Probabilistic models of cognition: exploring representations and inductive biases.

By T. Griffiths, N. Chater, Charles Kemp, Amy Perfors, Josh Tenenbaum. 2010

570 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Cultural Studies, Developmental and Educational Psychology

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Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, methodology): “In a Bayesian model, inductive biases are typically expressed through the choice and parameterization of the prior distribution (Griﬃths et al., 2010).”
Bayesian just-so stories in psychology and neuroscience. (2012, background): “Weiss et al. related this analysis to a variety of findings, including the tendency of automobile drivers to speed up in foggy conditions.”
The neuroscience of learning: beyond the Hebbian synapse. (2013, background): “According to much contemporary thinking in cognitive science, Bayesian inference is everywhere in cognition, from perception to learning and causal reasoning (Chater et al. 2006, Griffiths et al. 2010).”
Event-driven contrastive divergence for spiking neuromorphic systems (2013, background): “Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.”
The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology. (2016, background): “, [54]), and consistent with the Bayesian framework [55], this challenge can be solved through an appropriate inductive bias or prior.”
Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic (2015, background): “The central question is what function an aspect of cognition serves, leading recent proponents of computational-level analysis to call this approach a “function-first” strategy (Griffiths et al., 2010).”
Rapid Expectation Adaptation during Syntactic Comprehension (2013, background): “(Although many experience-based processing accounts are framed in terms of relative ‘‘activation’’, rather than probabilistically, these two views can often be translated into one another [50–52]; see, for example, the discussion in [26]).”
Relational inductive bias for physical construction in humans and machines (2018, background): “Such assumptions may be encoded in the prior of a Bayesian model (Griffiths et al., 2010), or instantiated via architectural assumptions in a neural network.”
Cognitive computational neuroscience (2018, background): “A third and critically important class of cognitive model is that of Bayesian models (Box 3).(33,35,36,34,143,144,145,32) Bayesian inference provides an essential normative perspective on cognition.”