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The MIT AgeLab n-back: a multi-modal android application implementation

Published: 17 September 2014 Publication History

Abstract

This paper briefly describes the background of the MIT AgeLab implementation of a delayed digit recall or n-back task, and the capabilities of an android application developed to implement a multi-modal version. The MIT AgeLab n-back task is a well-established methodology for inducing graded levels of cognitive workload. It has been adopted for broad use as a multi-modal surrogate demand and calibration task, and recently introduced as a driver and pedestrian distraction education tool.

References

[1]
Conti, A.S., Dlugosch, C., Schwarz, F., and Bengler, K. (2013). Driving and speaking: revelations by the headmounted detection response task. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 362--368.
[2]
Dlugosch, C., Conti, A.S., and Bengler, K. (2013). Driver distraction through conversation measured with pupillometry. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 198--204.
[3]
Donmez, B., Reimer, B., Mehler, B., Lavallière, M., and Coughlin, J.F. (2011). A pilot investigation of the impact of cognitive demand on turn signal use during lane changes in actual highway conditions across multiple age groups. Proc. 55th Annual Meeting of the Human Factors and Ergonomics Society.
[4]
Engström, J., Larsson, P., and Larsson, C. (2013). Comparison of static and driving simulator venues for the tactile detection response task. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 369--375.
[5]
Hajek, W., Gaponova, I., Fleischer, K.H., and Krems, J. (2013). Workload-adaptive cruise control--A new generation of advanced driver assistance systems. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 20, 108--120.
[6]
Harbluk, J.L., Burns, P.C., Hernandez, S., Tam, J., and Glazduri, V. (2013). Detection response tasks: Using remote, headmounted and Tactile signals to assess cognitive demand while driving. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 78--84.
[7]
Medeiros-Ward, N., Cooper, J.M., and Strayer, D.L. (2014). Hierarchical control and driving. Journal of Experimental Psychology: General, Vol. 143, 3, 953--958.
[8]
Mehler, B. and Reimer, B. (2013). An initial assessment of the significance of task pacing on self-report and physiological measures of workload while driving. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 170--176.
[9]
Mehler, B., Reimer, B., and Dusek, J. 2011. MIT AgeLab delayed digit recall task (n-back), MIT AgeLab White Paper Number 2011-3B. Massachusetts Institute of Technology, Cambridge, MA
[10]
Mehler, B., Reimer, B., and Coughlin, J.F. (2012). Sensitivity of physiological measures for detecting systematic variations in cognitive demand from a working memory task: an on-road study across three age groups. Human Factors, Vol. 54, 3, 396--412.
[11]
Mehler, B., Reimer, B., Coughlin, J.F., and Dusek, J.A. (2009). The impact of incremental increases in cognitive workload on physiological arousal and performance in young adult drivers. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2138, 6--12.
[12]
Mehler, B., Reimer, B., Dobres, J., McAnulty, H., Mehler, A., Munger, D., and Coughlin, J.F. (2014). Further Evaluation of the Effects of a Production Level "Voice-Command" Interface on Driver Behavior: Replication and a Consideration of the Significance of Training Method (Technical Report 2014-2). MIT AgeLab, Cambridge, MA
[13]
Ranney, T.A., Baldwin, G.H.S., Parmer, E., Domeyer, J., Martin, J., and Mazzae, E.N. 2011. Developing a Test to Measure Distraction Potential of In-Vehicle Information System Tasks in Production Vehicles (Report No. DOT HS 811 463). U.S. Department of Transportation National Highway Traffic Safety Administration (NHTSA), Washington, DC
[14]
Reimer, B. (2009). Cognitive task complexity and the impact on drivers' visual tunneling. Transportation Research Record: Journal of the Transportation Research Board, Vol. 2138, 13--19.
[15]
Reimer, B. and Mehler, B. (2011). The impact of cognitive workload on physiological arousal in young adult drivers: a field study and simulation validation. Ergonomics, Vol. 54, 10, 932--942.
[16]
Reimer, B., Mehler, B., Wang, Y., and Coughlin, J.F. (2012). A field study on the impact of variations in short-term memory demands on drivers' visual attention and driving performance across three age groups. Human Factors, Vol. 54, 3, 454--468.
[17]
Reimer, B., Mehler, B., Dobres, J., and Coughlin, J.F. 2013. The Effects of a Production Level "Voice-Command" Interface on Driver Behavior: Reported Workload, Physiology, Visual Attention, and Driving Performance (Technical Report 2013-17a). MIT AgeLab, Cambridge, MA
[18]
Reimer, B., Mehler, B., Coughlin, J.F., Godfrey, K.M., and Tan, C. (2009). An on-road assessment of the impact of cognitive workload on physiological arousal in young adult drivers. Proc. First International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI 2009), 115--118.
[19]
Reimer, B., Donmez, B., Lavallière, M., Mehler, B., Coughlin, J.F., and Teasdale, N. (2013). Impact of age and cognitive demand on lane choice and changing under actual highway conditions. Accident Analysis & Prevention, Vol. 52, 125--132.
[20]
Reimer, B., Mehler, B., McAnulty, H., Munger, D., Mehler, A., Perez, E.A.G., Manhardt, T., and Coughlin, J.F. (2013). A preliminary assessment of perceived and objectively scaled workload of a voice-based driver interface. Proc. Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 537--543.
[21]
Ross, V., Jongen, E.M., Wang, W., Brijs, T., Brijs, K., Ruiter, R.A., and Wets, G. (2014). Investigating the influence of working memory capacity when driving behavior is combined with cognitive load: An LCT study of young novice drivers. Accident Analysis & Prevention, Vol. 62, 377--387.
[22]
Solovey, E.T., Zec, M., Garcia Perez, E.A., reimer, B., and Mehler, B. (2014). Classifying driver workload using physiological and driving performance data: two field studies. Proc. ACM CHI Conference on Human Factors in Computing Systems.
[23]
Son, J., Lee, Y., and Kim, M.H. (2011). Impact of traffic environment and cognitive workload on older drivers' behavior in simulated driving. International Journal of Precision Engineering and Manufacturing, Vol. 12, 1, 135--141.
[24]
Son, J., Reimer, B., Mehler, B., Pohlmeyer, A., Godfrey, K., Orszulak, J., Long, J., Kim, M., Lee, Y., and Coughlin, J. (2010). Age and cross-cultural comparison of drivers' cognitive workload and performance in simulated urban driving. International Journal of Automotive Technology, Vol. 11, 4, 533--539.
[25]
Tan, Z.C., Reimer, B., Mehler, B., and Coughlin, J.F. (2011). Detection of Elevated States of Cognitive Demand in Drivers in a Naturalistic Driving Environment. Proc. 2nd Annual International Conference on Advanced Topics in Artificial Intelligence (ATAI 2011), 68--73.
[26]
Vilimek, R., Schäfer, J., and Keinath, A., Effects of task and presentation modality in detection response tasks, in Engineering Psychology and Cognitive Ergonomics. Understanding Human Cognitio, D. Harris, Editor. Springer Berlin, Heidelberg, pp.
[27]
Wang, Y., Reimer, B., Dobres, J., and Mehler, B. (2014). The Sensitivity of Different Methodologies for Characterizing Drivers' Gaze Concentration under Increased Cognitive Demand. Transportation Research Part F: Traffic Psychology and Behaviour, Vol.
[28]
Wang, Y., Reimer, B., Mehler, B., Zhang, J., Mehler, A., and Coughlin, J.F. (2010). The Impact of Repeated Cognitive Tasks on Driving Performance and Visual Attention. Proc. Proceedings of the 3rd International Conference on Applied Human Factors and Ergonomics.
[29]
Yang, Y., Reimer, B., Mehler, B., and Dobres, J. (2013). A field study assessing driving performance, visual attention, heart rate and subjective ratings in response to two types of cogntive workload. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, 397--403.
[30]
Young, R.A., Hsieh, L., and Seaman, S. (2013). The Tactile Detection Response Task: preliminary validation for measuring the attentional effects of cognitive load. Proc. 7th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 71--77.
[31]
Zeitlin, L.R. (1993). Subsidiary task measures of driver mental workload: A long-term field study. Transportation Research Record, Vol. 1403, 23--27.

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      cover image ACM Other conferences
      AutomotiveUI '14: Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
      September 2014
      271 pages
      ISBN:9781450307253
      DOI:10.1145/2667239
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 17 September 2014

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      Author Tags

      1. Cognitive workload
      2. calibration task
      3. driver distraction
      4. driving
      5. mixed mode
      6. surrogate task

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      Overall Acceptance Rate 248 of 566 submissions, 44%

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      • (2024)Effect of Expressway Exit Deceleration Markings on Distracted Drivers in ChinaHeliyon10.1016/j.heliyon.2024.e35291(e35291)Online publication date: Aug-2024
      • (2023)Young Novice Drivers’ Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control BehaviorSensors10.3390/s2303134523:3(1345)Online publication date: 25-Jan-2023
      • (2022)Looking out or Looking Away?—Exploring the Impact of Driving With a Passenger on Young Drivers’ Eye Glance BehaviorHuman Factors: The Journal of the Human Factors and Ergonomics Society10.1177/0018720822108120965:7(1306-1322)Online publication date: 23-Apr-2022
      • (2022)Predicting Secondary Task Performance: A Directly Actionable Metric for Cognitive Overload DetectionIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2021.311416214:4(1474-1485)Online publication date: Dec-2022
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      • (2019)Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasksHuman Factors and Ergonomics in Manufacturing & Service Industries10.1002/hfm.2079829:6(437-446)Online publication date: 14-Aug-2019

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