Probabilistic models of cognition: conceptual foundations.

By Nick Chater, Joshua B. Tenenbaum, Alan L. Yuille. Trends in Cognitive Sciences, 2006

382 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4ddb495661f1458ed54cb8643b5a459fe59836ca

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Citations in the corpus

A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop (2017, background): “More background on these problems can be found e.g. in (Chater et al., 2006), (Steyvers et al., 2003).”
Goal-directed decision making in prefrontal cortex: a computational framework (2008, background): “The interest of this idea, in the present context, derives from a recent movement toward framing human and animal information processing, as well as the underlying neural computations, in terms of structured probabilistic inference [13, 14].”
Consequences of the Now-or-Never bottleneck for signed versus spoken languages (2015, background): “…limitations, contrasts with a widespread interest in rational, rather processing-based, explanations in cognitive science (e.g., Anderson 1990; Chater et al. 2006 Griffiths & Tenenbaum 2009; Oaksford & Chater 1998; 2007; Tenenbaum et al. 2011), including language processing (Gibson et al.…”
Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience (2009, background): “It is also natural to have hierarchically structured belief networks, in which lower-level modules provide evidence to higher-level modules, which in turn structure the prior hypothesis space of the lower-level modules in the light of the bigger picture to which only the higher modules have access (”
The propositional approach to associative learning as an alternative for association formation models (2009, methodology): “Propositional models are compatible not only with many of the traditional mathematical models of associative learning but also with the more recent Bayesian approach of human cognition (see Chater, Tenenbaum, & Yuille, 2006, for an introduction).”
Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations (2015, background): “In the laboratory, humans have been shown to use information about uncertainty when solving a variety of sensory [9, 10, 11, 12], motor [13, 14] and cognitive [15, 16, 17] tasks.”
The Bias Bias in Behavioral Economics (2018, background): “It also conflicts with contemporary cognitive scientists’ view of people’s conscious and unconscious cognitive processes as largely consistent with Bayesian statistics (e.g., Anderson, 1990; Chater et al., 2006) and with neuroscience research that speaks of the Bayesian brain (e.g., Friston, 2010).”
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).”
On the assessment of landmark salience for human navigation (2007, background): “Probabilistic inference models are increasingly becoming important theoretical tools for understanding cognition (Chater et al. 2006; Scholl and Tremoulet 2000; Kersten and Yuille 2003; Kersten et al. 2004).”
Probabilistic brains: knowns and unknowns (2013, background): “These experiments have shown that human behavior is highly consistent with probabilistic reasoning not only in the sensory domain 8–12 , but also in the motor 13–15 and cognitive 16–22 domains.”
1 On what it means to see , and what we can do about it (2008, positive): “…the gradual realization that the prime candidate framework for managing uncertainty — graphical models, or Bayes networks — is ubiquitous in biological vision (Kersten et al., 2004; Kersten and Yuille, 2003; Knill and Richards, 1996), as it is, indeed, in cognition in general (Chater et al., 2006).”
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics (2014, background): “Human behaviour is consistent with Bayesian inference in many sensory [1, 2, 3, 4], motor [5, 6] and cognitive [7, 8, 9] tasks.”
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]).”