Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-25T18:35:03.313Z Has data issue: false hasContentIssue false
  • English
  • Français

Advancing rational analysis to the algorithmic level

Published online by Cambridge University Press:  11 March 2020

Falk Lieder Open the ORCID record for Falk Lieder [Opens in a new window]  and
Thomas L. Griffiths
Falk Lieder
Affiliation:
Max Planck Institute for Intelligent Systems, Tübingen72076, Germany. [email protected]; https://re.is.mpg.de
Thomas L. Griffiths
Affiliation:
Departments of Psychology and Computer Science, Princeton University, Princeton, New Jersey08544, USA. [email protected]; https://psych.princeton.edu/person/tom-griffiths
Get access Rights & Permissions [Opens in a new window]

Abstract

The commentaries raised questions about normativity, human rationality, cognitive architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We respond to these questions and clarify that RRA is a methodological advance that extends the scope of rational modeling to understanding cognitive processes, why they differ between people, why they change over time, and how they could be improved.

Type
Authors’ Response
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e27
Check for updates
Copyright
Copyright © Cambridge University Press 2020

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

Baron, J., Baron, J. H., Barber, J. P. & Nolen-Hoekseman, S. (1990) Rational thinking as a goal of therapy. Journal of Cognitive Psychotherapy 4(3):293.CrossRefGoogle Scholar
Callaway, F., Gul, S., Krueger, P.M., Griffiths, T.L., Lieder, F. (2018a) Learning to select computations. In: Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference.Google Scholar
Camerer, C. & Hua Ho, T. (1999) Experience-weighted attraction learning in normal form games. Econometrica 67(4):827–74.CrossRefGoogle Scholar
Cook, J. & Lewandowsky, S. (2016) Rational irrationality: Modeling climate change belief polarization using Bayesian networks. Topics in Cognitive Science 8(1):160–79.CrossRefGoogle ScholarPubMed
Frank, R. H. (1988) Passions within reason: The strategic role of the emotions. WW Norton & Co.Google Scholar
Gagne, C., Dayan, P. & Bishop, S. J. (2018) When planning to survive goes wrong: Predicting the future and replaying the past in anxiety and PTSD. Current Opinion in Behavioral Sciences 24:89–95.CrossRefGoogle Scholar
Gershman, S. J., Markman, A. B. & Otto, A. R. (2014) Retrospective revaluation in sequential decision making: A tale of two systems. Journal of Experimental Psychology: General 143(1):182.Google Scholar
Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L. & Lieder, F. (2018) Discovering rational heuristics for risky choice. KogWis 2018 [Abstract].Google Scholar
Halpern, J. Y. & Pass, R. (2015) Algorithmic rationality: Game theory with costly computation. Journal of Economic Theory 156(C):246–68. doi:10.1016/j.jet.2014.04.007.CrossRefGoogle Scholar
Jain, Y. R., Gupta, S., Rakesh, V., Dayan, P., Callaway, F. & Lieder, F. (in press) Testing models of how people learn how to plan.Google Scholar
Jern, A., Chang, K.-M. K. & Kemp, C. (2014) Belief polarization is not always irrational. Psychological Review 121(2):206–24.CrossRefGoogle Scholar
Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–91. doi:10.2307/1914185.CrossRefGoogle Scholar
Krueger, P. M. & Griffiths, T. (2018) Shaping model-free habits with model-based goals. In: CogSci 2018.Google Scholar
Krueger, P. M., Lieder, F. & Griffiths, T. (2017) Enhancing metacognitive reinforcement learning using reward structures and feedback. In: CogSci 2017.Google Scholar
Lewis, R. L., Howes, A. & Singh, S. (2014) Computational rationality: Linking mechanism and behavior through bounded utility maximization. Topics in Cognitive Science 6(2):279–311. doi:10.1111/tops.12086.CrossRefGoogle ScholarPubMed
Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L. & Gul, S. (2018a) Discovering and teaching optimal planning strategies. In: The 14th biannual conference of the German Society for Cognitive Science, GK.Google Scholar
Lieder, F., Callaway, F., Jain, Y. R., Krueger, P. M., Das, P., Gul, S. & Griffiths, T. L. (2019a) A cognitive tutor for helping people overcome present bias. RLDM 2019. doi:10.13140/RG.2.2.10467.20006.Google Scholar
Lieder, F. & Griffiths, T. L. (2017) Strategy selection as rational metareasoning. Psychological Review 124(6):762–94. doi:10.1037/rev0000075.CrossRefGoogle ScholarPubMed
Lieder, F., Griffiths, T. L. & Hsu, M. (2018b) Overrepresentation of extreme events in decision making reflects rational use of cognitive resources. Psychological Review 125(1):1–32. doi:10.1037/rev0000074.CrossRefGoogle Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. MIT Press.Google Scholar
Musslick, S., Dey, B., Ozcimder, K., Patwary, M. M. A., Willke, T. L. & Cohen, J. D. (2016) Controlled vs. automatic processing: A graph-theoretic approach to the analysis of serial vs. parallel processing in neural network architectures. In: Proceedings from The 38th Annual Conference of the Cognitive Science Society (Philadelphia, PA), pp. 1547–52. Cognitive Science Society.Google Scholar
Musslick, S., Saxe, A. M., Ozcimder, K., Dey, B., Henselman, G. & Cohen, J. D. (2017) Multitasking capability versus learning efficiency in neural network architectures. In: Proceedings from The 39th Cognitive Science Society Conference (London, UK), pp. 829–34. Cognitive Science Society.Google Scholar
Segev, Y., Musslick, S., Niv, Y. & Cohen, J. D. (2018) Efficiency of learning vs. processing: Towards a normative theory of multitasking. In: Proceedings from the 40th annual conference of the Cognitive Science Society (Madison, WI). Cognitive Science Society.Google Scholar
Shadlen, M. N. & Shohamy, D. (2016) Decision making and sequential sampling from memory. Neuron 90(5):927–39.CrossRefGoogle ScholarPubMed
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–31. doi:10.1126/science.185.4157.1124.CrossRefGoogle ScholarPubMed
van der Meer, M., Kurth-Nelson, Z. & Redish, A. D. (2012) Information processing in decision-making systems. The Neuroscientist 18(4):342–59.CrossRefGoogle ScholarPubMed
Allais, M. (1953) Le comportement de l'homme rationnel devant le risque: critique des postulats et axiomes de l'école américaine. Econometrica: Journal of the Econometric Society 21(4):503–46.CrossRefGoogle Scholar
Battaglia, P. W., Hamrick, J. B. & Tenenbaum, J. B. (2013) Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences 110(45):18327–32.CrossRefGoogle ScholarPubMed
Gold, J. I. & Shadlen, M. N. (2007) The neural basis of decision making. Annual Review of Neuroscience 30:535–74.CrossRefGoogle ScholarPubMed
Krajbich, I., Lu, D., Camerer, C. & Rangel, A. (2012) The attentional drift-diffusion model extends to simple purchasing decisions. Frontiers in Psychology 3:193.CrossRefGoogle ScholarPubMed