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Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

机译：贝叶斯原教旨主义还是启蒙运动？贝叶斯认知模型的解释地位和理论贡献

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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.
机译：贝叶斯认知模型的重要性最近得到了很大程度的提高，这是因为在从复杂的概率模型中指定和推导预测方面的数学进展。这项研究的大部分旨在证明，认知行为可以仅凭理性原理来解释，而不必求助于心理或神经过程和表征。我们注意到这种理性的方法与心理学的其他运动（即行为主义和进化心理学）之间的共同点，这些共同点搁置了机械学的解释或利用了最优性假设。通过这些比较，我们发现了一些挑战，这些挑战限制了理性程序对心理学理论的潜在贡献。具体而言，有理贝叶斯模型没有受到很大的限制，这既是因为它们不受大量过程级数据的影响，又是因为它们对环境的假设通常不基于经验度量。大多数贝叶斯模型的心理含义也不清楚。贝叶斯推理本身在概念上是微不足道的，但通常在假设集和用于得出模型预测的近似算法中嵌入了强大的假设，而在心理投入和实现细节之间没有明确的界限。比较同一任务的多个贝叶斯模型很少，因为许多贝叶斯模型可以概括现有（力学水平）理论。尽管当前的贝叶斯模型具有表达能力，但我们认为必须将其与力学考虑因素结合起来才能对认知进行实质性解释。我们提出了进行这种集成的几种方法，其中考虑了贝叶斯推理所基于的表示形式以及实现它的算法和启发式方法。我们认为这种统一将更好地促进对心理学理论的持久贡献，避免困扰先前理论运动的陷阱。

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