CoChat
Sign Up
Cited by 1

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Revealing User Confidence in Machine Learning-Based Decision Making

Zhou, Jianlong, Yu, Kun, Chen, Fang·2018·Human–Computer Interaction Series
DOI 10.1007/978-3-319-90403-0_11
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1007/978-3-319-90403-0_11

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 50

  1. Aimone, J.A., Houser, D., Weber, B.: Neural signatures of betrayal aversion: an fMRI study of trust. Proc. Biol. Sci. / Royal Soc. 281(1782), 20132127 (2014) · DOI
  2. Arshad, S., Wang, Y., Chen, F.: Interactive mouse stream as real-time indicator of user’s cognitive load. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1025–1030 (2015) · DOI
  3. Azizi, H., Ajirlu, S.F.: Measurement of overall performances of decision-making units using ideal and anti-ideal decision-making units. Comput. Ind. Eng. 59(3), 411–418 (2010) · DOI
  4. Beller, J., Heesen, M., Vollrath, M.: Improving the drivercautomation interaction an approach using automation uncertainty. Hum. Factors J. Human Factors Ergon. Soc. 55(6), 1130–1141 (2013) · DOI
  5. Camerer, C., Weber, M.: Recent developments in modeling preferences: uncertainty and ambiguity. J. Risk Uncertain. 5(4), 325–370 (1992) · DOI · Paper page
  6. Chen, F., Zhou, J., Wang, Y., Yu, K., Arshad, S.Z., Khawaji, A., Conway, D.: Robust Multimodal Cognitive Load Measurement, 1st edn. Springer, Berlin (2016) · DOI
  7. Clark, H.H., Brennan, S.E.: Grounding in communication. In: Perspectives on Socially Shared Cognition, pp. 127–149. American Psychological Association (1991) · DOI · Paper page
  8. Edwards, J.A., Snyder, F.J., Allen, P.M., Makinson, K.A., Hamby, D.M.: Decision making for risk management: a comparison of graphical methods for presenting quantitative uncertainty. Risk Anal. Off. Publ. Soc. Risk Anal. 32(12), 2055–2070 (2012) · DOI
  9. Eyster, E., Weizsacker, G.: Correlation Neglect in Financial Decision-Making. DIW Berlin Discussion Paper No. 1104 (2010). https://doi.org/10.2139/ssrn.1735339 · DOI
  10. Fiedler, S., Glockner, A.: The dynamics of decision making in risky choice: an eye-tracking analysis. Front. Psychol. 3 (2012) · DOI
  11. Figner, B., Murphy, R.O.: Using skin conductance in judgment and decision making research. In: A Handbook of Process Tracing Methods for Decision Research: A critical Review and User’s Guide, pp. 163–184 (2010)
  12. Franco-Watkins, A.M., Johnson, J.G.: Applying the decision moving window to risky choice: Comparison of eye-tracking and mouse-tracing methods. Judgm. Decis. Mak. 6(8), 740–749 (2011) · DOI
  13. Gog, T.V., Paas, F., Marcus, N., Ayres, P., Sweller, J.: The mirror neuron system and observational learning: implications for the effectiveness of dynamic visualizations. Educ. Psychol. Rev. 21(1), 21–30 (2008)
  14. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
  15. Hahn, T., Notebaert, K., Anderl, C., Teckentrup, V., Kaßecker, A., Windmann, S.: How to trust a perfect stranger: predicting initial trust behavior from resting-state brain-electrical connectivity. Soc. Cogn. Affect. Neurosci. 10(6), 809–813 (2015) · DOI
  16. Heekeren, H.R., Marrett, S., Ungerleider, L.G.: The neural systems that mediate human perceptual decision making. Nature Rev. Neurosci. 9(6), 467–479 (2008) · DOI
  17. Hill, B.: Confidence and decision. Games Econ. Behav. 82(C), 675–692 (2013) · DOI
  18. Hsu, M.: Neural systems responding to degrees of uncertainty in human decision-making. Science 310(5754), 1680–1683 (2005) · DOI · Paper page
  19. Huettel, S.A., Stowe, C.J., Gordon, E.M., Warner, B.T., Platt, M.L.: Neural signatures of economic preferences for risk and ambiguity. Neuron 49(5), 765–775 (2006) · DOI
  20. Ibrekk, H., Morgan, M.G.: Graphical communication of uncertain quantities to nontechnical people. Risk Anal. 7(4), 519–529 (1987) · DOI
  21. Jenkins, Q., Jiang, X.: Measuring trust and application of eye tracking in human robotic interaction. In: Proceedings of the 2010 Industrial Engineering Research Conference (2010)
  22. Kelsey, D.: Maxmin expected utility and weight of evidence. Oxf. Econ. Pap. 46(3), 425–444 (1994) · DOI
  23. Khawaji, A., Chen, F., Zhou, J., Marcus, N.: Trust and cognitive load in the text-chat environment: the role of mouse movement. In: Proceedings of the 26th Australian Computer-Human Interaction Conference on Designing Futures: The Future of Design, OzCHI ’14, pp. 324–327 (2014) · DOI · Paper page
  24. Kirschenbaum, S.S., Trafton, J.G., Schunn, C.D., Trickett, S.B.: Visualizing uncertainty: the impact on performance. Hum. Factors 56(3), 509–520 (2014) · DOI
  25. Köbberling, V.: Strength of preference and cardinal utility. Econ. Theory 27(2), 375–391 (2006) · DOI
  26. Lee, M.D., Dry, M.J.: Decision making and confidence given uncertain advice. Cogn. Sci. 30(6), 1081–1095 (2006) · DOI
  27. Liao, H., Xu, Z., Zeng, X.J., Merigó, J.M.: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets. Knowl.-Based Syst. 76, 127–138 (2015) · DOI
  28. Moll, J., De Oliveira-Souza, R., Zahn, R.: The neural basis of moral cognition: sentiments, concepts, and values. Ann. N. Y. Acad. Sci. 1124, 161–180 (2008) · DOI
  29. Moran, R., Teodorescu, A.R., Usher, M.: Post choice information integration as a causal determinant of confidence: novel data and a computational account. Cogn. Psychol. 78, 99–147 (2015) · DOI
  30. Morgado, P., Sousa, N., Cerqueira, J.J.: The impact of stress in decision making in the context of uncertainty. J. Neurosci. Res. 93(6), 839–847 (2015) · DOI
  31. Pallier, G., Wilkinson, R., Danthiir, V., Kleitman, S., Knezevic, G., Stankov, L., Roberts, R.D.: The role of individual differences in the accuracy of confidence judgments. J. Gen. Psychol. 129(3), 257–299 (2002) · DOI
  32. Peper, E., Harvey, R., Lin, I.M., Tylova, H., Moss, D.: Is there more to blood volume pulse than heart rate variability respiratory sinus arrhythmia, and cardiorespiratory synchrony? Biofeedback 35(2), 54–61 (2007)
  33. Preuschoff, K., ’t Hart, B.M., Einhäuser, W.: Pupil dilation signals surprise: evidence for noradrenaline’s role in decision making. Front. Neurosci. 5, 115 (2011) · DOI
  34. Ray, P.K., Sahu, S.: Productivity measurement through multi-criteria decision making. Eng. Costs Prod. Econ. 20(2), 151–163 (1990) · DOI
  35. Scantamburlo, T.: Machine learning in decisional process: a philosophical perspective. ACM SIGCAS Comput. Soc. 45(3), 218–224 (2015) · DOI
  36. Smith, L.J., Anand, P., Benattayallah, A., Hodgson, T.L.: An fMRI investigation of moral cognition in healthcare decision making. J. Neurosci. Psychol. Econ. 8(2), 116–133 (2015) · DOI
  37. Smith, P.J., Geddes, N.D., Beatty, R.: Human-centered design of decision-support systems. In: Sears, A., Jacko, J.A. (eds.) Human-Computer Interaction: Design Issues, Solutions, and Applications. CRC Press (2009) · DOI
  38. Stickel, C., Ebner, M., Steinbach-Nordmann, S., Searle, G., Holzinger, A.: Emotion detection: application of the valence arousal space for rapid biological usability testing to enhance universal access. In: Stephanidis, C. (ed.) Universal Access in Human-Computer Interaction. Addressing Diversity. Lecture Notes in Computer Science, vol. 5614, pp. 615–624. Springer, Berlin (2009) · DOI
  39. Taghavifard, M.T., Damghani, K.K., Moghaddam, R.T.: Decision Making Under Uncertain and Risky Situations. Enterprise Risk Management Symposium Monograph Society of Actuaries, Schaumburg, Illinois, Tech. rep (2009)
  40. Wagstaff, K.: Machine learning that matters. In: Proceedings of ICML2012, pp. 529–536 (2012)
  41. Watanabe, S.: Pattern Recognition: Human and Mechanical. John Wiley & Sons Inc, New York, USA (1985)
  42. White, T.P., Engen, N.H., Sørensen, S., Overgaard, M., Shergill, S.S.: Uncertainty and confidence from the triple-network perspective: voxel-based meta-analyses. Brain Cogn. 85, 191–200 (2014) · DOI
  43. Ye, J.: Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. Int. J. Gen. Syst. 42(4), 386–394 (2013) · DOI
  44. Zhou, J., Arshad, S.Z., Wang, X., Li, Z., Feng, D., Chen, F.: End-user development for interactive data analytics: uncertainty, correlation and user confidence. IEEE Trans. Affect. Comput. (2017) · DOI
  45. Zhou, J., Arshad, S.Z., Yu, K., Chen, F.: Correlation for user confidence in predictive decision making. In: Proceedings of the 28th Australian Conference on Computer-Human Interaction, OzCHI’16, pp. 252–256 (2016) · DOI
  46. Zhou, J., Bridon, C., Chen, F., Khawaji, A., Wang, Y.: Be informed and be involved: effects of uncertainty and correlation on user’s confidence in decision making. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 923–928 (2015) · DOI
  47. Zhou, J., Chen, F.: Making machine learning useable. Int. J. Intell. Syst. Tech. Appl. 14(2), 91 (2015)
  48. Zhou, J., Jung, J.Y., Chen, F.: Dynamic workload adjustments in human-machine systems based on GSR features. In: Human-Computer Interaction – INTERACT 2015. Lecture Notes in Computer Science, pp. 550–558. Springer, Berlin (2015) · DOI
  49. Zhou, J., Khawaja, M.A., Li, Z., Sun, J., Wang, Y., Chen, F.: Making machine learning useable by revealing internal states update & #45; a transparent approach. Int. J. Comput. Sci. Eng. 13(4), 378–389 (2016) · DOI
  50. Zhou, J., Sun, J., Chen, F., Wang, Y., Taib, R., Khawaji, A., Li, Z.: Measurable decision making with GSR and pupillary analysis for intelligent user interface. ACM Trans. Comput. Hum. Interact. 21(6), 33:1–33:23 (2015) · DOI

Cited by

Sorted by citations

Metadata

Authors
Zhou, J., Yu, K., & Chen, F.
Venue
Human–Computer Interaction Series
Publisher
Springer International Publishing
Published
2018
Pages
225-244
ISSN
  • 1571-5035
  • 2524-4477
DOI
10.1007/978-3-319-90403-0_11
Cited by
1
References
3
Licence
www.springer.com/tdm (unspecified)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review