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Time Cognition

The duration of incidents in dynamic human-machine system represent crucial information for system analysis and the regulation of task-performance. With rising task-demands people tend to be less reliable in time-estimations and cause errors in their interaction with a complex system.

A model of time perception was developed in the course of this project, which posits that working memory mechanisms distort time perception under certain circumstances. This approach was integrated into a cognitive architecture. With such an integrated approach of human time perception it is possible to predict temporal human errors in the context of complex dynamical systems. In early phases of system development a system could be evaluated and blueprints could be favoured or sources of error could be detected or prevented.

In order to verify the developed quantitative model, predictions for variations of a counting task were generated. These predictions were compared with date of a series of three experiments. In the first experiment the connection between the numbers of presented lists on time estimations was evaluated. The model as well as the experimental data shows no such connection for prospective time estimation. Operators in process plants often  experience severe changes in task demands and have to repeatedly estimate the duration of a certain processes. These situations are explored in the second experiment. A comparison with the model data reveals that predictions are met for the low to high demand switch. In the other condition some artefacts occurred that have to be explored in more detail. A third experiment was conducted with the simulation of a process plant. The experimental design was slightly modified due to the findings derived in the previous experiment. The experimental data of the third experiment corresponds to the model predictions.

The results show that the size, the spreading and the direction of time estimations are correctly predicted by the model. An additional influencing fact or on time estimation was found that occurs whenever participants experienced unexpected changes. This effect can also be explained by the theoretical account of time estimation but is difficult to model due to the absence of a modelling approach that explains how expectations develop during a task. The TaSTE (Task Sensitive Time Estimation) module is able to simulate most of the examined effects of human time-perception and therefore offers a tool which can be used for the evaluation of temporal aspects in the context of human-machine systems. 

 

Team Time Cognition

Nele Rußwinkel [1]

 

 

Project-related Publications

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Russwinkel, N. (2013). Modellierung von Zeitkognition bei der Fahrzeugführung [4]. In Jürgensohn, T. and Kohlrep, H. (Eds.), Fahrermodellierung in Wissenschaft und Wirtschaft.- 4. Berliner Fachtagung Fahrermodellierung, Berlin, 13./14. Juni 2013 (pp. 80-90), Düsseldorf: VDI Verlag.


Russwinkel, N. and Friedrich, M. (2012). Zeitabhängige Entscheidungsfindung im Fluglotsenkontext in Mensch-Maschine-Systemen (Symposium Mensch-Technik Interaktion) [5]. In 11. Fachtagung der Gesellschaft für Kognitionswissenschaft. Bamberg: Universität Bamberg.


Russwinkel, N., Urbas, L. and Thüring, M. (2011). Predicting temporal errors in complex task environments: A computational and experimental approach [6]. Cognitive Systems Research, 12(3-4), 336-354.


Pape, N. and Thüring, M. (2010). Vorhersage von Performanz und Dauerschätzung in einer Working Memory Updating Aufgabe [7]. In Tagungsband des 47. Kongress der Deutschen Gesellschaft für Psychologie. Lengerich: Pabst Science Publishers.


Russwinkel, N. (2010). Die Modellierung von Working Memory Updating in MMS [8]. In Tagungsband der 10. Fachtagung der Gesellschaft für Kognitionswissenschaft. Potsdam: Universität Potsdam. (Symposium Kognitive Modellierung in Mensch-Maschine-Systemen, Chairs: Nele Rußwinkel, Jeronimo Dzaack)


Pape, N. and Urbas, L. (2009). Testing a quantitative model of time estimation in a load switch scenario [9]. In Howes, A. and Peebles, D. and Cooper, R. (Eds.), 9th International Conference on Cognitive Modeling - ICCM 2009 (paper 214, 6 pages). Manchester, UK.


Pape, N. and Urbas, L. (2009). Validating a model of time perception with variations of a counting task [10]. In Taatgen, N. A. and van Rijn, H. (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.


Pape, N. and Urbas, L. (2009). A computational model of time estimation involving influences of task demands [11]. In Der Mensch im Mittelpunkt technischer Systeme - 8. Berliner Werkstatt Mensch-Maschine-Systeme. Düsseldorf: VDI Verlag.


Pape, N. and Urbas, L. (2008). Quantitatives Modell der Zeitschätzung mit Bezug auf Arbeitsgedächtnis Anforderungen [12]. In Tagungsband der 9. Fachtagung der Gesellschaft für Kognitionswissenschaft. Dresden: Technische Universität Dresden.


Pape, N. and Urbas, L. (2008). The influence of task demands in a model of time estimation [13]. In Proceedings of the Fifteenth Annual ACT-R Workshop. Pittsburgh, PA: Carnegie Mellon University.


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