Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Shared striatal activity in decisions to satisfy curiosity and hunger at the risk of electric shocks

Abstract

Curiosity is often portrayed as a desirable feature of human faculty. However, curiosity may come at a cost that sometimes puts people in harmful situations. Here, using a set of behavioural and neuroimaging experiments with stimuli that strongly trigger curiosity (for example, magic tricks), we examine the psychological and neural mechanisms underlying the motivational effect of curiosity. We consistently demonstrate that across different samples, people are indeed willing to gamble, subjecting themselves to electric shocks to satisfy their curiosity for trivial knowledge that carries no apparent instrumental value. Also, this influence of curiosity shares common neural mechanisms with that of hunger for food. In particular, we show that acceptance (compared to rejection) of curiosity-driven or incentive-driven gambles is accompanied by enhanced activity in the ventral striatum when curiosity or hunger was elicited, which extends into the dorsal striatum when participants made a decision.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Experimental task.
Fig. 2: Behavioural results of motivation-driven decision-making.
Fig. 3: Neural activity in the reward network is modulated by motivation-driven decision-making.
Fig. 4: Mediation path diagram.
Fig. 5: Functional connectivity of the caudate nucleus at the decision phase.

Similar content being viewed by others

Data availability

The behavioural data that support the findings of the current study are available at https://osf.io/mafe3/. The unthresholded statistical maps of the fMRI results can be accessed at https://neurovault.org/collections/AWZZIZCZ/.

Code availability

The analyses in this study were performed in standard software and based on published routines, as specified in detail in the Methods and the Supplementary Information. Custom codes can be accessed at https://osf.io/mafe3/ and are available from the corresponding authors upon request.

References

  1. Gottlieb, J., Oudeyer, P.-Y., Lopes, M. & Baranes, A. Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends Cogn. Sci. 17, 585–593 (2013).

    PubMed  PubMed Central  Google Scholar 

  2. Jirout, J. & Klahr, D. Children’s scientific curiosity: in search of an operational definition of an elusive concept. Dev. Rev. 32, 125–160 (2012).

    Google Scholar 

  3. Kidd, C. & Hayden, B. Y. The psychology and neuroscience of curiosity. Neuron 88, 449–460 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. von Stumm, S., Hell, B. & Chamorro-Premuzic, T. The hungry mind: intellectual curiosity is the third pillar of academic performance. Perspect. Psychol. Sci. 6, 574–588 (2011).

    Google Scholar 

  5. Gruber, M. J., Gelman, B. D. & Ranganath, C. States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron 84, 486–496 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Kang, M. J. et al. The wick in the candle of learning: epistemic curiosity activates reward circuitry and enhances memory. Psychol. Sci. 20, 963–973 (2009).

    PubMed  Google Scholar 

  7. Renninger, K. A. & Hidi, S. The Power of Interest for Motivation and Engagement (Routledge, 2016).

  8. Sakaki, M., Yagi, A. & Murayama, K. Curiosity in old age: a possible key to achieving adaptive aging. Neurosci. Biobehav. Rev. 88, 106–116 (2018).

    PubMed  Google Scholar 

  9. Rozek, C. S., Svoboda, R. C., Harackiewicz, J. M., Hulleman, C. S. & Hyde, J. S. Utility-value intervention with parents increases students’ STEM preparation and career pursuit. Proc. Natl Acad. Sci. USA 114, 909–914 (2017).

    CAS  PubMed  Google Scholar 

  10. Loewenstein, G. The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116, 75–98 (1994).

    Google Scholar 

  11. Berlyne, D. E. Conflict, Arousal, and Curiosity (McGraw-Hill, 1960).

  12. Silvia, P. J. Exploring the Psychology of Interest (Oxford Univ. Press, 2006).

  13. Marvin, C. B. & Shohamy, D. Curiosity and reward: valence predicts choice and information prediction errors enhance learning. J. Exp. Psychol. Gen. 145, 266–272 (2016).

    PubMed  Google Scholar 

  14. Gottlieb, J. & Oudeyer, P.-Y. Towards a neuroscience of active sampling and curiosity. Nat. Rev. Neurosci. 19, 758–770 (2018).

    CAS  PubMed  Google Scholar 

  15. Murayama, K. A reward-learning framework of autonomous knowledge acquisition: an integrated account of curiosity, interest, and intrinsic-extrinsic rewards. Preprint at OSF https://doi.org/10.31219/osf.io/zey4k (2019).

  16. Gruber, M. J. & Ranganath, C. How curiosity enhances hippocampus-dependent memory: the prediction, appraisal, curiosity, and exploration (PACE) framework. Trends Cogn. Sci. 23, 1014–1025 (2019).

    PubMed  PubMed Central  Google Scholar 

  17. Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M. & Gottlieb, J. Diverse motives for human curiosity. Nat. Hum. Behav. 3, 587–595 (2019).

    PubMed  Google Scholar 

  18. Blanchard, T. C., Hayden, B. Y. & Bromberg-Martin, E. S. Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity. Neuron 85, 602–614 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Daddaoua, N., Lopes, M. & Gottlieb, J. Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci. Rep. 6, 20202 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Vasconcelos, M., Monteiro, T. & Kacelnik, A. Irrational choice and the value of information. Sci. Rep. 5, 13874 (2015).

    PubMed  PubMed Central  Google Scholar 

  21. Bennett, D., Bode, S., Brydevall, M., Warren, H. & Murawski, C. Intrinsic valuation of information in decision making under uncertainty. PLoS Comput. Biol. 12, e1005020 (2016).

    PubMed  PubMed Central  Google Scholar 

  22. Brydevall, M., Bennett, D., Murawski, C. & Bode, S. The neural encoding of information prediction errors during non-instrumental information seeking. Sci. Rep. 8, 6134 (2018).

    PubMed  PubMed Central  Google Scholar 

  23. Eliaz, K. & Schotter, A. Paying for confidence: an experimental study of the demand for non-instrumental information. Games Econ. Behav. 70, 304–324 (2010).

    Google Scholar 

  24. Berridge, K. C. Motivation concepts in behavioral neuroscience. Physiol. Behav. 81, 179–209 (2004).

    CAS  PubMed  Google Scholar 

  25. Anselme, P. & Robinson, M. J. F. in The Cambridge Handbook of Motivation and Learning (eds Renninger, K. A. & Hidi, S.) 163–182 (Cambridge Univ. Press, 2019).

  26. Berridge, K. C. From prediction error to incentive salience: mesolimbic computation of reward motivation. Eur. J. Neurosci. 35, 1124–1143 (2012).

    PubMed  PubMed Central  Google Scholar 

  27. Robinson, T. E. & Berridge, K. C. The incentive sensitization theory of addiction: some current issues. Philos. Trans. R. Soc. B Biol. Sci. 363, 3137–3146 (2008).

    Google Scholar 

  28. Kringelbach, M. L. & Berridge, K. C. in Recent Developments in Neuroscience Research on Human Motivation (eds Kim, S., Reeve, J. & Bong, M.) 23–35 (Emerald Group Publishing Limited, 2016).

  29. Berridge, K. C. ‘Liking’ and ‘wanting’ food rewards: brain substrates and roles in eating disorders. Physiol. Behav. 97, 537–550 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Tang, D. W., Fellows, L. K., Small, D. M. & Dagher, A. Food and drug cues activate similar brain regions: a meta-analysis of functional MRI studies. Physiol. Behav. 106, 317–324 (2012).

    CAS  PubMed  Google Scholar 

  31. O’Doherty, J. P., Deichmann, R., Critchley, H. D. & Dolan, R. J. Neural responses during anticipation of a primary taste reward. Neuron 33, 815–826 (2002).

    PubMed  Google Scholar 

  32. Knutson, B., Adams, C. M., Fong, G. W. & Hommer, D. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J. Neurosci. 21, RC159 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Lawrence, N. S., Hinton, E. C., Parkinson, J. A. & Lawrence, A. D. Nucleus accumbens response to food cues predicts subsequent snack consumption in women and increased body mass index in those with reduced self-control. Neuroimage 63, 415–422 (2012).

    PubMed  Google Scholar 

  34. Litman, J. Curiosity and the pleasures of learning: wanting and liking new information. Cogn. Emot. 19, 793–814 (2005).

    Google Scholar 

  35. Oosterwijk, S., Snoek, L., Tekoppele, J., Engelbert, L. & Scholte, H. S. Choosing to view morbid information involves reward circuitry. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/795120v1 (2019).

  36. Kobayashi, K. & Hsu, M. Common neural code for reward and information value. Proc. Natl Acad. Sci. USA 116, 13061–13066 (2019).

    CAS  PubMed  Google Scholar 

  37. Gerdeman, G. L., Partridge, J. G., Lupica, C. R. & Lovinger, D. M. It could be habit forming: drugs of abuse and striatal synaptic plasticity. Trends Neurosci. 26, 184–192 (2003).

    CAS  PubMed  Google Scholar 

  38. Telzer, E. H. Dopaminergic reward sensitivity can promote adolescent health: a new perspective on the mechanism of ventral striatum activation. Dev. Cogn. Neurosci. 17, 57–67 (2016).

    PubMed  Google Scholar 

  39. Wright, W. F. & Bower, G. H. Mood effects on subjective probability assessment. Organ. Behav. Hum. Decis. Process. 52, 276–291 (1992).

    Google Scholar 

  40. van Doorn, J. et al. The JASP guidelines for conducting and reporting a Bayesian analysis. Preprint at OSF https://doi.org/10.31234/osf.io/yqxfr (2019).

  41. Moss, S. A., Irons, M. & Boland, M. The magic of magic: the effect of magic tricks on subsequent engagement with lecture material. Br. J. Educ. Psychol. 87, 32–42 (2017).

    PubMed  Google Scholar 

  42. Ligneul, R., Mermillod, M. & Morisseau, T. From relief to surprise: dual control of epistemic curiosity in the human brain. Neuroimage 181, 490–500 (2018).

    PubMed  Google Scholar 

  43. Baranes, A., Oudeyer, P.-Y. & Gottlieb, J. Eye movements reveal epistemic curiosity in human observers. Vision Res. 117, 81–90 (2015).

    PubMed  Google Scholar 

  44. Westfall, J., Nichols, T. E. & Yarkoni, T. Fixing the stimulus-as-fixed-effect fallacy in task fMRI. Wellcome Open Res. 1, 23 (2016).

    PubMed  Google Scholar 

  45. Adcock, R. A., Thangavel, A., Whitfield-Gabrieli, S., Knutson, B. & Gabrieli, J. D. E. Reward-motivated learning: mesolimbic activation precedes memory formation. Neuron 50, 507–517 (2006).

    CAS  PubMed  Google Scholar 

  46. Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19, 1286–1291 (2016).

    CAS  PubMed  Google Scholar 

  47. Kolling, N. et al. Value, search, persistence and model updating in anterior cingulate cortex. Nat. Neurosci. 19, 1280–1285 (2016).

    CAS  PubMed  Google Scholar 

  48. Pochon, J.-B., Riis, J., Sanfey, A. G., Nystrom, L. E. & Cohen, J. D. Functional imaging of decision conflict. J. Neurosci. 28, 3468–3473 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Botvinick, M. M., Cohen, J. D. & Carter, C. S. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn. Sci. 8, 539–546 (2004).

    PubMed  Google Scholar 

  50. Niv, Y. Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009).

    Google Scholar 

  51. O’Doherty, J. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).

    PubMed  Google Scholar 

  52. Marche, K., Martel, A.-C. & Apicella, P. Differences between dorsal and ventral striatum in the sensitivity of tonically active neurons to rewarding events. Front. Syst. Neurosci. 11, 52 (2017).

    PubMed  PubMed Central  Google Scholar 

  53. Morrison, I., Tipper, S. P., Fenton-Adams, W. L. & Bach, P. “Feeling” others’ painful actions: the sensorimotor integration of pain and action information. Hum. Brain Mapp. 34, 1982–1998 (2013).

    PubMed  Google Scholar 

  54. Guo, X. et al. Empathic neural responses to others’ pain depend on monetary reward. Soc. Cogn. Affect. Neurosci. 7, 535–541 (2012).

    PubMed  Google Scholar 

  55. Whitmarsh, S., Nieuwenhuis, I. L. C., Barendregt, H. P. & Jensen, O. Sensorimotor alpha activity is modulated in response to the observation of pain in others. Front. Hum. Neurosci. 5, 91 (2011).

    PubMed  PubMed Central  Google Scholar 

  56. Jepma, M., Verdonschot, R. G., van Steenbergen, H., Rombouts, S. A. R. B. & Nieuwenhuis, S. Neural mechanisms underlying the induction and relief of perceptual curiosity. Front. Behav. Neurosci. 6, 5 (2012).

    PubMed  PubMed Central  Google Scholar 

  57. Kruger, J. & Evans, M. The paradox of Alypius and the pursuit of unwanted information. J. Exp. Soc. Psychol. 45, 1173–1179 (2009).

    Google Scholar 

  58. Bromberg-Martin, E. S. & Hikosaka, O. Midbrain dopamine neurons signal preference for advance information about upcoming rewards. Neuron 63, 119–126 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Rodriguez Cabrero, J. A. M., Zhu, J.-Q. & Ludvig, E. A. Costly curiosity: people pay a price to resolve an uncertain gamble early. Behav. Processes 160, 20–25 (2019).

    PubMed  Google Scholar 

  60. Oosterwijk, S. Choosing the negative: a behavioral demonstration of morbid curiosity. PLoS ONE 12, e0178399 (2017).

    PubMed  PubMed Central  Google Scholar 

  61. Hsee, C. K. & Ruan, B. The Pandora effect: the power and peril of curiosity. Psychol. Sci. 27, 659–666 (2016).

    PubMed  Google Scholar 

  62. Noordewier, M. K. & van Dijk, E. Curiosity and time: from not knowing to almost knowing. Cogn. Emot. 31, 411–421 (2017).

    PubMed  Google Scholar 

  63. Dickinson, A. & Balleine, B. in Stevens’ Handbook of Experimental Psychology: Learning, Motivation, and Emotion (eds Pashler, H. & Gallistel, R.) 497–533 (Wiley, 2002).

  64. Litman, J., Hutchins, T. & Russon, R. Epistemic curiosity, feeling-of-knowing, and exploratory behaviour. Cogn. Emot. 19, 559–582 (2005).

    Google Scholar 

  65. Schonberg, T. et al. Selective impairment of prediction error signaling in human dorsolateral but not ventral striatum in Parkinson’s disease patients: evidence from a model-based fMRI study. Neuroimage 49, 772–781 (2010).

    PubMed  Google Scholar 

  66. Takahashi, Y., Schoenbaum, G. & Niv, A. Silencing the critics: understanding the effects of cocaine sensitization on dorsolateral and ventral striatum in the context of an actor/critic model. Front. Neurosci. 2, 86–99 (2008).

    PubMed  PubMed Central  Google Scholar 

  67. van Lieshout, L. L. F., Vandenbroucke, A. R. E., Müller, N. C. J., Cools, R. & de Lange, F. P. Induction and relief of curiosity elicit parietal and frontal activity. J. Neurosci. 38, 2579–2588 (2018).

    PubMed  PubMed Central  Google Scholar 

  68. Di Domenico, S. I. & Ryan, R. M. The emerging neuroscience of intrinsic motivation: a new frontier in self-determination research. Front. Hum. Neurosci. 11, 145 (2017).

    PubMed  PubMed Central  Google Scholar 

  69. Preuschoff, K., Quartz, S. R. & Bossaerts, P. Human insula activation reflects risk prediction errors as well as risk. J. Neurosci. 28, 2745–2752 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Alexander, W. H. & Brown, J. W. The role of the anterior cingulate cortex in prediction error and signaling surprise. Top. Cogn. Sci. 11, 119–135 (2019).

    PubMed  Google Scholar 

  71. Silvia, P. J. What Is interesting? Exploring the appraisal structure of interest. Emotion 5, 89–102 (2005).

    PubMed  Google Scholar 

  72. Silvia, P. J. Appraisal components and emotion traits: examining the appraisal basis of trait curiosity. Cogn. Emot. 22, 94–113 (2008).

    Google Scholar 

  73. Noordewier, M. K. & van Dijk, E. Interest in complex novelty. Basic Appl. Soc. Psych. 38, 98–110 (2016).

    Google Scholar 

  74. Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S. & Cohen, J. D. Conflict monitoring and cognitive control. Psychol. Rev. 108, 624–652 (2001).

    CAS  PubMed  Google Scholar 

  75. Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Murayama, K., FitzGibbon, L. & Sakaki, M. Process account of curiosity and interest: a reward-learning perspective. Educ. Psychol. Rev. 31, 875–895 (2019).

    Google Scholar 

  77. Zink, C. F., Pagnoni, G., Martin-Skurski, M. E., Chappelow, J. C. & Berns, G. S. Human striatal responses to monetary reward depend on saliency. Neuron 42, 509–517 (2004).

    CAS  PubMed  Google Scholar 

  78. Krebs, R. M., Boehler, C. N., Roberts, K. C., Song, A. W. & Woldorff, M. G. The involvement of the dopaminergic midbrain and cortico–striatal–thalamic circuits in the integration of reward prospect and attentional task demands. Cereb. Cortex 22, 607–615 (2012).

    PubMed  Google Scholar 

  79. Ozono, H. et al. Magic curiosity arousing tricks (MagicCATs): a novel stimulus collection to induce epistemic emotions. Preprint at OSF https://doi.org/10.31234/osf.io/qxdsn (2020).

  80. Fastrich, G. M., Kerr, T., Castel, A. D. & Murayama, K. The role of interest in memory for trivia questions: an investigation with a large-scale database. Motiv. Sci. 4, 227–250 (2018).

    PubMed  Google Scholar 

  81. Peirce, J. W. Generating stimuli for neuroscience using PsychoPy. Front. Neuroinform. 2, 10 (2009).

    PubMed  PubMed Central  Google Scholar 

  82. Murayama, K., Matsumoto, M., Izuma, K. & Matsumoto, K. Neural basis of the undermining effect of monetary reward on intrinsic motivation. Proc. Natl Acad. Sci. USA 107, 20911–20916 (2010).

    CAS  PubMed  Google Scholar 

  83. Murayama, K. et al. How self-determined choice facilitates performance: a key role of the ventromedial prefrontal cortex. Cereb. Cortex 25, 1241–1251 (2015).

    PubMed  Google Scholar 

  84. Worsley, K. J. et al. A unified statistical approach for determining significant signals in images of cerebral activation. Hum. Brain Mapp. 4, 58–73 (1996).

    CAS  PubMed  Google Scholar 

  85. Pauli, W. M., Nili, A. N. & Tyszka, J. M. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci. Data 5, 180063 (2018).

    PubMed  PubMed Central  Google Scholar 

  86. Mumford, J. A., Poline, J.-B. & Poldrack, R. A. Orthogonalization of regressors in fMRI models. PLoS ONE 10, e0126255 (2015).

    PubMed  PubMed Central  Google Scholar 

  87. Rissman, J., Gazzaley, A. & D’Esposito, M. Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage 23, 752–763 (2004).

    PubMed  Google Scholar 

  88. Göttlich, M., Beyer, F. & Krämer, U. M. BASCO: a toolbox for task-related functional connectivity. Front. Syst. Neurosci. 9, 126 (2015).

    PubMed  PubMed Central  Google Scholar 

  89. Tomasi, D. & Volkow, N. D. Laterality patterns of brain functional connectivity: gender effects. Cereb. Cortex 22, 1455–1462 (2012).

    PubMed  PubMed Central  Google Scholar 

  90. Wang, D., Buckner, R. L. & Liu, H. Functional specialization in the human brain estimated by intrinsic hemispheric interaction. J. Neurosci. 34, 12341–12352 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255–278 (2013).

    Google Scholar 

  92. Brauer, M. & Curtin, J. J. Linear mixed-effects models and the analysis of nonindependent data: a unified framework to analyze categorical and continuous independent variables that vary within-subjects and/or within-items. Psychol. Methods 23, 389–411 (2018).

    PubMed  Google Scholar 

  93. Murayama, K., Sakaki, M., Yan, V. X. & Smith, G. M. Type I error inflation in the traditional by-participant analysis to metamemory accuracy: a generalized mixed-effects model perspective. J. Exp. Psychol. Learn. Mem. Cogn. 40, 1287–1306 (2014).

    PubMed  Google Scholar 

  94. Ludbrook, J. Interim analyses of data as they accumulate in laboratory experimentation. BMC Med. Res. Methodol. 3, 15 (2003).

    PubMed  PubMed Central  Google Scholar 

  95. Šidák, Z. Rectangular confidence regions for the means of multivariate normal distributions. J. Am. Stat. Assoc. 62, 626–633 (1967).

    Google Scholar 

  96. Stone, M. Comments on model selection criteria of Akaike and Schwarz. J. R. Stat. Soc. Ser. B 41, 276–278 (1979).

    Google Scholar 

  97. Wagenmakers, E.-J. A practical solution to the pervasive problems of p values. Psychon. Bull. Rev. 14, 779–804 (2007).

    PubMed  Google Scholar 

  98. Masson, M. E. J. A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behav. Res. Methods 43, 679–690 (2011).

    PubMed  Google Scholar 

  99. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  100. Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R. & Kievit, R. A. Raincloud plots: a multi-platform tool for robust data visualization. Wellcome Open Res. 4, 63 (2019).

    PubMed  PubMed Central  Google Scholar 

  101. Muthén, L. K. & Muthén, B. O. Mplus User’s Guide 7th edn (Muthén & Muthén, 2012).

  102. Enders, C. K. & Tofighi, D. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychol. Methods 12, 121–138 (2007).

    PubMed  Google Scholar 

  103. McNeish, D., Stapleton, L. M. & Silverman, R. D. On the unnecessary ubiquity of hierarchical linear modeling. Psychol. Methods 22, 114–140 (2017).

    PubMed  Google Scholar 

  104. Arend, M. G. & Schäfer, T. Statistical power in two-level models: a tutorial based on Monte Carlo simulation. Psychol. Methods 24, 1–19 (2019).

    PubMed  Google Scholar 

  105. Leong, Y. C., Hughes, B. L., Wang, Y. & Zaki, J. Neurocomputational mechanisms underlying motivated seeing. Nat. Hum. Behav. 3, 962–973 (2019).

    PubMed  Google Scholar 

  106. Geuter, S., Qi, G., Welsh, R. C., Wager, T. D. & Lindquist, M. A. Effect size and power in fMRI group analysis. Preprint at bioRxiv https://doi.org/10.1101/295048 (2018).

Download references

Acknowledgements

The study was supported by the Marie Curie Career Integration Grant (CIG630680 to K.M.), the JSPS KAKENHI (15H05401, 16H06406, 18H01102 and 18K18696 to K.M.), the F. J. McGuigan Early Career Investigator Prize (to K.M.), the Jacobs Foundation Advanced Fellowship (to K.M.) and the Leverhulme Trust (RPG-2016-146 and RL-2016-030 to K.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to the magicians (S. Irieda, O. Kohei and Malka) for producing magic tricks for our research. We thank C. Ogulmus, E. Daveau and the rest of the MeMo Lab as well as S. Shen and the CINN for helping with data collection, C. Inaltay, A. Firat, A. Haffey, J. Raw and G. Fastrich for editing and pilot-testing the magic video clips, A. Mihalik for advice on further advanced analysis, and C. McNabb for providing useful comments on the drafts of the article.

Author information

Authors and Affiliations

Authors

Contributions

K.M. conceived the idea, and J.K.L.L. and K.M. jointly designed the study. J.K.L.L., H.O., A.K. and K.K. created the experimental materials. J.K.L.L. performed the research and analysed the data. J.K.L.L. and K.M. jointly wrote the paper. All authors provided critical comments.

Corresponding authors

Correspondence to Johnny King L. Lau or Kou Murayama.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Primary Handling Editor: Marike Schiffer

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Exploratory whole-brain analyses at elicitation phase showed differential brain activations when comparing ‘rejected’ with ‘accepted’ trials.

Activity was stronger within prefrontal cortex and insular gyrus at the elicitation phase of trials in which participants rejected the gamble. For visual illustration here, a voxel-wise threshold of P<0.001 (uncorrected) is applied; all clusters survived a FWE-corrected statistical significance threshold of P<0.05 (at cluster level). L, left; R, right.

Extended Data Fig. 2 Exploratory whole-brain analyses with a main effect of decision (accepted > rejected trials) at decision phase.

Peak activation is shown for the right caudate nucleus (MNI coordinate: 9, 12, 0) and the left caudate nucleus (MNI coordinate: −9, 6, −3) in an extensive medial reward network cluster, extending into the thalamus and the medial frontal cortex, as well as the right frontal cortex and anterior insula. For visual illustration here, a voxel-wise threshold of P < 0.001 (uncorrected) is applied; all clusters survived a FWE-corrected statistical significance threshold of P < 0.05 (at cluster level). See ROI results in Fig. 3. A, anterior; L, left; P, posterior; R, right.

Extended Data Fig. 3 ROI activations for motivation-driven decision-making in a parametric modulation analysis accounting for presented outcome probability.

Differential activations for accepted (> rejected) trials were observed within the ROIs of caudate nucleus, NAcc, and VTA/SN at the Decision phase, even when taking into account the shock/outcome probability presented as an additional parametric modulator in the model. For visual illustration, a voxel-wise threshold of P < 0.001 (uncorrected) is applied here; clusters survived the ROI analysis with an adjusted FWE-corrected statistical significance threshold of P < 0.0167 (at cluster level). A, anterior; L, left; P, posterior; R, right.

Supplementary information

Supplementary Information

Supplementary Methods, Supplementary Tables 1–11, Supplementary References.

Reporting Summary

Supplementary Video 1

Magic trick: disappearance of a pack of playing cards in a hollow box.

Supplementary Video 2

Magic trick: popping a cigarette into and through a metal coin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lau, J.K.L., Ozono, H., Kuratomi, K. et al. Shared striatal activity in decisions to satisfy curiosity and hunger at the risk of electric shocks. Nat Hum Behav 4, 531–543 (2020). https://doi.org/10.1038/s41562-020-0848-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-020-0848-3

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing