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The social sciences needs more than integrative experimental designs: We need better theories

Published online by Cambridge University Press:  05 February 2024

Moshe Hoffman Open the ORCID record for Moshe Hoffman [Opens in a new window] ,
Tadeg Quillien  and
Bethany Burum
Moshe Hoffman*
Affiliation:
Harvard University, Cambridge, MA, USA Hoffman.moshe@gmail.com, https://sites.google.com/site/hoffmanmoshe/ bethanyburum@gmail.com, http://bethanyburum.com
Tadeg Quillien
Affiliation:
School of Informatics, University of Edinburgh, Edinburgh, UK tadeg.quillien@gmail.com, https://sites.google.com/view/tadeg-quillien/homel
Bethany Burum
Affiliation:
Harvard University, Cambridge, MA, USA Hoffman.moshe@gmail.com, https://sites.google.com/site/hoffmanmoshe/ bethanyburum@gmail.com, http://bethanyburum.com
*
*Corresponding author.
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Abstract

Almaatouq et al.'s prescription for more integrative experimental designs is welcome but does not address an equally important problem: Lack of adequate theories. We highlight two features theories ought to satisfy: “Well-specified” and “grounded.” We discuss the importance of these features, some positive exemplars, and the complementarity between the target article's prescriptions and improved theorizing.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e47
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Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press

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References

Agrawal, M., Peterson, J. C., & Griffiths, T. L. (2020). Scaling up psychology via scientific regret minimization. Proceedings of the National Academy of Sciences of the United States of America, 117(16), 8825–8835.CrossRefGoogle ScholarPubMed
Ahn, W.-K., Kalish, C. W., Medin, D. L., & Gelman, S. A. (1995). The role of covariation versus mechanism information in causal attribution. Cognition, 54(3), 299–352.CrossRefGoogle ScholarPubMed
Boyd, R. (2018). A different kind of animal: How culture transformed our species. Princeton University Press.CrossRefGoogle Scholar
Burum, B., Nowak, M. A., & Hoffman, M. (2020). An evolutionary explanation for ineffective altruism. Nature Human Behaviour, 4(12), 1245–1257.CrossRefGoogle ScholarPubMed
Cosmides, L., Guzmán, R. A., & Tooby, J. (2018). The evolution of moral cognition. In The Routledge handbook of moral epistemology (pp. 174–228). Routledge.CrossRefGoogle Scholar
DeScioli, P., & Kurzban, R. (2013). A solution to the mysteries of morality. Psychological Bulletin, 139(2), 477.CrossRefGoogle Scholar
Goodman, N. D., Ullman, T. D., & Tenenbaum, J. B. (2011). Learning a theory of causality. Psychological Review, 118(1), 110.CrossRefGoogle ScholarPubMed
Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116(4), 661.CrossRefGoogle ScholarPubMed
Hoffman, M., & Yoeli, E. (2022). Hidden games: The surprising power of game theory to explain irrational human behavior. Hachette.Google Scholar
Lakatos, I. (1978). The methodology of scientific research programmes, J. Worrall & G. Currie (Eds.). Cambridge University Press.CrossRefGoogle Scholar
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2018a). The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review, 25, 322–349.CrossRefGoogle ScholarPubMed
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2018b). Empirical evidence for resource-rational anchoring and adjustment. Psychonomic Bulletin & Review, 25, 775–784.CrossRefGoogle ScholarPubMed
MacKay, D. J. (2003). Information theory, inference and learning algorithms. Cambridge University Press.Google Scholar
Muthukrishna, M., & Henrich, J. (2019). A problem in theory. Nature Human Behaviour, 3(3), 221–229.CrossRefGoogle ScholarPubMed
Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity. Nature, 437(7063), 1291–1298.CrossRefGoogle ScholarPubMed
Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101(4), 608.CrossRefGoogle Scholar
Pacer, M., & Lombrozo, T. (2017). Ockham's razor cuts to the root: Simplicity in causal explanation. Journal of Experimental Psychology: General, 146(12), 1761.CrossRefGoogle Scholar
Pearl, J. (2000). Causality. Cambridge University Press.Google Scholar
Quillien, T. (2020). Evolution of conditional and unconditional commitment. Journal of Theoretical Biology, 492, 110204.CrossRefGoogle ScholarPubMed
Quillien, T., & Lucas, C. G. (2023). Counterfactuals and the logic of causal selection. Psychological Review. Advance online publication. https://doi.org/10.1037/rev0000428CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Woodward, J. (2003). Making things happen: A theory of causal explanation. Oxford University Press.Google Scholar
Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114(2), 245.CrossRefGoogle ScholarPubMed