Modeling Interruption and Resumption in a Smartphone Task: An ACT-R Approach

  • 1 DFG Research Training Group “CrossWorlds”, TU Chemnitz, Chemnitz, Germany.
  • 2 Cognitive Modeling in dynamic Human-Machine Systems, TU Berlin, Berlin, Germany
Maria Wirzberger
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  • Maria Wirzberger, M. Sc. works as research assistant within the interdisciplinary DFG Research Training Group “CrossWorlds” at the TU Chemnitz. In 2012, she received a B. Sc. in Psychology from the University of Hagen, and completed the master’s program Human Factors at the TU Berlin in 2014. Her current PhD project focusses on connecting cognitive modeling and instructional design by exploring the construct of cognitive load with the cognitive architecture ACT-R.
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and Prof. Dr.-Ing. Nele Russwinkel
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  • Nele Russwinkel holds a junior professorship for Cognitive Modeling in dynamic Human-Machine Systems at the TU Berlin. Based on a B. Sc. and M. Sc. in Cognitive Science from the University of Osnabrueck and some experience in industry, she held a scholarship within the DFG Research Training Group “ProMeTeI” at the TU Berlin, where she obtained her PhD in 2009. Besides others, her research is concerned with cognitive modeling of human-computer interaction on purposes of usability, embodied spatial cognition and time estimation.
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Abstract

This research aims to inspect human cognition when being interrupted while performing a smartphone task with varying levels of mental demand. Due to its benefits especially in the early stages of interface development, a cognitive modeling approach is used. It applies the cognitive architecture ACT-R to shed light on task-related cognitive processing. The inspected task setting involves a shopping scenario, manipulating interruption via product advertisements and mental demands by the respective number of people shopping is done for. Model predictions are validated through a corresponding experimental setting with 62 human participants. Comparing model and human data in a defined set of performance-related parameters displays mixed results that indicate an acceptable fit – at least in some cases. Potential explanations for the observed differences are discussed at the end.

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