Rational approximations to rational models: alternative algorithms for category learning.

By Adam N. Sanborn, T. Griffiths, D. Navarro. Psychology Review, 2010

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/bf244633a58d0ca27f2b383ed225f3c42481eed9

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In Developmental and Educational Psychology

Citations in the corpus

Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning (2016, background): “The idea that stochastic local search plays an important role in cognition has some precedent (Gershman, Vul, & Tenenbaum, 2012; Sanborn et al., 2010).”
Discovering latent causes in reinforcement learning (2015, background): “The Chinese restaurant process has also appeared in many areas of cognitive science, ranging from categorization [42,44] to visual perception [18] and reconstructive memory [10].”
A Tutorial on Bayesian Nonparametric Models (2011, background): “A number of recent authors have extended this work (Griffiths et al., 2007; Heller et al., 2009; Sanborn et al., 2010) and applied it to other domains, such as classical conditioning (Gershman et al.”
Bootstrapping language acquisition. (2017, methodology): “Other recent models have also used particle filtering algorithms, including to model memory limitations in incremental sentence processing (Levy, Reali, & Griffiths, 2009; Sanborn et al., 2010; Shi et al., 2010).”
Bridging Levels of Analysis for Probabilistic Models of Cognition (2012, methodology): “For example, particle filters can be used to explain phenomena in category learning (Sanborn et al., 2010), sentence processing (Levy et al.”
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal (2012, background): “In fact, Darwinian algorithms have yet to be fully investigated within the new field of rational process models that study how optimal Bayesian calculations can be algorithmically approximated in practice (Sanborn et al., 2010).”
Cognitive control over learning: creating, clustering, and generalizing task-set structure. (2013, neutral): “Because it is a process model, it also affords predictions about the dynamics of action selection within a trial and, hence, response times.”
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic (2015, methodology): “Sanborn, Grifﬁths, and Navarro (2006) and Sanborn et al. (2010) showed how two algorithms commonly used to perform probabilistic inference in related models in statistics — Markov chain Monte Carlo and particle ﬁlters — could be used to approximate the average over all clusterings in Anderson’s…”