Skip to main content
SpringerLink
Log in

Description of Sequential Risky Decision-Making Choices in Human-Machine Teams Using Eye-Tracking and Decision Tree

  • Conference paper
  • First Online:
Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14028))

Included in the following conference series:

  • 861 Accesses

Abstract

Human-machine collaboration has shown great potential in sequential risky decision-making (SRDM). Human decision-makers made their decisions depending on the condition and their machine teammates. This paper aimed to explore attentional behaviors and decision-making processes under two human-machine collaboration contexts. To this end, 25 participants were asked to complete a modified Balloon Analog Risk Task with a highly accurate machine under different human-machine teams (HMTs). We collected and analyzed task performance, decision-making choices, eye-tracking data and subjective data. We employed the decision tree algorithm to describe decision-making processes and tested the performance through resubstitution validation and train-test validation. We found that both HMTs achieved comparable performance. Participants in the human-dominated team paid more attention to the machine-recommended value while participants in the human-machine joint team paid more attention to the inflation information of the machine. There were significant associations between choice ratios of inflation alternatives and decision choices for most subjects in both HMTs. In the human-machine joint team, we found a correlation between task profits and the fixation count on machine recommended value (r = 0.40, p = 0.05), and a correlation between the number of total explosions and the fixation count on whether the machine recommending to pump or not (r = –0.36, p = 0.07). Decision tree algorithm could cover and describe at least 67% of the decision-making choices and performed differently when subjects took different strategies. Our results revealed that eye-tracking and decision tree can be potential tools to describe and understand human SRDM behaviors.

Supported by the National Natural Science Foundation of China [Grant Nos. 72192824 and 71942005].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Causse, M., Lancelot, F., Maillant, J., Behrend, J., Cousy, M., Schneider, N.: Encoding decisions and expertise in the operator’s eyes: Using eye-tracking as input for system adaptation. Int. J. Hum Comput Stud. 125, 55–65 (2019)

    Article  Google Scholar 

  2. Cavanagh, J.F., Wiecki, T.V., Kochar, A., Frank, M.J.: Eye tracking and pupillometry are indicators of dissociable latent decision processes. J. Exp. Psychol. Gen. 143(4), 1476 (2014)

    Article  Google Scholar 

  3. Chen, J.Y., Lakhmani, S.G., Stowers, K., Selkowitz, A.R., Wright, J.L., Barnes, M.: Situation awareness-based agent transparency and human-autonomy teaming effectiveness. Theor. Issues Ergon. Sci. 19(3), 259–282 (2018)

    Article  Google Scholar 

  4. Dietvorst, B.J., Simmons, J.P., Massey, C.: Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144(1), 114 (2015)

    Article  Google Scholar 

  5. Dietvorst, B.J., Simmons, J.P., Massey, C.: Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them. Manage. Sci. 64(3), 1155–1170 (2018)

    Article  Google Scholar 

  6. Festinger, L.: A theory of social comparison processes. Hum. Relations 7(2), 117–140 (1954)

    Article  Google Scholar 

  7. France, K.R., Shah, R.H., Park, C.W.: The impact of emotional valence and intensity on ad evaluation and memory. ACR North American Advances . Adv. Consum. Res. 21, 583–588 (1994)

    Google Scholar 

  8. Franco-Watkins, A.M., Mattson, R.E., Jackson, M.D.: Now or later? attentional processing and intertemporal choice. J. Behav. Decis. Mak. 29(2–3), 206–217 (2016)

    Article  Google Scholar 

  9. Friedl, M.A., Brodley, C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61(3), 399–409 (1997)

    Article  Google Scholar 

  10. Haesevoets, T., De Cremer, D., Dierckx, K., Van Hiel, A.: Human-machine collaboration in managerial decision making. Comput. Hum. Behav. 119, 106730 (2021)

    Article  Google Scholar 

  11. Hristova, E., Grinberg, M.: Disjunction effect in prisoner’s dilemma: Evidences from an eye-tracking study. In: Proceedings of the 30th Annual conference of the cognitive science society. pp. 1225–1230. Cognitive Science Society Austin, TX, USA (2008)

    Google Scholar 

  12. Jian, J.Y., Bisantz, A.M., Drury, C.G.: Foundations for an empirically determined scale of trust in automated systems. Int. J. Cogn. Ergon. 4(1), 53–71 (2000)

    Article  Google Scholar 

  13. Kim, B.E., Seligman, D., Kable, J.W.: Preference reversals in decision making under risk are accompanied by changes in attention to different attributes. Front. Neurosci. 6, 109 (2012)

    Article  Google Scholar 

  14. Kotsiantis, S.B.: Decision trees: a recent overview. Artif. Intell. Rev. 39, 261–283 (2013)

    Article  Google Scholar 

  15. Kuo, F.Y., Hsu, C.W., Day, R.F.: An exploratory study of cognitive effort involved in decision under framing-an application of the eye-tracking technology. Decis. Support Syst. 48(1), 81–91 (2009)

    Article  Google Scholar 

  16. Lejuez, C.W., et al.: Evaluation of a behavioral measure of risk taking: the balloon analogue risk task (BART). J. Exp. Psychol. Appl. 8(2), 75 (2002)

    Article  Google Scholar 

  17. Lipton, Z.C.: The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue 16(3), 31–57 (2018)

    Article  Google Scholar 

  18. Liu, B.: In AI we trust? effects of agency locus and transparency on uncertainty reduction in human-Ai interaction. J. Comput.-Mediat. Commun. 26(6), 384–402 (2021)

    Article  Google Scholar 

  19. Madsen, M., Gregor, S.: Measuring human-computer trust. Citeseer

    Google Scholar 

  20. Maner, J.K., Gailliot, M.T., Butz, D.A., Peruche, B.M.: Power, risk, and the status quo: Does power promote riskier or more conservative decision making? Pers. Soc. Psychol. Bull. 33(4), 451–462 (2007)

    Article  Google Scholar 

  21. Patel, B., et al.: Human-machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit. Med. 2(1), 111 (2019)

    Article  Google Scholar 

  22. Schmitz, F., Manske, K., Preckel, F., Wilhelm, O.: The multiple faces of risk-taking. Eur. J. Psychol. Assess. 32(1), 17–38 (2016)

    Google Scholar 

  23. Wallsten, T.S., Pleskac, T.J., Lejuez, C.W.: Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychol. Rev. 112(4), 862 (2005)

    Article  Google Scholar 

  24. Xiong, W., Wang, C., Ma, L.: Partner or subordinate? sequential risky decision-making behaviors under human-machine collaboration contexts. Comput. Hum. Behav. 139, 107556 (2023)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lab of Enhanced Human-Machine Collaborative Decision-Making, Department of Industrial Engineering, Tsinghua University, Beijing, 100084, China

    Wei Xiong, Chen Wang & Liang Ma

Authors
  1. Wei Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Liang Ma .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xiong, W., Wang, C., Ma, L. (2023). Description of Sequential Risky Decision-Making Choices in Human-Machine Teams Using Eye-Tracking and Decision Tree. In: Duffy, V.G. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. HCII 2023. Lecture Notes in Computer Science, vol 14028. Springer, Cham. https://doi.org/10.1007/978-3-031-35741-1_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-35741-1_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35740-4

  • Online ISBN: 978-3-031-35741-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation