Influence of Emotional Imagery on Risk Perception and Decision Making in Autism Spectrum Disorder

  • TanuEmail author
  • D. KakkarEmail author

We investigated the effect of emotions evoked while imagination of the risk consequences in certain life situations on the risk perception and subsequent behavioral reactions in autism spectrum disorder (ASD). The participants (20 ASD and 20 typically developing, TD, subjects) were asked to imagine the consequences of a given risky scenario (the consequences could be either negative or positive) and then mark their risk assessment and reactions on a rating scale. During this process, EEG activities were traced by recording from the parietal (P3, P4), occipital (O1, O2), and frontal (F3, F4) lobes. During imagery, EEG spectral power and imagery alpha index (IAI) values were statistically evaluated, while the approximate entropy (ApEn) reflected the presence of emotions, as well as differentiation between imagery and general involvement in the task. The lower IAI and higher theta power values at both positive and negative consequences of the imaged situations reflected the risk-taking attitude of ASD individuals. The insignificant performance difference of both consequences suggests that the decisions are independent of the risk outcomes in ASD subjects relative to TD individuals. Moreover, the lower negative correlation value suggests that risk knowledge is poorly built in ASD persons and thus leads to impulsive risk taking. The higher imagery ApEn values relative to a neutral state in both ASD and TD individuals indicated intense engagement in the imagery rather than general involvement. However, the lower ApEn in ASDs relative to TDs reflected the poor influence of emotions on the risk sense and subsequent reactions of the former individuals. Thus, it can be concluded that the attenuated emotional imagery of the risk consequences is poorly associated with the risk perception and subsequent decisions in ASD subjects.


autism spectrum disorder EEG events complexity emotions imagery risk perception risk taking 


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  1. 1.
    Tanu and D. Kakkar, “Strengthening risk prediction using statistical learning in children with autism spectrum disorder,” Adv. Autism, 4, No. 3, 141–152 (2018).CrossRefGoogle Scholar
  2. 2.
    M. South, M. J. Larson, S. E. White, et al., “Better fear conditioning is associated with reduced symptom severity in autism spectrum disorders,” Autism Res., 4, No. 6, 412–421 (2011).PubMedCrossRefGoogle Scholar
  3. 3.
    A. Banerjee, C. T. Engineer, B. L. Sauls, et al., “Abnormal emotional learning in a rat model of autism exposed to valproic acid in utero,” Front. Behav. Neurosci., 8, 387 (2014).PubMedPubMedCentralGoogle Scholar
  4. 4.
    R. Bernier, G. Dawson, H. Panagiotides, and S. Webb, “Individuals with autism spectrum disorder show normal responses to a fear potential startle paradigm,” J. Autism Dev. Disord., 35, No. 5, 575–583 (2005).PubMedCrossRefGoogle Scholar
  5. 5.
    American Psychiatric Association. DSM-IV-TR: Diagnostic and statistical manual of mental disorders, text revision. Washington, DC, Am. Psychiatr. Assoc., vol. 75 (2000).Google Scholar
  6. 6.
    G. Loewenstein, E. U. Weber, C. K. Hsee, and N. Welch, “Risk as feelings,” Psychol. Bull., 127, No. 2, 267–286 (2001).PubMedCrossRefGoogle Scholar
  7. 7.
    E. A. Holmes and A. Mathews, “Mental imagery and emotion: A special relationship?” Emotion, 5, No. 4, 489 (2005).Google Scholar
  8. 8.
    M. Lauriola and I. P. Levin, “Personality traits and risky decision-making in a controlled experimental task: An exploratory study,” Pers. Indiv. Differ., 31, No. 2, 215–226 (2001).CrossRefGoogle Scholar
  9. 9.
    A. Öhman, and S. Mineka, “Fears, phobias, and preparedness: toward an evolved module of fear and fear learning,” Psychol. Rev., 108, No. 3, 483 (2001).PubMedCrossRefGoogle Scholar
  10. 10.
    P. Van Schaik and P. Kusev, “Human preferences and risky choices,” Front. Psychol., 2, 333 (2011).PubMedPubMedCentralGoogle Scholar
  11. 11.
    R. L. Reniers, L. Murphy, A. Lin, et al., “Risk perception and risk-taking behaviour during adolescence: the influence of personality and gender,” PloS One, 11, No. 4, e0153842 (2016).PubMedPubMedCentralCrossRefGoogle Scholar
  12. 12.
    J. Traczyk, A. Sobkow, and T. Zaleskiewicz, “Affectladen imagery and risk taking: the mediating role of stress and risk perception,” PloS One, 10, No. 3, e0122226 (2015).PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    M. South, J. Dana, S. E. White, and M. J. Crowley, “Failure is not an option: Risk-taking is moderated by anxiety and also by cognitive ability in children and adolescents diagnosed with an autism spectrum disorder,” J. Autism Dev. Disord., 41, No. 1, 55–65 (2011).PubMedCrossRefGoogle Scholar
  14. 14.
    M. South, S. Ozonoff, Y. Suchy, et al., “Intact emotion facilitation for nonsocial stimuli in autism: Is amygdala impairment in autism specific for social information?” J. Int. Neuropsych. Soc., 14, No. 1, 42–54 (2008).CrossRefGoogle Scholar
  15. 15.
    L. Sterling, J. Munson, A. Estes, et al., “Fear-potentiated startle response is unrelated to social or emotional functioning in adolescents with autism spectrum disorders,” Autism Res., 6, No. 5, 320–331 (2013).PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    M. South, P. D. Chamberlain, S. Wigham, et al., “Enhanced decision making and risk avoidance in high-functioning autism spectrum disorder,” Neuropsychology, 28, No. 2, 222–228 (2014).PubMedCrossRefGoogle Scholar
  17. 17.
    B. De Martino, N. A. Harrison, S. Knafo, et al., “Explaining enhanced logical consistency during decision making in autism,” J. Neurosci., 28, No. 42, 10746–10750 (2008).PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    A. Minassian, M. Paulus, A. Lincoln, and W Perry, “Adults with autism show increased sensitivity to outcomes at low error rates during decision-making,” J. Autism Dev. Disord., 37, No. 7, 1279–1288 (2007).PubMedCrossRefGoogle Scholar
  19. 19.
    J. Fujino, S. Tei, R. I. Hashimoto, et al., “Attitudes toward risk and ambiguity in patients with autism spectrum disorder,” Mol. Autism, 8, No. 1, 45 (2017).PubMedPubMedCentralCrossRefGoogle Scholar
  20. 20.
    M. Kunda and A. K. Goel, “Thinking in pictures as a cognitive account of autism,” J. Autism Dev. Disord.,41, No. 9, 1157–1177 (2011).PubMedCrossRefGoogle Scholar
  21. 21.
    R. K. Kana, Y. Liu, D. L. Williams, et al., “The local, global, and neural aspects of visuospatial processing in autism spectrum disorders,” Neuropsychologia, 51, No. 14, 2995–3003 (2013).PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    T. J. Silk, N. Rinehart, J. L. Bradshaw, et al., “Visuospatial processing and the function of prefrontal-parietal networks in autism spectrum disorders: a functional MRI study,” Am. J. Psychiat., 163, No. 8, 1440–1443 (2006).PubMedCrossRefGoogle Scholar
  23. 23.
    I. Soulieres, T. A. Zeffiro, M. L. Girard, and L. Mottron, “Enhanced mental image mapping in autism,” Neuropsychologia, 49, No. 5, 848–857 (2011).PubMedCrossRefGoogle Scholar
  24. 24.
    C. P. Sahyoun, J. W. Belliveau, I. Soulières, et al., “Neuroimaging of the functional and structural networks underlying visuospatial vs. linguistic reasoning in highfunctioning autism,” Neuropsychologia,48, No. 1, 86–95 (2010).PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    K. L. Maras, M. C. Wimmer, E. J. Robinson, and D. M. Bowler, “Mental imagery scanning in autism spectrum disorder,” Res. Autism Spect. Dis., 8, No. 10, 1416–1423 (2014).CrossRefGoogle Scholar
  26. 26.
    G. Esposito, S. Dellantonio, C. Mulatti, and R. Job, “Axiom, anguish, and amazement: how autistic traits modulate emotional mental imagery,” Front. Psychol, 7, 757 (2016).PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    A. Ozsivadjian, M. J. Hollocks, J. Southcott, et al., “Anxious imagery in children with and without autism spectrum disorder: an investigation into occurrence, content, features and implications for therapy,” J. Autism Dev. Disord., 47, No. 12, 3822–3832 (2017).PubMedCrossRefGoogle Scholar
  28. 28.
    X. Cui, C.B. Jeter, D. Yang, et al., “Vividness of mental imagery: individual variability can be measured objectively,” Vision Res., 47, No. 4, 474–478 (2007).PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    J. G. Cremades, “The effects of imagery perspective as a function of skill level on alpha activity,” Int. J. Psychophysiol., 43, No. 3, 261–271 (2002).CrossRefGoogle Scholar
  30. 30.
    R. S. Schaefer, R. J. Vlek, and P. Desain, “Music perception and imagery in EEG: Alpha band effects of task and stimulus,” Int. J. Psychophysiol., 82, No. 3, 254–259 (2011).PubMedCrossRefGoogle Scholar
  31. 31.
    J. Li, Y. Y. Tang, L. Zhou, et al., “EEG dynamics reflects the partial and holistic effects in mental imagery generation,” J. Zhejiang Univ. Sci., B, 11, No. 12, 944–951 (2010).CrossRefGoogle Scholar
  32. 32.
    D. F. Marks and A. R. Isaac, “Topographical distribution of EEG activity accompanying visual and motor imagery in vivid and non-vivid imagers,” Brit. J. Psychol., 86, No. 2, 271–282 (1995).PubMedCrossRefGoogle Scholar
  33. 33.
    F. Bartsch, G. Hamuni, V. Miskovic, et al., “Oscillatory brain activity in the alpha range is modulated by the content of word-prompted mental imagery,” Psychophysiology, 52, No. 6, 727–735 (2015).PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    A. Fink and M. Benedek, “EEG alpha power and creative ideation,” Neurosci. Biobehav. Rev., 44, 111–123 (2014).PubMedPubMedCentralCrossRefGoogle Scholar
  35. 35.
    A. Fink, C. Rominger, M. Benedek, et al., “EEG alpha activity during imagining creative moves in soccer decision-making situations,” Neuropsychologia, 114, 118–124 (2018).PubMedCrossRefGoogle Scholar
  36. 36.
    C. W. Quaedflieg, F. T. Smulders, T. Meyer, et al., “The validity of individual frontal alpha asymmetry EEG neurofeedback,” Soc. Cogn. Affect. Neurosci., 11, No. 1, 33–43 (2015).PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Y. Y. Lee and S. Hsieh, “Classifying different emotional states by means of EEG-based functional connectivity patterns,” PloS One, 9, No. 4, e95415 (2014).PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    M. Murugappan, N. Ramachandran, and Y. Sazali, “Clas-sification of human emotion from EEG using discrete wavelet transform” J. Biomed. Sci. Eng., 3, No. 4, 390–396 (2010).CrossRefGoogle Scholar
  39. 39.
    J. Li, G. Liu, and J. Gao, “Analysis of positive and negative emotions based on EEG signal,” in: 2016 Int. Conf. Artific. Intellig. Engineer. Appl. Atlantis Press (2016).Google Scholar
  40. 40.
    L. Wei, Y. Li, J. Ye, et al., “Emotion-induced higher wavelet entropy in the EEG with depression during a cognitive task,” in: 2009 Ann. Int. Conf. IEEE Engineer. Med. Biol. Soc. (2009, September) IEEE, pp. 5018–5021).Google Scholar
  41. 41.
    A. Pakhomov and N. Sudin, “Thermodynamic view on decision-making process: emotions as a potential power vector of realization of the choice,” Cogn. Neurodyn.,7, No. 6, 449–463 (2013).PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    L. I. Aftanas, N. V. Lotova, V. I. Koshkarov, et al., “Non-linear analysis of emotion EEG: calculation of Kolmogorov entropy and the principal Lyapunov exponent,” Neurosci. Lett., 226, No. 1, 13–16 (1997).PubMedCrossRefGoogle Scholar
  43. 43.
    K. H. Chon, C. G. Scully, and S. Lu, “Approximate entropy for all signals,” IEEE Eng. Med. Biol., 28, No. 6, 18–23 (2009).CrossRefGoogle Scholar
  44. 44.
    P. Zarjam, J. Epps, and N. H. Lovell, “Characterizing mental load in an arithmetic task using entropy-based features,” in: Inform. Sci., Sign. Proc. Appl. (ISSPA), 11th Int. Conf. (2012, July), IEEE, pp. 199–204.Google Scholar
  45. 45.
    N. Jaiswal, W. Ray, and S. Slobounov, “Encoding of visual–spatial information in working memory requires more cerebral efforts than retrieval: Evidence from an EEG and virtual reality study,” Brain Res., 1347, 80–89 (2010).PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    N. Shourie, M. Firoozabadi, and K. Badie, “Analysis of EEG signals related to artists and nonartists during visual perception, mental imagery, and rest using approximate entropy,” Biomed. Res. Int.,2014, 764382 (2014).PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    O. Jensen and C. D. Tesche, “Frontal theta activity in humans increases with memory load in a working memory task,” Eur. J. Neurosci., 15, No. 8, 1395–1399 (2002).PubMedCrossRefGoogle Scholar
  48. 48.
    S. A. Massar, J. L. Kenemans, and D. J. Schutter, “Resting-state EEG theta activity and risk learning: sensitivity to reward or punishment?” Int. J. Psychophysiol., 91, No. 3, 172–177 (2014).PubMedCrossRefGoogle Scholar
  49. 49.
    Z. Yaple, M. Martinez-Saito, M. Feurra, et al., “Transcranial alternating current stimulation modulates risky decision making in a frequency controlled experiment,” eNeuro, 4, No. 6, ENEURO.0136–17 (2017).PubMedPubMedCentralCrossRefGoogle Scholar
  50. 50.
    J. Jacobs, G. Hwang, T. Curran, and M. J. Kahana, “EEG oscillations and recognition memory: theta correlates of memory retrieval and decision making,” NeuroImage, 32, No. 2, 978–987 (2006).PubMedCrossRefGoogle Scholar
  51. 51.
    A. J. Malin, “Manual for Malin’s intelligence scale for Indian children (MISIC),” Ind. Psychol. Corp., Lucknow (1969).Google Scholar
  52. 52.
    E. U. Weber, A. R. Blais, and N. E. Betz, “A domainspecific risk-attitude scale: Measuring risk perceptions and risk behaviors,” J. Behav. Dec. Making, 15, No. 4, 263–290 (2002).CrossRefGoogle Scholar
  53. 53.
    A. Galentino, N. Bonini, and L. Savadori, “Positive arousal increases individuals’ preferences for risk,” Front. Psychol., 8, 2142 (2017).PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    A. Delorme and S. Makeig, “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis,” J. Neurosci. Meth., 134, No. 1, 9–21 (2004).CrossRefGoogle Scholar
  55. 55.
    Z. X. Liu, S. Woltering, and M. D. Lewis, “Developmental change in EEG theta activity in the medial prefrontal cortex during response control,” Neuroimage, 85, Pt. 2, 873–887 (2014).PubMedCrossRefGoogle Scholar
  56. 56.
    M. Simões, R. Monteiro, J. Andrade, et al., “A novel biomarker of compensatory recruitment of face emotional imagery networks in autism spectrum disorder,” Front. Neurosci.-Switz., 12, 791 (2018).CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Dr. B. R. Ambedkar National Institute of TechnologyJalandharIndia

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