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Chapter 1 - The Motivational Processes of Sense-Making

from Part I - What Drives Humans to Seek Information?

Published online by Cambridge University Press:  19 May 2022

By
Zachary Wojtowicz ,
Nick Chater  and
George Loewenstein
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
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Summary

In this chapter we discuss the psychological function of “the drive for sense-making,” or our innate desire to make sense of the world. We start by discussing why sense-making generates a drive, similar to those associated with the primary reinforcers of food, water, sleep, sex, shelter, and air. In our account, the drive for sense-making fills a critical gap in purely goal-oriented cognition by motivating us to continue investing in knowledge even when we cannot foresee exactly how it will benefit us. We then examine three different factors that shape the particular form sense-making takes: (1) the practical utility of holding accurate beliefs for attaining concrete goals, (2) the motivational significance of some beliefs, which generates a desire to make sense of the world in a way that feels good, and (3) the impact of computational limitations on the sense-making process, especially our limited ability to explicitly predict what information will turn out to be useful. Finally, we turn our attention to how these factors help to explain aberrant sense-making phenomena such as conspiracy theories, science denial, and political polarization.

Keywords

Sense-makingmotiveshedonicinstrumentalbeliefs
Type
Chapter
Information
The Drive for Knowledge
The Science of Human Information Seeking
 , pp. 3 - 30
Publisher: Cambridge University Press
Print publication year: 2022

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References

Anderson, C., Brion, S., Moore, D. A., & Kennedy, J. A. (2012). A status enhancement account of overconfidence. Journal of Personality and Social Psychology, 103(4), 718.Google Scholar
Aragones, E., Gilboa, I., Postlewaite, A., & Schmeidler, D. (2005). Fact-free learning. American Economic Review, 95(5), 1355–1368.CrossRefGoogle Scholar
Ariely, D., Gneezy, U., Loewenstein, G., & Mazar, N. (2009). Large stakes and big mistakes. The Review of Economic Studies, 76(2), 451–469.Google Scholar
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132.Google Scholar
Bamman, D., O’Connor, B., & Smith, N. (2012). Censorship and deletion practices in Chinese social media. First Monday, 17(3). https://doi.org/10.5210/fm.v17i3.3943.Google Scholar
Batson, C. D. (1975). Rational processing or rationalization? The effect of disconfirming information on a stated religious belief. Journal of Personality and Social Psychology, 32(1), 176.Google Scholar
Beatty, M. J. (1988). Situational and predispositional correlates of public speaking anxiety. Communication Education, 37(1), 28–39.CrossRefGoogle Scholar
Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 52(2), 336–372.Google Scholar
Bellman, R. E. (1957). Dynamic programming. Princeton University Press.Google Scholar
Blackwell, D. (1953). Equivalent comparisons of experiments. The annals of mathematical statistics, 24(2), 265–272. www.jstor.org/stable/2236332.CrossRefGoogle Scholar
Bramley, N. R., Dayan, P., Griffiths, T. L., & Lagnado, D. A. (2017). Formalizing Neurath’s ship: Approximate algorithms for online causal learning. Psychological Review, 124(3), 301.CrossRefGoogle ScholarPubMed
Bruder, M., Haffke, P., Neave, N., Nouripanah, N., & Imhoff, R. (2013). Measuring individual differences in generic beliefs in conspiracy theories across cultures: Conspiracy mentality questionnaire. Frontiers in Psychology, 4, 225.Google Scholar
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., & Efros, A. A. (2018). Large-scale study of curiosity-driven learning. arXiv preprint arXiv:1808.04355.Google Scholar
Cabanac, M. (1971). Physiological role of pleasure. Science, 173(4002), 1103–1107.Google Scholar
Cat, J. (2021). Otto Neurath. In Zalta, E. N. (Ed.), The Stanford encyclopedia of philosophy (Spring 2021 ed.). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/spr2021/entries/neurath/.Google Scholar
Chater, N. (1996). Reconciling simplicity and likelihood principles in perceptual organization. Psychological Review, 103(3), 566.Google Scholar
Chater, N. (2019). The mind is flat. Yale University Press.Google Scholar
Chater, N., & Loewenstein, G. (2016). The under-appreciated drive for sensemaking. Journal of Economic Behavior & Organization, 126, 137–154.CrossRefGoogle Scholar
Chater, N., & Vitányi, P. (2003). Simplicity: A unifying principle in cognitive science? Trends in Cognitive Sciences, 7(1), 19–22.Google Scholar
Chater, N., Zhu, J., Spicer, J., Sundh, J., León-Villagrá, P., & Sanborn, A. (2020). Probabilistic biases meet the Bayesian brain. Current Directions in Psychological Science, 29(5), 506–512.Google Scholar
Chen, S., & Heese, C. (2021). “Fishing for Good News: Motivated Information Acquisition.” CRC TR 224 Discussion Paper Series crctr224_2021_223v3, University of Bonn and University of Mannheim, Germany.Google Scholar
Cheng, X., Dale, C., & Liu, J. (2008). Statistics and social network of YouTube videos. In 2008 16th International Workshop on Quality of Service (pp. 229–238). IEEE. https://ieeexplore.ieee.org/document/4539688.Google Scholar
Christensen, D. (1994). Conservatism in epistemology. Noûs, 28(1), 69–89.CrossRefGoogle Scholar
Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317–332.Google Scholar
Colman, A. M. (2003). Cooperation, psychological game theory, and limitations of rationality in social interaction. Behavioral and Brain Sciences, 26(2), 139–153.CrossRefGoogle ScholarPubMed
Cook, J., & Lewandowsky, S. (2016). Rational irrationality: Modeling climate change belief polarization using Bayesian networks. Topics in Cognitive Science, 8(1), 160–179.Google Scholar
Cosmides, L., & Tooby, J. (2000). Evolutionary psychology and the emotions. In Lewis, M. & Haviland-Jones, J. M. (eds.), Handbook of Emotions (pp. 91–115). Guilford.Google Scholar
Coyle, K. (2006). Mass digitization of books. The Journal of Academic Librarianship, 32(6), 641–645.CrossRefGoogle Scholar
Cushman, F. (2020). Rationalization is rational. Behavioral and Brain Sciences, 43, E28.CrossRefGoogle Scholar
Damasio, A. R. (2006). Descartes’ error. Random House.Google Scholar
Darwin, H., Neave, N., & Holmes, J. (2011). Belief in conspiracy theories. The role of paranormal belief, paranoid ideation and schizotypy. Personality and Individual Differences, 50(8), 1289–1293.Google Scholar
Dasgupta, I., Schulz, E., & Gershman, S. J. (2017). Where do hypotheses come from? Cognitive Psychology, 96, 1–25.Google Scholar
Dayan, P. (1998). A hierarchical model of binocular rivalry. Neural Computation, 10(5), 1119–1135.Google Scholar
Deci, E. L., & Ryan, R. M. (1981). Curiosity and self-directed learning: The role of motivation in education. In Katz, L. (Ed.), Current topics in early childhood education (Vol. 4). Ablex Publishing Co.Google Scholar
DSM-5. (2013). Diagnostic and statistical manual of mental disorders. American Psychiatric Association.Google Scholar
Dubois, E., & Blank, G. (2018). The echo chamber is overstated: The moderating effect of political interest and diverse media. Information, Communication & Society, 21(5), 729–745.Google Scholar
Ely, J., Frankel, A., & Kamenica, E. (2015). Suspense and surprise. Journal of Political Economy, 123(1), 215–260.Google Scholar
Enke, B., & Zimmermann, F. (2019). Correlation neglect in belief formation. The Review of Economic Studies, 86(1), 313–332.Google Scholar
Epley, N., & Gilovich, T. (2016). The mechanics of motivated reasoning. Journal of Economic perspectives, 30(3), 133–140.CrossRefGoogle Scholar
Eyster, E., & Rabin, M. (2014). Extensive imitation is irrational and harmful. The Quarterly Journal of Economics, 129(4), 1861–1898.CrossRefGoogle Scholar
Festinger, L. (1957). A theory of cognitive dissonance (Vol. 2). Stanford University Press.Google Scholar
Flaxman, S., Goel, S., & Rao, J. M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298–320.CrossRefGoogle Scholar
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.Google Scholar
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521), 1211–1221.CrossRefGoogle ScholarPubMed
Friston, K., Thornton, C., & Clark, A. (2012). Free-energy minimization and the dark-room problem. Frontiers in Psychology, 3, 130.CrossRefGoogle ScholarPubMed
Gershman, S., Vul, E., & Tenenbaum, J. B. (2009). Perceptual multistability as Markov chain Monte Carlo inference. In Advances in neural information processing systems 22 (pp. 611–619). https://proceedings.neurips.cc/paper/2009/hash/692f93be8c7a41525c0baf2076aecfb4-Abstract.html.Google Scholar
Gilovich, T. (2008). How we know what isn’t so. Simon and Schuster.Google Scholar
Goldfarb, A. (2014). What is different about online advertising? Review of Industrial Organization, 44(2), 115–129.CrossRefGoogle Scholar
Golman, R., & Loewenstein, G. (2018). Information gaps: A theory of preferences regarding the presence and absence of information. Decision, 5(3), 143.CrossRefGoogle Scholar
Gopnik, A. (1998). Explanation as orgasm. Minds and Machines, 8(1), 101–118.CrossRefGoogle Scholar
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013). Information seeking, curiosity, and attention: Computational and neural mechanisms. Trends in Cognitive Sciences, 17(11), 585–593.Google Scholar
Griffiths, T. L., & Tenenbaum, J. B. (2003). Probability, algorithmic complexity, and subjective randomness. In Proceedings of the annual meeting of the cognitive science society (Vol. 25). https://escholarship.org/uc/item/6ts3j7bw.Google Scholar
Griffiths, T. L., Vul, E., & Sanborn, A. N. (2012). Bridging levels of analysis for probabilistic models of cognition. Current Directions in Psychological Science, 21(4), 263–268.Google Scholar
Hanoch, Y. (2002). “Neither an angel nor an ant”: Emotion as an aid to bounded rationality. Journal of Economic Psychology, 23(1), 1–25.CrossRefGoogle Scholar
Harman, G. (2003). Skepticism and foundations. In Luper, S. (ed.), The skeptics: Contemporary essays (pp. 1–11). Routledge.Google Scholar
Heath, C., & Heath, D. (2007). Made to stick: Why some ideas survive and others die. Random House.Google Scholar
Jacob, F. (1977). Evolution and tinkering. Science, 196(4295), 1161–1166.Google Scholar
Jeffrey, R. C. (2004). Subjective probability: The real thing. Cambridge University Press.CrossRefGoogle Scholar
Jepma, M., Verdonschot, R. G., Van Steenbergen, H., Rombouts, S. A., & Nieuwenhuis, S. (2012). Neural mechanisms underlying the induction and relief of perceptual curiosity. Frontiers in Behavioral Neuroscience, 6, 5. www.frontiersin.org/article/10.3389/fnbeh.2012.00005.Google Scholar
Jern, A., Chang, K.-M. K., & Kemp, C. (2014). Belief polarization is not always irrational. Psychological Review, 121(2), 206.Google Scholar
John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524–532.Google Scholar
Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31.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.CrossRefGoogle ScholarPubMed
Kehlmann, D. (2009). Measuring the world: A novel. Vintage.Google Scholar
Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88(3), 449–460.CrossRefGoogle ScholarPubMed
Kleine, D. (1990). Anxiety and sport performance: A meta-analysis. Anxiety Research, 2(2), 113–131.Google Scholar
Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790.Google Scholar
Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3), 480.CrossRefGoogle ScholarPubMed
Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013). An opportunity cost model of subjective effort and task performance. Behavioral and Brain Sciences, 36(6), 661–679.CrossRefGoogle ScholarPubMed
Kwisthout, J. (2011). Most probable explanations in Bayesian networks: Complexity and tractability. International Journal of Approximate Reasoning, 52(9), 1452–1469.Google Scholar
Levy, R. P., Reali, F., & Griffiths, T. L. (2009). Modeling the effects of memory on human online sentence processing with particle filters. In Advances in neural information processing systems (pp. 937–944). https://cocosci.princeton.edu/tom/papers/sentencepf1.pdf.Google Scholar
Liberman, A., & Chaiken, S. (1992). Defensive processing of personally relevant health messages. Personality and Social Psychology Bulletin, 18(6), 669–679.CrossRefGoogle Scholar
Litman, J. (2005). Curiosity and the pleasures of learning: Wanting and liking new information. Cognition & Emotion, 19(6), 793–814.CrossRefGoogle Scholar
Loewenstein, G. (1994). The psychology of curiosity: A review and reinterpretation. Psychological Bulletin, 116(1), 75.Google Scholar
Loewenstein, G., & Molnar, A. (2018). The renaissance of belief-based utility in economics. Nature Human Behaviour, 2(3), 166–167.Google Scholar
Lopes, M., Lang, T., Toussaint, M., & Oudeyer, P.-Y. (2012). Exploration in model-based reinforcement learning by empirically estimating learning progress. In Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q. (eds.), Advances in neural information processing systems (pp. 206–214). Curran Associates, Inc.Google Scholar
Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence. Journal of Personality and Social Psychology, 37(11), 2098.Google Scholar
MacLeod, W. B. (1996). Decision, contract, and emotion: Some economics for a complex and confusing world. Canadian Journal of Economics, 29(4), 788–810.Google Scholar
Markey, A., & Loewenstein, G. (2014). Curiosity. In Linnenbrink-Garcia, L. (ed.) International handbook of emotions in education (pp. 228–245). Routledge.Google Scholar
Marshall, G. (2015). Don’t even think about it: Why our brains are wired to ignore climate change. Bloomsbury Publishing USA.Google Scholar
Matz, S. C., Kosinski, M., Nave, G., & Stillwell, D. J. (2017). Psychological targeting as an effective approach to digital mass persuasion. Proceedings of the National Academy of Sciences, 114(48), 12714–12719.Google Scholar
McCabe, M. P. (2005). The role of performance anxiety in the development and maintenance of sexual dysfunction in men and women. International Journal of Stress Management, 12(4), 379.Google Scholar
McLean, B. F., Mattiske, J. K., & Balzan, R. P. (2017). Association of the jumping to conclusions and evidence integration biases with delusions in psychosis: A detailed meta-analysis. Schizophrenia Bulletin, 43(2), 344–354.Google Scholar
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.Google Scholar
Meder, B., Nelson, J. D., Jones, M., & Ruggeri, A. (2019). Stepwise versus globally optimal search in children and adults. Cognition, 191, 103965.Google Scholar
Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory. Behavioral and Brain Sciences, 34(2), 57–74.CrossRefGoogle ScholarPubMed
Moreno-Bote, R., Knill, D. C., & Pouget, A. (2011). Bayesian sampling in visual perception. Proceedings of the National Academy of Sciences, 108(30), 12491–12496.Google Scholar
Muramatsu, R., & Hanoch, Y. (2005). Emotions as a mechanism for boundedly rational agents: The fast and frugal way. Journal of Economic Psychology, 26(2), 201–221.Google Scholar
Pashler, H. (1999). The psychology of attention. MIT Press.Google Scholar
Pathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017). Curiosity-driven exploration by self-supervised prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 16– 17). http://proceedings.mlr.press/v70/pathak17a/pathak17a.pdf.Google Scholar
Pluck, G., & Johnson, H. (2011). Stimulating curiosity to enhance learning. GESJ: Education Sciences and Psychology, 2(19). ISSN 1512-1801.Google Scholar
Pronin, E., Lin, D. Y., & Ross, L. (2002). The bias blind spot: Perceptions of bias in self versus others. Personality and Social Psychology Bulletin, 28(3), 369–381.Google Scholar
Rabin, M., & Schrag, J. L. (1999). First impressions matter: A model of confirmatory bias. The Quarterly Journal of Economics, 114(1), 37–82.CrossRefGoogle Scholar
Rapoport, A., & Chammah, A. M. (1966). The game of chicken. American Behavioral Scientist, 10(3), 10–28.Google Scholar
Ruan, B., Hsee, C. K., & Lu, Z. Y. (2018). The teasing effect: An underappreciated benefit of creating and resolving an uncertainty. Journal of Marketing Research, 55(4), 556–570.Google Scholar
Samuelson, L., & Swinkels, J. M. (2006). Information, evolution and utility. Theoretical Economics, 1(1), 119–142.Google Scholar
Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883–893.CrossRefGoogle ScholarPubMed
Savage, L. J. (1972). The foundations of statistics. Courier Corporation.Google Scholar
Schelling, T. C. (1980). The strategy of conflict: With a new preface by the author. Harvard University Press.Google Scholar
Schmidhuber, J. (1991). A possibility for implementing curiosity and boredom in model-building neural controllers. In Meyer, J. A. and Wilson, S. W. (eds.), Proc. of the international conference on simulation of adaptive behavior: From animals to animats (pp. 222–227). MIT Press/Bradford Books.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.Google Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.Google Scholar
Simon, H. A. (1972). Complexity and the representation of patterned sequences of symbols. Psychological Review, 79(5), 369.Google Scholar
Sorg, J., Singh, S. P., & Lewis, R. L. (2010). Internal rewards mitigate agent boundedness. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) (pp. 1007–1014). https://icml.cc/Conferences/2010/papers/442.pdf.Google Scholar
Sun, Z., & Firestone, C. (2020). The dark room problem. Trends in Cognitive Sciences, 24. https://doi.org/10.1016/j.tics.2020.02.006.Google Scholar
Sunstein, C. R. (2002). The law of group polarization. The Journal of Political Philosophy, 10(2), 175–195.Google Scholar
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.Google Scholar
Tenenbaum, J. B., Griffiths, T. L., & Niyogi, S. (2007). Intuitive theories as grammars for causal inference. In Causal learning: Psychology, philosophy, and computation, 301–322. https://oxford.universitypressscholarship.com/view/10.1093/acprof:oso/9780195176803.001.0001/acprof-9780195176803-chapter-20.Google Scholar
Vallone, R. P., Ross, L., & Lepper, M. R. (1985). The hostile media phenomenon: Biased perception and perceptions of media bias in coverage of the Beirut massacre. Journal of Personality and Social Psychology, 49(3), 577–585.Google Scholar
Van Rooij, I. (2008). The tractable cognition thesis. Cognitive Science, 32(6), 939–984.Google Scholar
Voß, J. (2005). Measuring Wikipedia. In International Conference of the International Society for Scientometrics and Informetrics: 10th, Stockholm (Sweden), 24–28 July 2005 (pp. 221–231). http://eprints.rclis.org/6207/.Google Scholar
Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599–637.Google Scholar
Vul, E., & Pashler, H. (2008). Measuring the crowd within: Probabilistic representations within individuals. Psychological Science, 19(7), 645–647.Google Scholar
Wade, S., & Kidd, C. (2019). The role of prior knowledge and curiosity in learning. Psychonomic Bulletin & Review, 26(4), 1377–1387.Google Scholar
Wojtowicz, Z., Chater, N., & Loewenstein, G. (2020). Boredom and flow: An opportunity cost theory of attention-directing motivational states. Available at SSRN 3339123. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3339123.Google Scholar
Wojtowicz, Z., & DeDeo, S. (2020). From probability to consilience: How explanatory values implement Bayesian reasoning. Trends in Cognitive Sciences, 24(12), 981–993.Google Scholar
Wojtowicz, Z., & Loewenstein, G. (2020). Curiosity and the economics of attention. Current Opinion in Behavioral Sciences, 35, 135–140.Google Scholar
Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences, 10(7), 301–308.Google Scholar
Zarouali, B., Dobber, T., De Pauw, G., & de Vreese, C. (2020). Using a personality-profiling algorithm to investigate political microtargeting: Assessing the persuasion effects of personality-tailored ads on social media. Communication Research, 0093650220961965.Google Scholar
Zeidner, M. (2010). Test anxiety. In Weiner, I. B., & Craighead, W. E. (eds.), The Corsini encyclopedia of psychology, pp. 1–3. John Wiley.Google Scholar

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