Department of Learning, Data-Analytics and Technology, Section Cognition, Data and Education, Faculty of Behavioral, Management and Social sciences, University of Twente, PO Box 217, 7500 AE, Enschede, the Netherlands.
Psychon Bull Rev. 2024 Jun;31(3):931-978. doi: 10.3758/s13423-023-02377-0. Epub 2023 Oct 17.
An exhaustive review is reported of over 25 years of research with the Discrete Sequence Production (DSP) task as reported in well over 100 articles. In line with the increasing call for theory development, this culminates into proposing the second version of the Cognitive framework of Sequential Motor Behavior (C-SMB 2.0), which brings together known models from cognitive psychology, cognitive neuroscience, and motor learning. This processing framework accounts for the many different behavioral results obtained with the DSP task and unveils important properties of the cognitive system. C-SMB 2.0 assumes that a versatile central processor (CP) develops multimodal, central-symbolic representations of short motor segments by repeatedly storing the elements of these segments in short-term memory (STM). Independently, the repeated processing by modality-specific perceptual and motor processors (PPs and MPs) and by the CP when executing sequences gradually associates successively used representations at each processing level. The high dependency of these representations on active context information allows for the rapid serial activation of the sequence elements as well as for the executive control of tasks as a whole. Speculations are eventually offered as to how the various cognitive processes could plausibly find their neural underpinnings within the intricate networks of the brain.
本文全面回顾了超过 25 年使用离散序列生成(DSP)任务进行的研究,这些研究成果发表在 100 多篇文章中。为了响应越来越多的理论发展需求,本文提出了第二代序列运动行为认知框架(C-SMB 2.0),该框架整合了认知心理学、认知神经科学和运动学习领域的知名模型。该处理框架解释了使用 DSP 任务获得的许多不同行为结果,并揭示了认知系统的重要特性。C-SMB 2.0 假设一个多功能的中央处理器(CP)通过在短期记忆(STM)中反复存储这些片段的元素,为短的运动片段生成多模态的、中央符号表示。独立地,通过模态特定的感知和运动处理器(PP 和 MP)以及 CP 对序列的重复处理,在每个处理级别上逐渐关联连续使用的表示。这些表示高度依赖于活动的上下文信息,允许序列元素的快速连续激活,以及对整个任务的执行控制。最后,本文推测了各种认知过程如何在大脑复杂的网络中找到其神经基础。