Exemplar models as a mechanism for performing Bayesian inference

By Lei Shi, T. Griffiths, Naomi H Feldman, Adam N. Sanborn. 2010

143 citations recorded by the source corpus.

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

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Citations in the corpus

Generalization Through the Recurrent Interaction of Episodic Memories (2012, background): “Performance on both these tasks, like transitivity, is disrupted by damage to the hippocampal system across species (Buckmaster et al., 2004; Bunsey & Eichenbaum, 1996; Coutureau et al., 2002; Myers et al., 2003; for review, see Zeithamova et al., 2012).”
Amortized Inference in Probabilistic Reasoning (2014, background): “It has even been suggested that exemplar models may provide a general method for performing probabilistic inference (Shi et al., 2010), based on the idea that exemplars correspond to samples from a generative model.”
Bridging Levels of Analysis for Probabilistic Models of Cognition (2012, background): “This explains how individuals might use approximation algorithms in each specific computation but the average behavior of an individual across multiple settings or of many individuals in one setting ends up resembling the ideal solution (Shi et al., 2010).”
One and Done? Optimal Decisions From Very Few Samples (2014, background): “Although this decision will be informed by prior experiences that produced intricate cognitive representations of possible traffic flow, at the moment of decision these complex representations collapse onto a prediction about a binary variable: Is it best to turn left or right?”
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).”
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic (2015, background): “For example, Shi and colleagues (Shi, Feldman, & Grifﬁths, 2008; Shi et al., 2010) showed that exemplar models — a popular class of cognitive process models based on memorizing examples and recalling those examples most similar to a current stimulus — can be interpreted as approximating Bayesian…”