The pigeon as particle filter

By Nathaniel D. Daw, Aaron C. Courville. Neural Information Processing Systems, 2007

101 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/291e592316f2d1d9f9624e7b1e96b74e66da0857

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Citations in the corpus

Rational approximations to rational models: alternative algorithms for category learning. (2010, background): “Daw and Courville (2008) argued that a particle filter with a small number of particles could explain rapid transitions seen in associative learning tasks with animals.”
A Bayesian framework for word segmentation: exploring the effects of context. (2009, unknown): “…widespread as a means of translating Bayesian models into process models capable of making trial-by-trial predictions (Sanborn et al., 2006a; Daw and Courville, 2008; Brown and Steyvers, in press), along with other approximate methods for performing Bayesian inference (Kruschke, 2006, for…”
Learning latent structure: Carving nature at its joints (2010, background): “However, the solution can be approximated, for example by representing the posterior probability over structures with a set of samples [16 –19].”
Multistability and Perceptual Inference (2012, methodology): “…be employed in human (Sanborn, Griffiths, & Navarro, 2006; Levy, Reali, & Griffiths, 2009; Vul, Goodman, Griffiths, & Tenenbaum, 2009) and animal (Daw & Courville, 2007) cognition, and several authors have proposed that the spiking activity of neurons might be interpreted as a set of samples…”
Probabilistic models of cognition: exploring representations and inductive biases. (2010, unknown): “learning [48], associative learning [49], detecting changes in temporal sequences [50], and”
Perception, Action, and Utility: The Tangled Skein (2012, background): “Perception, Action and Utility: The Tangled Skein Samuel J. Gershman1 and Nathaniel D. Daw2 1 Neuroscience Institute and Department of Psychology, Princeton University 2 Center for Neural Science and Department of Psychology, New York University June 5, 2011”
Modeling the effects of memory on human online sentence processing with particle filters (2008, background): “A variety of “rational” models of online sentence processing [6, 7, 8, 9] take exactly this perspective, using the properties ofP (T |w1 ...i) or quantities derived from it to explain why people find some sentences more difficult to comprehend than others.”
Exemplar models as a mechanism for performing Bayesian inference (2010, methodology): “This idea has been explored in the context of the rational model of categorization by Sanborn et al. (2006), and similar models have been proposed as explanations of change-point detection (Brown & Steyvers, 2009), associative learning ( Daw & Courville, 2008 ), sentence processing (Levy, Reali, & G”