Bayesian Models of Cognition Revisited: Setting Optimality Aside and Letting Data Drive Psychological Theory

@article{Tauber2017BayesianMO,
  title={Bayesian Models of Cognition Revisited: Setting Optimality Aside and Letting Data Drive Psychological Theory},
  author={Sean Tauber and Daniel J. Navarro and Amy Perfors and Mark Steyvers},
  journal={Psychological Review},
  year={2017},
  volume={124},
  pages={410–441}
}
Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian… Expand
How to Explain Behavior?
TLDR
Three new frameworks for explanations that emerged after the cognitive revolution are distinguished, one of which describes mental processes in situations of uncertainty where an optimal solution is unknown and has considerable potential to inform each other and to generate points of integration. Expand
BAYESIAN METHODS IN COGNITIVE MODELING 2 Introduction
Bayesian statistical methods provide a flexible and principled framework for relating cognitive models to behavioral data. They allow for cognitive models to be formalized, evaluated, and applied,Expand
Suboptimality in Perceptual Decision Making
TLDR
A "LPCD approach" to perceptual decision making is proposed that focuses exclusively on uncovering the LPCD components, without debating whether the uncovered LPCDs are "optimal" or not. Expand
Believing in one’s power: a counterfactual heuristic for goal-directed control
TLDR
This model closely mirrors people’s belief in their causal power –a belief that is well-suited to learning action-outcome associations in controllable environments and agrees with the intuitive way of construing causal power as “difference-making”. Expand
On the Irrationality of Being in Two Minds
TLDR
A general framework that allows irrational decision making to be theoretically investigated and simulated is presented and it is found that bistable probabilities can be formalized by positive-operator-valued projections in quantum mechanics. Expand
If mathematical psychology did not exist we would need to invent it: A case study in cumulative theoretical development
It is commonplace, when discussing the subject of psychological theory, to write articles from the assumption that psychology differs from physical sciences in that we have no psychological theoriesExpand
Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference
TLDR
It is described how without a grounding in theories of human statistical inference, research in exploratory visual analysis can lead to contradictory interface objectives and representations of uncertainty that can discourage users from drawing valid inferences. Expand
Coincidence judgment in causal reasoning: How coincidental is this?
TLDR
It is argued that coincidentality is a marker for causal suspicion/discovery in terms of a flag that a new, unknown causal mechanism may be operating. Expand
Using Occam’s razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements
TLDR
This paper contrasts discrete and continuous prior formats within the domain of numerical estimation using both direct comparisons of computational models of this process using these representations, as well as empirical contrasts exploiting different predicted reactions of these formats to uncertainty via Occam's razor. Expand
Large prospective losses lead to sub-optimal sensorimotor decisions in humans
TLDR
This work rigorously tested the hypothesis that incentivized sensorimotor decisions maximizes expected gain, suggesting that it may be impervious to cognitive biases and heuristics, and obtained strong evidence that people deviated from the objectively rational strategy when potential losses were large. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 108 REFERENCES
Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition
TLDR
It is argued that the expressive power of current Bayesian models must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition, and this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. Expand
Pinning down the theoretical commitments of Bayesian cognitive models
Abstract Mathematical developments in probabilistic inference have led to optimism over the prospects for Bayesian models of cognition. Our target article calls for better differentiation of theseExpand
Bayesian just-so stories in psychology and neuroscience.
TLDR
It is argued that many of the important constraints in Bayesian theories in psychology and neuroscience come from biological, evolutionary, and processing considerations that have no adaptive relevance to the problem per se. Expand
Moving beyond qualitative evaluations of Bayesian models of cognition
TLDR
A cross-validation analysis shows that the Bayesian memory model with inferred subjective priors predicts withheld data better than a Bayesian model where the priors are based on environmental statistics. Expand
Using alien coins to test whether simple inference is Bayesian.
TLDR
The current results highlight the importance of close quantitative comparisons between Bayesian inference and human data at the individual-subject level when evaluating models of cognition. Expand
Is that what Bayesians believe? reply to Griffiths, Chater, Norris, and Pouget (2012).
TLDR
It is argued that many Bayesian researchers often appear to be make claims regarding optimality, and often appears to be making claims regarding how the mind computes at algorithmic and implementational levels of descriptions. Expand
Are humans good intuitive statisticians after all? Rethinking some conclusions from the literature on judgment under uncertainty
Abstract Professional probabilists have long argued over what probability means, with, for example, Bayesians arguing that probabilities refer to subjective degrees of confidence and frequentistsExpand
Conditionalization and observation
I take bayesianism to be the doctrine which maintains that (i) a set of reasonable beliefs can be represented by a probability function defined over sentences or propositions, and that (ii)Expand
Bayesian data analysis.
  • J. Kruschke
  • Computer Science, Medicine
  • Wiley interdisciplinary reviews. Cognitive science
  • 2010
TLDR
A fatal flaw of NHST is reviewed and some benefits of Bayesian data analysis are introduced and illustrative examples of multiple comparisons in Bayesian analysis of variance and Bayesian approaches to statistical power are presented. Expand
Three case studies in the Bayesian analysis of cognitive models
TLDR
This article presents Bayesian analyses of three influential psychological models: multidimensional scaling models of stimulus representation, the generalized context model of category learning, and a signal detection theory model of decision making. Expand
...
1
2
3
4
5
...