Toward Interfaces that Help Users Identify Misinformation Online: Using fNIRS to Measure Suspicion

Abstract

With terms like ‘fake news’ and ‘cyber attack’ dominating the news, skepticism toward the media and other online individuals has become a major facet of modern life. This paper views the way we process information during HCI through the lens of suspicion, a mentally taxing state that people enter before making a judgment about whether or not to trust information. With the goal of enabling objective, real-time measurements of suspicion during HCI, we describe an experiment where fNIRS was used to identify the neural correlates of suspicion in the brain. We developed a convolutional long short-term memory classifier that predicts suspicion using a leave-one-participant-out cross-validation scheme, with average accuracy greater than 76%. Notably, the brain regions implicated by our results dovetail with prior theoretical definitions of suspicion. We describe implications of this work for HCI, to augment users’ capabilities by enabling them to develop a ‘healthy skepticism’ to parse out truth from fiction online.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. 1.

    This was the estimate included in prepared congressional testimony by Facebook on October 30, 2017.

  2. 2.

    We note concern about the measure of suspicion in this study—which was operationalized as the standard deviation (SD) of buyer price suggestions. That is, two different participants can experience the same SD across trials, yet the means of the suggested values could be lower for one seller (i.e., 1, 2, 3 have the same SD as 11, 12, 13)—and thus, different mean levels can be associated with different levels of suspicion (different levels of feelings of being taken advantage of).

References

  1. 1.

    Groys B (2012) Under Suspicion: a phenomenology of media. Columbia University Press, New York

    Google Scholar 

  2. 2.

    Daniller A, Allen D, Tallevi A, Mutz D (2017) Measuring trust in the press in a changing media environment. Commun Methods Meas 11(1):76–85

    Google Scholar 

  3. 3.

    Weeks B (2015) Emotions, partisanship, and misperceptions: how anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. J Commun 65:699–719

    Google Scholar 

  4. 4.

    Friedman B, Kahn PHJ, Howe C (2000) Trust online. Commun ACM 43(12):34–40

    Google Scholar 

  5. 5.

    McEvily WJ, Wilson JM, Straus SG (2006) All in due time: the development of trust in electronic and face-to-face groups. Organ Behav Hum Decis Process 99:16–33

    Google Scholar 

  6. 6.

    Toma C (2010) Perceptions of trustworthiness online: the role of visual and textual information. In: CSCW 2010, ACM, Savannah, Georgia, USA

  7. 7.

    Mitchell A, Gottfried J, Barthel M, Shearer E (2016) The Modern News Consumer News attitudes and practices in the digital era. Pew Research Center Report

  8. 8.

    Beldad A, de Jong M, Steehouder M (2010) How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Comput Hum Behav 26:857–869

    Google Scholar 

  9. 9.

    Shneiderman B (2000) Designing trust into online experiences. Commun ACM 43(12):57–59

    Google Scholar 

  10. 10.

    Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131

    Google Scholar 

  11. 11.

    Parasuraman R, Manzey D (2010) Complacency and bias in human use of automation: an attentional integration. Hum Factors 52(3):381–410

    Google Scholar 

  12. 12.

    IBM (2013) The 2013 IBM Cyber Security Intelligence Index

  13. 13.

    Hirshfield L, Bobko P, Barelka A, Costa M, Funke G, Mancuso V, Finomore V, Knott B (2015) The role of human operators’ suspicion in the detection of cyber attacks. Int J Cyber Warf Terror 5(3):28–44

    Google Scholar 

  14. 14.

    Bean J (2017) The medium is the fake news. Interactions 24(3):24–25

    Google Scholar 

  15. 15.

    Pogue D (2017) The ultimate cure for the fake news epidemic will be more skeptical readers. Scientific American

  16. 16.

    Colquitt J, Scott B, LePine J (2007) Trust, trustworthiness, and trust propensity: a meta-analytic test of their unique relationship with risk taking and job performance. J Appl Psychol 92(4):909–927

    Google Scholar 

  17. 17.

    Bobko P, Barelka A, Hirshfield LM (2014) The construct of state-level suspicion: a model and research agenda for automated and information technology (IT) contexts. Hum Factors 56(3):489–508

    Google Scholar 

  18. 18.

    Evans A, Revelle W (2008) Survey and behavioral measurements of interpersonal trust. J Res Pers 42:1585–1593

    Google Scholar 

  19. 19.

    Mayer R, Davis J, Schoorman D (1995) An integrative model of organizational trust. Acad Manag Rev 20(3):709–734

    Google Scholar 

  20. 20.

    Krueger F, McCabe K, Moll J, Kriegeskorte N, Zahn R, Strenziok M, Heinecke A, Grafman J (2007) Neural correlates of trust. The National Academy of Sciences of the USA. PNAS 104(50):20084–20089

    Google Scholar 

  21. 21.

    Dimoka A (2010) What does the brain tell us about trust and distrust? Evidence from a functional neuroimaging study. Manag Inf Syst Q 34(2):373–396

    Google Scholar 

  22. 22.

    Bhatt M, Lohrenz T, Camerer C, Montague R (2012) Distinct contributions of the amygdala and parahippocampal gyrus to suspicion in a repeated bargaining game. In: Proceedings of the National Academy of the Sciences of the United States of America

  23. 23.

    Watabe M, Hiroshi B, Yamamoto H (2011) Judgments about others’ trustworthiness: an fMRI study. Hum Behav Evol Soc Jpn 2(2):28–32

    Google Scholar 

  24. 24.

    Craig A, Loureiro Y, Wood S, Vendemia J (2012) Suspicious minds: exploring neural processes during exposure to deceptive advertising. J Mark Res 49:361–372

    Google Scholar 

  25. 25.

    Dimoka A (2012) How to conduct a functional magnetic resonance (fMRI) study in social science research. MIS Q 36(3):811–840

    Google Scholar 

  26. 26.

    Medvedev A (2013) Shedding near-infrared light on brain networks. J Radiol Radiat Ther 1:1002

    Google Scholar 

  27. 27.

    Gratton G, Fabiani F (2009) Fast optical signals: principles, methods, and experimental results. In: Frostig RD (ed) Cover of in vivo optical imaging of brain function in vivo optical imaging of brain function. Taylor and Francis, Routledge

    Google Scholar 

  28. 28.

    Chance B, Zhuang Z, Chu U, Alter C, Lipton L (1993) Cognition activated low frequency modulation of light absorption in human brain. Proc Natl Acad Sci 90:2660–2774

    Google Scholar 

  29. 29.

    Izzetoglu K, Bunce S, Onaral B, Pourrezaei K, Chance B (2004) Functional optical brain imaging using near-infrared during cognitive tasks. Int J Hum-Comput Interact 17(2):211–231

    Google Scholar 

  30. 30.

    Hirshfield L, Gulotta R, Hirshfield S, Hincks S, Russell M, Williams T, Jacob R (2011) This is your brain on interfaces: enhancing usability testing with functional near infrared spectroscopy. In: SIGCHI. ACM

  31. 31.

    Solovey E, Girouard A, Chauncey K, Hirshfield L, Sassaroli A, Zheng F, Fantini S, Jacob R (2009) Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines. In: ACM UIST symposium on user interface software and technology. ACM Press

  32. 32.

    Solovey E, Afergan D, Peck E, Hincks S, Jacob R (2015) Designing implicit interfaces for physiological computing: guidelines and lessons learned using fNIRS. ACM Trans Hum Comput Interact 21(6):35

    Google Scholar 

  33. 33.

    Fairclough S (2009) Fundamentals of physiological computing. Interact Comput 21:133–145

    Google Scholar 

  34. 34.

    Plácido Da Silva H, Fairclough S, Holzinger A, Jacob J, Tan D (2015) Introduction to the special issue on physiological computing for human–computer interaction. ACM Trans Hum Comput Interact 21(6):29

    Google Scholar 

  35. 35.

    Tan D, Nijholt A (2010) Brain–computer interfaces: applying our minds to human–computer interaction. Springer, Berlin

    Google Scholar 

  36. 36.

    Mandryk R, Atkins M, Inkpen K (2006) A continuous and objective evaluation of emotional experience with interactive play environments. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM Press, Montreal, Quebec, Canada

  37. 37.

    Poldrack RA (2006) Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci 10(2):59–63

    Google Scholar 

  38. 38.

    Parasuraman R, Rizzo M (2008) Neuroergonomics: the brain at work. Oxford University Press, Oxford

    Google Scholar 

  39. 39.

    Boas D, Elwell C, Ferrari M, Taga G (2014) Twenty years of functional near-infrared spectroscopy: introduction for the special issue. Neuroimage 15:1–5

    Google Scholar 

  40. 40.

    Ferrari M, Quaresima V (2012) A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage 63(2):921–935

    Google Scholar 

  41. 41.

    Kruger F, Mccabe K, Moll J, Kriegeskorte N, Zahn R, Strenziok M, Heinecke A, Grafman J (2008) The neural correlates of trust. PNAS 104(50):20084–20089

    Google Scholar 

  42. 42.

    Fett A, Gromann P, Giampietro V, Shergill S, Krabbendam L (2014) Default distrust? An fMRI investigation of the neural development of trust and cooperation. Soc Cogn Affect Neurosci 9(4):395–402

    Google Scholar 

  43. 43.

    Sebastian C, Fontaine N, Bird G, Blakemore S, De Brito S, McCrory E, Viding E (2012) Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults. Soc Cogn Affect Neurosci 7(1):53–63

    Google Scholar 

  44. 44.

    Mahya C, Mosesa L, Pfeifera J (2014) How and where: theory-of-mind in the brain. Dev Cogn Neurosci 9:68–81

    Google Scholar 

  45. 45.

    Etkin A, Egner T, Kalisch R (2011) Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci 15(2):85–93

    Google Scholar 

  46. 46.

    Golkar A, Lonsdorf TB, Olsson A, Lindstrom KM, Berrebi J, Fransson P, Schalling M, Ingvar M, Öhman A (2012) Distinct contributions of the dorsolateral prefrontal and orbitofrontal cortex during emotion regulation. PLoS ONE 7:e48107

    Google Scholar 

  47. 47.

    Niendam T, Laird A, Ray K, Dean M, Glahn D, Carter C (2012) Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci 12(2):241–268

    Google Scholar 

  48. 48.

    Green M, Horan W, Lee J (2015) Social cognition in schizophrenia. Nat Rev Neurosci 16(10):620–631

    Google Scholar 

  49. 49.

    Lavin C, Melis C, Mikulan E, Gelormini C, Huepe D, Ibanez A (2013) The anterior cingulate cortex: an integrative hub for human socially-driven interactions. Front Neurosci 7:64

    Google Scholar 

  50. 50.

    Xiang T, Lohrenz T (2013) Computational substrates of norms and their violations during social exchange. J Neurosci 33(3):1099–1108

    Google Scholar 

  51. 51.

    Krain A, Wilson AM, Arbuckle R, Castellanos FX, Milham MP (2006) Distinct neural mechanisms of risk and ambiguity: a meta-analysis of decision-making. Neuroimage 32(1):477–484

    Google Scholar 

  52. 52.

    Saxe R, Kanwisher N (2003) People thinking about thinking people: the role of the temporo-parietal junction in “theory of mind”. NeuroImage 19(4):1835–1842

    Google Scholar 

  53. 53.

    Koster-Hale J, Saxe R (2013) Functional neuroimaging of theory of mind. In: Understanding other minds: perspectives from developmental social neuroscience, Oxford Scholarship Online

  54. 54.

    Ciaramidaro A, Adenzato M, Enrici I, Erk S, Pia L, Bara B, Walte H (2007) The intentional network: How the brain reads varieties of intentions. Neuropsychologia 45:3105–3113

    Google Scholar 

  55. 55.

    Brunet E, Sarfati Y (2000) A PET investigation of the attribution of intentions with a nonverbal task. Neuroimage 11:157–166

    Google Scholar 

  56. 56.

    Aimone J, DHouser D, Weber B (2014) Neural signatures of betrayal aversion: an fMRI study of trust. In: Proceedings of the royal society of biological sciences

  57. 57.

    Tang H, Mai X, Wang S, Zhu C, Krueger F, Liu C (2015) Interpersonal brain synchronization in the right temporo-parietal junction during face-to-face economic exchange. Soc Cogn Affect Neurosci 11:23–32

    Google Scholar 

  58. 58.

    Hahn T, Notebaert K, Anderl C, Teckentrup V, Kaßecker A, Windmann S (2015) How to trust a perfect stranger: predicting initial trust behavior from resting-state brain-electrical connectivity. Soc Cogn Affect Neurosci 10(6):809–813

    Google Scholar 

  59. 59.

    Rudoy J, Paller K (2003) Who can you trust? Behavioral and neural differences between perceptual and memory-based influences. Front Hum Neurosci 3:16

    Google Scholar 

  60. 60.

    Ma Q, Meng L, Qiang S (2015) You have my word: reciprocity expectation modulates feedback-related negativity in the trust game. PLOS One 10(2):e0119129

    Google Scholar 

  61. 61.

    Wang Y, Zhang Z, Jing Y, Valadez E, Simons R (2016) How do we trust strangers? The neural correlates of decision making and outcome evaluation of generalized trust. Soc Cogn Affect Neurosci 11:1666–1676

    Google Scholar 

  62. 62.

    Ferraz P, Millan J (2005) You are wrong!—Automatic detection of interaction errors from brain waves. In: Proceedings of the 19th international joint conference on artificial intelligence, pp 1413–1418

  63. 63.

    Hajcak G, McDonald N, Simons R (2004) Error-related psychophysiology and negative affect. Brain Cogn 56(2):189–197

    Google Scholar 

  64. 64.

    Vi C, Subramanian S (2012) Detecting error-related negativity for interaction design. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI ‘12)

  65. 65.

    Lafferty JC, Eady P, Elmers J (1974) The desert survival problem

  66. 66.

    Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Hancock P, Meshkati N (eds) Human mental workload. North Holland, Amsterdam, pp 139–183

    Google Scholar 

  67. 67.

    Bracley M, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59

    Google Scholar 

  68. 68.

    Ye JC, Tak S, Jang KE, Jung J, Jang J (2009) NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy. Neuroimage 44(2):428–447

    Google Scholar 

  69. 69.

    Burgoon JK, Blair P, Strom R (2008) Cognitive biases and nonverbal cue availability in detecting deception. Hum Commun Res 34(4):572–599

    Google Scholar 

  70. 70.

    Shneiderman B, Plaisant C (2005) Designing the user interface: strategies for effective human-computer interaction, 4th edn. Addison-Wesley, Reading

    Google Scholar 

  71. 71.

    Adamczyk P, Bailey B (2004) If not now, when?: The effects of interruption at different moments within task execution. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI ‘04). ACM

  72. 72.

    Chawya N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    MATH  Google Scholar 

  73. 73.

    Seiffert C, Khoshgoftaar T, Van Hulse J, Napolitano A (2010) RUSBoost: a Hybrid Approach to Alleviating Class Imbalance. IEEE Trans Syst Man Cybern A Syst Hum 40(1):185–197

    Google Scholar 

Download references

Acknowledgements

We would like to thank the Air Force Research Laboratory and the Air Force Office of Sponsored Research (FA9550-15-1-0021) for sponsoring this research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Leanne Hirshfield.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hirshfield, L., Bobko, P., Barelka, A. et al. Toward Interfaces that Help Users Identify Misinformation Online: Using fNIRS to Measure Suspicion. Augment Hum Res 4, 1 (2019). https://doi.org/10.1007/s41133-019-0011-8

Download citation

Keywords

  • Brain–computer interfaces
  • Adaptive interface
  • Suspicion
  • Skepticism
  • Fake news
  • Trust
  • Functional near-infrared spectroscopy
  • Usability testing