Tenison Caitlin, Fincham Jon M, Anderson John R
Department of Psychology, Carnegie Mellon University, United States.
Department of Psychology, Carnegie Mellon University, United States.
Cogn Psychol. 2016 Jun;87:1-28. doi: 10.1016/j.cogpsych.2016.03.001. Epub 2016 Mar 25.
This fMRI study examines the changes in participants' information processing as they repeatedly solve the same mathematical problem. We show that the majority of practice-related speedup is produced by discrete changes in cognitive processing. Because the points at which these changes take place vary from problem to problem, and the underlying information processing steps vary in duration, the existence of such discrete changes can be hard to detect. Using two converging approaches, we establish the existence of three learning phases. When solving a problem in one of these learning phases, participants can go through three cognitive stages: Encoding, Solving, and Responding. Each cognitive stage is associated with a unique brain signature. Using a bottom-up approach combining multi-voxel pattern analysis and hidden semi-Markov modeling, we identify the duration of that stage on any particular trial from participants brain activation patterns. For our top-down approach we developed an ACT-R model of these cognitive stages and simulated how they change over the course of learning. The Solving stage of the first learning phase is long and involves a sequence of arithmetic computations. Participants transition to the second learning phase when they can retrieve the answer, thereby drastically reducing the duration of the Solving stage. With continued practice, participants then transition to the third learning phase when they recognize the problem as a single unit and produce the answer as an automatic response. The duration of this third learning phase is dominated by the Responding stage.
这项功能磁共振成像（fMRI）研究考察了参与者在反复解决同一数学问题时信息处理过程的变化。我们发现，与练习相关的速度提升主要是由认知处理的离散变化产生的。由于这些变化发生的点因问题而异，并且潜在的信息处理步骤在持续时间上也有所不同，所以这种离散变化的存在可能很难被检测到。我们使用两种相互印证的方法，确定了三个学习阶段的存在。当在这些学习阶段之一中解决问题时，参与者会经历三个认知阶段：编码、求解和反应。每个认知阶段都与一种独特的大脑特征相关联。通过结合多体素模式分析和隐半马尔可夫模型的自下而上方法，我们从参与者的大脑激活模式中确定任何特定试验中该阶段的持续时间。对于自上而下的方法，我们开发了这些认知阶段的ACT - R模型，并模拟了它们在学习过程中的变化。第一个学习阶段的求解阶段较长，涉及一系列算术运算。当参与者能够检索到答案时，他们就会过渡到第二个学习阶段，从而大幅缩短求解阶段的持续时间。随着持续练习，参与者随后在将问题识别为一个单一单元并作为自动反应给出答案时，会过渡到第三个学习阶段。第三个学习阶段的持续时间主要由反应阶段主导。
