Part III - Which Machinery Supports the Drive for Knowledge?
Published online by Cambridge University Press: 19 May 2022
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- The Drive for KnowledgeThe Science of Human Information Seeking, pp. 193 - 290Publisher: Cambridge University PressPrint publication year: 2022
References
References
Badre, D., Doll, B. B., Long, N. M., & Frank, M. J. (2012). Rostrolateral prefrontal cortex and individual differences in uncertainty-driven exploration. Neuron, 73(3), 595–607. https://doi.org/10.1016/j.neuron.2011.12.025.Google Scholar
Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage, 76, 412–427. https://doi.org/10.1016/j.neuroimage.2013.02.063.CrossRefGoogle ScholarPubMed
Bénabou, R. (2016). Mindful economics: The production, consumption, and value of beliefs. Journal of Economic Perspectives, 30, 141–164.Google Scholar
Berlyne, D. E. (1957). Uncertainty and conflict: A point of contact between information-theory and behavior-theory concepts. Psychological Review, 64(6), 329–339.CrossRefGoogle Scholar
Berns, G. S., Chappelow, J., Cekic, M., Zink, C. F., Pagnoni, G., & Martin-Skurski, M. E. (2006). Neurobiological substrates of dread. Science, 312(5774), 754–758. https://doi.org/10.1126/science.1123721.CrossRefGoogle ScholarPubMed
Blanchard, T. C., Hayden, B. Y., & Bromberg-Martin, E. S. (2015). Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity. Neuron, 85(3), 602–614. https://doi.org/10.1016/j.neuron.2014.12.050.CrossRefGoogle ScholarPubMed
Bromberg-Martin, E. S., & Hikosaka, O. (2009). Midbrain dopamine neurons signal preference for advance information about upcoming rewards. Neuron, 63(1), 119–126. https://doi.org/10.1016/j.neuron.2009.06.009.CrossRefGoogle ScholarPubMed
Bromberg-Martin, E. S., & Hikosaka, O. (2011). Lateral habenula neurons signal errors in the prediction of reward information. Nature Neuroscience, 14(9), 1209–1216. https://doi.org/10.1038/nn.2902.CrossRefGoogle ScholarPubMed
Bromberg-Martin, E. S., & Monosov, I. E. (2020). Neural circuitry of information seeking. Current Opinion in Behavioral Sciences, 35, 62–70, https://doi.org/10.1016/j.cobeha.2020.07.006.CrossRefGoogle ScholarPubMed
Brydevall, M., Bennett, D., Murawski, C., & Bode, S. (2018). The neural encoding of information prediction errors during non-instrumental information seeking. Scientific Reports, 8(1), 6134. https://doi.org/10.1038/s41598-018-24566-x.CrossRefGoogle ScholarPubMed
Caplin, A., & Leahy, J. (2001). Psychological expected utility theory and anticipatory feelings. The Quarterly Journal of Economics, 116(1), 55–79.Google Scholar
Charpentier, C. J., Bromberg-Martin, E. S., & Sharot, T. (2018). Valuation of knowledge and ignorance in mesolimbic reward circuitry. Proceedings of the National Academy of Sciences of the United States of America, 115(31), E7255-E7264. https://doi.org/10.1073/pnas.1800547115.Google Scholar
Chib, V. S., Yun, K., Takahashi, H., & Shimojo, S. (2013). Noninvasive remote activation of the ventral midbrain by transcranial direct current stimulation of prefrontal cortex. Translational Psychiatry, 3, e268. https://doi.org/10.1038/tp.2013.44.Google Scholar
Cogliati Dezza, I., Cleeremans, A., & Alexander, W. (2020). Independent and interacting value systems for reward and information in the human brain. bioRxiv.Google Scholar
Cogliati Dezza, I., & Sharot, T. (2021). People adaptively use information to improve their internal and external states. PsyArXiv. https://psyarxiv.com/f5vyq.Google Scholar
Cogliati Dezza, I., Yu, A. J., Cleeremans, A., & Alexander, W. (2017). Learning the value of information and reward over time when solving exploration-exploitation problems. Sci Rep, 7(1), 16919. https://doi.org/10.1038/s41598-017-17237-w.CrossRefGoogle ScholarPubMed
Diederen, K. M. J., & Fletcher, P. C. (2021). Dopamine, prediction error and beyond. Neuroscientist, 27(1), 30–46. https://doi.org/10.1177/1073858420907591.CrossRefGoogle ScholarPubMed
Dubey, R., & Griffiths, T. L. (2020). Reconciling novelty and complexity through a rational analysis of curiosity. Psychological Review, 127(3), 455–476. https://doi.org/10.1037/rev0000175.Google Scholar
Evans, B. M., & Chi, E. H. (2010). An elaborated model of social search. Information Processing & Management, 46, 656–678.Google Scholar
Evans, B. M., Kairam, S., & Pirolli, P. (2009). Do your friends make you smarter? An analysis of social strategies in online information seeking. Information Processing & Management, 46(6), 679–692.Google Scholar
Filimon, F., Nelson, J. D., Sejnowski, T. J., Sereno, M. I., & Cottrell, G. W. (2020). The ventral striatum dissociates information expectation, reward anticipation, and reward receipt. Proceedings of the National Academy of Sciences of the United States of America, 117(26), 15200–15208. https://doi.org/10.1073/pnas.1911778117.Google Scholar
Foley, N. C., Kelly, S. P., Mhatre, H., Lopes, M., & Gottlieb, J. (2017). Parietal neurons encode expected gains in instrumental information. Proceedings of the National Academy of Sciences of the United States of America, 114(16), E3315–E3323. https://doi.org/10.1073/pnas.1613844114.Google ScholarPubMed
Frank, M. J., Doll, B. B., Oas-Terpstra, J., & Moreno, F. (2009). Prefrontal and striatal dopaminergic genes predict individual differences in exploration and exploitation. Nature Neuroscience, 12(8), 1062–1068. https://doi.org/10.1038/nn.2342.Google Scholar
Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive Neuroscience, 6(4), 187–214. https://doi.org/10.1080/17588928.2015.1020053.Google Scholar
Friston, K. J., Lin, M., Frith, C. D., Pezzulo, G., Hobson, J. A., & Ondobaka, S. (2017). Active inference, curiosity and insight. Neural Computation, 29(10), 2633–2683. https://doi.org/10.1162/neco_a_00999.Google Scholar
Gershman, S. J. (2018). Deconstructing the human algorithms for exploration. Cognition, 173, 34–42. https://doi.org/10.1016/j.cognition.2017.12.014.Google Scholar
Gershman, S. J. (2019). Uncertainty and exploration. Decision, 6(3), 277–286.CrossRefGoogle ScholarPubMed
Goldman-Rakic, P. S., Lidow, M. S., Smiley, J. F., & Williams, M. S. (1992). The anatomy of dopamine in monkey and human prefrontal cortex. In Stricker, E. M., Tuma, A. H., & Gershon, S. (Eds.), Advances in neuroscience and schizophrenia. Vienna: Springer.Google Scholar
Golman, R., Hagmann, D., & Loewenstein, G. (2017). Information avoidance. Journal of Economic Literature, 55(1), 96–135.Google Scholar
Gottlieb, J., Oudeyer, P. Y., Lopes, M., & Baranes, A. (2013). Information-seeking, curiosity, and attention: computational and neural mechanisms. Trends in Cognitive Science, 17(11), 585–593. https://doi.org/10.1016/j.tics.2013.09.001.CrossRefGoogle ScholarPubMed
Horan, M., Daddaoua, N., & Gottlieb, J. (2019). Parietal neurons encode information sampling based on decision uncertainty. Nature Neuroscience, 22(8), 1327–1335. https://doi.org/10.1038/s41593-019-0440-1.Google Scholar
Hunt, L. T., Malalasekera, W. M. N., de Berker, A. O., Miranda, B., Farmer, S. F., Behrens, T. E. J., & Kennerley, S. W. (2018). Triple dissociation of attention and decision computations across prefrontal cortex. Nature Neuroscience, 21(10), 1471–1481. https://doi.org/10.1038/s41593-018-0239-5.Google Scholar
Iigaya, K., Hauser, T. U., Kurth-Nelson, Z., O’Doherty, J. P., Dayan, P., & Dolan, R. J. (2020). The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation. Sci Adv, 6 (25), eaba3828. https://doi.org/10.1126/sciadv.aba3828.Google Scholar
Iigaya, K., Story, G. W., Kurth-Nelson, Z., Dolan, R. J., & Dayan, P. (2016). The modulation of savouring by prediction error and its effects on choice. Elife, 5. https://doi.org/10.7554/eLife.13747.Google Scholar
Jessup, R. K., & O’Doherty, J. P. (2014). Distinguishing informational from value-related encoding of rewarding and punishing outcomes in the human brain. European Journal of Neuroscience, 39(11), 2014–2026. https://doi.org/10.1111/ejn.12625.Google Scholar
Kaanders, P., Juechems, K., O’Reilly, J. X., & Hunt, L. T. (2021). Dissociable mechanisms of information sampling in prefrontal cortex and the dopaminergic system. Current Opinion in Behavioral Sciences, 41, 63–70.CrossRefGoogle Scholar
Kaanders, P., Nili, H., O’Reilly, J. X., & Hunt, L. T. (2020). Medial frontal cortex activity predicts information sampling in economic choice. bioRxiv preprint. https://doi.org/10.1101/2020.11.24.395814.Google Scholar
Karlsson, N., Loewenstein, G., & Seppi, D. (2009). The ostrich effect: Selective attention to information. Journal of Risk and Uncertainty, 38(2), 95–115.Google Scholar
Kelly, C. A., & Sharot, T. (2021). Individual differences in information-seeking. Nature Communications 12(7062). https://doi.org/10.1038/s41467-021-27046-5.Google Scholar
Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88(3), 449–460. https://doi.org/10.1016/j.neuron.2015.09.010.CrossRefGoogle ScholarPubMed
Kobayashi, K., & Hsu, M. (2019). Common neural code for reward and information value. Proceedings of the National Academy of Sciences of the United States of America, 116(26), 13061–13066. https://doi.org/10.1073/pnas.1820145116.Google Scholar
Kobayashi, K., Lee, S., Filipowicz, A., McGaughey, K., Kable, J. W., & Nassar, M. R. (2021). Dynamic Representation of the Subjective Value of Information. bioRxiv.Google Scholar
Kobayashi, K., Ravaioli, S., Baranes, A., Woodford, M., & Gottlieb, J. (2019). Diverse motives for human curiosity. Nature Human Behavior, 3(6), 587–595. https://doi.org/10.1038/s41562-019-0589-3.Google Scholar
Ligneul, R., Mermillod, M., & Morisseau, T. (2018). From relief to surprise: Dual control of epistemic curiosity in the human brain. Neuroimage, 181, 490–500. https://doi.org/10.1016/j.neuroimage.2018.07.038.Google Scholar
Loewenstein, G. (1987). Anticipation and the valuation of delayed consumption. The Economic Journal, 97(387), 666–684.Google Scholar
Loewenstein, G., & Molnar, A. (2018). The renaissance of belief-based utility in economics. Nature Human Behavior, 2, 166–167.CrossRefGoogle Scholar
Lopez-Persem, A., Bastin, J., Petton, M., Abitbol, R., Lehongre, K., Adam, C., … Pessiglione, M. (2020). Four core properties of the human brain valuation system demonstrated in intracranial signals. Nature Neuroscience, 23(5), 664–675. https://doi.org/10.1038/s41593-020-0615-9.Google Scholar
Matsumoto, M., & Hikosaka, O. (2007). Lateral habenula as a source of negative reward signals in dopamine neurons. Nature, 447(7148), 1111–1115. https://doi.org/10.1038/nature05860.Google Scholar
Morris, L. S., Kundu, P., Dowell, N., Mechelmans, D. J., Favre, P., Irvine, M. A., … Voon, V. (2016). Fronto-striatal organization: Defining functional and microstructural substrates of behavioural flexibility. Cortex, 74, 118–133. https://doi.org/10.1016/j.cortex.2015.11.004.Google Scholar
Murayama, K. (2019a). A reward-learning framework of autonomous knowledge acquisition: An integrated account of curiosity, interest, and intrinsic-extrinsic rewards. preprint. https://doi.org/10.31219/osf.io/zey4k.Google Scholar
Murayama, K. (2019b). A reward-learning framework of autonomous knowledge acquisition: An integrated account of curiosity, interest, and intrinsic-extrinsic rewards. OSFPREPRINTS. https://doi.org/10.31219/osf.io/zey4k.Google Scholar
Oudeyer, P.-Y. (2018). Computational theories of curiosity-driven learning. In Gordon, G (Ed.), The new science of curiosity (pp. 43–72). Nova Science Publishers.Google Scholar
Oudeyer, P.-Y., Lopes, M., Kidd, C., & Gottlieb, J. (2016). Curiosity and intrinsic motivation for autonomous machine learning. ERCIM News, 107, 34–35.Google Scholar
Oudeyer, P. Y., & Kaplan, F. (2007). What is intrinsic motivation? A typology of computational approaches. Frontiers in Neurorobotics, 1, 6. https://doi.org/10.3389/neuro.12.006.2007.Google Scholar
Padoa-Schioppa, C., & Conen, K. E. (2017). Orbitofrontal cortex: A neural circuit for economic decisions. Neuron, 96(4), 736–754. https://doi.org/10.1016/j.neuron.2017.09.031.CrossRefGoogle ScholarPubMed
Pessiglione, M., & Lebreton, M. (2014). From the reward circuit to the valuation system: How the brain motivates behavior. In Gendolla, G. H. E, Koole, S. L. & Tops, M (Eds.), Handbook of biobehavioral approaches to self-regulation. New York: Springer.Google Scholar
Pierson, E., & Goodman, N. (2014). Uncertainty and denial: A resource-rational model of the value of information. PLoS One, 9(11), e113342. https://doi.org/10.1371/journal.pone.0113342.Google Scholar
Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593–1599. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/9054347.Google Scholar
Schulz, E., & Gershman, S. J. (2019). The algorithmic architecture of exploration in the human brain. Current Opinion in Neurobiology, 55, 7–14. https://doi.org/10.1016/j.conb.2018.11.003.Google Scholar
Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H., Kronbichler, M., & Friston, K. J. (2019). Computational mechanisms of curiosity and goal-directed exploration. Elife, 8. https://doi.org/10.7554/eLife.41703.Google Scholar
Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behavior, 4(1), 14–19. https://doi.org/10.1038/s41562-019-0793-1.Google Scholar
Smith, V. D., Rigney, A. E., & Delgado, M. R. (2016). Distinct reward properties are encoded via corticostriatal interactions. Scientific Reports. https://doi.org/10.1038/srep20093.CrossRefGoogle Scholar
Story, G. W., Vlaev, I., Seymour, B., Winston, J. S., Darzi, A., & Dolan, R. J. (2013). Dread and the disvalue of future pain. PLoS Computational Biology, 9(11), e1003335. https://doi.org/10.1371/journal.pcbi.1003335.Google Scholar
Tomov, M. S., Truong, V. Q., Hundia, R. A., & Gershman, S. J. (2020). Dissociable neural correlates of uncertainty underlie different exploration strategies. Nature Communications, 11(1), 2371. https://doi.org/10.1038/s41467-020-15766-z.Google Scholar
van Lieshout, L. L. F., van den Bosch, R., Hofmans, L., de Lange, F. P., & Cools, R. (2020). Does dopamine synthesis capacity predict individual variation in curiosity? bioRxiv.CrossRefGoogle Scholar
van Lieshout, L. L. F., Vandenbroucke, A. R. E., Muller, N. C. J., Cools, R., & de Lange, F. P. (2018). Induction and relief of curiosity elicit parietal and frontal activity. Journal of Neuroscience, 38(10), 2579–2588. https://doi.org/10.1523/JNEUROSCI.2816-17.2018..Vellani, V., de Vries, L. P., Gaule, A., & Sharot, T. (2021). A selective effect of dopamine on information-seeking. Elife, 9, e59152.Google Scholar
White, J. K., Bromberg-Martin, E. S., Heilbronner, S. R., Zhang, K., Pai, J., Haber, S. N., & Monosov, I. E. (2019). A neural network for information seeking. Nature Communications, 10(1), 5168. https://doi.org/10.1038/s41467-019-13135-zGoogle Scholar
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2014). Humans use directed and random exploration to solve the explore-exploit dilemma. Journal of Experimental Psychology: General, 143(6), 2074–2081. https://doi.org/10.1037/a0038199.CrossRefGoogle ScholarPubMed
Wu, C. M., Schulz, E., Speekenbrink, M., Nelson, J. D., & Meder, B. (2018). Generalization guides human exploration in vast decision spaces. Nature Human Behavior, 2(12), 915–924. https://doi.org/10.1038/s41562-018-0467-4.Google Scholar
Zajkowski, W. K., Kossut, M., & Wilson, R. C. (2017). A causal role for right frontopolar cortex in directed, but not random, exploration. Elife, 6. https://doi.org/10.7554/eLife.27430.Google Scholar
Zurn, P., & Bassett, D. S. (2018). On curiosity: A fundamental aspect of personality, a practice of network growth. Personality Neuroscience, 1, e13. https://doi.org/10.1017/pen.2018.3.Google Scholar
References
Awh, E., Belopolsky, A. V., & Theeuwes, J. (2012). Top-down versus bottom-up attentional control: A failed theoretical dichotomy. Trends in Cognitive Science, 16(8), 437–443.Google Scholar
Bisley, J. W., & Goldberg, M. E. (2010). Attention, intention, and priority in the parietal lobe. Annual Review of Neuroscience, 33, 1–21.Google Scholar
Callaway, F., Rangel, A., & Griffiths, T. L. (2021). Fixation patterns in simple choice reflect optimal information sampling. PLoS Comp Biol. https://doi.org/10.1371/journal.pcbi.1008863.Google Scholar
Carrasco, M., Eckstein, M., Verghese, P., Boynton, G., & Treue, S. (2009). Visual attention: Neurophysiology, psychophysics and cognitive neuroscience. Vision Research, 49(10), 1033–1036. https://doi.org/10.1016/j.visres.2009.04.022.Google Scholar
Charpentier, C. J., Bromberg-Martin, E. S., & Sharot, , T., S. (2018). Valuation of knowledge and ignorance in mesolimbic reward circuitry. Proceedings of the National Academy of Sciences of the United States of America., 115(31), E7255-E7264.Google Scholar
Coenen, A., Nelson, J. D., & Gureckis, T. M. (2018). Asking the right questions about the psychology of human inquiry: Nine open challenges. Psychonomic Bulletin & Review, Jun. 4. https://doi.org/10.3758/s13423-018–1470–5. [Epub ahead of print]CrossRefGoogle Scholar
Daddaoua, N., Lopes, M., & Gottlieb, J. (2016). Intrinsically motivated oculomotor exploration guided by uncertainty reduction and conditioned reinforcement in non-human primates. Sci Rep, 6(20202). https://doi.org/10.1038/srep20202.Google Scholar
Foley, N. C., Kelley, S. P., Mhatre, H., Lopes, M., & Gottlieb, J. (2017). Parietal neurons encode expected gains in instrumental information. Proceedings of the National Academy of Science, 114(16), E3315–E3323.Google Scholar
Gottlieb, J., Kusunoki, M., & Goldberg, M. E. (1998). The representation of visual salience in monkey parietal cortex. Nature, 391, 481–484.CrossRefGoogle ScholarPubMed
Gottlieb, J., & Oudeyer, P. Y. (2018). Toward a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), 758–770.Google Scholar
Horan, M., Daddaoua, N., & Gottlieb, J. (2019). Parietal neurons encode information sampling based on decision uncertainty. Nature Neuroscience, 22(8), 1327–1335. https://doi.org/10.1038/s41593-019-0440-1.Google Scholar
Hunt, L. T., Rutledge, R. B., Malalasekera, W. M., Kennerley, S. W., & Dolan, R. J. (2016). Approach-induced biases in human information sampling. PLoS Biol., 14(11), e2000638. https://doi.org/10.1371/journal.pbio.2000638.Google Scholar
Iigaya, K., Story, G. W., Kurth-Nelson, Z., Dolan, R. J., & Dayan, P. (2016). The modulation of savouring by prediction error and its effects on choice. eLife, Apr. 21(5), e13747. https://doi.org/10.7554/eLife.13747.Google Scholar
Kobayashi, K., Ravaioli, S., Baranès, A., Woodford, M., & Gottlieb, J. (2019). Diverse motives for human curiosity. Nature Human Behavior., 3(6), 587–595. https://doi.org/10.1038/s41562-019-0589-3.CrossRefGoogle ScholarPubMed
Krajbich, I., Armel, C., & Rangel, A. (2010). Visual fixations and the computation and comparison of value in simple choice. Nature Neuroscience, 13(10), 1292–1298. https://doi.org/nn.2635%5Bpii%5D10.1038/nn.2635.Google Scholar
Krauzlis, R. J., Lovejoy, L. P., & Zenon, A. (2013). Superior colliculus and visual spatial attention. Annual Review of Neuroscience, 36, 165–182. https://doi.org/10.1146/annurev-neuro-062012-170249.Google Scholar
Leong, Y., Radulescu, A., Daniel, R., DeWoskin, V., & Niv, Y. (2017). Dynamic interaction between reinforcement learning and attention in multidimensional environments. Neuron, 93(2), 451–463.Google Scholar
Loewenstein, G. (1987). Anticipation and the valuation of delayed consumption. The Economic Journal, 97(387), 666–684.Google Scholar
Maunsell, J. H. (2004). Neuronal representations of cognitive state: reward or attention? Trends in Cognitive Science, 8(6), 261–265.Google Scholar
Morvan, C., & Maloney, L. (2012). Human visual search does not maximize the post-saccadic probability of identifying targets. PLoS Computational Biology, 8(2), e1002342. https://doi.org/10.1371/journal.pcbi.1002342.Google Scholar
Najemnik, J., & Geisler, W. S. (2008). Eye movement statistics in humans are consistent with an optimal search strategy. Journal of Vision, 8 (3), 1–14. https://doi.org/10.1167/8.3.4/8/3/4/ [pii].Google Scholar
Nelson, J., McKenzie, C., Cottrell, G., & Sejnowski, T. (2010). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21(7), 960–969.Google Scholar
Padmala, S., & Pessoa, L. (2011). Reward reduces conflict by enhancing attentional control and biasing visual cortical processing. Journal of Cognitive Neuroscience, 23(11), 3419–3432. https://doi.org/10.1162/jocn_a_00011.Google Scholar
Rothe, A., Lake, B., & Gureckis, T. M. (2016). Asking and evaluating natural language questions. In Papafragou, A, Grodner, D, Mirman, D, & Trueswell, J. C (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 2051–2056). Austin, TX: Cognitive Science Society.Google Scholar
Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behavior, 4(1), 14–19. https://doi.org/10.1038/s41562-019-0793-1.CrossRefGoogle ScholarPubMed
Shenhav, A., Botvinick, M., & Cohen, J. (2013). The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217–240.Google Scholar
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D., & Botvinick, M. M. (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience, 40, 99–124. https://doi.org/10.1146/annurev-neuro-072116–031526.Google Scholar
Silvetti, M., Vassena, E., Abrahamse, E., & Verguts, T. (2018). Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner. PLoS Computational Biology, 14(8), e1006370. https://doi.org/10.1371/journal.pcbi.1006370.Google Scholar
Simons, D. J. (2000). Attentional capture and inattentional blindness. Trends in Cognitive Science, 4(4), 147–155. https://doi.org/10.1016/s1364-6613(00)01455-8.Google Scholar
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004). Matching behavior and the representation of value in the parietal cortex. Science, 304(5678), 1782–1787.Google Scholar
Taghizadeh, B., Foley, N. C., Karimimehr, S., Cohanpour, M., Semework, M., Sheth, S. A., … Gottlieb, J. (2020). Reward uncertainty asymmetrically affects information transmission within the monkey fronto-parietal network. Communications Biology, 3(1), 594. https://doi.org/10.1038/s42003-020-01320-6.Google Scholar
Tatler, B. W., Hayhoe, M. N., Land, M. F., & Ballard, D. H. (2011). Eye guidance in natural vision: reinterpreting salience. Journal of Vision, 11(5), 5–25.Google Scholar
Thompson, K. G., & Bichot, N. P. (2005). A visual salience map in the primate frontal eye field. Progress in Brain Research, 147, 251–262.Google Scholar
Wei, X. X., & Stocker, A. A. (2015). A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience 18(10), 1509–1517.Google Scholar
Yang, S. C., Lengyel, M., & Wolpert, D. M. (2016). Active sensing in the categorization of visual patterns. eLife. https://doi.org/10.7554/eLife.12215.CrossRefGoogle Scholar
References
Abbott, J. T., Austerweil, J. L., & Griffiths, T. L. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122(3), 558–569.Google Scholar
Adamic, L. A. (1999). The small world web. International Conference on Theory and Practice of Digital Libraries, 1696, 443–452.Google Scholar
Anderson, M. C., Bjork, R. A., & Bjork, E. (1994). Remembering can cause forgetting: Retrieval dynamics in long-term memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 20(5), 1063–1087.Google ScholarPubMed
Anderson, J. R., & Pirolli, P. L. (1984). Spread of activation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 791–798.Google Scholar
Anderson, J. R., & Schooler, L. J. (1991). Reflections of the environment in memory. Psychological Science, 2(6), 396–408.Google Scholar
Benhamou, S. (2007). How many animals really do the Lévy walk? Ecology, 88(8), 1962–1969.CrossRefGoogle ScholarPubMed
Bhatia, S., Richie, R., & Zou, W. (2019). Distributed semantic representations for modeling human judgment. Current Opinion in Behavioral Sciences, 29, 31–36.Google Scholar
Birn, R. M., Kenworthy, L., Case, L., Caravella, R., Jones, T. B., Bandettini, P. A., & Martin, A. (2010). Neural systems supporting lexical search guided by letter and semantic category cues: A self-paced overt response fMRI study of verbal fluency. Neuroimage, 49(1), 1099–1107.Google Scholar
Blanchard, T. C., & Hayden, B. Y. (2014). Neurons in dorsal anterior cingulate cortex signal postdecisional variables in a foraging task. Journal of Neuroscience, 34(2), 646–655.Google Scholar
Boettcher, S. E., Drew, T., & Wolfe, J. M. (2018). Lost in the supermarket: Quantifying the cost of partitioning memory sets in hybrid search. Memory & Cognition, 46(1), 43–57.Google Scholar
Bokat, C. E., & Goldberg, T. E. (2003). Letter and category fluency in schizophrenic patients: A meta-analysis. Schizophrenia Research, 64(1), 73–78.Google Scholar
Bousfield, W. A., & Sedgewick, C. H. W. (1944). An analysis of sequences of restricted associative responses. The Journal of General Psychology, 30(2), 149–165.Google Scholar
Brown, J. W., & Alexander, W. H. (2017). Foraging value, risk avoidance, and multiple control signals: How the anterior cingulate cortex controls value-based decision-making. Journal of Cognitive Neuroscience, 29(10), 1656–1673.Google Scholar
Buzsáki, G., & Moser, E. I. (2013). Memory, navigation and theta rhythm in the hippocampal-entorhinal system. Nature Neuroscience, 16(2), 130–138.Google Scholar
Chadwick, M. J., Hassabis, D., Weiskopf, N., & Maguire, E. A. (2010). Decoding individual episodic memory traces in the human hippocampus. Current Biology, 20(6), 544–547.Google Scholar
Charnov, E. L. (1976). Optimal foraging, the marginal value theorem. Theoretical Population Biology, 9(2), 129–136.Google Scholar
Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. (2016). Organizing conceptual knowledge in humans with a gridlike code. Science, 352(6292), 1464–1468.Google Scholar
Costafreda, S. G., Fu, C. H., Lee, L., Everitt, B., Brammer, M. J., & David, A. S. (2006). A systematic review and quantitative appraisal of fMRI studies of verbal fluency: Role of the left inferior frontal gyrus. Human Brain Mapping, 27(10), 799–810.Google Scholar
Davidsen, J., Ebel, H., & Bornholdt, S. (2002). Emergence of a small world from local interactions: Modeling acquaintance networks. Physical Review Letters, 88(12), 128701.Google Scholar
Dubossarsky, H., De Deyne, S., & Hills, T. (2017). Quantifying the structure of free association networks across the lifespan. Developmental Psychology, 53, 1560–1570.Google Scholar
Eichenbaum, H. (2004). Hippocampus: Cognitive processes and neural representations that underlie declarative memory. Neuron, 44(1), 109–120.Google Scholar
Ferrer i Cancho, R., & Solé, R. V. (2001). The small world of human language. Proceedings of The Royal Society B, 268, 2261–2265.Google Scholar
Fu, W. T., & Pirolli, P. (2007). SNIF-ACT: A cognitive model of user navigation on the World Wide Web. Human–Computer Interaction, 22(4), 355–412.Google Scholar
Gates, A. I. (1917). Recitation as a factor in memorizing. Archives of Psychology, 6, 40.Google Scholar
Gauthier, C. T., Duyme, M., Zanca, M., & Capron, C. (2009). Sex and performance level effects on brain activation during a verbal fluency task: A functional magnetic resonance imaging study. Cortex, 45(2), 164–176.Google Scholar
Gelbard-Sagiv, H., Mukamel, R., Harel, M., Malach, R., & Fried, I. (2008). Supporting online material internally generated reactivation of single neurons in human hippocampus during free recall. Science Reports, 322, 96–101.Google Scholar
Gruber, M. J., & Ranganath, C. (2019). How curiosity enhances hippocampus-dependent memory: The prediction, appraisal, curiosity, and exploration (PACE) framework. Trends in Cognitive Sciences, 23(12), 1014–1025.Google Scholar
Gruenewald, P. J., & Lockhead, G. R. (1980). The free recall of category examples. Journal of Experimental Psychology: Human Learning and Memory, 6(3), 225–240.Google Scholar
Gurd, J. M., Amunts, K., Weiss, P. H., Zafiris, O., Zilles, K., Marshall, J. C., & Fink, G. R. (2002). Posterior parietal cortex is implicated in continuous switching between verbal fluency tasks: An fMRI study with clinical implications. Brain, 125(5), 1024–1038.Google Scholar
Henry, J. D., & Crawford, J. R. (2005). A meta-analytic review of verbal fluency deficits in depression. Journal of Clinical and Experimental Neuropsychology, 27(1), 78–101.Google Scholar
Henry, J. D., Crawford, J. R., & Phillips, L. H. (2004). Verbal fluency performance in dementia of the Alzheimer’s type: A meta-analysis. Neuropsychologia, 42(9), 1212–1222.Google Scholar
Heylighen, F. (2016). Stigmergy as a universal coordination mechanism II: Varieties and evolution. Cognitive Systems Research, 38, 50–59.Google Scholar
Hills, T. (2003). Towards a unified theory of animal event timing. In H. Meck, W (Ed.), Functional and neural mechanisms of interval timing (pp. 77–111). New York: CRC Press.Google Scholar
Hills, T. T. (2006). Animal foraging and the evolution of goal-directed cognition. Cognitive Science, 30(1), 3–41.Google Scholar
Hills, T. T. (2019). Neurocognitive free will. Proceedings of the Royal Society B, 286(1908), 20190510.CrossRefGoogle ScholarPubMed
Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in semantic memory. Psychological Review, 119(2), 431–440.Google Scholar
Hills, T. T., Kalff, C., & Wiener, J. M. (2013). Adaptive Lévy processes and area-restricted search in human foraging. PLoS One, 8(4), e60488.Google Scholar
Hills, T. T., & Kenett, Y. (2021). Is the mind a network? Maps, vehicles, and skyhooks in cognitive network science. Topics in Cognitive Science. https://onlinelibrary.wiley.com/doi/abs/10.1111/tops.12570Google Scholar
Hills, T. T., Mata, R., Wilke, A., & Samanez-Larkin, G. R. (2013). Mechanisms of age-related decline in memory search across the adult life span. Developmental Psychology, 49(12), 2396.Google Scholar
Hills, T. T., & Pachur, T. (2012). Dynamic search and working memory in social recall. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38(1), 218.Google Scholar
Hills, T. T., Todd, P. M., Lazer, D., Redish, A. D., Couzin, I. D., & Cognitive Search Research Group (2015). Exploration versus exploitation in space, mind, and society. Trends in Cognitive Science, 19(1), 46–54. doi:10.1016/j.tics.2014.10.004Google Scholar
Hirshorn, E. A., & Thompson-Schill, S. L. (2006). Role of the left inferior frontal gyrus in covert word retrieval: Neural correlates of switching during verbal fluency. Neuropsychologia, 44(12), 2547–2557.Google Scholar
Hupbach, A., Gomez, R., Hardt, O., & Nadel, L. (2007). Reconsolidation of episodic memories: A subtle reminder triggers integration of new information. Learning & Memory, 14, 47–53.Google Scholar
Jones, H. E. (1923). The effects of examination on the performance of learning. Archives of Psychology, 10, 1–70.Google Scholar
Jones, M. N., Hills, T. T., & Todd, P. M. (2015). Hidden processes in structural representations: A reply to Abbott, Austerweil, and Griffiths (2015). Psychological Review, 122(3), 570–574.Google Scholar
Jones, M. N., & Mewhort, D. J. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114(1), 1–37.Google Scholar
Joyce, E. M., Collinson, S. L., & Crichton, P. (1996). Verbal fluency in schizophrenia: Relationship with executive function, semantic memory and clinical alogia. Psychological Medicine, 26(1), 39–49.Google Scholar
Kolling, N., Behrens, T. E., Mars, R. B., & Rushworth, M. F. (2012). Neural mechanisms of foraging. Science, 336(6077), 95–98.Google Scholar
Laisney, M., Matuszewski, V., Mézenge, F., Belliard, S., de la Sayette, V., Eustache, F., & Desgranges, B. (2009). The underlying mechanisms of verbal fluency deficit in frontotemporal dementia and semantic dementia. Journal of Neurology, 256(7), 1083–1094.Google Scholar
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240.Google Scholar
Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.Google Scholar
Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73(6), 1943–1967.Google Scholar
Lundin, N. B. (2022). Disorganized speech in psychosis: Computational and neural markers of semantic foraging and discourse cohesion. Unpublished doctoral dissertation. Indiana University Bloomington.Google Scholar
Lundin, N. B., Todd, P. M., Jones, M. N., Avery, J. E., O’Donnell, B. F., & Hetrick, W. P. (2020). Semantic search in psychosis: Modeling local exploitation and global exploration. Schizophrenia Bulletin Open, 1(1), sgaa011.Google Scholar
Luthra, M., Izquierdo, E. J., & Todd, P. M. (2020). Cognition evolves with the emergence of environmental patchiness. In Bongard, J, Lovato, J, Herbert-Dufresne, L, Dasari, R, & Soros, L (Eds.), Proceedings of the Artificial Life Conference 2020 (pp. 450–458). MIT Press. https://direct.mit.edu/isal/proceedings/isal2020/450/98395Google Scholar
Luthra, M. & Todd, P. M. (2021). Social search evolves with the emergence of clustered environments. In Čejková, J, Holler, S, Soros, L, & Witkowski, O (Eds.), Proceedings of the Artificial Life Conference 2021 (pp. 182–190). MIT Press.Google Scholar
Lydon-Staley, D. M., Zhou, D., Blevins, A. S., Zurn, P., & Bassett, D. S. (2021). Hunters, busybodies and the knowledge network building associated with deprivation curiosity. Nature Human Behaviour, 5(3), 327–336.Google Scholar
O’Keefe, J., & Nadel, L. (1978). The hippocampus as a cognitive map. Oxford University Press.Google Scholar
Mack, M. L., Love, B. C., & Preston, A. R. (2016). Dynamic updating of hippocampal object representations reflects new conceptual knowledge. Proceedings of the National Academy of Sciences, 113(46), 13203–13208.Google Scholar
Mehta, P. S., Tu, J. C., LoConte, G. A., Pesce, M. C., & Hayden, B. Y. (2019). Ventromedial prefrontal cortex tracks multiple environmental variables during search. Journal of Neuroscience, 39(27), 5336–5350.Google Scholar
Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: Evidence of a dependence between retrieval operations. Journal of Experimental Psychology, 90(2), 227.Google Scholar
Montez, P., Thompson, G., & Kello, C. T. (2015). The role of semantic clustering in optimal memory foraging. Cognitive Science, 39(8), 1925–1939.Google Scholar
Montoya, J. M., & Solé, R. V. (2002). Small world patterns in food webs. Journal of Theoretical Biology, 214(3), 405–412.Google Scholar
Morais, A. S., Olsson, H., & Schooler, L. J. (2013). Mapping the structure of semantic memory. Cognitive Science, 37(1), 125–145.Google Scholar
Morton, N. W., Sherrill, K. R., & Preston, A. R. (2017). Memory integration constructs maps of space, time, and concepts. Current Opinion in Behavioral Sciences, 17, 161–168.Google Scholar
Nader, K., Schafe, G. E., & Le Doux, J. E. (2000). Fear memories require protein synthesis in the amygdala for reconsolidation after retrieval. Nature, 406(6797), 722–726.Google Scholar
Nyberg, L., Habib, R., Mcintosh, A. R., & Tulving, E. (2000). Reactivation of encoding-related brain activity during memory retrieval. Proceedings of the National Academy of Sciences, 97(20), 11120–11124.Google Scholar
Olesen, J. M., Bascompte, J., Dupont, Y. L., & Jordano, P. (2006). The smallest of all worlds: Pollination networks. Journal of Theoretical Biology, 240(2), 270–276.Google Scholar
Pezzulo, G., van der Meer, M.A., Lansink, C.S., & Pennartz, C.M. (2014). Internally generated sequences in learning and executing goal-directed behavior. Trends in Cognitive Sciences, 18, 647–657. doi:10.1016/j.tics. 2014.06.011Google Scholar
Pyc, M. A., & Rawson, K. A. (2009). Testing the retrieval effort hypothesis: Does greater difficulty correctly recalling information lead to higher levels of memory? Journal of Memory and Language, 60(4), 437–447.Google Scholar
Ratcliff, R., & McKoon, G. (1988). A retrieval theory of priming in memory. Psychological Review, 95(3), 385–408.Google Scholar
Rhodes, T., & Turvey, M. T. (2007). Human memory retrieval as Lévy foraging. Physica A: Statistical Mechanics and its Applications, 385(1), 255–260.Google Scholar
Rips, L. J., Shoben, E. J., & Smith, E. E. (1973). Semantic distance and the verification of semantic relations. Journal of Verbal Learning and Verbal Behavior, 12(1), 1–20.Google Scholar
Sandoval, T. C., Gollan, T. H., Ferreira, V. S., & Salmon, D. P. (2017). What causes the bilingual disadvantage in verbal fluency? The dual-task analogy. Bilingualism: Language and Cognition, 13(2), 231–252.Google Scholar
Sen, P., Dasgupta, S., Chatterjee, A., Sreeram, P. A., Mukherjee, G., & Manna, S. S. (2003). Small-world properties of the Indian railway network. Physical Review, 67, 036106.Google Scholar
Shenhav, A., Straccia, M. A., Botvinick, M. M., & Cohen, J. D. (2016). Dorsal anterior cingulate and ventromedial prefrontal cortex have inverse roles in both foraging and economic choice. Cognitive, Affective, & Behavioral Neuroscience, 16(6), 1127–1139.Google Scholar
Shenhav, A., Straccia, M. A., Cohen, J. D., & Botvinick, M. M. (2014). Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value. Nature Neuroscience, 17(9), 1249–1254.Google Scholar
Shima, K., & Tanji, J. (1998). Role for cingulate motor area cells in voluntary movement selection based on reward. Science, 282(5392), 1335–1338.Google Scholar
Steyvers, M., & Tenenbaum, J. B. (2005). The large-scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science, 29(1), 41–78.Google Scholar
Strange, B. A., Witter, M. P., Lein, E. S., & Moser, E. I. (2014). Functional organization of the hippocampal longitudinal axis. Nature Reviews Neuroscience, 15(10), 655–669.Google Scholar
Taler, V., Johns, B., Sheppard, C., & Jones, M. (2015, December). Determining the linguistic information sources underlying verbal fluency performance across aging and cognitive impairment. Canadian Journal of Experimental Psychology-Revue Canadienne De Psychologie Experimentale, 69(4), 369–369.Google Scholar
Thompson, G. W., & Kello, C. (2014). Walking across Wikipedia: A scale-free network model of semantic memory retrieval. Frontiers in Psychology, 5, 86.Google Scholar
Todd, P. M., & Hills, T.T. (2020). Foraging in mind. Current Directions in Psychological Science, 20(3), 309–315. https://doi.org/10.1177/0963721420915861Google Scholar
Todd, P. M., Hills, T. T., & Robbins, T. W. (Eds.). (2012). Cognitive search: Evolution, algorithms, and the brain. MIT Press.Google Scholar
Tolman, E. C. (1948). Cognitive maps in rats and men. Psychological Review, 55, 189–208.Google Scholar
Tolman, E. C., & Gleitman, H. (1949). Studies in learning and motivation: I. Equal reinforcements in both end-boxes, followed by shock in one end-box. Journal of Experimental Psychology, 39, 810–819.Google Scholar
Troyer, A. K., Moscovitch, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults. Neuropsychology, 11(1), 138–146.Google Scholar
Troyer, A. K., Moscovitch, M., Winocur, G., Alexander, M. P., & Stuss, D. O. N. (1998). Clustering and switching on verbal fluency: The effects of focal frontal- and temporal-lobe lesions. Neuropsychologia, 36(6), 499–504.Google Scholar
van Beilen, M., Pijnenborg, M., van Zomeren, E. H., van den Bosch, R. J., Withaar, F. K., & Bouma, A. (2004). What is measured by verbal fluency tests in schizophrenia? Schizophrenia Research, 69(2–3), 267–276.Google Scholar
Viswanathan, G. M., Buldyrev, S. V., Havlin, S., Da Luz, M. G. E., Raposo, E. P., & Stanley, H. E. (1999). Optimizing the success of random searches. Nature, 401(6756), 911–914.Google Scholar
Wang, M. Z., & Hayden, B. Y. (2021). Latent learning, cognitive maps, and curiosity. Current Opinion in Behavioral Sciences, 38, 1–7.Google Scholar
Wang, X., & Pleimling, M. (2017). Foraging patterns in online searches. Physical Review E, 95(3), 032145.Google Scholar
Wheeler, M. A., & Roediger, H. L. (1992). Disparate effects of repeated testing: Reconciling Ballard’s (1913) and Bartlett’s (1932) results. Psychological Science, 3(4), 240–245.Google Scholar
Williams, S. C. (2019). Neural correlates of adaptive behavior: Structure, dynamics, and information processing (Publication No. 27543239) [Doctoral dissertation, Indiana University Bloomington]. ProQuest Dissertations Publishing.Google Scholar
Wimber, M., Alink, A., Charest, I., Kriegeskorte, N., & Anderson, M. C. (2015). Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression. Nature Neuroscience, 18(4), 582–589.Google Scholar
Winstanley, C. A., Robbins, T. W., Balleine, B. W., Brown, J. W., Büchel, C., Cools, R., … & Seamans, J. K. (2012). Search, goals, and the brain. In Todd, P. M., Hills, T. T., & Robbins, T. W. (Eds.), Cognitive search: Evolution, algorithms, and the brain. Strüngmann Forum reports (pp. 125–156). MIT Press.Google Scholar
References
Ahmed, S. (2012). On being included: Racism and diversity in institutional life. Duke University Press.Google Scholar
Bassett, D. S. (2020). A network science of the practice of curiosity. In Zurn, P. & Shankar, A. (Eds.), Curiosity studies: A new ecology of knowledge (pp. 57–74). Minnesota Press.Google Scholar
Bassett, D. S., Zurn, P., & Gold, J. I. (2018). On the nature and use of models in network neuroscience. Nature Reviews Neuroscience, 19(9), 566–578.Google Scholar
Begus, K., & Southgate, V. (2018). Curious learners: How infants’ motivation to learn shapes and is shaped by infants’ interactions with the social world. In Active learning from infancy to childhood (pp. 13–37). Springer.CrossRefGoogle Scholar
Benedict, B. (2003). Curiosity: A cultural history of early modern inquiry. University of Chicago Press.Google Scholar
Bertolero, M. A., Dworkin, J. D., David, S. U., Lloreda, C. L., Srivastava, P., Stiso, J., Zhou, D., … Bassett, D. S. (2020). Racial and ethnic imbalance in neuroscience reference lists and intersections with gender. bioRxiv, 10.12.336230. https://doi.org/10.1101/2020.10.12.336230.CrossRefGoogle Scholar
Birhane, A. (2021). The impossibility of automating ambiguity. Artificial Life, 27(1), 44–61.Google Scholar
Caplar, N., Tacchella, S., & Birrer, S. (2017). Quantitative evaluation of gender bias in astronomical publications from citation counts. Nature Astronomy, 1(6), 0141. https://doi.org/10.1038/s41550-017-0141.Google Scholar
Christianson, N. H., Blevins, A. S., & Bassett, D. S. (2020). Architecture and evolution of semantic networks in mathematics texts. Philosophical Transactions of the Royal Society, A, 476(2239), 20190741.Google Scholar
Chu, J., & Schulz, L. E. (2020). Play, curiosity, and cognition. Annual Review of Developmental Psychology, 2, 317–343.Google Scholar
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1481), 933–942.Google Scholar
Constantinescu, A. O., OReilly, J. X., & Behrens, T. E. J. (2016). Organizing conceptual knowledge in humans with a gridlike code. Science, 352(6292), 1464–1468. https://doi.org/10.1126/science.aaf0941.Google Scholar
Crawford, K., Gray, M. L., & Miltner, K. M. (2014). Big data. Critiquing big data: Politics, ethics, epistemology. Special Section Introduction. International Journal of Communication, 8(10).Google Scholar
Descartes, R. (1649). Passions of the soul. In Cottingham, J. (Ed.), The philosophical writings of Rene Descartes (p. sec. 88). Cambridge University Press, 1985.Google Scholar
Diener, E., & Diener, C. (1996). Most people are happy. Psychological Science, 7(3), 181–185.Google Scholar
Dion, M. L., Sumner, J. L., & Mitchell, S. M. (2018). Gendered citation patterns across political science and social science methodology fields. Political Analysis, 26(3), 312–327. https://doi.org/10.1017/pan.2018.12.Google Scholar
Dworkin, J. D., Linn, K. A., Teich, E. G., Zurn, P., Shinohara, R. T., & Bassett, D. S. (2020). The extent and drivers of gender imbalance in neuroscience reference lists. Nature Neuroscience, 23, 918–926.Google Scholar
Dworkin, J., Zurn, P., & Bassett, D. S. (2020). (In)citing action to realize an equitable future. Neuron, 106(6), 890–894.Google Scholar
Fredrickson, B. L. (2004). The broaden–and–build theory of positive emotions. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 359(1449), 1367–1377.Google Scholar
Fredrickson, B. L., & Branigan, C. (2005). Positive emotions broaden the scope of attention and thought-action repertoires. Cognition & Emotion, 19(3), 313–332.Google Scholar
Fricker, M. (2009). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.Google Scholar
Garrosa, E., Blanco-Donoso, L. M., Carmona-Cobo, I., & Moreno-Jiménez, B. (2017). How do curiosity, meaning in life, and search for meaning predict college students’ daily emotional exhaustion and engagement? Journal of Happiness Studies, 18(1), 17–40.Google Scholar
Garvert, M. M., Dolan, R. J., & Behrens, T. E. (2017). A map of abstract relational knowledge in the human hippocampal–entorhinal cortex. Elife, 6, e17086.Google Scholar
George, M. J., Russell, M. A., Piontak, J. R., & Odgers, C. L. (2018). Concurrent and subsequent associations between daily digital technology use and high‐risk adolescents’ mental health symptoms. Child Development, 89(1), 78–88.Google Scholar
Gershman, S. J., & Niv, Y. (2015). Novelty and inductive generalization in human reinforcement learning. Topics in Cognitive Science, 7(3), 391–415. https://doi.org/10.1111/tops.12138.Google Scholar
Gopnik, A. (2020). Childhood as a solution to explore–exploit tensions. Philosophical Transactions of the Royal Society B, 375(1803), 20190502.Google Scholar
Gottlieb, J. (2012). Attention, learning, and the value of information. Neuron, 76(2), 281–295.Google Scholar
Gottlieb, J., & Oudeyer, P.-Y. (2018). Towards a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), 758–770.Google Scholar
Gottlieb, J., Hayhoe, M., Hikosaka, O., & Rangel, A. (2014). Attention, reward, and information seeking. Journal of Neuroscience, 34(46), 15497–15504.Google Scholar
Gray, M. L., Suri, S., Ali, S. S., & Kulkarni, D. (2016, February). The crowd is a collaborative network. In Proceedings of the 19th ACM conference on computer-supported cooperative work & social computing (pp. 134–147).Google Scholar
Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486–496.Google Scholar
Gruber, M. J., Valji, A., & Ranganath, C. (2019). Curiosity and learning: A neuroscientific perspective. In Renniger, K. & Hidi, S. E. (Eds.), The Cambridge handbook of motivation and learning (pp. 397–417). Cambridge University Press.Google Scholar
Hidi, S. E., & Renninger, K. A. (2019). Interest development and its relation to curiosity: needed neuroscientific research. Educational Psychology Review, 31(4), 833–852.Google Scholar
Huntenburg, J. M., Bazin, P.-L., & Margulies, D. S. (2018). Large-scale gradients in human cortical organization. Trends in Cognitive Sciences, 22(1), 21–31.Google Scholar
James, W. (1925). Talks to teachers on psychology: And to students on some of life’s ideals. Henry Holt Company.Google Scholar
Ju, H., & Bassett, D. S. (2020). Dynamic representations in networked neural systems. Nature Neuroscience, 23(8), 908–917.Google Scholar
Ju, H., Zhou, D., Blevins, A. S., Lydon-Staley, D. M., Kaplan, J., Tuma, J. R., & Bassett, D. S. (2020). The network structure of scientific revolutions. arXiv Preprint arXiv:2010.08381.Google Scholar
Kahn, A. E., Karuza, E. A., Vettel, J. M., & Bassett, D. S. (2018). Network constraints on learnability of probabilistic motor sequences. Nature Human Behaviour, 2(12), 936–947.Google Scholar
Kang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T.-y., & Camerer, C. F. (2009). The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory. Psychological Science, 20(8), 963–973.Google Scholar
Karuza, E. A., Kahn, A. E., & Bassett, D. S. (2019). Human sensitivity to community structure is robust to topological variation. Complexity, 2019 (8379321).Google Scholar
Karuza, E. A., Kahn, A. E., Thompson-Schill, S. L., & Bassett, D. S. (2017). Process reveals structure: How a network is traversed mediates expectations about its architecture. Scientific Reports, 7(1), 1–9.Google Scholar
Karuza, E. A., Thompson-Schill, S. L., & Bassett, D. S. (2016). Local patterns to global architectures: Influences of network topology on human learning. Trends in Cognitive Sciences, 20(8), 629–640.Google Scholar
Kashdan, T. B., & Steger, M. F. (2007). Curiosity and pathways to well-being and meaning in life: Traits, states, and everyday behaviors. Motivation and emotion, 31(3), 159–173.Google Scholar
Kashdan, T. B., Gallagher, M. W., Silvia, P. J., Winterstein, B. P., Breen, W. E., Terhar, D., & Steger, M. F. (2009). The curiosity and exploration inventory-ii: Development, factor structure, and psychometrics. Journal of Research in Personality, 43(6), 987–998.Google Scholar
Kashdan, T. B., Stiksma, M. C., Disabato, D. J., McKnight, P. E., Bekier, J., Kaji, J., & Lazarus, R. (2018). The five-dimensional curiosity scale: Capturing the bandwidth of curiosity and identifying four unique subgroups of curious people. Journal of Research in Personality, 73, 130–149.Google Scholar
Kemp, C., & Tenenbaum, J. B. (2008). The discovery of structural form. Proceedings of the National Academy of Sciences, 105(31), 10687–10692.Google Scholar
Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88(3), 449–460.Google Scholar
Kim, J. Z., Lu, Z., & Bassett, D. S. (2019a). Design of large sequential conformational change in mechanical networks. arXiv, 1906, 08400.Google Scholar
Kim, J. Z., Lu, Z., Strogatz, S. H., & Bassett, D. S. (2019b). Conformational control of mechanical networks. Nature Physics, 15, 714–720.Google Scholar
Larson, R., & Csikszentmihalyi, M. (2014). The experience sampling method. In Flow and the foundations of positive psychology (pp. 21–34). Springer. https://link.springer.com/book/10.1007/978-94-017-9088-8.Google Scholar
León, C. (2020). Curious entanglements: Opacity and ethical relation in Latina/or aesthetics. In Zurn, P. and Shankar, A. (Ed.), Curiosity studies: A new ecology of knowledge (pp. 167–187). University of Minnesota Press.Google Scholar
Litman, J. A. (2008). Interest and deprivation factors of epistemic curiosity. Personality and Individual Differences, 44(7), 1585–1595.Google Scholar
Litman, J. A., & Jimerson, T. L. (2004). The measurement of curiosity as a feeling of deprivation. Journal of Personality Assessment, 82(2), 147–157.Google Scholar
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75.Google Scholar
Lydon-Staley, D. M., Barnett, I., Satterthwaite, T. D., & Bassett, D. S. (2019a). Digital phenotyping for psychiatry: Accommodating data and theory with network science methodologies. Current Opinion in Biomedical Engineering, 9, 8–13.Google Scholar
Lydon-Staley, D. M., Falk, E. B., & Bassett, D. S. (2019b). Within-person variability in sensation-seeking during daily life: Positive associations with alcohol use and self-defined risky behaviors. Psychology of Addictive Behaviors, 34(2), 257–268.Google Scholar
Lydon-Staley, D. M., Zhou, D., Blevins, A. S., Zurn, P., & Bassett, D. S. (2019c). Hunters, busybodies, and the knowledge network building associated with curiosity. Nature Human Behavior, 6(3), 327–336.Google Scholar
Lydon-Staley, D. M., Zurn, P., & Bassett, D. S. (2019d). Within-person variability in curiosity during daily life and associations with well-being. Journal of Personality, 88(4), 625–641.Google Scholar
Lynn, C. W., & Bassett, D. S. (2020). How humans learn and represent networks. Proceedings of the National Academy of Sciences of the United States of America, 117(47), 29407–29415.Google Scholar
Lynn, C. W., & Bassett, D. S. (2021). Quantifying the compressibility of complex networks. Proceedings of the National Academy of Sciences 118(32): e2023473118. https://doi.org/10.1073/pnas.2023473118.Google Scholar
Lynn, C. W., Kahn, A. E., & Bassett, D. S. (2020). Abstract representations of events arise from mental errors in learning and memory. Nature Communications, 11(1), 2313.Google Scholar
Lynn, C. W., Papadopoulos, L., Kahn, A. E., & Bassett, D. S. (2020). Human information processing in complex networks. Nature Physics, 16, 965–973. https://doi.org/10.1038/s41567-020-0924-7.Google Scholar
Maliniak, D., Powers, R., & Walter, B. F. (2013). The gender citation gap in international relations. International Organization, 67(4), 889–922. https://doi.org/10.1017/S0020818313000209.Google Scholar
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., … et al. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences, 113(44), 12574–12579.Google Scholar
Mitchell, S. M., Lange, S., & Brus, H. (2013). Gendered citation patterns in international relations journals. International Studies Perspectives, 14(4), 485–492. https://doi.org/10.1111/insp.12026.Google Scholar
Mott, C., & Cockayne, D. (2017). Citation matters: Mobilizing the politics of citation toward a practice of “conscientious engagement.” Gender, Place and Culture, 24(7), 954–973.Google Scholar
Ocko, S. A., Hardcastle, K., Giocomo, L. M., & Ganguli, S. (2018). Emergent elasticity in the neural code for space. Proceedings of the National Academy of Sciences, 115(50), E11798–E11806.Google Scholar
Onnela, J.-P., Saramäki, J., Kertész, J., & Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Physical Review E, 71(6), 065103.Google Scholar
Oudeyer, P.-Y. (2018). Computational theories of curiosity-driven learning. In Gordon, G. (Ed.), The new science of curiosity (pp. 43–72). Nova Science Publishers.Google Scholar
Oudeyer, P.-Y., Gottlieb, J., & Lopes, M. (2016). Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies. In Progress in brain research (Vol. 229, pp. 257–284). Elsevier.Google Scholar
Pekrun, R. (2019). The murky distinction between curiosity and interest: State of the art and future prospects. Educational Psychology Review, 31(4), 905–914.Google Scholar
Plutarch, . (2005). On being a busybody. In Moralia VI (pp. 473–517). Harvard University Press.Google Scholar
Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., … & others. (2019). Screenomics: A framework to capture and analyze personal life experiences and the ways that technology shapes them. Human–Computer Interaction, 1–52.Google Scholar
Reio, T. G., & Callahan, J. L. (2004). Affect, curiosity, and socialization-related learning: A path analysis of antecedents to job performance. Journal of Business and Psychology, 19(1), 3–22.Google Scholar
Rodrigue, J. R., Olson, K. R., & Markley, R. P. (1987). Induced mood and curiosity. Cognitive Therapy and Research, 11(1), 101–106.Google Scholar
Rossiter, M. W. (1993). The Matthew/Matilda Effect in science. Social Studies of Science, 23(2), 325–341.Google Scholar
Schapiro, A. C., Rogers, T. T., Cordova, N. I., Turk-Browne, N. B., & Botvinick, M. M. (2013). Neural representations of events arise from temporal community structure. Nature Neuroscience, 16(4), 486–492.Google Scholar
Schapiro, A. C., Turk-Browne, N. B., Botvinick, M. M., & Norman, K. A. (2017). Complementary learning systems within the hippocampus: A neural network modelling approach to reconciling episodic memory with statistical learning. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1711), 20160049.Google Scholar
Schmidhuber, J. (2008). Driven by compression progress: A simple principle explains essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. Workshop on Anticipatory Behavior in Adaptive Learning Systems, 48–76. https://link.springer.com/chapter/10.1007/978-3-642-02565-5_4.Google Scholar
Scholtes, I. (2017). When is a network a network? Multi-order graphical model selection in pathways and temporal networks. Proceedings of the 23rd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, 1037–1046. https://dl.acm.org/doi/10.1145/3097983.3098145.Google Scholar
Sizemore, A. E., Karuza, E. A., Giusti, C., & Bassett, D. S. (2018). Knowledge gaps in the early growth of semantic networks. Nature Human Behavior, 2(9), 682–692.Google Scholar
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017). The hippocampus as a predictive map. Nature Neuroscience, 20(11), 1643.Google Scholar
Suárez, L. E., Markello, R. D., Betzel, R. F., & Misic, B. (2020). Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences, 24(4), 302–315.Google Scholar
Swanson, H. (2020). Curious ecologies of knowledge: More-than-human anthropology. In Zurn, P. and Shankar, A. (Eds.), Curiosity studies: A new ecology of knowledge (pp. 15–36). University of Minnesota Press.Google Scholar
Tompson, S. H., Falk, E. B., O’Donnell, M. B., Cascio, C. N., Bayer, J. B., Vettel, J. M., & Bassett, D. S. (2020). Response inhibition in adolescents is moderated by brain connectivity and social network structure. Social Cognitive and Affective Neuroscience, 15(8), 827–837.Google Scholar
Watson, L. (2018). Curiosity and inquisitiveness. In Battaly, H. (Ed.), The Routledge handbook of virtue epistemology (pp. 155–166). Routledge.Google Scholar
West, R., & Leskovec, J. (2012). Human wayfinding in information networks. Proceedings of the 21st International Conference on World Wide Web, 619–628. https://dl.acm.org/doi/10.1145/2187836.2187920.Google Scholar
Yonge, C. D., & Philo, . (1854). The works of Philo Judaeus, the contemporary of Josephus (Vol. 1). Bohn.Google Scholar
Zhou, D., Cornblath, E. J., Stiso, J., Teich, E. G., Dworkin, J. D., Blevins, A. S., & Bassett, D. S. (2020a). Gender diversity statement and code notebook v1.0. https://doi.org/10.5281/zenodo.3672110.Google Scholar
Zhou, D., Lydon-Staley, D. M., Zurn, P., & Bassett, D. S. (2020b). The growth and form of knowledge networks by kinesthetic curiosity. Current Opinion in Behavirioral Sciences, 35, 125–134. https://doi.org/10.1016/j.cobeha.2020.09.007.Google Scholar
Zhou, D., Lynn, C. W., Cui, Z., Ciric, R., Baum, G. L., Moore, T. M., Roalf, D. R., … & others. (2020c). Efficient coding in the economics of human brain connectomics. Network Neuroscience.Google Scholar
Zuckerman, M. (1994). Behavioral expressions and biosocial bases of sensation seeking. Cambridge University Press.Google Scholar
Zurn, P. (2019). Busybody, hunter, and dancer: Three historical models of curiosity. In Papastephanou, M. (Ed.), Toward new philosophical explorations of the epistemic desire to know: Just curious about curiosity (pp. 26–49). Cambridge Scholars Press.Google Scholar
Zurn, P. (2021). Curiosity and power: The politics of inquiry. University of Minnesota Press.Google Scholar
Zurn, P., & Bassett, D. S. (2018). On curiosity: A fundamental aspect of personality, a practice of network growth. Personality Neuroscience, 1.Google Scholar
Zurn, P., & Bassett, D. S. (2020). Network architectures supporting learnability. Philosophical Transactions of the Royal Society B, 375(1796), 20190323.Google Scholar
Zurn, P., Bassett, D. S., & Rust, N. C. (2020). The citation diversity statement: A practice of transparency, a way of life. Trends in Cognitive Science, 24(9), 669–672.Google Scholar
References
Bhui, R., Lai, L., & Gershman, S. J. (2021). Resource-rational decision making. Current Opinion in Behavioral Sciences, 41, 15–21.Google Scholar
Bromberg-Martin, E. S., & Sharot, T. (2020). The value of beliefs. Neuron, 106(4), 561–565. https://doi.org/10.1016/j.neuron.2020.05.001.Google Scholar
Charpentier, C. J., Bromberg-Martin, E. S., & Sharot, T. (2018). Valuation of knowledge and ignorance in mesolimbic reward circuitry. Proceedings of the National Academy of Sciences of the United States of America, 115(31), E7255–E7264. https://doi.org/10.1073/pnas.1800547115.Google Scholar
Cogliati Dezza, I., Cleeremans, A., & Alexander, W. (2019). Should we control? The interplay between cognitive control and information integration in the resolution of the exploration-exploitation dilemma. Journal of Experimental Psychology: General, 148(6), 977–993. https://doi.org/10.1037/xge0000546.Google Scholar
Cogliati Dezza, I., Noel, X., Cleeremans, A., & Yu, A. J. (2021). Distinct motivations to seek out information in healthy individuals and problem gamblers. Translational Psychiatry, 11(408). doi.org/10.1038/s41398-021-01523-3.Google Scholar
Constantinescu, A. O., O’Reilly, J. X., & Behrens, T. E. J. (2016). Organizing conceptual knowledge in humans with a gridlike code. Science, 352(6292), 1464–1468. https://doi.org/10.1126/science.aaf0941.Google Scholar
Crupi, V., Nelson, J. D., Meder, B., Cevolani, G., & Tentori, K. (2018). Generalized information theory meets human cognition: Introducing a unified framework to model uncertainty and information search. Cognitive Science, https://doi.org/10.1111/cogs.12613.Google Scholar
Csibra, G., & Gergely, G. (2009). Natural pedagogy. Trends in Cognitive Science, 13(4), 148–153. https://doi.org/10.1016/j.tics.2009.01.005.Google Scholar
Esposito, G., Terlizzi, A., & Crutzen, N. (2020). Policy narratives and megaprojects: the case of the Lyon-Turin high-speed railway. Public Management Review. https://doi.org/10.1080/14719037.2020.1795230.Google Scholar
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience and Biobehavioral Reviews, 68, 862–879. https://doi.org/10.1016/j.neubiorev.2016.06.022.Google Scholar
Gigerenzer, G., & Todd, P. M. (1999). Fast and frugal heuristics: The adaptive toolbox. In Gigerenzer, G, Todd, P. M, & The ABC Research Group, Simple heuristics that make us smart (pp. 3–34). Oxford University Press.Google Scholar
Hauser, T. U., Moutoussis, M., Consortium, N., Dayan, P., & Dolan, R. J. (2017). Increased decision thresholds trigger extended information gathering across the compulsivity spectrum. Translational Psychiatry, 7(12), 1296. https://doi.org/10.1038/s41398-017-0040-3.Google Scholar
Ho, M. K., MacGlashan, J., Littman, M. L., & Cushman, F. (2017). Social is special: A normative framework for teaching with and learning from evaluative feedback. Cognition, 167, 91–106.Google Scholar
Iigaya, K., Hauser, T. U., Kurth-Nelson, Z., O’Doherty, J. P., Dayan, P., & Dolan, R. J. (2020). The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation. Sci Adv, 6 (25), eaba3828. https://doi.org/10.1126/sciadv.aba3828.Google Scholar
Jones, M., Shanahan, E., & McBeth, M. (2014). The science of stories. Palgrave Macmillan.Google Scholar
Keramati, M., & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. Elife, 3. https://doi.org/10.7554/eLife.04811.Google Scholar
Kobayashi, K., & Hsu, M. (2019). Common neural code for reward and information value. Proceedings of the National Academy of Sciences of the United States of America, 116(26), 13061–13066. https://doi.org/10.1073/pnas.1820145116.Google Scholar
Kobayashi, K., Ravaioli, S., Baranes, A., Woodford, M., & Gottlieb, J. (2019). Diverse motives for human curiosity. Nature Human Behavior, 3(6), 587–595. https://doi.org/10.1038/s41562-019-0589-3.Google Scholar
Lieder, F., & Griffiths, T. L. (2019). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, e1. https://doi.org/10.1017/S0140525X1900061X.Google Scholar
Mehlhorn, K., Newell, B. R., Todd, P. M., Lee, M. D., Morgan, K., Braithwaite, V. A., …, & Gonzalez, C. (2015). Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures. Decision, 2(3), 191–215.Google Scholar
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590–595. https://doi.org/10.1038/s41586-021-033442.Google Scholar
Pennycook, G., & Rand, D. G. (2021). The Psychology of Fake News. Trends in Cognitive Science, 25(5), 388–402. https://doi.org/10.1016/j.tics.2021.02.007.Google Scholar
Pezzulo, G., Rigoli, F., & Friston, K. (2015). Active Inference, homeostatic regulation and adaptive behavioural control. Progress in Neurobiology, 134, 17–35. https://doi.org/10.1016/j.pneurobio.2015.09.001.Google Scholar
Rollwage, M., Loosen, A., Hauser, T. U., Moran, R., Dolan, R. J., & Fleming, S. M. (2020). Confidence drives a neural confirmation bias. Nature Communications, 11(1), 2634. https://doi.org/10.1038/s41467-020-16278-6.Google Scholar
Schwartenbeck, P., Passecker, J., Hauser, T. U., FitzGerald, T. H., Kronbichler, M., & Friston, K. J. (2019). Computational mechanisms of curiosity and goal-directed exploration. Elife, 8. https://doi.org/10.7554/eLife.41703.Google Scholar
Sharot, T., Korn, C. W., & Dolan, R. J. (2011). How unrealistic optimism is maintained in the face of reality. Nature Neuroscience, 14(11), 1475–1479. https://doi.org/10.1038/nn.2949.Google Scholar
Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behavior, 4(1), 14–19. https://doi.org/10.1038/s41562-019-0793-1.Google Scholar
Srinivas, N., Krause, A., Kakade, S. M., & Seeger, M. (2009). Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint.Google Scholar
Sunstein, C. R., Bobadilla-Suarez, S., Lazzaro, S., & Sharot, T. (2017). How people update beliefs about climate change: Good news and bad news. Cornell Law Review. https://scholarship.law.cornell.edu/cgi/viewcontent.cgi?article=4736&context=clr.Google Scholar
Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A., & Cohen, J. D. (2014). Humans use directed and random exploration to solve the explore-exploit dilemma. Journal of Experimental Psychology: General, 143(6), 2074–2081. https://doi.org/10.1037/a0038199.Google Scholar
Wu, C. M., Schulz, E., Pleskac, T. J., & Speekenbrink, M. (2021). Time pressure changes how people explore and respond to uncertainty PsyArXiv. https://doi.org/10.31234/osf.io/dsw7q.Google Scholar