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.
This is a preview of subscription content, log in via an institution to check access.
REFERENCES
Tanu and D. Kakkar, “Strengthening risk prediction using statistical learning in children with autism spectrum disorder,” Adv. Autism, 4, No. 3, 141–152 (2018).
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).
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).
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).
American Psychiatric Association. DSM-IV-TR: Diagnostic and statistical manual of mental disorders, text revision. Washington, DC, Am. Psychiatr. Assoc., vol. 75 (2000).
G. Loewenstein, E. U. Weber, C. K. Hsee, and N. Welch, “Risk as feelings,” Psychol. Bull., 127, No. 2, 267–286 (2001).
E. A. Holmes and A. Mathews, “Mental imagery and emotion: A special relationship?” Emotion, 5, No. 4, 489 (2005).
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).
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).
P. Van Schaik and P. Kusev, “Human preferences and risky choices,” Front. Psychol., 2, 333 (2011).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
I. Soulieres, T. A. Zeffiro, M. L. Girard, and L. Mottron, “Enhanced mental image mapping in autism,” Neuropsychologia, 49, No. 5, 848–857 (2011).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
A. Fink and M. Benedek, “EEG alpha power and creative ideation,” Neurosci. Biobehav. Rev., 44, 111–123 (2014).
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).
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).
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).
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).
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).
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).
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).
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).
K. H. Chon, C. G. Scully, and S. Lu, “Approximate entropy for all signals,” IEEE Eng. Med. Biol., 28, No. 6, 18–23 (2009).
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.
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).
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).
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).
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).
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).
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).
A. J. Malin, “Manual for Malin’s intelligence scale for Indian children (MISIC),” Ind. Psychol. Corp., Lucknow (1969).
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).
A. Galentino, N. Bonini, and L. Savadori, “Positive arousal increases individuals’ preferences for risk,” Front. Psychol., 8, 2142 (2017).
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).
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).
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).
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Tanu, Kakkar, D. Influence of Emotional Imagery on Risk Perception and Decision Making in Autism Spectrum Disorder. Neurophysiology 51, 281–292 (2019). https://doi.org/10.1007/s11062-019-09822-8
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11062-019-09822-8