Department of Psychology, Carnegie Mellon University.
Center for the Neural Basis of Cognition, Carnegie Mellon University.
Cogn Sci. 2023 Feb;47(2):e13250. doi: 10.1111/cogs.13250.
Hierarchical cognitive mechanisms underlie sophisticated behaviors, including language, music, mathematics, tool-use, and theory of mind. The origins of hierarchical logical reasoning have long been, and continue to be, an important puzzle for cognitive science. Prior approaches to hierarchical logical reasoning have often failed to distinguish between observable hierarchical behavior and unobservable hierarchical cognitive mechanisms. Furthermore, past research has been largely methodologically restricted to passive recognition tasks as compared to active generation tasks that are stronger tests of hierarchical rules. We argue that it is necessary to implement learning studies in humans, non-human species, and machines that are analyzed with formal models comparing the contribution of different cognitive mechanisms implicated in the generation of hierarchical behavior. These studies are critical to advance theories in the domains of recursion, rule-learning, symbolic reasoning, and the potentially uniquely human cognitive origins of hierarchical logical reasoning.
层级认知机制是复杂行为(包括语言、音乐、数学、工具使用和心理理论)的基础。层级逻辑推理的起源一直是,并且仍然是认知科学的一个重要难题。先前的层级逻辑推理方法往往未能区分可观察的层级行为和不可观察的层级认知机制。此外,过去的研究在很大程度上受到方法学的限制,只能进行被动的识别任务,而无法进行更具挑战性的主动生成任务,后者是对层级规则的更强有力的测试。我们认为,有必要在人类、非人类物种和机器中实施学习研究,并使用形式模型对不同认知机制在生成层级行为中的贡献进行分析。这些研究对于推进递归、规则学习、符号推理以及层级逻辑推理可能具有人类独特认知起源等领域的理论至关重要。