Department of Psychology and Zlotowski Center for Neuroscience, Ben-Gurion University, Beersheba84105,
Department of Psychology, New York University, New York,
Behav Brain Sci. 2019 Jul 18;43:e121. doi: 10.1017/S0140525X19002000.
In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the "here-and-now." Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans' predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.
近年来，科学家越来越倾向于研究认知的预测性质。我们认为，预测依赖于抽象，因此预测认知理论需要明确的抽象表示理论。我们提出了这样一种抽象表示能力的理论，这种能力使人类能够超越“此时此地”。与预测认知文献一致，我们认为，心灵的表象基质是作为一个层次结构构建的，从具体到抽象；然而，我们认为，沿着这个层次结构存在着质的区别，产生了有意义的、往往未被承认的多样性。呼应哲学观点，我们认为，表象层次结构可以被解析为：特定于模态的表示，实例化为知觉相似性；多模态表示，主要实例化为时空连续性的发现；以及类别表示，主要实例化为社会互动。这些元素是认知心理学中讨论的复杂结构的构建块（例如，情节、脚本），也是像功能一样表现的心理表象的输入，通常在语言学中讨论（即，谓词）。我们通过解释我们本体论中的元素如何都需要解释人类的预测认知（例如，在支持基于逻辑的预测中；在准确和详细预测之间的权衡中进行优化），并通过检查神经科学证据如何与我们的解释一致，来支持我们对表示多样性的论点。这样，我们提供了一个可测试的概念认知神经基础模型，并强调了自我投射、强化学习和精神病理学预测处理模型研究的几个重要意义。
