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20

Probabilistic approaches of this sort are usually referred to more specifically as the bayesian approach and Chater and Tanenbaum are definitely bayesians (I have not read much by Yuille and can't comment). Bayesianism is more than just increasing in popularity and being encouraged; it is considered one of the big-4 approaches to cognitive-modeling, with the ...


14

When performing certain tasks, people’s inferences approximate Bayesian inference to a remarkable degree. For example, when people receive both haptic and visual information about the size of an object, they combine this information in a manner that very closely resembles Bayesian inference, taking account of the uncertainties associated with the visual and ...


12

Artem gave a very good answer, but I want to add one more weaknesses of probabilistic/Bayesian models: they are not mechanistic. This is related to Artem's point about neural grounding, but is a little different. The issue is that probabilistic models don't really provide insight into the underlying mechanism that produces the observed behavior -- if you ask ...


10

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 ...


8

This should perhaps be a comment, but I don't have the reputation. The other two answers mention that a major drawback to the Bayesian approach is its lack of biological plausibility. However, see for instance: Bayesian inference with probabilistic population codes Ma, W.J. and Beck, J.M. and Latham, P.E. and Pouget, A. Nature Neuroscience, ...


7

@CHCH has provided a good broad overview, but I thought I would also append some specific experiments that are considered to be a weakness of Bayesian models. The whole theme of this answer is an extension of Tversky and Kahneman's program of rationality-violation. All of these experiments can be fitted by some Bayesian-ish just-so model of the sort Bowers ...


5

Your question is predicated on the assumption that Bayesian modeling has been successful in all domains. I think this is a stance that many (except hardened Bayesians) would disagree with. For instance, consider the classic Tversky & Shafir experiments on the violation of the sure thing principle: What are popular rationalist responses to Tversky & ...


5

Quantitative papers There are a number of papers that didn't use a Bayesian approach but provide a relevant basis for developing quantitative Bayesian models: Zickar et al (2004) performed a mixed-model using item response theory to examine different classes of respondents to personality tests. While it doesn't appear to be a Bayesian analysis, it is an ...


5

Reading list As @Jeff has mentioned Tom Griffiths has several useful resources. In particular Tom Griffiths has an extensive reading list that you might find relevant. To quote the summary of the content: This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in ...


5

+1 to Speldosa's suggestion. Griffiths and colleagues have written several primers on the use of Bayesian models in cogsci. Many of them can be found on Griffiths' website under 'Foundations': http://cocosci.berkeley.edu/publications.php?topic=Foundations e.g. Perfors, A., Tenenbaum, J.B., Griffiths, T. L., & Xu, F. (2011). A tutorial ...


5

I think you are doing the computation correctly, but Gigerenzer and Blank did not provide us with the full results of their experiment, preventing us from repeating their computations exactly: The data provided in columns 1 and 2 of the table are only the averages. The data in column 4 (Bayesian) is not a transformation of the average value using some ...


4

To what degree can the brain move resources from the "what" to the "when" to achieve a precise level of timing for conversion between sensory and motor output? I think that you may be making a false assumption here. I don't think that the 'what' and the 'when' are in competition for resources. According to Arnal and Giraud (and many others), having a valid ...


3

In the fairly recent book "The Cambridge Handbook of Computational Psychology", chapter three is devoted to bayesian modeling. It's written by Thomas Griffiths, Charles Kemp, and Joshua Tenenbaum. I haven't read this chapter yet myself but will update this answer when I have.


3

The number of samples that are necessary for a good parameter estimation does indeed depend on the estimation method. I am not aware of a simple rule of thumb to determine an optimal sample size, but there has been a lot of literature on this topic. A paper that might be a good starting point for a literature search is Van Zandt T. (2000) How to fit a ...


3

Disclaimer: I'm not generally doing experiments where reaction time is the primary DV. But I thought I'd look at this issue and explored RTs from a neuroimaging dataset, and I think the findings are relevant to the question. I think without further qualification, this question doesn't have an answer. Here I've plotted the estimation of reaction time/RT over ...


2

It depends what you mean by a biological mechanism. If you mean that there should be a protein cascade that implements normalization, that doesn't seem plausible, in my opinion. Normalization in probabilistic population codes is just one of many computations that can be performed in a neural system. If you're okay with the notion that there's nothing ...


2

In a paper published recently (actually, today) by myself and my 2 advisers, we analyze results of an auditory perceptual discrimination task, and show that the Bayesian model can be used to explain some aspects of behaviour, but not others. We provide a simple heuristic model that accounts for a wider range of phenomena in that task, such as the imperfect ...



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