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
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DOI：
10.1017/s0140525x10003134
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发表时间：
2011-08-01
影响因子：
29.3
通讯作者：
Love, Bradley C.
中科院分区：
文献类型：
--
作者：
Jones, Matt;Love, Bradley C.
贝叶斯认知模型的重要性最近有所增加，很大程度上是因为在复杂概率模型中指定和导出预测方面的数学进步。这项研究的大部分目的是证明认知行为可以仅用理性原则来解释，而不需要求助于心理或神经过程和表征。我们注意到这种理性方法与心理学中的其他运动（即行为主义和进化心理学）之间的共性，这些运动搁置机械解释或利用最优性假设。通过这些比较，我们发现了一些限制理性程序对心理学理论的潜在贡献的挑战。具体来说，理性贝叶斯模型明显不受约束，这既是因为它们不了解广泛的过程级数据，也因为它们对环境的假设通常不以经验测量为基础。大多数贝叶斯模型的心理影响也尚不清楚。贝叶斯推理本身在概念上是微不足道的，但强有力的假设通常嵌入在假设集和用于推导模型预测的近似算法中，而在心理承诺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见，因为认识到许多贝叶斯模型概括了现有（机械层面）理论。尽管当前贝叶斯模型具有表达能力，但我们认为它们必须与机械考虑结合起来发展，以提供认知的实质性解释。我们为这种集成提出了几种方法，其中考虑了贝叶斯推理运行的表示以及执行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。
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.