What tasks does Bayesian decision-making model poorly? Bayesianism has been a relatively successful paradigm for modeling decision-making. However, not every psychologist is a bayesian, and there are tasks such as the Tversky &amp; Shafir (1992) experiments that are hard to reconcile with the bayesian approach. 

Is there a list (or suvery-paper) of decision-making experiments that are hard to account for with the probabilistic approach? What are these experiments? What are some of their common features?



References and related questions


Shafir, E., &amp; Tversky, A. (1992). Thinking through uncertainty: Nonconsequential reasoning and choice. Cognitive Psychology, 24, 449-474. PDF
Tversky, A., &amp; Shafir, E. (1992). The disjunction effect in choice under uncertainty. Psychological Science, 3, 305-309.
What are some of the drawbacks to probabilistic models of cognition?
Introductory resources on bayesian modeling for cognitive sciences
How can the success of Bayesian models be reconciled with demonstrations of heuristic and biased reasoning?
There is currently a lot of debate surrounding what questions Bayesian modeling is appropriate for answering within cognitive science, as well what makes a "poor model."  Unfortunately these become extremely thorny issues very quickly, partly because what is called "bayesian modeling" actually refers to a rather heterogeneous set of approaches and philosophies, and partly because different models serve very different purposes, making the definition of a "poor model" very difficult to agree on.

Nonetheless, I can strongly recommend two papers that deal with these issues in depth.  The first is Jones &amp; Love's 2011 Behavioral &amp; Brain Sciences paper "Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition."  Fortunately, BBS articles include open peer commentary - so you can see the reaction of the larger field to their claims.  

The second paper is Bowers &amp; Davis's 2012 Psychonomic Bulletin &amp; Review article titled "Bayesian just-so stories in psychology and neuroscience." These papers both make very credible arguments that Bayesian models are actually "poor models" even when they would claim to be good; both papers highlight a number of phenomena that are difficult to fit with Bayesian models without resorting to the same kinds of parameter twiddling that Bayesians tend to criticize other modelers for. 

Finally, there is a paper that just came out today describing the failure of a Bayesian model to capture some phenomena in the domain of causal reasoning.  This has been traditionally understood as a relative strong suit of Bayesian modeling, making the findings of this paper somewhat striking.  I have not read it, but it's in press by Bes, Sloman, Lucas &amp; Raufaste at Cognitive Science, titled "Non-Bayesian Inference: Causal Structure Trumps Correlation."

References


Bowers, J.S. &amp; Davis, C.J. (2012). Bayesian just-so stories in psychology and neuroscience.. Psychological bulletin, 138, 389.PDF
Jones, M. &amp; Love, B.C. (2011). Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34, 169-188. PDF
Bes, B., Sloman, S., Lucas, C.G. &amp; Raufaste, E. (2012). Non-Bayesian inference: Causal structure trumps correlation. Cognitive Science. PDF