Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-24T12:37:25.050Z Has data issue: false hasContentIssue false

Chapter 9 - Information-Seeking in the Brain

from Part III - Which Machinery Supports the Drive for Knowledge?

Published online by Cambridge University Press:  19 May 2022

By
Caroline J. Charpentier  and
Irene Cogliati Dezza
Edited by
Irene Cogliati Dezza ,
Eric Schulz  and
Charley M. Wu
Irene Cogliati Dezza
Affiliation:
University College London
Eric Schulz
Affiliation:
Max-Planck-Institut für biologische Kybernetik, Tübingen
Charley M. Wu
Affiliation:
Eberhard-Karls-Universität Tübingen, Germany
Get access

Summary

Recent advancements in psychology, behavioral economics, and neuroscience have shown the human pursuit of knowledge to be an essential aspect of human cognition. It drives intellectual development, is integral to social interactions, and is crucial for learning, decision-making, and goal-directed behavior. Information appears to be valuable in and of itself, even when it has no apparent use, whereas at other times, instrumental information is actively and paradoxically avoided. With this complex role, a wide range of neural mechanisms can be deployed to assign value to information and drive decisions to seek (or avoid) information. Evidence points toward key roles for the mesolimbic system and the prefrontal cortex in these processes. Specifically, two different networks appear to be involved in the implementation of information-seeking behaviors. One network, overlapping with areas involved in processing primary and monetary rewards, appears to drive a general preference for information, as well as valence-dependent information-seeking. The other network, independent of reward processing, is recruited when information is acquired to reduce uncertainty. In this chapter, we review some of the most recent discoveries in the field to provide an overview of the neural basis of information-seeking.

Keywords

Information-seekingvalenceinstrumentaluncertainty reductionbrain
Type
Chapter
Information
The Drive for Knowledge
The Science of Human Information Seeking
 , pp. 195 - 216
Publisher: Cambridge University Press
Print publication year: 2022

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.CrossRefGoogle ScholarPubMed
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.Google Scholar
Bénabou, R. (2016). Mindful economics: The production, consumption, and value of beliefs. Journal of Economic Perspectives, 30, 141–164.CrossRefGoogle 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.Google 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.Google Scholar
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.Google Scholar
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.Google Scholar
Caplin, A., & Leahy, J. (2001). Psychological expected utility theory and anticipatory feelings. The Quarterly Journal of Economics, 116(1), 55–79.CrossRefGoogle 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.CrossRefGoogle 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.Google Scholar
Diederen, K. M. J., & Fletcher, P. C. (2021). Dopamine, prediction error and beyond. Neuroscientist, 27(1), 30–46. https://doi.org/10.1177/1073858420907591.Google Scholar
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.CrossRefGoogle ScholarPubMed
Evans, B. M., & Chi, E. H. (2010). An elaborated model of social search. Information Processing & Management, 46, 656–678.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.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., 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.Google 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.CrossRefGoogle ScholarPubMed
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.Google 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.CrossRefGoogle 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

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×