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Motivational processes and autonomic responsivity in Asperger's disorder: Evidence from the Iowa Gambling Task

Published online by Cambridge University Press:  08 September 2006

SHANNON A. JOHNSON
Affiliation:
Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
ELDAD YECHIAM
Affiliation:
Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
ROBIN R. MURPHY
Affiliation:
Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
SARAH QUELLER
Affiliation:
Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
JULIE C. STOUT
Affiliation:
Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana

Abstract

Asperger's disorder (ASP), like other autism spectrum disorders, is associated with altered responsiveness to social stimuli. This study investigated learning and responsiveness to nonsocial, but motivational, stimuli in ASP. We examined choice behavior and galvanic skin conductance responses (SCRs) during the Iowa Gambling Task (IGT; Bechara et al., 1994) in 15 adolescents and young adults with ASP and 14 comparison subjects. We examined aspects of learning, attention to wins and losses, and response style with a formal cognitive model, the Expectancy–Valence Learning model (Busemeyer & Stout, 2002). The ASP group did not differ from the comparison group in proportions of selections from advantageous decks. However, ASP participants showed a distinct pattern of selection characterized by frequent shifts between the four IGT decks, whereas comparison participants developed clear deck preferences. SCR results showed some evidence of reduced responsiveness in the ASP group during the IGT. Results from the cognitive model indicated that, in contrast to the comparison group, the ASP group's selections were less consistent with the motivational significance they assigned to decks. Findings are discussed in the context of the neurobiological substrates associated with IGT performance (JINS, 2006, 12, 668–676.)

Type
Research Article
Copyright
© 2006 The International Neuropsychological Society

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References

REFERENCES

Althaus, M., van Roon, A.M., Mulder, L.J.M., Mulder, G., Aarnoudse, C.C., & Minderaa, R.B. (2004). Autonomic response patterns observed during the performance of an attention-demanding task in two groups of children with autistic-type difficulties in social adjustment. Psychophysiology, 41, 893904.Google Scholar
American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders: DSM-IV (4th ed.). Washington, DC: The American Psychiatric Association.
Bar-On, R., Tranel, D., Denburg, N.L., & Bechara, A. (2003). Exploring the neurological substrate of emotional and social intelligence. Brain, 126, 17901800.Google Scholar
Baron-Cohen, S., Ring, H.A., Bullmore, E.T., Wheelright, S., Ashwin, C., & Williams, S.C.R. (2000). The amygdala theory of autism. Neuroscience and Biobehavioral Reviews, 24, 355364.Google Scholar
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The Autism-Spectrum Quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31, 517.Google Scholar
Bechara, A. (2004). The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage. Brain and Cognition, 55, 3040.Google Scholar
Bechara, A., Damasio, H., & Damasio, A.R. (2003). Role of the amygdala in decision-making. Annals of New York Academy of Science, 985, 356369.Google Scholar
Bechara, A., Damasio, A.R., Damasio, H., & Anderson, S.W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50, 715.Google Scholar
Bechara, A., Damasio, H., Damasio, A.R., & Lee, G.P. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. The Journal of Neuroscience, 19, 54735481.Google Scholar
Bechara, A., Damasio, H., Tranel, D., & Damasio, A.R. (1997). Deciding advantageously before knowing the advantageous strategy. Science, 275, 12931295.Google Scholar
Bechara, A., Tranel, D., Damasio, H., & Damasio, A.R. (1996). Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex, 6, 215225.Google Scholar
Brothers, L. (1996). Brain mechanisms of social cognition. Journal of Psychopharmacology, 10, 28.Google Scholar
Busemeyer, J.R. & Myung, I.J. (1992). An adaptive approach to human decision-making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121, 177194.Google Scholar
Busemeyer, J.R. & Stout, J.C. (2002). A contribution of cognitive decision models to clinical assessment: Decomposing performance on the Bechara gambling task. Psychological Assessment, 14, 253262.Google Scholar
Busemeyer, J.R. & Wang, Y.M. (2000). Model comparisons and model selections based on generalization criterion methodology. Journal of Mathematical Psychology, 44, 171189.Google Scholar
Campbell, M.C., Stout, J.C., & Finn, P.R. (2004). Reduced autonomic responsiveness to gambling task losses in Huntington's disease. Journal of the International Neuropsychological Society, 10, 239245.Google Scholar
Courchesne, E. & Pierce, K. (2005). Why the frontal cortex in autism might be talking only to itself: Local over-connectivity but long-distance disconnection. Current Opinions in Neurobiology, 15, 225230.Google Scholar
Davidson, R.J. (1998). Anterior electrophysiological asymmetries, emotion, and depression: Conceptual and methodological conundrums. Psychophysiology, 35, 607614.Google Scholar
Dawson, G., Webb, S.J., Carver, L., Panagiotides, H., & MacPartland, J. (2004). Young children with autism show atypical brain responses to fearful versus neutral facial expressions of emotions. Developmental Science, 7, 340359.Google Scholar
Ernst, M., Bolla, K., Mouratidis, M., Contoreggi, C.S., Matochik, J.A., Kurian, V., Cadet, J.L., Kimes, A.S., & London, E.D. (2002). Decision-making in a risk-taking task: A PET study. Neuropsychopharmacology, 26, 682691.Google Scholar
Ernst, M., Nelson, E.E., McClure, E.B., Monk, C.S., Munson, S., Eshel, N., Zarahn, E., Leibenluft, E., Zametkin, A., Towbin, K., Blair, J., Charney, D., & Pine, D.S. (2004). Choice selection and reward anticipation: An fMRI study. Neuropsychologia, 42, 15851597.Google Scholar
Frost, R.O. & Shows, D.L. (1993). The nature and measurement of compulsive indecisiveness. Behavior Research Therapy, 31, 683692.Google Scholar
Garretson, H.B., Fein, D., & Waterhouse, L. (1990). Sustained attention in children with autism. Journal of Autism and Developmental Disorders, 20, 101114.Google Scholar
Gillberg, C., Rastam, M., & Wentz, E. (2001). The Asperger Syndrome (and high-functioning autism) Diagnostic Interview (ASDI): A preliminary study of a new structured clinical interview. Autism, 5, 5766.Google Scholar
Hirstein, W., Iversen, P., & Ramachandran, V.S. (2001). Autonomic responses of autistic children to people and objects. Proceedings. Biological sciences/The Royal Society, 268, 18831888.Google Scholar
Ingersoll, B., Schreibman, L., & Tran, Q.H. (2003). Effect of sensory feedback on immediate object imitation in children with autism. Journal of Autism and Developmental Disorders, 33, 673683.Google Scholar
Just, M.A., Cherkassky, V.L., Keller, T.A., & Minshew, N.J. (2004). Cortical activation and synchronization during sentence comprehension in high-functioning autism: Evidence of underconnectivity. Brain, 127, 18111821.Google Scholar
Klin, A., Jones, W., Schultz, R., & Volkmar, F. (2003). The enactive mind, or from actions to cognition: Lessons from autism. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358, 345360.Google Scholar
Klinger, L.G. & Dawson, G. (2001). Prototype formation in autism. Development and Psychopathology, 1, 111124.Google Scholar
Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659686.Google Scholar
Minshew, N.J., Meyer, J., & Goldstein, G. (2002). Abstract reasoning in autism: A dissociation between concept formation and concept identification. Neuropsychology, 16, 327334.Google Scholar
Ozonoff, S. (1995). Reliability and validity of Wisconsin Card Sorting Test in studies of autism. Neuropsychology, 9, 491500.Google Scholar
Pascualvaca, D.M., Fantie, B.D., Papageorgiou, M., & Mirsky, A.F. (1998). Attentional capacities in children with autism: Is there a general deficit in shifting focus? Journal of Autism and Developmental Disorders, 28, 467478.Google Scholar
Salmond, C.H., de Haan, M., Friston, K.J., Gadian, D.G., & Vargha-Khadem, F. (2003). Investigating individual differences in brain abnormalities in autism. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358, 405413.Google Scholar
Sarin, R. & Vahid, F. (1999). Payoff assessments without probabilities: A simple dynamic model of choice. Games and Economic Behavior, 28, 294309.Google Scholar
Schultz, R.T., Romanski, L.M., & Tsatsanis, K.D. (2000). Neurofunctional models of Autistic disorder and Asperger syndrome: Clues from neuroimaging. In A. Klin, F.R. Volkmar, & S.S. Sparrow (Eds.), Asperger syndrome. New York: Guilford Press.
Stout, J.C., Busemeyer, J.R., Bechara, A., & Lin, A. (2002). Cognitive modeling of decision making in a simulated gambling task in frontal or somatosensory cortex damage. Paper presented at the Cognitive Neuroscience Society, San Francisco, CA.
Stout, J.C., Busemeyer, J.R., Lin, A., Grant, S.R., & Bonson, K.R. (2004). Cognitive modeling analysis of the decision-making processes used by cocaine abusers. Psychonomic Bulletin and Review, 11, 742747.Google Scholar
Stout, J.C., Rock, S.L., Campbell, M.C., Busemeyer, J.R., & Finn, P.R. (2005). Psychological processes underlying risky decisions in drug abusers. Psychology of Addictive Behaviors, 19, 148157.Google Scholar
Sutton, S.K., Burnette, C.P., Mundy, P.C., Meyer, J., Vaughan, A., Sanders, C., et al. (2005). Resting cortical brain activity and social behavior in higher functioning children with autism. Journal of Child Psychology and Psychiatry, 46, 211222.Google Scholar
Tager-Flusberg, H. & Joseph, R.M. (2003). Identifying neurocognitive phenotypes in autism. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 358(1430), 303314.Google Scholar
van Engeland, H., Roelofs, J.W., Verbaten, M.N., & Slangen, J.L. (1991). Abnormal electrodermal reactivity to novel visual stimuli in autistic children. Psychiatry Research, 38, 2738.Google Scholar
Wechsler, D. (1999). Wechsler Abbreviated Scale of Intelligence. San Antonio, TX: The Psychological Corporation.
Yechiam, E., Busemeyer, J.R., Stout, J.C., & Bechara, A. (2005). Using cognitive models to map relations between neuropsychological disorders and human decision making deficits. Psychological Science, 16, 973978.Google Scholar