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Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

  • Falk Lieder (a1) and Thomas L. Griffiths (a2)


Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.



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Allport, D. A., Antonis, B. & Reynolds, P. (1972) On the division of attention: A disproof of the single channel hypothesis. The Quarterly Journal of Experimental Psychology 24(2):225–35. doi:10.1080/00335557243000102.
Anderson, J. R. (1978) Arguments concerning representations for mental imagery. Psychological Review 85(4):249–77. doi:10.1037/0033-295X.85.4.249.
Anderson, J. R. (1990) The adaptive character of thought. Psychology Press.
Anderson, J. R. (1996) ACT: A simple theory of complex cognition. American Psychologist 51(4):355.
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C. & Qin, Y. (2004) An integrated theory of the mind. Psychological Review 111(4):1036–60. doi:10.1037/0033-295X.111.4.1036.
Anderson, J. R. & Milson, R. (1989) Human memory: An adaptive perspective. Psychological Review 96(4):703–19. doi:10.1037/0033-295X.96.4.703.
Anderson, J. R. & Schooler, L. J. (1991) Reflections of the environment in memory. Psychological Science 2(6):396408. doi:10.1111/j.1467-9280.1991.tb00174.x.
Ariely, D. (2009) Predictably irrational. Harper Collins.
Atkinson, R. C., Holmgren, J. E. & Juola, J. F. (1969) Processing time as influenced by the number of elements in a visual display. Perception & Psychophysics 6(6):321–26. doi:10.3758/BF03212784.
Austerweil, J. & Griffiths, T. (2011) Seeking confirmation is rational for deterministic hypotheses. Cognitive Science 35(3):499526. doi:10.1111/j.1551-6709.2010.01161.x.
Bacon, P.-L., Harb, J. & Precup, D. (2017) The option-critic architecture. In: Proceedings from AAAI-17: The 31st Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (San Francisco, CA), pp. 1726–34.
Bateson, M., Healy, S. D. & Hurly, T. A. (2002) Irrational choices in hummingbird foraging behaviour. Animal Behaviour 63(3):587–96.
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. & Pouget, A. (2012) Not noisy, just wrong: The role of suboptimal inference in behavioral variability. Neuron 74(1):3039. doi:10.1016/j.neuron.2012.03.016.
Beer, R. D. (2000) Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3):9199.
Bhui, R. & Gershman, S. J. (2017) Decision by sampling implements efficient coding of psychoeconomic functions. Psychological Review 125(6):985-1001. doi:10.1037/rev0000123.
Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. (2006) The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review 113(4):700–65. doi:10.1037/0033-295x.113.4.700.
Bossaerts, P. & Murawski, C. (2017) Computational complexity and human decision-making. Trends in Cognitive Sciences 21(12):917–29. doi:10.1016/j.tics.2017.09.005.
Bossaerts, P., Yadav, N. & Murawski, C. (2018) Uncertainty and computational complexity. Philosophical Transactions of the Royal Society B 374(1766):20180138.
Botvinick, M. (2008) Hierarchical models of behavior and prefrontal function. Trends in Cognitive Sciences 12(5):201–08. doi:10.1016/j.tics.2008.02.009.
Boureau, Y.-L., Sokol-Hessner, P. & Daw, N. D. (2015) Deciding how to decide: Self-control and meta-decision making. Trends in Cognitive Sciences 19(11):700-10 doi:10.1016/j.tics.2015.08.013.
Braine, M. D. (1978) On the relation between the natural logic of reasoning and standard logic. Psychological Review 85(1):121. doi:10.1037/0033-295X.85.1.1.
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):301–38. doi:10.1037/rev0000061.
Buss, D. M. (1995) Evolutionary psychology: A new paradigm for psychological science. Psychological Inquiry 6(1):130.
Butko, N. J. & Movellan, J. R. (2008) I-POMDP: An infomax model of eye movement. In: Proceedings from ICDL 2008: 7th IEEE International Conference on Development and Learning (Monterey, CA), pp. 139–44. doi:10.1109/DEVLRN.2008.4640819.
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.
Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M. & Griffiths, T. L. (2018b) A resource-rational analysis of human planning. In: Proceedings from 40th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L. & Lieder, F. (in preparation). Discovering rational heuristics for risky choice.
Caplin, A. & Dean, M. (2015) Revealed preference, rational inattention, and costly information acquisition. American Economic Review 105(7):2183–203. doi:10.3386/w19876.
Caplin, A., Dean, M. & Leahy, J. (2017) Rationally inattentive behavior: Characterizing and Generalizing Shannon Entropy. NBER Working Paper No. 23652.National Bureau of Economic Research.
Caplin, A., Dean, M. & Martin, D. (2011) Search and satisficing. American Economic Review 101(7):2899–922. doi:10.1257/aer.101.7.2899.
Carver, C. S. & Scheier, M. F. (2001) On the self-regulation of behavior. Cambridge University Press.
Chater, N. & Oaksford, M. (1999) Ten years of the rational analysis of cognition. Trends in Cognitive Sciences 3(2):5765. doi:10.1016/S1364-6613(98)01273-X.
Chater, N., Tenenbaum, J. B. & Yuille, A. (2006) Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Sciences 10(7):287–91. doi:10.1016/j.tics.2006.05.007.
Dasgupta, I., Schulz, E. & Gershman, S. J. (2017) Where do hypotheses come from? Cognitive Psychology 96:125. doi:10.1016/j.cogpsych.2017.05.001.
Dasgupta, I., Schulz, E., Goodman, N. D. & Gershman, S. J. (2018) Remembrance of inferences past: Amortization in human hypothesis generation. Cognition 178:67-81. doi:10.1016/j.cognition.2018.04.017.
Daw, N., Niv, Y. & Dayan, P. (2005) Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nature Neuroscience 8(12):1704–11. doi:10.1038/nn1560.
Dawes, R. M. & Mulford, M. (1996) The false consensus effect and overconfidence: Flaws in judgment or flaws in how we study judgment? Organizational Behavior and Human Decision Processes 65(3):201–11.
Dayan, P. & Abbott, L. F. (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, 1st edition. MIT Press.
Dickhaut, J., Rustichini, A. & Smith, V. (2009) A neuroeconomic theory of the decision process. Proceedings of the National Academy of Sciences 106(52):22145–50. doi:10.1073/pnas.0912500106.
Dolan, R. & Dayan, P. (2013) Goals and habits in the brain. Neuron 80(2):312–25. doi:10.1016/j.neuron.2013.09.007.
Dukas, R., ed. (1998a) Cognitive ecology: The evolutionary ecology of information processing and decision making. University of Chicago Press.
Dukas, R. (1998b) Constraints on information processing and their effects on behavior. In: Cognitive ecology: The evolutionary ecology of information processing and decision making, ed. Dukas, R.. University of Chicago Press.
Eckstein, M. P. (1998) The lower visual search efficiency for conjunctions is due to noise and not serial attentional processing. Psychological Science 9(2):111–18. doi:10.1111/1467-9280.00020.
Edwards, W. (1954) The theory of decision making. Psychological Bulletin 51(4):380.
Epley, N. & Gilovich, T. (2004) Are adjustments insufficient? Personality and Social Psychology Bulletin 30(4):447–60. doi:10.1177/0146167203261889.
Evans, J. St. B. T. (2008) Dual-processing accounts of reasoning, judgment and social cognition. Annual Review of Psychology 59:255–78. doi:10.1146/annurev.psych.59.103006.093629.
Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E., Trimmer, P. C. & McNamara, J. M. (2014) The evolution of decision rules in complex environments. Trends in Cognitive Sciences 18(3):153–61.
Feng, S. F., Schwemmer, M., Gershman, S. J. & Cohen, J. D. (2014) Multitasking versus multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors. Cognitive, Affective, & Behavioral Neuroscience 14(1):129–46. doi:10.3758/s13415-013-0236-9.
Fischer, R. & Plessow, F. (2015) Efficient multitasking: Parallel versus serial processing of multiple tasks. Frontiers in Psychology 6:1366. doi:10.3389/fpsyg.2015.01366.
Fiser, J., Berkes, P., Orbán, G. & Lengyel, M. (2010) Statistically optimal perception and learning: From behavior to neural representations. Trends in Cognitive Sciences 14(3):119–30. doi:10.1016/j.tics.2010.01.003.
Fodor, J. A. (1987) Modules, frames, fridgeons, sleeping dogs, and the music of the spheres. In: The robot's dilemma: The frame problem in artificial intelligence, ed. Pylyshyn, Z. W., pp. 139–50. Ablex.
Frank, M. C. & Goodman, N. D. (2012) Predicting pragmatic reasoning in language games. Science 336(6084):998. doi:10.1126/science.1218633.
Friedman, M. & Savage, L. J. (1948) The utility analysis of choices involving risk. The Journal of Political Economy 56(4):279304. doi:10.1086/256692.
Friedman, M. & Savage, L. J. (1952) The expected-utility hypothesis and the measurability of utility. Journal of Political Economy 60(6):463–74.
Friston, K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38. Available at:
Fudenberg, D., Strack, P. & Strzalecki, T. (2018) Speed, accuracy, and the optimal timing of choices (Working paper). MIT Press.
Gabaix, X. (2014) A sparsity-based model of bounded rationality. The Quarterly Journal of Economics 129(4):1661–710. doi:10.1093/qje/qju024.
Gabaix, X. (2016) Behavioral macroeconomics via sparse dynamic programming. NBER Working Paper No. w21848. National Bureau of Economic Research.
Gabaix, X. (2017) Behavioral inattention. NBER Working Paper No. 24096. National Bureau of Economic Research.
Gabaix, X. & Laibson, D. (2005) Bounded rationality and directed cognition (NBER and Harvard working paper). National Bureau of Economic Research.
Gabaix, X., Laibson, D., Moloche, G. & Weinberg, S. (2006) Costly information acquisition: Experimental analysis of a boundedly rational model. American Economic Review 96(4):1043–68. doi:10.1257/aer.96.4.1043.
Ganguli, D. & Simoncelli, E. P. (2014) Efficient sensory encoding and Bayesian inference with heterogeneous neural populations. Neural Computation 26(10):2103–34. doi:10.1162/NECO_a_00638.
Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. (2015) Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349(6245):273–78. doi:10.1126/science.aac6076.
Gigerenzer, G. (2015) On the supposed evidence for libertarian paternalism. Review of Philosophy and Psychology 6:363–83. doi:10.1007/s13164-015-0248-1.
Gigerenzer, G., Fiedler, K. & Olsson, H. (2012) Rethinking cognitive biases as environmental consequences. In: Ecological rationality: Intelligence in the world, ed. Todd, P. M., Gigerenzer, G. & ABC Research Group, pp. 80110. Oxford University Press.
Gigerenzer, G. & Gaissmaier, W. (2011) Heuristic decision making. Annual Review of Psychology 62(1):451–82. doi:10.1146/annurev-psych-120709-145346.
Gigerenzer, G. & Goldstein, D. G. (1996) Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review 103(4):650–69. doi:10.1037/0033-295X.103.4.650.
Gigerenzer, G. & Hoffrage, U. (1995) How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review 102(4):684704. doi:10.1037/0033-295X.102.4.684.
Gigerenzer, G. & Selten, R. (2002) Bounded rationality: The adaptive toolbox. MIT Press.
Gigerenzer, G., Todd, P. M. & Research Group, ABC. (1999) Simple heuristics that make us smart. Oxford University Press.
Gilovich, T., Griffin, D. & Kahneman, D., eds. (2002) Heuristics and biases: The psychology of intuitive judgment. Cambridge University Press.
Glymour, C. (1987) Android epistemology and the frame problem. In: The robot's dilemma: The frame problem in artificial intelligence, ed. Pylyshyn, Z. W., pp. 6375. Ablex.
Gobet, F., Lane, P. C. R., Croker, S., Cheng, P. C. H., Jones, G., Oliver, I. & Pine, J. M. (2001) Chunking mechanisms in human learning. Trends in Cognitive Sciences 5(6):236–43. doi:10.1016/S1364-6613(00)01662-4.
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):585–93. doi:10.1016/j.tics.2013.09.001.
Griffiths, T., Chater, N., Kemp, C., Perfors, A. & Tenenbaum, J. (2010) Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14(8):357–64. doi:10.1016/j.tics.2010.05.004.
Griffiths, T. L., Kemp, C. & Tenenbaum, J. B. (2008) Bayesian models of cognition. In: The Cambridge handbook of computational cognitive modeling, ed. Sun, R.. Cambridge University Press.
Griffiths, T. L., Lieder, F. & Goodman, N. D. (2015) Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science 7(2):217–29. doi:10.1111/tops.12142.
Griffiths, T. L. & Tenenbaum, J. B. (2001) Randomness and coincidences: Reconciling intuition and probability theory. In: Proceedings from The 23rd Annual Conference of the Cognitive Science Society (Edinburgh, Scotland), pp. 370–75. Cognitive Science Society.
Griffiths, T. L. & Tenenbaum, J. B. (2006) Optimal predictions in everyday cognition. Psychological Science 17(9):767–73. doi:10.1111/j.1467-9280.2006.01780.x.
Griffiths, T. L. & Tenenbaum, J. B. (2009) Theory-based causal induction. Psychological Review 116(4):661716. doi:10.1037/a0017201.
Griffiths, T. L., Vul, E. & Sanborn, A. N. (2012) Bridging levels of analysis for probabilistic models of cognition. Current Direction in Psychological Science 21(4):263–68. doi:10.1177/0963721412447619.
Hahn, U. & Oaksford, M. (2007) The rationality of informal argumentation: A Bayesian approach to reasoning fallacies. Psychological Review 114(3):704–32. doi:10.1037/0033-295X.114.3.704.
Hahn, U. & Warren, P. A. (2009) Perceptions of randomness: Why three heads are better than four. Psychological Review 116(2):454–61. doi:10.1037/a0017522.
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.
Harman, G. (2013) Rationality. In: International Encyclopedia of Ethics. ed. LaFollette, H., Deigh, J. & Stroud, S.. Blackwell Publishing Ltd.
Haselton, M. G. & Nettle, D. (2006) The paranoid optimist: An integrative evolutionary model of cognitive biases. Personality and Social Psychology Review 10(1):4766.
Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. (2017) Neuroscience-inspired artificial intelligence. Neuron 95(2):245–58. doi:10.1016/j.neuron.2017.06.011.
Hawkins, J. A. (2004) Efficiency and complexity in grammars. Oxford University Press.
Hedström, P. & Stern, C. (2008) Rational choice and sociology. In: The new Palgrave dictionary of economics (2nd edition), ed. Durlauf, S. N. & Blume, L. E.. Palgrave Macmillan.
Herrnstein, R. J. (1961) Relative and absolute strength of responses as a function of frequency of reinforcement. Journal of the Experimental Analysis of Behaviour 4:267–72. doi:10.1901/jeab.1961.4-267.
Hertwig, R. & Hoffrage, U. (2013) Simple heuristics in a social world. Oxford University Press.
Hertwig, R., Pachur, T., & Kurzenhäuser, S. (2005) Judgments of risk frequencies: Tests of possible cognitive mechanisms. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(4):621. doi:10.1037/0278-7393.31.4.621.
Hilbert, M. (2012) Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making. Psychological Bulletin 138(2):211–37. doi:10.1037/a0025940.
Holmes, P. & Cohen, J. D. (2014) Optimality and some of its discontents: Successes and shortcomings of existing models for binary decisions. Topics in Cognitive Science 6(2):258–78. doi:10.1111/tops.12084.
Horvitz, E. J. (1987) Reasoning about beliefs and actions under computational resource constraints. In: Proceedings of the third conference on uncertainty in artificial intelligence, pp. 429-44.
Horvitz, E. J. (1990) Computation and action under bounded resources. PhD Dissertation, Stanford University.
Horvitz, E. J., Cooper, G. F. & Heckerman, D. E. (1989) Reflection and action under scarce resources: Theoretical principles and empirical study. In: Proceedings from IJCAI-89: The 11th international joint conference on artificial intelligence (Detroit, Michigan), Volume 2, pp. 1121–27.
Houston, A. I. & McNamara, J. M. (1999) Models of adaptive behaviour: An approach based on state. Cambridge University Press.
Howes, A., Duggan, G. B., Kalidindi, K., Tseng, Y. -C. & Lewis, R. L. (2016) Predicting short-term remembering as boundedly optimal strategy choice. Cognitive Science 40(5):1192–223. doi:10.1111/cogs.12271.
Howes, A., Lewis, R. L. & Vera, A. H. (2009) Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action. Psychological Review 116(4):717–51. doi:10.1037/a0017187
Howes, A., Warren, P. A., Farmer, G., El-Deredy, W. & Lewis, R. L. (2016) Why contextual preference reversals maximize expected value. Psychology Review 123(4):368–91. doi:10.1037/a0039996.
Huys, Q. J. M., Lally, N., Faulkner, P., Eshel, N., Seifritz, E., Gershman, S. J., Dayan, P. & Roiser, J. P. (2015) Interplay of approximate planning strategies. Proceedings of the National Academy of Sciences 112(10):3098–103. doi:10.1073/pnas.1414219112.
Icard, T. (2014) Toward boundedly rational analysis. In: Proceedings from the 36th annual conference of the Cognitive Science Society (Quebec, Canada), Volume 1, pp. 637–42. Cognitive Science Society.
Icard, T. & Goodman, N. D. (2015) A resource-rational approach to the causal frame problem. In: Proceedings from the 37th annual meeting of the Cognitive Science Society (Pasadena, CA). Cognitive Science Society.
Johnstone, R. A., Dall, S. R. X. & Dukas, R. (2002) Behavioural and ecological consequences of limited attention. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357. Available at:
Kahneman, D. (2003) Maps of bounded rationality: Psychology for behavioral economics. American Economic Review 93(5):1449-75. doi:10.1257/000282803322655392.
Kahneman, D. & Frederick, S. (2002) Representativeness revisited: Attribute substitution in intuitive judgment. In: Heuristics and biases: The psychology of intuitive judgment, ed. Gilovich, T., Griffin, D. & Kahneman, D.. Cambridge University Press. doi:10.1017/CBO9780511808098.004.
Kahneman, D. & Frederick, S. (2005) A model of heuristic judgment. In: The Cambridge handbook of thinking and reasoning, ed. Holyoak, K. J. & Morrison, R. G., pp. 267–93. Cambridge University Press.
Kahneman, D. & Tversky, A. (1972) Subjective probability: A judgment of representativeness. Cognitive Psychology 3(3):430–54. doi:10.1016/0010-0285(72)90016-3.
Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–91. doi:10.2307/1914185.
Kemp, C. & Regier, T. (2012) Kinship categories across languages reflect general communicative principles. Science 336(6084):1049–54. doi:10.1126/science.1218811.
Keramati, M., Dezfouli, A. & Piray, P. (2011) Speed/accuracy trade-off between the habitual and the goal-directed processes. The Public Library of Science Computational Biology 7(5):e1002055, 1–21. doi:10.1371/journal.pcbi.1002055.
Keramati, M., Smittenaar, P., Dolan, R. J. & Dayan, P. (2016) Adaptive integration of habits into depth-limited planning defines a habitual-goal-directed spectrum. Proceedings of the National Academy of Sciences 113(45):12868–73. doi:10.1073/pnas.1609094113.
Khaw, M. W., Li, Z. & Woodford, M. (2017) Risk aversion as a perceptual bias. NBER Working Paper No. 23294. National Bureau of Economic Research.
Knill, D. C. & Pouget, A. (2004) The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences 27(12):712–19. doi:10.1016/j.tins.2004.10.007.
Knill, D. C. & Richards, W. (1996) Perception as Bayesian inference. Cambridge University Press.
Kool, W. & Botvinick, M. M. (2013) The intrinsic cost of cognitive control. The Behavioral and Brain Sciences 36(6):697–98. doi:10.1017/S0140525X1300109X.
Körding, K. P. & Wolpert, D. M. (2004) Bayesian integration in sensorimotor learning. Nature 427(6971):244–47. doi:10.1038/nature02169.
Lake, B. M., Ullman, T. D., Tenenbaum, J. B. & Gershman, S. J. (2017) Building machines that learn and think like people. Behavioral and Brain Sciences 40(253):1-72. doi:10.1017/S0140525X16001837.
Langley, P., Laird, J. E. & Rogers, S. (2009) Cognitive architectures: Research issues and challenges. Cognitive Systems Research 10(2):141–60. doi:10.1016/j.cogsys.2006.07.004.
Larrick, R. P. (2004) Debiasing. In: Blackwell handbook of judgment and decision making, ed. Koehler, D. J. & Harvey, N., pp. 316–38. Blackwell Publishing.
Latty, T. & Beekman, M. (2010) Irrational decision-making in an amoeboid organism: Transitivity and context-dependent preferences. Proceedings of the Royal Society B: Biological Sciences 278(1703): 307–12.
Lennie, P. (2003) The cost of cortical computation. Current Biology 13(6):493–97. doi:10.1016/S0960-9822(03)00135-0.
Levy, W. B. & Baxter, R. A. (1996) Energy efficient neural codes. Neural Computation 8(3):531–43. doi:10.1162/neco.1996.8.3.531.
Levy, W. B. & Baxter, R. A. (2002) Energy-efficient neuronal computation via quantal synaptic failures. Journal of Neuroscience 22(11):4746–55.
Lewis, R. L., Howes, A. & Singh, S. (2014) Computational rationality: Linking mechanism and behavior through bounded utility maximization. Topics in Cognitive Science 6(2):279311. doi:10.1111/tops.12086.
Lichtenstein, S., Slovic, P., Fischhoff, B., Layman, M. & Combs, B. (1978) Judged frequency of lethal events. Journal of Experimental Psychology: Human Learning and Memory 4(6):551–78.
Lieder, F. (2018) Beyond bounded rationality: Reverse-engineering and enhancing human intelligence (Doctoral dissertation). University of California, Berkeley.
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.
Lieder, F., Chen, O. X., Krueger, P. M. & Griffiths, T. L. (2019b) Cognitive prostheses for goal achievement. Nature Human Behavior 3:10961106.
Lieder, F. & Griffiths, T. L. (2017) Strategy selection as rational metareasoning. Psychological Review 124(6):762–94. doi:10.1037/rev0000075.
Lieder, F., Griffiths, T. L. & Goodman, N. D. (2012) Burn-in, bias, and the rationality of anchoring. In: Advances in Neural Information Processing Systems, vol. 26, ed. Bartlett, P., Pereira, F. C. N., Bottou, L., Burges, C. J. C. & Weinberger, K. Q., pp. 2690–798. Curran Associates, Inc.
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):132. doi:10.1037/rev0000074.
Lieder, F., Griffiths, T. L., Huys, Q. J., & Goodman, N. D. (2018c) Empirical evidence for resource-rational anchoring and adjustment. Psychonomic Bulletin & Review 25(2):775-84. doi:10.3758/s13423-017-1288-6.
Lieder, F., Griffiths, T. L., Huys, Q. J. M. & Goodman, N. D. (2018d) The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review 25(1):322–49. doi:10.3758/s13423-017-1286-8.
Lieder, F., Hsu, M. & Griffiths, T. L. (2014) The high availability of extreme events serves resource-rational decision-making. in Proceedings of the Annual Meeting of the Cognitive Science Society. Cognitive Science Society.
Lieder, F., Krueger, P. M. & Griffiths, T. L. (2017) An automatic method for discovering rational heuristics for risky choice. In: Proceedings from the 39th annual conference of the Cognitive Science Society (London, UK), pp. 2567–72. Cognitive Science Society.
Lieder, F., Shenhav, A., Musslick, S. & Griffiths, T. L. (2018e) Rational metareasoning and the plasticity of cognitive control. The Public Library of Science Computational Biology 14(4):e1006043.
Locke, E. & Latham, G. (2002) Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist 57(9):705–17. doi:10.1037/0003-066x.57.9.705.
Lohmann, S. (2008) Rational choice and political science. In: The new Palgrave dictionary of economics, 2nd edition, ed. Durlauf, S. N. & Blume, L. E.. Palgrave Macmillan. doi:10.1007/978-1-349-58802-2_1383.
Mahowald, K., Fedorenko, E., Piantadosi, S. T. & Gibson, E. (2013) Info/information theory: Speakers choose shorter words in predictive contexts. Cognition 126(2):313–18. doi:10.1016/j.cognition.2012.09.010.
Marcus, G. (2008) Kluge: The haphazard evolution of the human mind. Houghton Mifflin Harcourt.
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. MIT Press.
Matějka, F. & McKay, A. (2015) Rational inattention to discrete choices: A new foundation for the multinomial logit model. American Economic Review 105(1):272–98. doi:10.1257/aer.20130047.
McNamara, J. M. & Weissing, F. J. (2010) Evolutionary game theory. In: Social behaviour: genes, ecology and evolution, ed. Székely, T., Moore, A. J. & Komdeur, J., pp. 88106. Cambridge University Press.
Meyer, D. E. & Kieras, D. E. (1997a) A computational theory of executive cognitive processes and multiple-task performance: Part I. Basic mechanisms. Psychological Review 104(1):365. doi:10.1037/0033-295X.104.1.3.
Meyer, D. E. & Kieras, D. E. (1997b) A computational theory of executive cognitive processes and multiple-task performance: Part 2. Accounts of psychological refractory-period phenomena. Psychological Review 104(4):749–91. doi:10.1037//0033-295X.104.4.749.
Mill, J. S. (1882) A system of logic, ratiocinative and inductive, 8th edition. Harper and Brothers.
Milli, S., Lieder, F. & Griffiths, T. L. (2017) When does bounded-optimal metareasoning favor few cognitive systems? In: Proceedings from AAAI-17: The 31st Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence, vol. 31, 4422–28. Palo Alto, CA: Association for the Advancement of Artificial Intelligence Press.
Milli, S., Lieder, F. & Griffiths, T. L. (2019) A rational reinterpretation of dual-process theories. Preprint. doi:10.13140/RG.2.2.14956.46722/1.
Moore, D. A. & Healy, P. J. (2008) The trouble with overconfidence. Psychological Review 115(2):502–17.
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.
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.
Navon, D. & Gopher, D. (1979) On the economy of the human-processing system. Psychological Review 86(3):214–55. doi:10.1037/0033-295X.86.3.214.
Neuman, R., Rafferty, A. & Griffiths, T. (2014) A bounded rationality account of wishful thinking. In: Proceedings from the 36th annual meeting of the Cognitive Science Society. Cognitive Science Society.
Newell, A., Shaw, J. C. & Simon, H. A. (1958) Elements of a theory of human problem solving. Psychological Review 65(3):151–66. doi:10.1037/h0048495.
Newell, A. & Simon, H. A. (1972) Human problem solving. Prentice-Hall.
Niven, J. E. & Laughlin, S. B. (2008) Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology 211(11):1792–804. Available at:
Nobandegani, A. (2017) The minimalist mind: On minimality in learning, reasoning. McGill-Queen's University Press.
Nobandegani, A. S., Castanheira, K. da S., Otto, A. R. & Shultz, T. R. (2018) Over-representation of extreme events in decision-making: A rational metacognitive account. In: Proceedings from the 40th annual conference of the Cognitive Science Society, pp. 2394–99. Cognitive Science Society.
Nobandegani, A. S. & Psaromiligkos, I. N. (2017) The causal frame problem: An algorithmic perspective. In: Proceedings from the 39th annual conference of the Cognitive Science Society (London, UK), pp. 2567–72. Cognitive Science Society.
Oaksford, M. & Chater, N. (1994) A rational analysis of the selection task as optimal data selection. Psychological Review 101(4):608–31. doi:10.1037/0033-295X.101.4.608.
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning (Oxford cognitive science). Oxford University Press.
Olshausen, B. A. & Field, D. J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–09. doi:10.1038/381607a0.
Olshausen, B. A. & Field, D. J. (1997) Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research 37(23):3311–25. doi:10.1016/S0042-6989(97)00169-7.
Olshausen, B. A. & Field, D. J. (2004) Sparse coding of sensory inputs. Current Opinion in Neurobiology 14(4):481–87. doi:10.1016/j.conb.2004.07.007.
Orhan, A. E., Sims, C. R., Jacobs, R. A. & Knill, D. C. (2014) The adaptive nature of visual working memory. Current Directions in Psychological Science 23(3):164–70. doi:10.1177/0963721414529144.
Pashler, H. E. & Sutherland, S. (1998) The psychology of attention, vol. 15. MIT Press.
Payne, J. W., Bettman, J. R. & Johnson, E. J. (1993) The adaptive decision maker. Cambridge University Press.
Piantadosi, S. T., Tily, H. & Gibson, E. (2011) Word lengths are optimized for efficient communication. Proceedings of the National Academy of Sciences 108(9):3526–29. doi:10.1073/pnas.1012551108.
Polania, R., Woodford, M. & Ruff, C. C. (2019) Efficient coding of subjective value. Nature Neuroscience 22(1):134.
Ratcliff, R. (1978) A theory of memory retrieval. Psychological Review 85(2):59108. doi:10.1037/0033-295X.85.2.59.
Regier, T., Kay, P. & Khetarpal, N. (2007) Color naming reflects optimal partitions of color space. Proceedings of the National Academy of Sciences 104(4):1436–41. doi:10.1073/pnas.0610341104.
Reis, R. (2006) Inattentive consumers. Journal of Monetary Economics 53(8):17611800. doi:10.3386/w10883.
Rozenblit, L. & Keil, F. (2002) The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science 26(5):521–62. doi:10.1207/s15516709cog2605_1.
Rumelhart, D. E. & McClelland, J. L. (1987) Parallel distributed processing, vol. 1. MITPress.
Russell, S. J. (1997) Rationality and intelligence. Artificial Intelligence 94(1–2):5777. doi:10.1016/S0004-3702(97)00026-X.
Russell, S. J. & Subramanian, D. (1995) Provably bounded-optimal agents. Journal of Artificial Intelligence Research 2(1):575609. doi: 10.1613/jair.133.
Sanborn, A. N. & Chater, N. (2016) Bayesian brains without probabilities. Trends in Cognitive Sciences 20(12):883–93. doi:10.1016/j.tics.2016.10.003.
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117(4):1144–67. doi:10.1037/a0020511.
Sanjurjo, A. (2017) Search with multiple attributes: Theory and empirics. Games and Economic Behavior 104:535–62. doi:10.2139/ssrn.2460129.
Sedlmeier, P. & Gigerenzer, G. (2001) Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General 130(3):380400. doi:10.1037//0096-3445.130.3.380.
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.
Shafir, S., Waite, T. A. & Smith, B. H. (2002) Context-dependent violations of rational choice in honeybees (Apis mellifera) and gray jays (Perisoreus canadensis). Behavioral Ecology and Sociobiology 51(2):180–87.
Shanks, D., Tunney, R. & McCarthy, J. (2002) A re-examination of probability matching and rational choice. Journal of Behavioral Decision Making 15(3):233–50. doi:10.1002/bdm.413.
Shenhav, A., Botvinick, M. M. & Cohen, J. (2013) The expected value of control: An integrative theory of anterior cingulate cortex function. Neuron 79(2):217–40. doi:10.1016/j.neuron.2013.07.007.
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 40:99124. doi:10.1146/annurev-neuro-072116-031526.
Shrager, J. & Siegler, R. S. (1998) SCADS: A model of children's strategy choices and strategy discoveries. Psychological Science 9(5):405–10.
Shugan, S. M. (1980) The cost of thinking. Journal of Consumer Research 7(2):99111. doi:10.1086/208799.
Siegler, R. & Jenkins, E. A. (1989) How children discover new strategies. Psychology Press.
Simon, H. A. (1955) A behavioral model of rational choice. The Quarterly Journal of Economics 69(1):99118. doi:10.2307/1884852.
Simon, H. A. (1956) Rational choice and the structure of the environment. Psychological Review 63(2):129–38. doi:10.1037/h0042769.
Simon, H. A. (1982) Models of bounded rationality: Empirically grounded economic reason, vol. 3. MIT Press.
Sims, C. A. (2003) Implications of rational inattention. Journal of Monetary Economics 50(3):665–90. doi:10.1016/S0304-3932(03)00029-1.
Sims, C. A. (2006) Rational inattention: Beyond the linear-quadratic case. American Economic Review 96(2):158–63. doi:10.1257/000282806777212431.
Sims, C. R. (2016) Rate-distortion theory and human perception. Cognition 152:181–98. doi:10.1016/j.cognition.2016.03.020.
Sims, C. R., Jacobs, R. A. & Knill, D. C. (2012) An ideal observer analysis of visual working memory. Psychological Review 119(4):807–30. doi:10.1037/a0029856.
Solway, A., Diuk, C., Córdova, N., Yee, D., Barto, A. G., Niv, Y. & Botvinick, M. M. (2014) Optimal behavioral hierarchy. The Public Library of Science Computational Biology 10(8):e1003779. doi:10.1371/journal.pcbi.1003779.
Sosis, C. & Bishop, M. (2014) Rationality. Wiley Interdisciplinary Reviews: Cognitive Science 5(1):2737. doi:10.1002/wcs.1263.
Stanovich, K. E. (2011) Rationality and the reflective mind. Oxford University Press.
Sterling, P. & Laughlin, S. (2015) Principles of neural design. MIT Press.
Sternberg, S. (1966) High-speed scanning in human memory. Science 153(3736):652–54. doi:10.1126/science.153.3736.652.
Stewart, N. (2009) Decision by sampling: The role of the decision environment in risky choice. The Quarterly Journal of Experimental Psychology 62(6):1041–62. doi:10.1080/17470210902747112.
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53(1):126. doi:10.1016/j.cogpsych.2005.10.003.
Stigler, G. J. (1961) The economics of information. Journal of Political Economy 69(3):213–25.
Stocker, A., Simoncelli, E. & Hughes, H. (2006) Sensory adaptation within a Bayesian framework for perception. In: Advances in neural information processing systems, vol. 18, ed. Weiss, Y., Schölkopf, B. & Platt, J., pp. 1291–98. MIT Press.
Suchow, J. W. (2014) Measuring, monitoring, and maintaining memories in a partially observable mind (Doctoral dissertation). Harvard University.
Suchow, J. W. & Griffiths, T. L. (2016) Deciding to remember: Memory maintenance as a Markov decision process. In: Proceedings from the 38th annual conference of the Cognitive Science Society, pp. 2063–68. Cognitive Science Society.
Sutherland, S. (2013) Irrationality: The enemy within. Pinter & Martin Ltd.
Tajima, S., Drugowitsch, J. & Pouget, A. (2016) Optimal policy for value-based decision-making. Nature Communications 7:12400–11. doi:10.1038/ncomms12400.
Tenenbaum, J. & Griffiths, T. (2001) The rational basis of representativeness. In: Proceedings from the 23rd annual conference of the Cognitive Science Society, 84–98. Cognitive Science Society.
Todd, P. M. & Brighton, H. (2016) Building the theory of ecological rationality. Minds and Machines 26(1–2):930. doi:10.1007/s11023-015-9371-0.
Todd, P. M. & Gigerenzer, G. (2012) Ecological rationality: Intelligence in the world. Oxford University Press.
Todorov, E. (2004) Optimality principles in sensorimotor control. Nature Neuroscience 7(9):907–15. doi:10.1038/nn1309.
Treisman, A. M. & Gelade, G. (1980) A feature-integration theory of attention. Cognitive Psychology 12(1):97136. doi:10.1016/0010-0285(80)90005-5.
Tsetsos, K., Moran, R., Moreland, J., Chater, N., Usher, M. & Summerfield, C. (2016) Economic irrationality is optimal during noisy decision making. Proceedings of the National Academy of Sciences 113(11):3102–07. doi:10.1073/pnas.1519157113.
Tversky, A. & Kahneman, D. (1973) Availability: A heuristic for judging frequency and probability. Cognitive Psychology 5(2):207–32. doi:10.1016/0010-0285(73)90033-9.
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–31. doi:10.1126/science.185.4157.1124.
Tversky, A. & Kahneman, D. (1992) Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4):297323. doi:10.1007/BF00122574.
van den Berg, R. & Ma, W. J. (2018) A resource-rational theory of set size effects in human visual working memory. ELife 7:e34963.
Van Ravenzwaaij, D., van der Maas, H. L. J. & Wagenmakers, E.-J. (2012) Optimal decision making in neural inhibition models. Psychological Review 119(1):201–15. doi:10.1037/a0026275.
Van Rooij, I. (2008) The tractable cognition thesis. Cognitive Science 32(6):939–84. doi:10.1080/03640210801897856.
Verrecchia, R. E. (1982) Information acquisition in a noisy rational expectations economy. Econometrica: Journal of the Econometric Society 50(6):1415–30. doi:10.2307/1913389.
Von Neumann, J. & Morgenstern, O. (1944) The theory of games and economic behavior. Princeton University Press.
Vul, E., Goodman, N. D., Griffiths, T. L. & Tenenbaum, J. B. (2014) One and done? Optimal decisions from very few samples. Cognitive Science 38(4):599637. doi:10.1111/cogs.12101.
Vulkan, N. (2000) An economist's perspective on probability matching. Journal of Economic Surveys 14(1):101–18. doi:10.1111/1467-6419.00106.
Wang, Z., Wei, X.-X., Stocker, A. A. & Lee, D. D. (2016) Efficient neural codes under metabolic constraints. In: Advances in neural information processing systems, vol. 29, ed. Lee, D. D., Sugiyama, M., Luxburg, U. V, Guyon, I. & Garnett, R., pp. 4619–27. Curran Associates, Inc.
Wason, P. C. (1968) Reasoning about a rule. Quarterly Journal of Experimental Psychology 20(3):273–81. doi:10.1080/14640746808400161.
Wei, X.-X. & Stocker, A. A. (2015) A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience 18(10):1509–17. doi:10.1038/nn.4105.
Wei, X.-X. & Stocker, A. A. (2017) Lawful relation between perceptual bias and discriminability. Proceedings of the National Academy of Sciences 114(38):10244–49. doi:10.1073/pnas.1619153114.
Wilson, M. (2002) Six views of embodied cognition. Psychonomic Bulletin & Review 9(4):625–36.
Wolfe, J. M. (1994) Guided search 2.0 a revised model of visual search. Psychonomic Bulletin & Review 1(2):202–38. doi:10.3758/BF03200774.
Wolpert, D. M. & Ghahramani, Z. (2000) Computational principles of movement neuroscience. Nature Neuroscience 3(11):1212–17. doi:10.1038/81497.
Woodford, M. (2014) Stochastic choice: An optimizing neuroeconomic model. American Economic Review 104(5):495500. doi:10.1257/aer.104.5.495.
Woodford, M. (2016) Optimal evidence accumulation and stochastic choice (Technical report). Columbia University.
Zaslavsky, N., Kemp, C., Regier, T. & Tishby, N. (2018) Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences 115(31):7937–42. doi:10.1073/pnas.1800521115.
Zipf, G. K. (1949) Human behavior and the principle of least effort: An introduction to human ecology. Addison-Wesley Press.
Dukas, R. (2004) Evolutionary biology of animal cognition. Annual Review of Ecology, Evolution, and Systematics 35:347–74.


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Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

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