Topic
Bayesian cognitive science
About: Bayesian cognitive science is a research topic. Over the lifetime, 22 publications have been published within this topic receiving 1817 citations.
Papers
TL;DR: This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems.
Abstract: In coming to understand the world-in learning concepts, acquiring language, and grasping causal relations-our minds make inferences that appear to go far beyond the data available. How do we do it? This review describes recent approaches to reverse-engineering human learning and cognitive development and, in parallel, engineering more humanlike machine learning systems. Computational models that perform probabilistic inference over hierarchies of flexibly structured representations can address some of the deepest questions about the nature and origins of human thought: How does abstract knowledge guide learning and reasoning from sparse data? What forms does our knowledge take, across different domains and tasks? And how is that abstract knowledge itself acquired?
1,980 citations
27 Mar 2008
TL;DR: This chapter introduces the probabilistic approach to cognition; describes the different levels of explanation at which it can apply; reviewes past work; and considers potential challenges to the probable approach.
Abstract: This chapter introduces the probabilistic approach to cognition; describes the different levels of explanation at which it can apply; reviewes past work; and considers potential challenges to the probabilistic approach. The approach has been widely applied in the areas of perception, motor control, and language, where the performance of dedicated computational modules exceeds the abilities of any artificial computational methods by an enormous margin. Theories of perception based on decorrelation and information compression can be viewed as part of the Bayesian probabilistic approach to perception. The study of perceptuo-motor control provides a second important area of Bayesian analysis. A wide range of experimental evidence has indicated that movement trajectories are accurately predictable. Bayesian decision theory has been widely applied as theoretical framework for understanding the control of movement.
369 citations
TL;DR: It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power, but this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation.
Abstract: It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal-mechanical explanation.
72 citations
TL;DR: In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine as mentioned in this paper. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramati...
Abstract: 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 dramati...
57 citations
TL;DR: In this paper, the authors argue that the realist attitude of some naturalistic philosophers of mind towards the results of Bayesian cognitive science is unwarranted and that such philosophers should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true.
Abstract: Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science In this paper, we argue that this realist attitude is unwarranted The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives It is also contentious that the Bayesian approach is overall better supported by the empirical evidence So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind
54 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 3 |
| 2020 | 3 |
| 2019 | 2 |
| 2018 | 1 |
| 2017 | 1 |
| 2016 | 5 |