Cognitive Modeling of Task Switching in Discretionary Multitasking Based on the ACT-R Cognitive Architecture

Hyungseok Oh,Rohae Myung,Yongdeok Yun

doi:10.3390/app11093967

Abstract

Discretionary multitasking has emerged as a prevalent and important domain in research on human–computer interaction. Studies on modeling based on cognitive architectures such as ACT-R to gain insight into and predict human behavior in multitasking are critically important. However, studies on ACT-R modeling have mainly focused on concurrent and sequential multitasking, including scheduled task switching. Therefore, in this study, an ACT-R cognitive model of task switching in discretionary multitasking was developed to provide an integrated account of when and how humans decide on switching tasks. Our model contains a symbolic structure and subsymbolic equations that represent the cognitive process of task switching as self-interruption by the imposed demands and a decision to switch. To validate our model, it was applied to an illustrative dual task, including a memory game and a subitizing task, and the results were compared with human data. The results demonstrate that our model can provide a relatively accurate representation, in terms of task-switching percent just after the subtask, the number of task-switching during the subtask, and performance time depending on the task difficulty level; it exhibits enhanced performance in predicting human behavior in multitasking and demonstrates how ACT-R facilitates accounts of voluntary task switching.

Highlights

Many observational studies have demonstrated that multitasking is prevalent and important in everyday life, and especially in modern office environments where workers are frequently are immersed in information overloaded environments [1,2,3]
Considerable efforts have been devoted toward accounting for the cognitive process of task switching in discretionary multitasking
The proposed model contributes toward such efforts using an adaptive control of thought-rational (ACT-R)-based approach for integrating different accounts of task switching

Summary

Introduction

Many observational studies have demonstrated that multitasking is prevalent and important in everyday life, and especially in modern office environments where workers are frequently are immersed in information overloaded environments [1,2,3]. In the field of human–computer interaction, studies on human performance in multitasking environments to gain insight into and predict cognition and human behavior in multitasking environments are critically important. In this way, over the past several decades, considerable progress has been achieved in the cognitive modeling of human behavior in diverse multitasking environments through various cognitive architectures—for example, executive-process interactive control (EPIC) [5], queuing network-model human processor (QN-MHP) [6], and adaptive control of thought-rational (ACT-R) [7,8]. ACT-R has been used in several studies on human performance modeling in multitasking environments (e.g., [13,14,15])

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Conclusion