Modeling Task Immersion based on Goal Activation Mechanism
This work addresses the negative aspects of task immersion for cognitive modeling and daily life applications, but it is incremental as it builds on existing ACT-R frameworks.
The study tackled the problem of excessive task immersion hindering task transitions by constructing a computational arousal dynamics model within the ACT-R cognitive architecture. The results showed consistency between human and model behaviors in simulations, validating the model's assumptions.
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.