• Godfrey-Smith, P. Complexity and the Function of Mind in Nature, Vol. 328 (Cambridge University Press, 1998).

  • Hertwig, R., Barron, G., Weber, E. U. & Erev, I. Decisions from experience and the effect of rare events in risky choice. Psychol. Sci. 15, 534–539 (2004).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • Piaget, J. The Construction of Reality in the Child 1st edn, Vol. 400 (Routledge & Kegan Paul, 1955).

  • Mehlhorn, K. et al. Unpacking the exploration–exploitation tradeoff: a synthesis of human and animal literatures. Decision 2, 191–215 (2015).

    Article 
    MATH 

    Google Scholar
     

  • Wulff, D. U., Mergenthaler-Canseco, M. & Hertwig, R. A meta-analytic review of two modes of learning and the description–experience gap. Psychol. Bull. 144, 140–176 (2018).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • Ruggeri, A., Swaboda, N., Sim, Z. L. & Gopnik, A. Shake it baby, but only when needed: preschoolers adapt their exploratory strategies to the information structure of the task. Cognition 193, 104013 (2019).

    Article 
    PubMed 

    Google Scholar
     

  • Stahl, A. E. & Feigenson, L. Observing the unexpected enhances infants’ learning and exploration. Science 348, 91–94 (2015).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar
     

  • Fawcett, T. W. et al. The evolution of decision rules in complex environments. Trends Cogn. Sci. 18, 153–161 (2014).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • Lejarraga, T., Hertwig, R. & Gonzalez, C. How choice ecology influences search in decisions from experience. Cognition 124, 334–342 (2012).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • Zhang, Z., Yao, X., Yuan, S., Deng, Y. & Guo, C. Big five personality influences trajectories of information seeking behavior. Pers. Individ. Dif. 173, 110631 (2021).

    Article 
    MATH 

    Google Scholar
     

  • van den Bos, W. & Hertwig, R. Adolescents display distinctive tolerance to ambiguity and to uncertainty during risky decision making. Sci. Rep. 7, 40962 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Beran, M. J. & Smith, J. D. Information seeking by rhesus monkeys (Macaca mulatta) and capuchin monkeys (Cebus apella). Cognition 120, 90–105 (2011).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bohn, M., Allritz, M., Call, J. & Völter, C. J. Information seeking about tool properties in great apes. Sci. Rep. 7, 10923 (2017).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Call, J. Do apes know that they could be wrong? Anim. Cogn. 13, 689–700 (2010).

    Article 
    PubMed 

    Google Scholar
     

  • Call, J. & Carpenter, M. Do apes and children know what they have seen? Anim. Cogn. 3, 207–220 (2001).

    Article 
    MATH 

    Google Scholar
     

  • Rosati, A. G. & Santos, L. R. Spontaneous metacognition in rhesus monkeys. Psychol. Sci. 27, 1181–1191 (2016).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar
     

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

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Bode, S. et al. When knowledge hurts: humans are willing to receive pain for obtaining non-instrumental information. Proc. R. Soc. B. 290, 20231175 (2023).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar
     

  • Wyckoff, L. B. Jr. The role of observing responses in discrimination learning. Psychol. Rev. 59, 431–442 (1952).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • 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).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bromberg-Martin, E. S. & Monosov, I. E. Neural circuitry of information seeking. Curr. Opin. Behav. Sci. 35, 62–70 (2020).

    Article 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar
     

  • 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).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 
    MATH 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cohen, J. D., McClure, S. M. & Yu, A. J. Should I stay or should I go? how the human brain manages the trade-off between exploitation and exploration. Philos. Trans. R. Soc. B. 362, 933–942 (2007).

    Article 

    Google Scholar
     

  • Hills, T. T. & Hertwig, R. Information search in decisions from experience: do our patterns of sampling foreshadow our decisions? Psychol. Sci. 21, 1787–1792 (2010).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • Hertwig, R. & Pleskac, T. J. Decisions from experience: why small samples? Cognition 115, 225–237 (2010).

    Article 
    PubMed 
    MATH 

    Google Scholar
     

  • Haux, L. M., Engelmann, J. M., Arslan, R. C., Hertwig, R. & Herrmann, E. Chimpanzee and human risk preferences show key similarities. Psychol. Sci. 34, 358–369 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • My, K. B., Brunette, M., Couture, M. & Van Driessche, S. Are ambiguity preferences aligned with risk preferences? J. Behav. Exp. Econ. 111, 102237 (2024).

    Article 
    MATH 

    Google Scholar
     

  • Huettel, S. A., Stowe, C. J., Gordon, E. M., Warner, B. T. & Platt, M. L. Neural signatures of economic preferences for risk and ambiguity. Neuron 49, 765–775 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • R Core Team: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org (2024).

  • Bürkner, P. C. Advanced Bayesian multilevel modeling with the R package brms. arXiv https://arxiv.org/abs/1705.11123 (2017).

  • Gelman, A. Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1, 515–534 (2006).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Article 
    MATH 

    Google Scholar
     

  • Wagenmakers, E., Lee, M. D., Rouder, J. N. & Morey, R. D. The principle of predictive irrelevance, or why intervals should not be used for model comparison featuring a point null hypothesis. PsyArXiv https://doi.org/10.31234/osf.io/rqnu5 (2019).

  • Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

    Article 
    MathSciNet 
    MATH 

    Google Scholar
     

  • McElreath, R. Statistical Rethinking: A Bayesian Course With Examples in R and Stan 1st en, Vol. 469 (Chapman & Hall, 2016).

  • Haux, L. M., Engelmann, J. M., Herrmann, E. & Hertwig, R. Adaptive_exploration. Zenodo https://doi.org/10.5281/zenodo.13907943 (2024).

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