A Realist Perspective on Bayesian Cognitive Science

2019 ◽  
pp. 40-73
Author(s):  
Michael Rescorla
Keyword(s):  
Cognitive Science ◽  
Bayesian Cognitive Science ◽  
Realist Perspective

scholarly journals Bayesian cognitive science, predictive brains, and the nativism debate

Synthese ◽  
2017 ◽  
Vol 195 (11) ◽  
pp. 4817-4838 ◽  
Author(s):  
Matteo Colombo
Keyword(s):  
Cognitive Science ◽  
Bayesian Cognitive Science

scholarly journals The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science

2011 ◽  
Vol 34 (4) ◽  
pp. 194-196 ◽  
Author(s):  
Nick Chater ◽  
Noah Goodman ◽  
Thomas L. Griffiths ◽  
Charles Kemp ◽  
Mike Oaksford ◽  
...  
Keyword(s):  
Cognitive Science ◽  
Neural Circuits ◽  
Behavioral Data ◽  
Multiple Models ◽  
Close Attention ◽  
Environmental Structure ◽  
Bayesian Cognitive Science ◽  
Future Practice

AbstractIf Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

scholarly journals Probabilistic Biases Meet the Bayesian Brain

2020 ◽  
Vol 29 (5) ◽  
pp. 506-512
Author(s):  
Nick Chater ◽  
Jian-Qiao Zhu ◽  
Jake Spicer ◽  
Joakim Sundh ◽  
Pablo León-Villagrá ◽  
...  
Keyword(s):  
Decision Making ◽  
Cognitive Science ◽  
Mental Simulation ◽  
Judgment And Decision Making ◽  
Heuristics And Biases ◽  
Bayesian Brain ◽  
Anchoring And Adjustment ◽  
The Mind ◽  
The Brain ◽  
Bayesian Cognitive Science

In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramatically and systematically people depart from rational norms. In this article, we outline recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities but approximates probabilistic calculations by drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, which offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment.

scholarly journals Bayesian Cognitive Science, Unification, and Explanation

2017 ◽  
Vol 68 (2) ◽  
pp. 451-484 ◽  
Author(s):  
Matteo Colombo ◽  
Stephan Hartmann
Keyword(s):  
Cognitive Science ◽  
Bayesian Cognitive Science

scholarly journals Comparing probabilistic accounts of probability judgments

2021 ◽  
Author(s):  
Derek Powell
Keyword(s):  
Cognitive Science ◽  
Model Comparison ◽  
Predictive Accuracy ◽  
Information Criteria ◽  
Model Complexity ◽  
Analysis Framework ◽  
Informative Priors ◽  
Probability Judgments ◽  
Bayesian Data Analysis ◽  
Bayesian Cognitive Science

Bayesian theories of cognitive science hold that cognition is fundamentally probabilistic, but people’s explicit probability judgments often violate the laws of probability. Two recent proposals, the “Probability Theory plus Noise” (Costello & Watts, 2014) and “Bayesian Sampler” (Zhu et al., 2020) theories of probability judgments, both seek to account for these biases while maintaining that mental credences are fundamentally probabilistic. These theories fit quite differently into the larger project of Bayesian cognitive science, but their many similarities complicate comparisons of their predictive accuracy. In particular, comparing the models demands a careful accounting of model complexity. Here, I cast these theories into a Bayesian data analysis framework that supports principled model comparison using information criteria. Comparing the fits of both models on data collected by Zhu and colleagues (2020) I find the data are best explained by a modified version of the Bayesian Sampler model under which people may hold informative priors about probabilities.

Language as a mental travel guide

2020 ◽  
Vol 43 ◽  
Author(s):  
Charles P. Davis ◽  
Gerry T. M. Altmann ◽  
Eiling Yee
Keyword(s):  
Cognitive Science ◽  
Human Cognition ◽  
Travel Guide

Abstract Gilead et al.'s approach to human cognition places abstraction and prediction at the heart of “mental travel” under a “representational diversity” perspective that embraces foundational concepts in cognitive science. But, it gives insufficient credit to the possibility that the process of abstraction produces a gradient, and underestimates the importance of a highly influential domain in predictive cognition: language, and related, the emergence of experientially based structure through time.

Embodiment and Cognitive Science

2005 ◽  
Author(s):  
Raymond W. Gibbs, Jr
Keyword(s):  
Cognitive Science

Minding the Heart: Cognitive Science and Process Spirituality

2003 ◽  
Vol 48 (6) ◽  
pp. 745-748 ◽  
Author(s):  
Michael Mahoney
Keyword(s):  
Cognitive Science
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