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-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-05-04T05:17:20.140Z Has data issue: false hasContentIssue false

Future Challenges

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

Published online by Cambridge University Press:  19 May 2022

By
Irene Cogliati Dezza ,
Eric Schulz  and
Charley M. Wu
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

This book has covered a wide range of new and exciting research in the science of information-seeking. Yet many open questions still remain. For example, how is information-seeking related to reward-seeking? What are the principles that enable us to acquire useful information with computational efficiency, despite possessing limited cognitive capacities and knowledge? Which aspects of our neural machinery are unique to information-seeking, and what is shared across other cognitive systems? How does the science of information-seeking inform important societal issues, such as fake news, conspiracy theories, and education?

Type
Chapter
Information
The Drive for Knowledge
The Science of Human Information Seeking
 , pp. 279 - 290
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

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.CrossRefGoogle ScholarPubMed
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 ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.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., 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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org 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
×