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.
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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.
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).
Jirout, J. & Klahr, D. Children’s scientific curiosity: in search of an operational definition of an elusive concept. Dev. Rev. 32, 125–160 (2012).
Kidd, C. & Hayden, B. Y. The psychology and neuroscience of curiosity. Neuron 88, 449–460 (2015).
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).
Gruber, M. J., Gelman, B. D. & Ranganath, C. States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron 84, 486–496 (2014).
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).
Renninger, K. A. & Hidi, S. The Power of Interest for Motivation and Engagement (Routledge, 2016).
Sakaki, M., Yagi, A. & Murayama, K. Curiosity in old age: a possible key to achieving adaptive aging. Neurosci. Biobehav. Rev. 88, 106–116 (2018).
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).
Loewenstein, G. The psychology of curiosity: a review and reinterpretation. Psychol. Bull. 116, 75–98 (1994).
Berlyne, D. E. Conflict, Arousal, and Curiosity (McGraw-Hill, 1960).
Silvia, P. J. Exploring the Psychology of Interest (Oxford Univ. Press, 2006).
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).
Gottlieb, J. & Oudeyer, P.-Y. Towards a neuroscience of active sampling and curiosity. Nat. Rev. Neurosci. 19, 758–770 (2018).
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).
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).
Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M. & Gottlieb, J. Diverse motives for human curiosity. Nat. Hum. Behav. 3, 587–595 (2019).
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).
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).
Vasconcelos, M., Monteiro, T. & Kacelnik, A. Irrational choice and the value of information. Sci. Rep. 5, 13874 (2015).
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).
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).
Eliaz, K. & Schotter, A. Paying for confidence: an experimental study of the demand for non-instrumental information. Games Econ. Behav. 70, 304–324 (2010).
Berridge, K. C. Motivation concepts in behavioral neuroscience. Physiol. Behav. 81, 179–209 (2004).
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).
Berridge, K. C. From prediction error to incentive salience: mesolimbic computation of reward motivation. Eur. J. Neurosci. 35, 1124–1143 (2012).
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).
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).
Berridge, K. C. ‘Liking’ and ‘wanting’ food rewards: brain substrates and roles in eating disorders. Physiol. Behav. 97, 537–550 (2009).
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).
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).
Knutson, B., Adams, C. M., Fong, G. W. & Hommer, D. Anticipation of increasing monetary reward selectively recruits nucleus accumbens. J. Neurosci. 21, RC159 (2001).
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).
Litman, J. Curiosity and the pleasures of learning: wanting and liking new information. Cogn. Emot. 19, 793–814 (2005).
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).
Kobayashi, K. & Hsu, M. Common neural code for reward and information value. Proc. Natl Acad. Sci. USA 116, 13061–13066 (2019).
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).
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).
Wright, W. F. & Bower, G. H. Mood effects on subjective probability assessment. Organ. Behav. Hum. Decis. Process. 52, 276–291 (1992).
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).
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).
Ligneul, R., Mermillod, M. & Morisseau, T. From relief to surprise: dual control of epistemic curiosity in the human brain. Neuroimage 181, 490–500 (2018).
Baranes, A., Oudeyer, P.-Y. & Gottlieb, J. Eye movements reveal epistemic curiosity in human observers. Vision Res. 117, 81–90 (2015).
Westfall, J., Nichols, T. E. & Yarkoni, T. Fixing the stimulus-as-fixed-effect fallacy in task fMRI. Wellcome Open Res. 1, 23 (2016).
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).
Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19, 1286–1291 (2016).
Kolling, N. et al. Value, search, persistence and model updating in anterior cingulate cortex. Nat. Neurosci. 19, 1280–1285 (2016).
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).
Botvinick, M. M., Cohen, J. D. & Carter, C. S. Conflict monitoring and anterior cingulate cortex: an update. Trends Cogn. Sci. 8, 539–546 (2004).
Niv, Y. Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009).
O’Doherty, J. Dissociable roles of ventral and dorsal striatum in instrumental conditioning. Science 304, 452–454 (2004).
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).
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).
Guo, X. et al. Empathic neural responses to others’ pain depend on monetary reward. Soc. Cogn. Affect. Neurosci. 7, 535–541 (2012).
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).
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).
Kruger, J. & Evans, M. The paradox of Alypius and the pursuit of unwanted information. J. Exp. Soc. Psychol. 45, 1173–1179 (2009).
Bromberg-Martin, E. S. & Hikosaka, O. Midbrain dopamine neurons signal preference for advance information about upcoming rewards. Neuron 63, 119–126 (2009).
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).
Oosterwijk, S. Choosing the negative: a behavioral demonstration of morbid curiosity. PLoS ONE 12, e0178399 (2017).
Hsee, C. K. & Ruan, B. The Pandora effect: the power and peril of curiosity. Psychol. Sci. 27, 659–666 (2016).
Noordewier, M. K. & van Dijk, E. Curiosity and time: from not knowing to almost knowing. Cogn. Emot. 31, 411–421 (2017).
Dickinson, A. & Balleine, B. in Stevens’ Handbook of Experimental Psychology: Learning, Motivation, and Emotion (eds Pashler, H. & Gallistel, R.) 497–533 (Wiley, 2002).
Litman, J., Hutchins, T. & Russon, R. Epistemic curiosity, feeling-of-knowing, and exploratory behaviour. Cogn. Emot. 19, 559–582 (2005).
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).
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).
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).
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).
Preuschoff, K., Quartz, S. R. & Bossaerts, P. Human insula activation reflects risk prediction errors as well as risk. J. Neurosci. 28, 2745–2752 (2008).
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).
Silvia, P. J. What Is interesting? Exploring the appraisal structure of interest. Emotion 5, 89–102 (2005).
Silvia, P. J. Appraisal components and emotion traits: examining the appraisal basis of trait curiosity. Cogn. Emot. 22, 94–113 (2008).
Noordewier, M. K. & van Dijk, E. Interest in complex novelty. Basic Appl. Soc. Psych. 38, 98–110 (2016).
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).
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).
Murayama, K., FitzGibbon, L. & Sakaki, M. Process account of curiosity and interest: a reward-learning perspective. Educ. Psychol. Rev. 31, 875–895 (2019).
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).
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).
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).
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).
Peirce, J. W. Generating stimuli for neuroscience using PsychoPy. Front. Neuroinform. 2, 10 (2009).
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).
Murayama, K. et al. How self-determined choice facilitates performance: a key role of the ventromedial prefrontal cortex. Cereb. Cortex 25, 1241–1251 (2015).
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).
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).
Mumford, J. A., Poline, J.-B. & Poldrack, R. A. Orthogonalization of regressors in fMRI models. PLoS ONE 10, e0126255 (2015).
Rissman, J., Gazzaley, A. & D’Esposito, M. Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage 23, 752–763 (2004).
Göttlich, M., Beyer, F. & Krämer, U. M. BASCO: a toolbox for task-related functional connectivity. Front. Syst. Neurosci. 9, 126 (2015).
Tomasi, D. & Volkow, N. D. Laterality patterns of brain functional connectivity: gender effects. Cereb. Cortex 22, 1455–1462 (2012).
Wang, D., Buckner, R. L. & Liu, H. Functional specialization in the human brain estimated by intrinsic hemispheric interaction. J. Neurosci. 34, 12341–12352 (2014).
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).
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).
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).
Ludbrook, J. Interim analyses of data as they accumulate in laboratory experimentation. BMC Med. Res. Methodol. 3, 15 (2003).
Šidák, Z. Rectangular confidence regions for the means of multivariate normal distributions. J. Am. Stat. Assoc. 62, 626–633 (1967).
Stone, M. Comments on model selection criteria of Akaike and Schwarz. J. R. Stat. Soc. Ser. B 41, 276–278 (1979).
Wagenmakers, E.-J. A practical solution to the pervasive problems of p values. Psychon. Bull. Rev. 14, 779–804 (2007).
Masson, M. E. J. A tutorial on a practical Bayesian alternative to null-hypothesis significance testing. Behav. Res. Methods 43, 679–690 (2011).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
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).
Muthén, L. K. & Muthén, B. O. Mplus User’s Guide 7th edn (Muthén & Muthén, 2012).
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).
McNeish, D., Stapleton, L. M. & Silverman, R. D. On the unnecessary ubiquity of hierarchical linear modeling. Psychol. Methods 22, 114–140 (2017).
Arend, M. G. & Schäfer, T. Statistical power in two-level models: a tutorial based on Monte Carlo simulation. Psychol. Methods 24, 1–19 (2019).
Leong, Y. C., Hughes, B. L., Wang, Y. & Zaki, J. Neurocomputational mechanisms underlying motivated seeing. Nat. Hum. Behav. 3, 962–973 (2019).
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).
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.
The authors declare no competing interests.
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 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.
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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
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