research-article

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface

Authors:

Zhidong LiAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 21, Issue 6

Article No.: 33, Pages 1 - 23

https://doi.org/10.1145/2687924

Published: 14 January 2015 Publication History

Abstract

This article presents a framework of adaptive, measurable decision making for Multiple Attribute Decision Making (MADM) by varying decision factors in their types, numbers, and values. Under this framework, decision making is measured using physiological sensors such as Galvanic Skin Response (GSR) and eye-tracking while users are subjected to varying decision quality and difficulty levels. Following this quantifiable decision making, users are allowed to refine several decision factors in order to make decisions of high quality and with low difficulty levels. A case study of driving route selection is used to set up an experiment to test our hypotheses. In this study, GSR features exhibit the best performance in indexing decision quality. These results can be used to guide the design of intelligent user interfaces for decision-related applications in HCI that can adapt to user behavior and decision-making performance.

References

[1]

M. A. Abdel-Aty, K. M. Vaughn, R. Kitamura, P. P. Jovanis, and F. L. Mannering. 1994. Models of commuters’ information use and route choice: Initial results based on Southern California Commuter Route Choice Survey. Transportation Research Record 1453, 46--55.

[2]

M. Acevedo and J. I. Krueger. 2004. Two egocentric sources of the decision to vote: The voter's illusion and the belief in personal relevance. Political Psychology 25, 1, 115--134.

[3]

F. S. Azar. 2000. Multiattribute Decision-Making: Use of Three Scoring Methods to Compare the Performance of Imaging Techniques for Breast Cancer Detection. University of Pennsylvania.

[4]

H. Azizi and S. F. Ajirlu. 2010. Measurement of overall performances of decision-making units using ideal and anti-ideal decision-making units. Computers and Industrial Engineering 59, 3, 411--418.

Digital Library

[5]

A. Bechara, A. R. Damasio, H. Damasio, and S. W. Anderson. 1994. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition 50, 7--15.

[6]

A. Bechara, H. Damasio, A. R. Damasio, and G. P. Lee. 1999. Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. Journal of Neuroscience 19, 5473--5481.

[7]

J. R. Bettman, E. J. Johnson, and J. W. Payne. 1990. A componential analysis of cognitive effort in choice. Organizational Behavior and Human Decision Processes 45, 1, 111--139.

[8]

M. M. Botvinick and Z. B. Rosen. 2009. Anticipation of cognitive demand during decision-making. Psychological Research 73, 6, 835--842.

[9]

W. Boucsein. 2012. Electrodermal Activity, (2nd ed.). Springer.

[10]

O. Bousquet. 2005. Machine Learning Thoughts—Decision Making. Retrieved September 20, 2013 from http://ml.typepad.com/machine_learning_thoughts/2005/06/decisionmaking.html.

[11]

W. Bruine de Bruin, A. M. Parker, and B. Fischhoff. 2007. Individual differences in adult decision-making competence. Journal of Personality and Social Psychology 92, 5, 938--956.

[12]

S. Chen, J. Epps, N. Ruiz, and F. Chen. 2011. Eye activity as a measure of human mental effort in HCI. In Proceedings of the 16th International Conference on Intelligent User Interfaces (IUI’11). ACM, New York, NY, 315--318.

Digital Library

[13]

M. E. Dawson, A. M. Schell, and C. G. Courtney. 2011. The skin conductance response, anticipation, and decision-making. Journal of Neuroscience, Psychology, and Economics 4, 2, 111--116.

[14]

Z. Duric et al. 2002. Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction. Proceedings of the IEEE 90, 7, 1272--1289.

[15]

S. Fiedler and A. Glockner. 2012. The dynamics of decision making in risky choice: An eye-tracking analysis. Frontiers in Psychology 3.

[16]

B. Figner and R. O. Murphy. 2011. Using skin conductance in judgment and decision making research. In A Handbook of Process Tracing Methods for Decision Research. M. Schulte-Mecklenbeck, A. Kuehberger, and R. Ranyard (eds.). Psychology Press, New York, 163--184.

[17]

A. M. Franco-Watkins and J. G. Johnson. 2011. Applying the decision moving window to risky choice: Comparison of eye-tracking and mouse-tracing methods. Judgment and Decision Making 6, 8, 740--749.

[18]

L. A. Gutnik, A. F. Hakimzada, N. A. Yoskowitz, and V. L. Patel. 2006. The role of emotion in decision-making: A cognitive neuroeconomic approach towards understanding sexual risk behavior. Journal of Biomedical Informatics 39, 6, 720--736.

Digital Library

[19]

J. Healey and R. Picard. 2000. SmartCar: Detecting driver stress. In Proceedings of the 15th International Conference on Pattern Recognition 2000. 218--221.

Digital Library

[20]

E. A. Helgee. 2010. Improving Drug Discovery Decision Making Using Machine Learning and Graph Theory in QSAR Modeling. PhD thesis. University of Gothenburg, Gothenburg, Sweden.

[21]

A. Holzinger. 2013. Human-computer interaction and knowledge discovery (HCI-KDD): What is the benefit of bringing those two fields to work together&quest; In A. Cuzzocrea, C. Kittl, D. E. Simos, E. Weippl, and L. Xu (eds.). Availability, Reliability, and Security in Information Systems and HCI. Lecture Notes in Computer Science. Springer, Berlin, 319--328.

[22]

C.-L. Hwang and K. Yoon. 1981. Multiple Attribute Decision Making: Methods and Applications: A State-of-the-Art Survey. Springer-Verlag.

[23]

E. Á. Juliusson, N. Karlsson, and T. Gärling. 2005. Weighing the past and the future in decision making. European Journal of Cognitive Psychology 17, 4, 561--575.

[24]

A. Kelemen, Y. Liang, and S. Franklin. 2002. A comparative study of different machine learning approaches for decision making. In Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 84--141.

[25]

M. Kendall. 1962. Rank Correction Methods (3rd ed). Hafner, New York.

[26]

S.-H. Kim, Z. Dong, H. Xian, B. Upatising, and J. S. Yi. 2012. Does an eye tracker tell the truth about visualizations&quest;: Findings while investigating visualizations for decision making. IEEE Transactions on Visualization and Computer Graphics 18, 12, 2421--2430.

Digital Library

[27]

J. H. Knorring. 2003. Basic Human Decision Making: An Analysis of Route Choice Decisions by Long-Haul Truckers. Bachelor thesis. Princeton University, Princeton, NJ.

[28]

V. Köbberling. 2006. Strength of preference and cardinal utility. Economic Theory 27, 2, 375--391.

[29]

S. Krishnamurthy, G. Thamilarasu, and C. Bauckhage. 2009. MALADY: A machine learning-based autonomous decision-making system for sensor networks. In Proceedings of International Conference on Computational Science and Engineering 2009. 93--100.

Digital Library

[30]

S. Lertprapai. 2013. Review: Multiple criteria decision making method with applications. International Mathematical Forum 8, 7, 347--355.

[31]

L. Luo and R. Taib. 2013. Assessing recovery from cognitive load through pen input. In CHI’13 Extended Abstracts on Human Factors in Computing Systems. 1353--1358.

Digital Library

[32]

K. R. MacCrimon. 1968. Decision Making among Multiple Attribute Alternatives: A Survey and Consolidated Approach. RAND Memorandum. The Rand Corporation, Santa Monica, CA.

[33]

J. M. McWilliams, C. C. Afendulis, T. G. McGuire, and B. E. Landon. 2011. Complex Medicare advantage choices may overwhelm seniors--especially those with impaired decision making. Health Affairs (Project Hope) 30, 9, 1786--1794.

[34]

J. V. Neumann. 1944. Theory of Games and Economic Behavior. Princeton University Press.

[35]

N. Nourbakhsh, Y. Wang, and F. Chen. 2013. GSR and blink features for cognitive load classification. In Proceedings of INTERACT 2013.

[36]

A. V. Oppenheim and R. W. Schafer. 2010. Discrete-Time Signal Processing. Pearson, Upper Saddle River, NJ.

Digital Library

[37]

L. K. Payne. 2008. Skin Conductance Response as a Marker of Intuitive Decision Making in Nursing. ProQuest.

[38]

K. Preuschoff, B. M.'t Hart, and W. Einhäuser. 2011. Pupil dilation signals surprise: Evidence for noradrenaline's role in decision making. Frontiers in Neuroscience 5, 115.

[39]

P. K. Ray and S. Sahu. 1990. Productivity measurement through multi-criteria decision making. Engineering Costs and Production Economics 20, 2, 151--163.

[40]

V. S. Rotenberg and A. B. Vedenyapin. 1985. GSR as reflection of decision-making under conditions of delay. The Pavlovian Journal of Biological Science: Official Journal of the Pavlovian 20, 1, 11--14.

[41]

M. N. Rothbard. 1977. Toward a Reconstruction of Utility and Welfare Economics. Center for Libertarian Studies.

[42]

K. J. Rothman. 2010. Curbing type I and type II errors. European Journal of Epidemiology, 25(4), 223--224.

[43]

S. Sethi-Iyengar, G. Huberman, and W. Jiang. 2004. How much choice is too much&quest; Contributions to 401(k) retirement plans. In Pension Design and Structure: New Lessons from Behavioral Finance. Oxford University Press, 83--95.

[44]

Y. B. Shin, S. Lee, S. G. Chun, and D. Chung. 2013. A critical review of popular multi-criteria decision making methodologies. Issues in Information Systems 14, 1, 358--365.

[45]

P. J. Smith, N. D. Geddes, and R. Beatty. 2009. Human-centered design of decision-support systems. In A. Sears and J. A. Jacko (eds). Human-Computer Interaction: Design Issues, Solutions, and Applications. CRC Press.

[46]

K. E. Stanovich and R. F. West. 2008. On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology 94, 4, 672--695.

[47]

C. Stickel, M. Ebner, S. Steinbach-Nordmann, G. Searle, and A. Holzinger. 2009. Emotion detection: Application of the valence arousal space for rapid biological usability testing to enhance universal access. In C. Stephanidis (ed). Universal Access in Human-Computer Interaction. Addressing Diversity. Lecture Notes in Computer Science. Springer, Berlin, 615--624.

Digital Library

[48]

P. K. Vemulapalli, V. Monga, and S. N. Brennan. 2013. Robust extrema features for time-series data analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 6, 1464--1479.

Digital Library

[49]

K. Veropoulos. 2001. Machine Learning Approaches to Medical Decision Making. PhD thesis. University of Bristol, Bristol, UK.

[50]

W. Wang, Z. Li, Y. Wang, and F. Chen. 2013. Indexing cognitive workload based on pupillary response under luminance and emotional changes. In Proceedings of the 2013 International Conference on Intelligent User Interfaces (IUI’13). ACM, New York, NY, 247--256.

Digital Library

[51]

XEROX. 2013. Xerox Researchers Hit The Streets to Help Unclog Highways, Reduce Pollution and Find You a Parking Space. Retrieved September 20, 2013 from http://news.xerox.com/news/Xerox-researchers-create-new-transportation-applications.

[52]

H. Xu. 2009. Robust Decision Making and Its Applications in Machine Learning. PhD thesis. McGill University, Montréal, Canada.

Digital Library

[53]

J. Xu et al. 2011. Pupillary response-based cognitive workload index under luminance and emotional changes. In CHI’11 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY, 1627--1632.

Digital Library

[54]

J.-B. Yang and P. Sen. 1994. A general multi-level evaluation process for hybrid MADM with uncertainty. IEEE Transactions on Systems, Man and Cybernetics 24, 10, 1458--1473.

[55]

J.-B. Yang and D.-L. Xu. 2013. Evidential reasoning rule for evidence combination. Artificial Intelligence 205, 1--29.

Digital Library

[56]

Q. Zhou and Z. Huang. 2012. A decision-making method using knowledge-based machine learning. In Proceedings of International Conference on Computer Science and Electronics Engineering 2012. 616--620.

Digital Library

Cited By

Carrera-Rivera ALarrinaga FLasa GMartinez-Arellano GUnamuno G(2024)AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service SystemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09414-0Online publication date: 12-Aug-2024
https://doi.org/10.1007/s11257-024-09414-0
Stangl FRiedl R(2024)Neurophysiological Measurements in the Research Field of Interruption Science: Insights into Applied Methods for Different Interruption Types Based on an Umbrella ReviewInformation Systems and Neuroscience10.1007/978-3-031-58396-4_11(123-152)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-58396-4_11
Sobota BKorečko ŠMattová MSteingartner WSzabό CStromp G(2023)User Interfaces in Virtual Reality Environments2023 21st International Conference on Emerging eLearning Technologies and Applications (ICETA)10.1109/ICETA61311.2023.10343832(469-473)Online publication date: 26-Oct-2023
https://doi.org/10.1109/ICETA61311.2023.10343832
Show More Cited By

Index Terms

Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interface
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

Recommendations

The design of AHPEC in web-based decision support system for making decision
AIC'10/BEBI'10: Proceedings of the 10th WSEAS international conference on applied informatics and communications, and 3rd WSEAS international conference on Biomedical electronics and biomedical informatics

This paper presents the architecture of the WDSS using AHPEC for making decision. The WDSS-AHPEC involves client, user interface, server, web server, database, and DSS model. The DSS model in this study is using AHPEC method. In applying AHPEC, it ...
Experience-Based Decision Making: A Satisficing Decision Tree Approach

This paper introduces a framework of experienced-based decision making as an extension of case-based decision making, a recently proposed alternative to expected utility theory. In experienced-based decision making, an agent faced with a new decision ...
Collaborative Decision Making

<P>A synthesis of the work of three noted authors provides a framework for collaborative decisions built on the foundation of decision analysis. A Nobel Prize winner provides a psychological foundation for the framework, an authority on harnessing the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 21, Issue 6

Special Issue on Physiological Computing for Human-Computer Interaction

January 2015

144 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/2722827

Editor:
Shumin Zhai
Google

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 January 2015

Accepted: 01 September 2014

Revised: 01 September 2014

Received: 01 December 2013

Published in TOCHI Volume 21, Issue 6

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Asian Office of Aerospace Research & Development (AOARD)

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

71
Total Citations
View Citations
1,012
Total Downloads

Downloads (Last 12 months)61
Downloads (Last 6 weeks)7

Reflects downloads up to 28 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Carrera-Rivera ALarrinaga FLasa GMartinez-Arellano GUnamuno G(2024)AdaptUI: A Framework for the development of Adaptive User Interfaces in Smart Product-Service SystemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09414-0Online publication date: 12-Aug-2024
https://doi.org/10.1007/s11257-024-09414-0
Stangl FRiedl R(2024)Neurophysiological Measurements in the Research Field of Interruption Science: Insights into Applied Methods for Different Interruption Types Based on an Umbrella ReviewInformation Systems and Neuroscience10.1007/978-3-031-58396-4_11(123-152)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-58396-4_11
Sobota BKorečko ŠMattová MSteingartner WSzabό CStromp G(2023)User Interfaces in Virtual Reality Environments2023 21st International Conference on Emerging eLearning Technologies and Applications (ICETA)10.1109/ICETA61311.2023.10343832(469-473)Online publication date: 26-Oct-2023
https://doi.org/10.1109/ICETA61311.2023.10343832
Jalil N(2022)Introduction to Intelligent User Interfaces (IUIs)Software Usability10.5772/intechopen.97789Online publication date: 2-Feb-2022
https://doi.org/10.5772/intechopen.97789
Angerschmid AZhou JTheuermann KChen FHolzinger A(2022)Fairness and Explanation in AI-Informed Decision MakingMachine Learning and Knowledge Extraction10.3390/make40200264:2(556-579)Online publication date: 16-Jun-2022
https://doi.org/10.3390/make4020026
Krausman ANeubauer CForster DLakhmani SBaker AFitzhugh SGremillion GWright JMetcalfe JSchaefer K(2022)Trust Measurement in Human-Autonomy Teams: Development of a Conceptual ToolkitACM Transactions on Human-Robot Interaction10.1145/353087411:3(1-58)Online publication date: 2-Sep-2022
https://dl.acm.org/doi/10.1145/3530874
Gao HHasenbein LBozkir EGöllner RKasneci E(2022)Exploring Gender Differences in Computational Thinking Learning in a VR Classroom: Developing Machine Learning Models Using Eye-Tracking Data and Explaining the ModelsInternational Journal of Artificial Intelligence in Education10.1007/s40593-022-00316-z33:4(929-954)Online publication date: 4-Nov-2022
https://doi.org/10.1007/s40593-022-00316-z
Li YYou FJi MZhang RYou X(2022)Effect of gestures and smartphone sizes on user experience of text input methodsUniversal Access in the Information Society10.1007/s10209-021-00863-722:2(537-554)Online publication date: 24-Jan-2022
https://dl.acm.org/doi/10.1007/s10209-021-00863-7
Zhou JLi ZXiao CChen F(2022)Does a Compromise on Fairness Exist in Using AI Models?AI 2022: Advances in Artificial Intelligence10.1007/978-3-031-22695-3_14(191-204)Online publication date: 3-Dec-2022
https://doi.org/10.1007/978-3-031-22695-3_14
Angerschmid ATheuermann KHolzinger AChen FZhou J(2022)Effects of Fairness and Explanation on Trust in Ethical AIMachine Learning and Knowledge Extraction10.1007/978-3-031-14463-9_4(51-67)Online publication date: 23-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-14463-9_4
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents