioneers in social cognition Fritz Heider [38], Harold Kelley [39], and Roger Brown [40], but with the development of new computational cognitive modeling tools these ideas can be formulated and tested more precisely [41-46].”