A Comparison of Different Interaction Modes for Measuring Simple and Peripheral Reaction Times

Authors

  • René Yahir Rodríguez Robledo Universidad Autónoma de Baja California
  • Ernesto Miguel Vera Uribe Universidad Autónoma de Baja California
  • Marcela D. Rodríguez Universidad Autónoma de Baja California
  • Carlos Aguilar-Avelar Universidad Autónoma de Baja California

DOI:

https://doi.org/10.47756/aihc.y9i1.177

Keywords:

Interaction modes, Reaction Time Test, User Involvement, Driving Behavior, Driving simulator

Abstract

The objective of this work is to assess whether the voice interaction mode can replace the button in simple reaction time (SRT) and peripheral reaction time (PRT) tests. To achieve the above, we have carried out a first study with 26 young subjects to whom these tests were applied with both modes of interaction, that is, they used the four conditions: SRT-button, SRT-voice, PRT-button, and SRT-voice. We found that reaction time means are greater than the button conditions. We conclude that modifying the tests (SRT or PRT) to include voice as a reaction mode resulted in a more cognitively demanding reaction test. This work contributes to Human-Computer Interaction because it demonstrates the feasibility of using an interaction mode different from that typically used in reaction time tests, which would allow studies to be carried out to measure reaction time in other contexts, such as people with disabilities, or mobility issues.

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References

Fatality Facts 2021 Older people, https://www.iihs.org/topics/fatality-statistics/detail/older-people Acedido 24 Julio 2024.

Ledger, S., Bennett, J.M., Chekaluk, E., Batchelor, J.: Cognitive function and driving: Important for young and old alike. Transp. Res. Part F Traffic Psychol. Behav. 60, 262–273 (2019). https://doi.org/10.1016/J.TRF.2018.10.024. DOI: https://doi.org/10.1016/j.trf.2018.10.024

Guo, F., Klauer, S.G., Fang, Y., Hankey, J.M., Antin, J.F., Perez, M.A., Lee, S.E., Dingus, T.A.: The effects of age on crash risk associated with driver distraction. Int. J. Epidemiol. 46, 258–265 (2017). https://doi.org/10.1093/ije/dyw234. DOI: https://doi.org/10.1093/ije/dyw234

Asimakopulos, J., Boychuck, Z., Sondergaard, D., Poulin, V., Ménard, I., Korner-Bitensky, N. Assessing executive function in relation to fitness to drive: A review of tools and their ability to predict safe driving, (2012). Australian Occupational Therapy J. https://doi.org/10.1111/j.1440-1630.2011.00963.x DOI: https://doi.org/10.1111/j.1440-1630.2011.00963.x

Rashid, R., Standen, P., Carpenter, H., & Radford, K. Systematic review and meta-analysis of association between cognitive tests and on-road driving ability in people with dementia. Neuropsychol. Rehabil. 30, 1720–1761 (2020). https://doi.org/10.1080/09602011.2019.1603112. DOI: https://doi.org/10.1080/09602011.2019.1603112

Volantes y pedales Logitech G920 y G29 Driving Force, https://www.logitechg.com/es-mx/products/driving/driving-force-racing-wheel.html Accedido 05 Diciembre 2022

CARLA Simulator, https://carla.org/, last accessed 2022/07/31.

Armenta, J.S., Andrade, A.G., Rodriguez, M.D. An Intelligent Multi-Sourced Sensing System to Study Driver’s Visual Behaviors. IEEE Sens. J. 21 (2021), 12295–12305. https://doi.org/10.1109/JSEN.2021.3064080. DOI: https://doi.org/10.1109/JSEN.2021.3064080

Vera-Uribe, E.M., Armenta, J.S., Rodríguez, M.D. Understanding the Association of Driving Safety and Visual Behaviors Collected Through Smart Sensing Technology. In: Bravo, J., Urzáiz, G. (eds) Proceedings of the 15th International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2023), Lecture Notes in Networks and Systems, vol 842. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-48642-5_20 DOI: https://doi.org/10.1007/978-3-031-48642-5_20

Vera-Uribe, E.M., Rodríguez, M.D., Armenta, J.S., López-Nava, I.H. Validity of Using a Driving Game Simulator to Study the Visual Attention Differences in Young and Older Adults. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). Lecture Notes in Networks and Systems, vol 594. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-21333-5_3 DOI: https://doi.org/10.1007/978-3-031-21333-5_3

Stinchcombe, A., Gagnon, S., Zhang, J., Montembeault, P., Bedard, M.: Fluctuating Attentional Demand in a Simulated Driving Assessment: The Roles of Age and Driving

Java SE 19 Archive Downloads https://www.oracle.com/java/technologies/javase/jdk19-archive-downloads.html. Accedido 25 Nov. 2024

JSR-000113 Java Speech API 2.0.6 Final Release https://download.oracle.com/otndocs/jcp/speech-2.0.6-fr-eval-oth-JSpec/ Accedido 25 Nov. 2024

Complexity. Traffic Inj. Prev. 12, 576–587 (2011). https://doi.org/10.1080/15389588.2011.607479. DOI: https://doi.org/10.1080/15389588.2011.607479

Social Science Statistics https://www.socscistatistics.com/

Normality Calculaltor https://www.gigacalculator.com/.

Published

2024-11-30

How to Cite

[1]
Rodríguez Robledo, R.Y. et al. 2024. A Comparison of Different Interaction Modes for Measuring Simple and Peripheral Reaction Times. Avances en Interacción Humano-Computadora. 9, 1 (Nov. 2024), 245–248. DOI:https://doi.org/10.47756/aihc.y9i1.177.

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Section

Work in Progress

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