用认知模型模拟任务沉浸中的唤醒机制，发现过高唤醒阻碍任务切换。
Modeling Task Immersion based on Goal Activation Mechanism
- 基于ACT-R架构，将唤醒设为影响整体激活的系数
- 高低唤醒条件下，模型行为与人类实验一致
- 提示日常中需控制唤醒水平以避免注意力固化
任务沉浸是创造力的前提。然而单一任务中过度唤醒会带来负面影响，如忽视任务外事件。为探究这一负面效应，本研究构建了基于认知架构ACT-R的唤醒动态计算模型，其中唤醒被视作影响整体激活水平的系数。通过设置低唤醒和高唤醒两种模拟条件，分别对应不同的人类实验设定，并采用两组不同的ACT-R参数进行仿真。结果表明，模型在两种条件下表现出的行为与人类实验高度一致，验证了假设的有效性，对日常生活中的唤醒调控具有启示意义。
原文摘要 · Abstract (English)
Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task transition difficult. The model was developed using functions integrated into the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). Under the framework, arousal is treated as a coefficient affecting the overall activation level in the model. In our simulations, we set up two conditions demanding low and high arousal, trying to replicate corresponding human experiments. In each simulation condition, two sets of ACT-R parameters were assumed from the different interpretations of the human experimental settings. The results showed consistency of behavior between humans and models both in the two different simulation settings. This result suggests the validity of our assumptions and has implications of controlling arousal in our daily life.
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