Department of Psychology, University of Utah, 380 S. 1530 E. Rm. 502, Salt Lake City, UT, 84112, USA.
Neurosci Biobehav Rev. 2018 Nov;94:93-112. doi: 10.1016/j.neubiorev.2018.08.013. Epub 2018 Aug 25.
Hierarchical predictive processing (PP) has recently emerged as a candidate theoretical paradigm for neurobehavioral research. To date, PP has found support through its success in offering compelling explanations for a number of perceptual, cognitive, and psychiatric phenomena, as well as from accumulating neurophysiological evidence. However, its implications for understanding intelligence and its neural basis have received relatively little attention. The present review outlines the key tenets and evidence for PP, and assesses its implications for intelligence research. It is argued that PP suggests indeterminacy as a unifying principle from which to investigate the cognitive hierarchy and brain-ability correlations. The resulting framework not only accommodates prominent psychometric models of intelligence, but also incorporates key findings from neuroanatomical and functional activation research, and motivates new predictions via the mechanisms of prediction-error minimization. Because PP also suggests unique neural signatures of experience-dependent activity, it may also help clarify environmental contributions to intellectual development. It is concluded that PP represents a plausible, integrative framework that could enhance progress in the neuroscience of intelligence.
分层预测加工（PP）最近已成为神经行为研究的候选理论范例。迄今为止，PP 通过成功地为许多感知、认知和精神现象提供了引人注目的解释，以及积累的神经生理学证据，得到了支持。然而，它对理解智力及其神经基础的影响却相对较少受到关注。本综述概述了 PP 的主要原则和证据，并评估了其对智力研究的意义。有人认为，PP 提出了不确定性作为一个统一的原则，从这个原则可以研究认知层次结构和大脑能力相关性。由此产生的框架不仅可以容纳智力的主要心理计量模型，还可以纳入神经解剖学和功能激活研究的关键发现，并通过预测误差最小化的机制来激发新的预测。由于 PP 还暗示了经验依赖性活动的独特神经特征，它也可能有助于澄清环境对智力发展的贡献。综上所述，PP 代表了一个合理的、综合的框架，可以促进智力神经科学的进展。
