Show Reference: "Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition"

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. The Behavioral and brain sciences, Vol. 34, No. 4. (August 2011), pp. 169-188, doi:10.1017/s0140525x10003134 by Matt Jones, Bradley C. Love
@article{jones-and-love-2011,
    abstract = {
                The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.
            },
    author = {Jones, Matt and Love, Bradley C.},
    doi = {10.1017/s0140525x10003134},
    issn = {1469-1825},
    journal = {The Behavioral and brain sciences},
    keywords = {bayes, philosophy, research},
    month = aug,
    number = {4},
    pages = {169--188},
    pmid = {21864419},
    posted-at = {2013-08-30 09:12:47},
    priority = {2},
    title = {Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.},
    url = {http://dx.doi.org/10.1017/s0140525x10003134},
    volume = {34},
    year = {2011}
}

See the CiteULike entry for more info, PDF links, BibTex etc.

Bayesian models have been used to model natural cognition.

Bayesian models cannot explain why natural cognition is not always optimal or predict bahavior in cases when it is not.

Computational models cannot predict non-functional effects, like response timing.

A particular deviation from optimality in natural cognition is pathological cognition.

Purely computational, Bayesian accounts of cognition are underconstrained.

Evolutionary psychology assumes that evolution has lead to ecologically optimal behavior and behavior can therefore predicted and understood by considering optimal behavior within an environment.

Without constrains from ecological and biological (mechanistic) knowledge, computational and evolutionary accounts of natural cognition run the risk of finding optimality wherever they look, as there will always be some combination of model and assumptions to match the data.

Bounded rationality, the idea that an organism may be as rational as possible given its limitations, can be useful, but it is prone to producing tautologies: Any organism is as rational as it can be given its limitations if those limitations are taken to be everything that limits its rationality.

Jones and Love propose three ways of `Bayesian Enlightenment'.

Bayesian theory can be used to describe hypotheses and prior beliefs. These two can then be tested against actual behavior.

In contrast with `Bayesian Fundamentalism', this approach views prior and hypotheses as the scientific theory to be tested as opposed to the only (if handcrafted) way to describe the situation, which is used to see whether once again optimality can be demonstrated.

The components of `Bayesian Fundamentalist's' psychological models critically are not assumed to correspond to anything in the subject's mind.

Cognitive science must not only provide predictive generative models predicting natural cognitive behavior within a normative framework, but also tie in these models with theories on how the necessary computations are realised.

Love and Jones accuse `Bayesian Fundamentalism' of focussing too much on the computational theory and neglecting more biologically constrained levels of understanding cognition.

There often are multiple computational theories of a given problem, differing in assumptions on hardware and problem.

Computational theories of cognition alone are underconstrained.

`Bayesian Fundamentalism', like Behaviorism and evolutionary psychology, explain behavior purely from the point of view of the environment---they completely ignore the inner workings of the organism.

Connectionism used to use telegraph networks as its founding metaphor. Information processing units and physical neurons came later.

Connectionism has suffered theoretically unfounded euphorias twice.

Connectionism has been criticised for

  • being too opaque,
  • lacking compositionality,
  • lacking productivity,
  • for using biologically implausible learning rules,
  • being mostly generalized regression.

In many Bayesian models, the prior and hypothesis space are solely chosen for the convenience of the modeler, not for their plausibility.

`Fundamentalist Bayesians' posit that they can predict behavior purely on the basis of optimality.

Jones and Love talk about Bayesian theory as psychological theories---not so much as neuroscientific theories... I guess?