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

  • Leanne HirshfieldEmail author
  • Phil Bobko
  • Alex Barelka
  • Natalie Sommer
  • Senem Velipasalar
Original Paper


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.


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



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.

Compliance with Ethical Standards

Conflict of interest

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


  1. 1.
    Groys B (2012) Under Suspicion: a phenomenology of media. Columbia University Press, New YorkGoogle 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):76–85Google 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 65:699–719Google Scholar
  4. 4.
    Friedman B, Kahn PHJ, Howe C (2000) Trust online. Commun ACM 43(12):34–40Google 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 99:16–33Google Scholar
  6. 6.
    Toma C (2010) Perceptions of trustworthiness online: the role of visual and textual information. In: CSCW 2010, ACM, Savannah, Georgia, USAGoogle Scholar
  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 ReportGoogle Scholar
  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 26:857–869Google Scholar
  9. 9.
    Shneiderman B (2000) Designing trust into online experiences. Commun ACM 43(12):57–59Google Scholar
  10. 10.
    Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157):1124–1131Google Scholar
  11. 11.
    Parasuraman R, Manzey D (2010) Complacency and bias in human use of automation: an attentional integration. Hum Factors 52(3):381–410Google Scholar
  12. 12.
    IBM (2013) The 2013 IBM Cyber Security Intelligence IndexGoogle Scholar
  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):28–44Google Scholar
  14. 14.
    Bean J (2017) The medium is the fake news. Interactions 24(3):24–25Google Scholar
  15. 15.
    Pogue D (2017) The ultimate cure for the fake news epidemic will be more skeptical readers. Scientific AmericanGoogle Scholar
  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):909–927Google 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):489–508Google Scholar
  18. 18.
    Evans A, Revelle W (2008) Survey and behavioral measurements of interpersonal trust. J Res Pers 42:1585–1593Google Scholar
  19. 19.
    Mayer R, Davis J, Schoorman D (1995) An integrative model of organizational trust. Acad Manag Rev 20(3):709–734Google 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):20084–20089Google 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):373–396Google 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 AmericaGoogle Scholar
  23. 23.
    Watabe M, Hiroshi B, Yamamoto H (2011) Judgments about others’ trustworthiness: an fMRI study. Hum Behav Evol Soc Jpn 2(2):28–32Google 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 49:361–372Google Scholar
  25. 25.
    Dimoka A (2012) How to conduct a functional magnetic resonance (fMRI) study in social science research. MIS Q 36(3):811–840Google Scholar
  26. 26.
    Medvedev A (2013) Shedding near-infrared light on brain networks. J Radiol Radiat Ther 1:1002Google 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, RoutledgeGoogle 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 90:2660–2774Google 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):211–231Google 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. ACMGoogle Scholar
  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 PressGoogle Scholar
  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):35Google Scholar
  33. 33.
    Fairclough S (2009) Fundamentals of physiological computing. Interact Comput 21:133–145Google 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):29Google Scholar
  35. 35.
    Tan D, Nijholt A (2010) Brain–computer interfaces: applying our minds to human–computer interaction. Springer, BerlinGoogle 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, CanadaGoogle Scholar
  37. 37.
    Poldrack RA (2006) Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci 10(2):59–63Google Scholar
  38. 38.
    Parasuraman R, Rizzo M (2008) Neuroergonomics: the brain at work. Oxford University Press, OxfordGoogle 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 15:1–5Google 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):921–935Google 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):20084–20089Google 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):395–402Google 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):53–63Google Scholar
  44. 44.
    Mahya C, Mosesa L, Pfeifera J (2014) How and where: theory-of-mind in the brain. Dev Cogn Neurosci 9:68–81Google Scholar
  45. 45.
    Etkin A, Egner T, Kalisch R (2011) Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci 15(2):85–93Google 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:e48107Google 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):241–268Google Scholar
  48. 48.
    Green M, Horan W, Lee J (2015) Social cognition in schizophrenia. Nat Rev Neurosci 16(10):620–631Google 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 7:64Google Scholar
  50. 50.
    Xiang T, Lohrenz T (2013) Computational substrates of norms and their violations during social exchange. J Neurosci 33(3):1099–1108Google 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):477–484Google 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):1835–1842Google 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 OnlineGoogle Scholar
  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 45:3105–3113Google Scholar
  55. 55.
    Brunet E, Sarfati Y (2000) A PET investigation of the attribution of intentions with a nonverbal task. Neuroimage 11:157–166Google 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 sciencesGoogle Scholar
  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 11:23–32Google 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):809–813Google 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 3:16Google 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):e0119129Google 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 11:1666–1676Google 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 1413–1418Google Scholar
  63. 63.
    Hajcak G, McDonald N, Simons R (2004) Error-related psychophysiology and negative affect. Brain Cogn 56(2):189–197Google 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)Google Scholar
  65. 65.
    Lafferty JC, Eady P, Elmers J (1974) The desert survival problemGoogle Scholar
  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 139–183Google 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):49–59Google 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):428–447Google Scholar
  69. 69.
    Burgoon JK, Blair P, Strom R (2008) Cognitive biases and nonverbal cue availability in detecting deception. Hum Commun Res 34(4):572–599Google Scholar
  70. 70.
    Shneiderman B, Plaisant C (2005) Designing the user interface: strategies for effective human-computer interaction, 4th edn. Addison-Wesley, ReadingGoogle 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). ACMGoogle Scholar
  72. 72.
    Chawya N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357zbMATHGoogle 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):185–197Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Syracuse UniversitySyracuseUSA
  2. 2.Virginia TechBlacksburgUSA
  3. 3.Illinois State UniversityNormalUSA

Personalised recommendations