Skip to main content

Advertisement

SpringerLink
Log in

Impact of past experiences on decision-making in autism spectrum disorder

  • Original Paper
  • Published:
European Archives of Psychiatry and Clinical Neuroscience Aims and scope Submit manuscript

Abstract

People are often influenced by past costs in their current decision-making, thus succumbing to a well-known bias recognized as the sunk cost effect. A recent study showed that the sunk cost effect is attenuated in individuals with autism spectrum disorder (ASD). However, the study only addressed one situation of utilization decision by focusing on the choice between similar attractive alternatives with different levels of sunk costs. Thus, it remains unclear how individuals with ASD behave under sunk costs in different types of decision situations, particularly progress decisions, in which the decision-maker allocates additional resources to an initially chosen alternative. The sunk cost effect in progress decisions was estimated using an economic task designed to assess the effect of the past investments on current decision-making. Twenty-four individuals with ASD and 21 age-, sex-, smoking status-, education-, and intelligence quotient-level-matched typical development (TD) subjects were evaluated. The TD participants were more willing to make the second incremental investment if a previous investment was made, indicating that their decisions were influenced by sunk costs. However, unlike the TD group, the rates of investments were not significantly increased after prior investments in the ASD group. The results agree with the previous evidence of a reduced sensitivity to context stimuli in individuals with ASD and help us obtain a broader picture of the impact of sunk costs on their decision-making. Our findings will contribute to a better understanding of ASD and may be useful in addressing practical implications of their socioeconomic behavior.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Fond G, Bayard S, Capdevielle D et al (2013) A further evaluation of decision-making under risk and under ambiguity in schizophrenia. Eur Arch Psychiatry Clin Neurosci 263:249–257

    Google Scholar 

  2. Fujino J, Tei S, Jankowski KF et al (2017) Role of spontaneous brain activity in explicit and implicit aspects of cognitive flexibility under socially conflicting situations: a resting-state fMRI study using fractional amplitude of low-frequency fluctuations. Neuroscience 367:60–71

    CAS  Google Scholar 

  3. Luk MSK, Chang WC, Chong CSY et al (2019) Altered risky decision making in patients with early non-affective psychosis. Eur Arch Psychiatry Clin Neurosci [Epub ahead of print]

  4. Schmidt SNL, Fenske SC, Kirsch P et al (2019) Nucleus accumbens activation is linked to salience in social decision making. Eur Arch Psychiatry Clin Neurosci 269:701–712

    Google Scholar 

  5. Sharp C, Monterosso J, Montague PR (2012) Neuroeconomics: a bridge for translational research. Biol Psychiatry 72:87–92

    Google Scholar 

  6. Tei S, Kauppi JP, Fujino J et al (2019) Inter-subject correlation of temporoparietal junction activity is associated with conflict patterns during flexible decision-making. Neurosci Res 144:67–70

    Google Scholar 

  7. Brosnan M, Lewton M, Ashwin C (2016) Reasoning on the autism spectrum: a dual process theory account. J Autism Dev Disord 46:2115–2125

    Google Scholar 

  8. Brosnan M, Ashwin C, Lewton M (2017) Brief report: intuitive and reflective reasoning in autism spectrum disorder. J Autism Dev Disord 47:2595–2601

    Google Scholar 

  9. Damiano CR, Aloi J, Treadway M et al (2012) Adults with autism spectrum disorders exhibit decreased sensitivity to reward parameters when making effort-based decisions. J Neurodev Disord 4:13

    Google Scholar 

  10. Mussey JL, Travers BG, Klinger LG, Klinger MR (2015) Decision-making skills in ASD: performance on the Iowa Gambling Task. Autism Res. 8:105–114

    Google Scholar 

  11. Vella L, Ring HA, Aitken MR et al (2018) Understanding self-reported difficulties in decision-making by people with autism spectrum disorders. Antism 22:549–559

    Google Scholar 

  12. Zhang L, Tang J, Dong Y et al (2015) Similarities and differences in decision-making impairments between autism spectrum disorder and schizophrenia. Front Behav Neurosci 9:259

    Google Scholar 

  13. Farmer GD, Baron-Cohen S, Skylark WJ (2017) People with autism spectrum conditions make more consistent decisions. Psychol Sci 28:1067–1076

    Google Scholar 

  14. Fujino J, Tei S, Hashimoto RI et al (2017) Attitudes toward risk and ambiguity in patients with autism spectrum disorder. Mol Autism 8:45

    Google Scholar 

  15. Pushkarskaya H, Tolin D, Ruderman L et al (2015) Decision-making under uncertainty in obsessive–compulsive disorder. J Psychiatr Res 69:166–173

    Google Scholar 

  16. Takahashi H (2013) Molecular neuroimaging of emotional decision-making. Neurosci Res 75:269–274

    Google Scholar 

  17. Weller RE, Avsar KB, Cox JE et al (2014) Delay discounting and task performance consistency in patients with schizophrenia. Psychiatry Res 215:286–293

    Google Scholar 

  18. Arkes HR, Blumer C (1985) The psychology of sunk cost. Organ Behav Hum Decis Process 35:124–140

    Google Scholar 

  19. Bogdanov M, Ruff CC, Schwabe L (2017) Transcranial stimulation over the dorsolateral prefrontal cortex increases the impact of past expenses on decision-making. Cereb Cortex 27:1094–1102

    Google Scholar 

  20. Friedman D, Pommerenke K, Lukose R et al (2007) Searching for the sunk cost fallacy. Exp Econ 10:79–104

    Google Scholar 

  21. Haller A, Schwabe L (2014) Sunk costs in the human brain. Neuroimage 97:127–133

    Google Scholar 

  22. Thaler R (1980) Toward a positive theory of consumer choice. J Econ Behav Organ 1:39–60

    Google Scholar 

  23. Augenblick N (2016) The sunk-cost fallacy in penny auctions. Rev Econ Stud 83:58–86

    Google Scholar 

  24. Fujino J, Fujimoto S, Kodaka F et al (2016) Neural mechanisms and personality correlates of the sunk cost effect. Sci Rep 6:33171

    CAS  Google Scholar 

  25. Fujino J, Kawada R, Tsurumi K et al (2018) An fMRI study of decision-making under sunk costs in gambling disorder. Eur Neuropsychopharmacol 28:1371–1381

    CAS  Google Scholar 

  26. Roth S, Robbert T, Straus L (2015) On the sunk-cost effect in economic decision-making: a meta-analytic review. Bus Res 8:99–138

    Google Scholar 

  27. Zeng J, Zhang Q, Chen C et al (2013) An fMRI study on sunk cost effect. Brain Res 1519:63–70

    CAS  Google Scholar 

  28. Moon H (2001) Looking forward and looking back: integrating completion and sunk-cost effects within an escalation-of-commitment progress decision. J Appl Psychol 86:104–113

    CAS  Google Scholar 

  29. Conlon DE, Garland H (1993) The role of project completion information in resource allocation decisions. Acad Manage J 36:402–413

    Google Scholar 

  30. Fujino J, Tei S, Itahashi T et al (2019) Sunk cost effect in individuals with autism spectrum disorder. J Autism Dev Disord 49:1–10

    Google Scholar 

  31. De Martino B, Harrison NA, Knafo S et al (2008) Explaining enhanced logical consistency during decision making in autism. J Neurosci 28:10746–10750

    Google Scholar 

  32. Shah P, Catmur C, Bird G (2016) Emotional decision-making in autism spectrum disorder: the roles of interoception and alexithymia. Mol Autism 7:43

    Google Scholar 

  33. Luke L, Clare IC, Ring H et al (2012) Decision-making difficulties experienced by adults with autism spectrum conditions. Autism 16:612–621

    Google Scholar 

  34. Robic S, Sonie S, Fonlupt P et al (2015) Decision-making in a changing world: a study in autism spectrum disorders. J Autism Dev Disord 45:1603–1613

    CAS  Google Scholar 

  35. Fujino J, Tei S, Itahashi T et al (2019) Need for closure and cognitive flexibility in individuals with autism spectrum disorder: a preliminary study. Psychiatry Res 271:247–252

    Google Scholar 

  36. Tei S, Fujino J, Hashimoto RI et al (2018) Inflexible daily behaviour is associated with the ability to control an automatic reaction in autism spectrum disorder. Sci Rep 8:8082

    Google Scholar 

  37. Tei S, Fujino J, Itahashi T et al (2019) Egocentric biases and atypical generosity in autistic individuals. Autism Res [Epub ahead of print]

  38. Lejuez C, Aklin WM, Jones HA et al (2003) The balloon analogue risk task (BART) differentiates smokers and nonsmokers. Exp Clin Psychopharmacol 11:26–33

    CAS  Google Scholar 

  39. Lord C, Risi S, Lambrecht L et al (2003) The autism diagnostic observation schedule—generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 30:205–223

    Google Scholar 

  40. Matsuoka K, Kim Y (2006) Japanese adult reading test (JART). Shinkou-Igaku Publishers, Tokyo

    Google Scholar 

  41. Matsuoka K, Uno M, Kasai K et al (2006) Estimation of premorbid IQ in individuals with Alzheimer’s disease using Japanese ideographic script (Kanji) compound words: Japanese version of national adult reading test. Psychiatry Clin Neurosci 60:332–339

    Google Scholar 

  42. Tanaka Y, Fujino J, Ideno T et al (2014) Are ambiguity aversion and ambiguity intolerance identical?: a neuroeconomics investigation. Front Psychol 5:1550

    Google Scholar 

  43. Jolliffe T, Baron-Cohen S (1997) Are people with autism and Asperger syndrome faster than normal on the embedded figures test? J Child Psychol Psychiatry 38:527–534

    CAS  Google Scholar 

  44. Plaisted K, O’Riordan M, Baron-Cohen S (1998) Enhanced visual search for a conjunctive target in autism: a research note. J Child Psychol Psychiatry 39:777–783

    CAS  Google Scholar 

  45. Happé F, Frith U (2006) The weak coherence account: detail-focused cognitive style in autism spectrum disorders. J Autism Dev Disord 36:5–25

    Google Scholar 

  46. Decety J, Lamm C (2007) The role of the right temporoparietal junction in social interaction: how low-level computational processes contribute to meta-cognition. Neuroscientist 13:580–593

    Google Scholar 

  47. Tei S, Fujino J, Kawada R et al (2017) Collaborative roles of temporoparietal junction and dorsolateral prefrontal cortex in different types of behavioural flexibility. Sci Rep 7:6415

    Google Scholar 

  48. Gigerenzer G, Goldstein DG et al (1996) Reasoning the fast and frugal way: models of bounded rationality. Psychol Rev 103:650–669

    CAS  Google Scholar 

  49. Goddard MN, Swaab H, Rombouts SA et al (2016) Neural systems for social cognition: gray matter volume abnormalities in boys at high genetic risk of autism symptoms, and a comparison with idiopathic autism spectrum disorder. Eur Arch Psychiatry Clin Neurosci 266:523–531

    Google Scholar 

  50. Fujino J, Hirose K, Tei S et al (2016) Ambiguity aversion in schizophrenia: an fMRI study of decision-making under risk and ambiguity. Schizophr Res 178:94–101

    Google Scholar 

  51. South M, Dana J, White SE et al (2011) 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 41:55–65

    Google Scholar 

  52. Wu HC, White S, Rees G et al (2018) Executive function in high-functioning autism: decision-making consistency as a characteristic gambling behaviour. Cortex 107:21–36

    Google Scholar 

  53. Gosling CJ, Moutier S (2018) Brief Report: risk-aversion and rationality in autism spectrum disorders. J Autism Dev Disord 48:3623–3628

    Google Scholar 

  54. Levin IP, Gaeth GJ, Foley-Nicpon M et al (2015) Extending decision making competence to special populations: a pilot study of persons on the autism spectrum. Front Psychol 6:539

    Google Scholar 

  55. Doya K (2008) Modulators of decision making. Nat Neurosci 11:410–416

    CAS  Google Scholar 

  56. St Onge JR, Floresco SB (2009) Dopaminergic modulation of risk-based decision making. Neuropsychopharmacol 34:681–697

    CAS  Google Scholar 

  57. Rogers RD (2011) The roles of dopamine and serotonin in decision making: evidence from pharmacological experiments in humans. Neuropsychopharmacol 36:114–132

    CAS  Google Scholar 

  58. Beninger RJ, Wasserman J, Zanibbi K et al (2003) Typical and atypical antipsychotic medications differentially affect two nondeclarative memory tasks in schizophrenic patients: a double dissociation. Schizophr Res 61:281–292

    Google Scholar 

  59. Bray S, Shimojo S, O’Doherty JP (2010) Human medial orbitofrontal cortex is recruited during experience of imagined and real rewards. J Neurophysiol 103:2506–2512

    Google Scholar 

  60. Kang MJ, Rangel A, Camus M (2011) Hypothetical and real choice differentially activate common valuation areas. J Neurosci 31:461–468

    CAS  Google Scholar 

  61. Locey ML, Jones BA, Rachlin H (2011) Real and hypothetical rewards. Judgm Decis Mak 6:552–564

    Google Scholar 

  62. Olivola CY (2018) The interpersonal sunk-cost effect. Psychol Sci 29:1072–1083

    Google Scholar 

  63. Hafenbrack AC, Kinias Z, Barsade SG (2014) Debiasing the mind through meditation: mindfulness and the sunk-cost bias. Psychol Sci 25:369–376

    Google Scholar 

  64. Tan HT, Yates JF (1995) Sunk cost effects: the influences of instruction and future return estimates. Organ Behav Hum Decis Process 63:311–319

    Google Scholar 

Download references

Acknowledgements

The authors wish to extend their gratitude to the research team of the Medical Institute of Developmental Disabilities Research at Showa University for their assistance in data acquisition. This work was supported by grants-in-aid for scientific research A (24243061), C (17K10326), Young Scientists B (17K16398), and on Innovative Areas (23120009, 16H06572), from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT); and the Takeda Science Foundation. A part of this study is the result of the Strategic Research Program for Brain Sciences (JP19dm0107151) by Japan Agency for Medical Research and Development, “Research and development of technology for enhancing functional recovery of elderly and disabled people based on non-invasive brain imaging and robotic assistive devices,” the Commissioned Research of National Institute of Information and Communications Technology, JAPAN, and the Joint Usage/Research Program of Medical Institute of Developmental Disabilities Research, Showa University. These agencies had no further role in the study design, the collection, analysis, and interpretation of data, the writing of the report, or in the decision to submit the paper for publication.

Author information

Authors and Affiliations

  1. Medical Institute of Developmental Disabilities Research, Showa University, 6-11-11 Kita-karasuyama, Setagaya-ku, Tokyo, 157-8577, Japan

    Junya Fujino, Shisei Tei, Takashi Itahashi, Yuta Y. Aoki, Haruhisa Ohta, Manabu Kubota, Ryu-ichiro Hashimoto, Motoaki Nakamura, Nobumasa Kato & Hidehiko Takahashi

  2. Department of Psychiatry, Graduate School of Medicine, Kyoto University, 54 Shogoin-Kawaracho, Sakyo-ku, Kyoto, Japan

    Junya Fujino, Shisei Tei, Manabu Kubota & Hidehiko Takahashi

  3. Institute of Applied Brain Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, Japan

    Shisei Tei

  4. School of Human and Social Sciences, Tokyo International University, 2509 Matoba, Kawagoe, Saitama, Japan

    Shisei Tei

  5. Department of Psychiatry, School of Medicine, Showa University, 6-11-11 Kita-karasuyama, Setagaya-ku, Tokyo, Japan

    Haruhisa Ohta

  6. Department of Functional Brain Imaging, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, 4-9-1 Anagawa, Inage-ku, Chiba, Japan

    Manabu Kubota

  7. Department of Language Sciences, Graduate School of Humanities, Tokyo Metropolitan University, 1-1 Minami-Osawa, Hachioji-shi, Tokyo, Japan

    Ryu-ichiro Hashimoto

  8. Kanagawa Psychiatric Center, 2-5-1 Serigaya, Yokohama, Kanagawa, Japan

    Motoaki Nakamura

  9. Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, Japan

    Hidehiko Takahashi

Authors
  1. Junya Fujino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shisei Tei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takashi Itahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuta Y. Aoki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haruhisa Ohta
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manabu Kubota
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ryu-ichiro Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Motoaki Nakamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nobumasa Kato
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hidehiko Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JF, ST, TI, YYA, HO, R-IH, MN, NK, and HT designed research; JF, ST, TI, and YYA participated in the data acquisition; JF, YYA, HO, MN, and NK were in charge of the clinical assessment. JF and ST analyzed data; TI, YYA, HO, MK, R-IH, MN, NK, and HT helped with interpretation of data. JF, ST, TI, YYA, HO, MK, R-IH, MN, NK, and HT wrote the paper. All authors have made intellectual contribution to the work and approved the final version of the manuscript for submission.

Corresponding author

Correspondence to Junya Fujino.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The study was approved by the institutional ethical review board and has, therefore, been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Informed consent

All participants gave their informed consent to participate prior to inclusion in the study.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 180 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fujino, J., Tei, S., Itahashi, T. et al. Impact of past experiences on decision-making in autism spectrum disorder. Eur Arch Psychiatry Clin Neurosci 270, 1063–1071 (2020). https://doi.org/10.1007/s00406-019-01071-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00406-019-01071-4

Keywords

Search

Navigation