ICCM2016

Halbrügge, M. & Russwinkel, N. (2016). The Sum of Two Models: How a Composite Model Explains Unexpected User Behavior in a Dual-Task Scenario. In Proc. ICCM 2016

Abstract: Maintaining cognitive control while pursuing several tasks at the same time is hard, especially when the current problem states of these tasks need to be represented in memory. We are investigating the mutual influence of a self-paced and a reactive task with regard to completion time and error rates. Against initial expectations, the interruptions from the reactive task did not lead to more errors in the self-paced task, but only prolonged the completion time. Our understanding of this result is guided by a combined version of two previously published cognitive models of the individual tasks. The combined model reproduces the empirical findings concerning error rates and task completion times, but not an unexpected change in the error pattern. These results feed back into our theoretical understanding of cognitive control during sequential action.

CogSci2016

Halbruegge, M., Quade, M. & Engelbrecht, K.-P. (2016). Cognitive Strategies in HCI and Their Implications on User Error. In Proc. CogSci 2016

Abstract: Human error while performing well-learned tasks on a computer is an infrequent, but pervasive problem. Such errors are often attributed to memory deficits, such as loss of activation or interference with other tasks (Altmann & Trafton, 2002). We are arguing that this view neglects the role of the environment. As embodied beings, humans make extensive use of external cues during the planning and execution of tasks. In this paper, we study how the visual interaction with a computer interface is linked to user errors. Gaze recordings confirm our hypothesis that the use of the environment increases when memory becomes weak. An existing cognitive model of sequential action and procedural error (Halbrügge, Quade, & Engelbrecht, 2015) is extended to account for the observed gaze behavior.

KogWis2014

Halbruegge, M., and Engelbrecht, K.-P. (2014). An activation-based model of execution delays of specific task steps. Cognitive Processing, 15, pages S107-S110

Abstract: When humans use devices like ticket vending machines, their actions can be categorized into task-oriented (e.g. selecting a ticket) and device-oriented (e.g. removing the bank card after having paid). Device-oriented steps contribute only indirectly to the user's goal; they take longer than their task-oriented counterparts and are more likely to be forgotten. A promising explanation is provided by the activation-based memory for goals model (Altmann and Trafton 2002). The objectives of this paper are, first, to replicate the step prolongation effect of device-orientation in a kitchen assistance context, and secondly, to investigate whether the activation construct can explain this effect using cognitive modeling. Finally, a necessity and sensitivity analysis provides more insights into the relationship between goal activation and device-orientation effects.

ICCM2015

Halbrügge, M., Quade, M. & Engelbrecht, K.-P. (2015). A Predictive Model of Human Error based on User Interface Development Models and a Cognitive Architecture. In Taatgen, N. A., van Vugt, M. K., Borst, J. P. & Mehlhorn, K. (Eds.), Proceedings of the 13th International Conference on Cognitive Modeling (pp. 238-243). Groningen, the Netherlands: University of Groningen.

Abstract: The concept of device- vs. task-orientation allows to identify subtasks that are especially prone to errors. Device-oriented tasks occur whenever a user interface requires additional steps that do not directly contribute to the users' goals. They comprise, but are not limited to, initialization errors and postcompletion errors (e.g., removing a bank card after having received money). The vulnerability of device-oriented tasks is often counteracted by making them obligatory (e.g., by not handing out the money before the bank card has been removed), making it even harder to predict where users will have problems with a given interface without dedicated user tests. In this paper we show how cognitive modeling can be used to predict error rates of device-oriented and task-oriented subtasks with respect to a given application logic. The process is facilitated by exploiting user interface meta information from model-based user interface development.

AGI2015

Halbrügge, M., Quade, M. and Engelbrecht, K.-P. (2015). How can Cognitive Modeling Benefit from Ontologies? Evidence from the HCI Domain. In Bieger, J., Goertzel, B. & Potapov, A. (Eds.) AGI 2015, (pp. 261-271). Berlin: Springer.
DOI: 10.1007/978-3-319-21365-1_27

Abstract: Cognitive modeling as a method has proven successful at reproducing and explaining human intelligent behavior in specific laboratory situations, but still struggles to produce more general intelligent capabilities. A promising strategy to address this weakness is the addition of large semantic resources to cognitive architectures. We are investigating the usefulness of this approach in the context of human behavior during software use. By adding world knowledge from a Wikipedia-based ontology to a model of human sequential behavior, we achieve quantitatively and qualitatively better fits
to human data.The combination of model and ontology yields additional insights that cannot be explained by the model or the ontology alone.