Hostname: page-component-848d4c4894-v5vhk Total loading time: 0 Render date: 2024-06-22T09:29:22.018Z Has data issue: false hasContentIssue false
  • English
  • Français

Narratives, probabilities, and the currency of thought

Published online by Cambridge University Press:  08 May 2023

Samuel G. B. Johnson Open the ORCID record for Samuel G. B. Johnson [Opens in a new window] ,
Avri Bilovich  and
David Tuckett Open the ORCID record for David Tuckett [Opens in a new window]
Samuel G. B. Johnson
Affiliation:
Department of Psychology, University of Warwick, Coventry CV4 7AL, UK. sgbjohnson@gmail.com Centre for the Study of Decision-Making Uncertainty, University College London, London W1CE 6BT, UK. a.bilovich@ucl.ac.uk d.tuckett@ucl.ac.uk University of Bath School of Management, Bath BA2 7AY, UK Department of Psychology, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Avri Bilovich
Affiliation:
Centre for the Study of Decision-Making Uncertainty, University College London, London W1CE 6BT, UK. a.bilovich@ucl.ac.uk d.tuckett@ucl.ac.uk
David Tuckett
Affiliation:
Centre for the Study of Decision-Making Uncertainty, University College London, London W1CE 6BT, UK. a.bilovich@ucl.ac.uk d.tuckett@ucl.ac.uk Blavatnik School of Government, University of Oxford, Oxford OX2 6GG, UK
Get access Rights & Permissions [Opens in a new window]

Abstract

Whereas most commentators agree about the centrality of narratives in decision-making, the commentaries revealed little consensus about the nature of radical uncertainty. Here we consider thirteen objections to our views, including our characterization of the uncertain decision environment and associated cognitive, affective, and social processes. We conclude that under radical uncertainty, narratives rather than probabilities are the currency of thought.

Type
Authors' Response
Information
Behavioral and Brain Sciences , Volume 46 , 2023 , e110
Check for updates
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press

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

Anderson, J. R. (1990). The adaptive character of thought. Erlbaum.Google Scholar
Becker, G. S. (1968). Crime and punishment: An economic approach. Journal of Political Economy, 76, 169–217.CrossRefGoogle Scholar
Beckert, J., & Bronk, R. (Eds.) (2018). Uncertain futures: Imaginaries, narratives, and calculation in the economy. Oxford University Press.CrossRefGoogle Scholar
Caplan, B. (2007). The myth of the rational voter: Why democracies choose bad policies. Princeton University Press.Google Scholar
Chater, N. (2018). The mind is flat: The remarkable shallowness of the improvising brain. Yale University Press.Google Scholar
Chater, N., & Oaksford, M. (1999). Ten years of the rational analysis of cognition. Trends in Cognitive Sciences, 3, 57–65.CrossRefGoogle ScholarPubMed
Cushman, F. (2020). Rationalization is rational. Behavioral and Brain Sciences, 43, e28.CrossRefGoogle Scholar
Dawkins, R. (1976). The selfish gene. Oxford University Press.Google Scholar
Feigenson, L., Dehaene, S., & Spelke, E. (2004). Core systems of number. Trends in Cognitive Sciences, 8, 307–314.CrossRefGoogle ScholarPubMed
Fenker, D. B., Waldmann, M. R., & Holyoak, K. J. (2005). Accessing causal relations in semantic memory. Memory & Cognition, 33, 1036–1046.CrossRefGoogle ScholarPubMed
Fenton O'Creevy, M., & Tuckett, D. (2022). Selecting futures: The role of conviction, narratives, ambivalence, and constructive doubt. Futures & Foresight Science, 4. https://onlinelibrary.wiley.com/doi/full/10.1002/ffo2.111.CrossRefGoogle Scholar
Fodor, J. A., & Pylyshyn, Z. (1981). How direct is visual perception? Some reflections on Gibson's “ecological approach”. Cognition, 9, 139–196.CrossRefGoogle ScholarPubMed
Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11, 127–138.CrossRefGoogle ScholarPubMed
Griffiths, T. L., Chater, N., Norris, D., & Pouget, A. (2012). How the Bayesians got their beliefs (and what those beliefs actually are): Comment on Bowers and Davis (2012). Psychological Bulletin, 138, 415–422.CrossRefGoogle ScholarPubMed
Hirst, W., Yamashiro, J. K., & Coman, A. (2018). Collective memory from a psychological perspective. Trends in Cognitive Sciences, 22, 438–451.CrossRefGoogle ScholarPubMed
Holyoak, K. J. (1985). The pragmatics of analogical transfer. Psychology of Learning and Motivation, 19, 59–87.CrossRefGoogle Scholar
Horne, Z., Muradoglu, M., & Cimpian, A. (2019). Explanation as a cognitive process. Trends in Cognitive Sciences, 23, 187–199.CrossRefGoogle ScholarPubMed
Johnson, S. G. B., & Ahn, W. (2015). Causal networks or causal islands? The representation of mechanisms and the transitivity of causal judgment. Cognitive Science, 39, 1468–1503.CrossRefGoogle ScholarPubMed
Johnson, S. G. B., & Ahn, W. (2017). Causal mechanisms. In Waldmann, M. R. (Ed.). Oxford handbook of causal reasoning (pp. 127–146). Oxford University Press.Google Scholar
Johnson, S. G. B., Jin, A., & Keil, F. C. (2014). Simplicity and goodness-of-fit in explanation: The case of intuitive curve-fitting. In Bello, P., Guarini, M., McShane, M. & Scassellati, B. (Eds.), Proceedings of the 36th annual conference of the Cognitive Science Society (pp. 701–706). Cognitive Science Society.Google Scholar
Johnson, S. G. B., & Keil, F. C. (2014). Causal inference and the hierarchical structure of experience. Journal of Experimental Psychology: General, 143, 2223–2241.CrossRefGoogle ScholarPubMed
Johnson, S. G. B., Merchant, T., & Keil, F. C. (2020). Belief digitization: Do we treat uncertainty as probabilities or as bits? Journal of Experimental Psychology: General, 149, 1417–1434.CrossRefGoogle ScholarPubMed
Johnson, S. G. B., Murphy, G. L., Rodrigues, M., & Keil, F. C. (2019a). Predictions from uncertain moral character. In Goel, A. K., Seifert, C. M. & Freksa, C. (Eds.), Proceedings of the 41st annual conference of the Cognitive Science Society (pp. 506–512). Cognitive Science Society.Google Scholar
Johnson, S. G. B., Rajeev-Kumar, G., & Keil, F. C. (2016). Sense-making under ignorance. Cognitive Psychology, 89, 39–70.CrossRefGoogle ScholarPubMed
Johnson, S. G. B., Valenti, J. J., & Keil, F. C. (2019b). Simplicity and complexity preferences in causal explanation: An opponent heuristic account. Cognitive Psychology, 113, 101222.CrossRefGoogle ScholarPubMed
Kahan, D. M., Peters, E., Dawson, E. C., & Slovic, P. (2017). Motivated numeracy and enlightened self-government. Behavioural Public Policy, 1, 54–86.CrossRefGoogle Scholar
Keynes, J. M. (1937). The general theory of employment. Quarterly Journal of Economics, 51, 209–223.CrossRefGoogle Scholar
Khemlani, S. S., Sussman, A. B., & Oppenheimer, D. M. (2011). Harry Potter and the sorcerer’s scope: Latent scope biases in explanatory reasoning. Memory and Cognition, 39, 527–535.CrossRefGoogle Scholar
Korman, J., & Khemlani, S. (2020). Explanatory completeness. Acta Psychologica, 209, 103139.CrossRefGoogle ScholarPubMed
Lagnado, D. A., Waldmann, M. R., Hagmayer, Y., & Sloman, S. A. (2007). Beyond covariation: Cues to causal structure. In Gopnik, A. & Schulz, L. (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 154–172). Oxford University Press.CrossRefGoogle Scholar
Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43, e1.CrossRefGoogle Scholar
Lombrozo, T. (2007). Simplicity and probability in causal explanation. Cognitive Psychology, 55, 232–257.CrossRefGoogle ScholarPubMed
Lombrozo, T. (2016). Explanatory preferences shape learning and inference. Trends in Cognitive Sciences, 20, 748–759.CrossRefGoogle ScholarPubMed
Marr, D. (1982). Vision. Freeman.Google Scholar
Mills, C. W. (1940). Situated actions and vocabularies of motive. American Sociological Review, 5, 904–913.CrossRefGoogle Scholar
Murphy, G. L., & Ross, B. H. (1994). Predictions from uncertain categorizations. Cognitive Psychology, 27, 148–193.CrossRefGoogle ScholarPubMed
Newell, A. (1973). You can't play 20 questions with nature and win. In Chase, W. G. (Ed.), Visual information processing (pp. 287–307). Academic Press.Google Scholar
Rottman, B. M., & Keil, F. C. (2012). Causal structure learning over time: Observations and interventions. Cognitive Psychology, 64, 93–125.CrossRefGoogle ScholarPubMed
Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20, 883–893.CrossRefGoogle ScholarPubMed
Savage, L. J. (1954). The foundations of statistics. Wiley.Google Scholar
Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 44, 695–729.CrossRefGoogle Scholar
Shiller, R. J. (2019). Narrative economics: How stories go viral and drive major economic events. Princeton University Press.Google Scholar
Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41, 1–19.CrossRefGoogle ScholarPubMed
Taleb, N. N. (2007). The black swan. Random House.Google Scholar
Walasek, L., & Brown, G. D. A. (in press). Incomparability and incommensurability in choice: No common currency of value? Perspectives on Psychological Science. https://psyarxiv.com/suw47/.Google Scholar
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford University Press.Google Scholar
Yin, P., & Sun, J. (2021). Is causation deterministic or probabilistic? A critique of Frosch and Johnson-Laird (2011). Journal of Cognitive Psychology, 33, 899–918.CrossRefGoogle Scholar
Zacks, J. M., & Tversky, B. (2001). Event structure in perception and conception. Psychological Bulletin, 127, 3–21.CrossRefGoogle ScholarPubMed
Zhu, J., & Murphy, G. L. (2013). The effect of emotionally charged information on category-based induction. PLoS ONE, 8, e54286.CrossRefGoogle ScholarPubMed