Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-24T16:22:25.548Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  04 February 2019

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]://re.is.mpg.de
Thomas L. Griffiths
Affiliation:
Departments of Psychology and Computer Science, Princeton University, Princeton, New Jersey08544, [email protected]://psych.princeton.edu/person/tom-griffiths
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

bounded rationalitycognitive biasescognitive mechanismscognitive modelingrepresentationsresource rationality
Type
Target Article
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e1
Check for updates
Copyright
Copyright © Cambridge University Press 2019

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

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.Google Scholar
Anderson, J. R. (1978) Arguments concerning representations for mental imagery. Psychological Review 85(4):249–77. doi:10.1037/0033-295X.85.4.249.Google Scholar
Anderson, J. R. (1990) The adaptive character of thought. Psychology Press.Google Scholar
Anderson, J. R. (1996) ACT: A simple theory of complex cognition. American Psychologist 51(4):355.Google Scholar
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.Google Scholar
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.Google Scholar
Anderson, J. R. & Schooler, L. J. (1991) Reflections of the environment in memory. Psychological Science 2(6):396–408. doi:10.1111/j.1467-9280.1991.tb00174.x.Google Scholar
Ariely, D. (2009) Predictably irrational. Harper Collins.Google Scholar
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.Google Scholar
Austerweil, J. & Griffiths, T. (2011) Seeking confirmation is rational for deterministic hypotheses. Cognitive Science 35(3):499–526. doi:10.1111/j.1551-6709.2010.01161.x.Google Scholar
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.Google Scholar
Bateson, M., Healy, S. D. & Hurly, T. A. (2002) Irrational choices in hummingbird foraging behaviour. Animal Behaviour 63(3):587–96.Google Scholar
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):30–39. doi:10.1016/j.neuron.2012.03.016.Google Scholar
Beer, R. D. (2000) Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3):91–99.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Bossaerts, P., Yadav, N. & Murawski, C. (2018) Uncertainty and computational complexity. Philosophical Transactions of the Royal Society B 374(1766):20180138.Google Scholar
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.Google Scholar
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.Google Scholar
Braine, M. D. (1978) On the relation between the natural logic of reasoning and standard logic. Psychological Review 85(1):1–21. doi:10.1037/0033-295X.85.1.1.Google 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):301–38. doi:10.1037/rev0000061.Google Scholar
Buss, D. M. (1995) Evolutionary psychology: A new paradigm for psychological science. Psychological Inquiry 6(1):1–30.Google Scholar
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.Google 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
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.Google Scholar
Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L. & Lieder, F. (in preparation). Discovering rational heuristics for risky choice.Google Scholar
Caplin, A. & Dean, M. (2015) Revealed preference, rational inattention, and costly information acquisition. American Economic Review 105(7):2183–203. doi:10.3386/w19876.Google Scholar
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.Google Scholar
Caplin, A., Dean, M. & Martin, D. (2011) Search and satisficing. American Economic Review 101(7):2899–922. doi:10.1257/aer.101.7.2899.Google Scholar
Carver, C. S. & Scheier, M. F. (2001) On the self-regulation of behavior. Cambridge University Press.Google Scholar
Chater, N. & Oaksford, M. (1999) Ten years of the rational analysis of cognition. Trends in Cognitive Sciences 3(2):57–65. doi:10.1016/S1364-6613(98)01273-X.Google Scholar
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.Google Scholar
Dasgupta, I., Schulz, E. & Gershman, S. J. (2017) Where do hypotheses come from? Cognitive Psychology 96:1–25. doi:10.1016/j.cogpsych.2017.05.001.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Dayan, P. & Abbott, L. F. (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems, 1st edition. MIT Press.Google Scholar
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.Google Scholar
Dolan, R. & Dayan, P. (2013) Goals and habits in the brain. Neuron 80(2):312–25. doi:10.1016/j.neuron.2013.09.007.Google Scholar
Dukas, R., ed. (1998a) Cognitive ecology: The evolutionary ecology of information processing and decision making. University of Chicago Press.Google Scholar
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.Google Scholar
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.Google Scholar
Edwards, W. (1954) The theory of decision making. Psychological Bulletin 51(4):380.Google Scholar
Epley, N. & Gilovich, T. (2004) Are adjustments insufficient? Personality and Social Psychology Bulletin 30(4):447–60. doi:10.1177/0146167203261889.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Frank, M. C. & Goodman, N. D. (2012) Predicting pragmatic reasoning in language games. Science 336(6084):998. doi:10.1126/science.1218633.Google Scholar
Friedman, M. & Savage, L. J. (1948) The utility analysis of choices involving risk. The Journal of Political Economy 56(4):279–304. doi:10.1086/256692.Google Scholar
Friedman, M. & Savage, L. J. (1952) The expected-utility hypothesis and the measurability of utility. Journal of Political Economy 60(6):463–74.Google Scholar
Friston, K. (2010) The free-energy principle: A unified brain theory? Nature Reviews Neuroscience 11(2):127–38. Available at: https://doi.org/10.1038/nrn2787.Google Scholar
Fudenberg, D., Strack, P. & Strzalecki, T. (2018) Speed, accuracy, and the optimal timing of choices (Working paper). MIT Press.Google Scholar
Gabaix, X. (2014) A sparsity-based model of bounded rationality. The Quarterly Journal of Economics 129(4):1661–710. doi:10.1093/qje/qju024.Google Scholar
Gabaix, X. (2016) Behavioral macroeconomics via sparse dynamic programming. NBER Working Paper No. w21848. National Bureau of Economic Research.Google Scholar
Gabaix, X. (2017) Behavioral inattention. NBER Working Paper No. 24096. National Bureau of Economic Research.Google Scholar
Gabaix, X. & Laibson, D. (2005) Bounded rationality and directed cognition (NBER and Harvard working paper). National Bureau of Economic Research.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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. 80–110. Oxford University Press.Google Scholar
Gigerenzer, G. & Gaissmaier, W. (2011) Heuristic decision making. Annual Review of Psychology 62(1):451–82. doi:10.1146/annurev-psych-120709-145346.Google Scholar
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.Google Scholar
Gigerenzer, G. & Hoffrage, U. (1995) How to improve Bayesian reasoning without instruction: Frequency formats. Psychological Review 102(4):684–704. doi:10.1037/0033-295X.102.4.684.Google Scholar
Gigerenzer, G. & Selten, R. (2002) Bounded rationality: The adaptive toolbox. MIT Press.Google Scholar
Gigerenzer, G., Todd, P. M. & Research Group, ABC. (1999) Simple heuristics that make us smart. Oxford University Press.Google Scholar
Gilovich, T., Griffin, D. & Kahneman, D., eds. (2002) Heuristics and biases: The psychology of intuitive judgment. Cambridge University Press.Google Scholar
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. 63–75. Ablex.Google Scholar
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.Google 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):585–93. doi:10.1016/j.tics.2013.09.001.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Griffiths, T. L. & Tenenbaum, J. B. (2009) Theory-based causal induction. Psychological Review 116(4):661–716. doi:10.1037/a0017201.Google Scholar
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.Google Scholar
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.Google Scholar
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.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.Google Scholar
Harman, G. (2013) Rationality. In: International Encyclopedia of Ethics. ed. LaFollette, H., Deigh, J. & Stroud, S.. Blackwell Publishing Ltd.Google Scholar
Haselton, M. G. & Nettle, D. (2006) The paranoid optimist: An integrative evolutionary model of cognitive biases. Personality and Social Psychology Review 10(1):47–66.Google Scholar
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.Google Scholar
Hawkins, J. A. (2004) Efficiency and complexity in grammars. Oxford University Press.Google Scholar
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.Google Scholar
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.Google Scholar
Hertwig, R. & Hoffrage, U. (2013) Simple heuristics in a social world. Oxford University Press.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Horvitz, E. J. (1990) Computation and action under bounded resources. PhD Dissertation, Stanford University.Google Scholar
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.Google Scholar
Houston, A. I. & McNamara, J. M. (1999) Models of adaptive behaviour: An approach based on state. Cambridge University Press.Google Scholar
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.Google Scholar
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/a0017187Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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: http://doi.org/10.1098/rstb.2002.1063.Google Scholar
Kahneman, D. (2003) Maps of bounded rationality: Psychology for behavioral economics. American Economic Review 93(5):1449-75. doi:10.1257/000282803322655392.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–91. doi:10.2307/1914185.Google Scholar
Kemp, C. & Regier, T. (2012) Kinship categories across languages reflect general communicative principles. Science 336(6084):1049–54. doi:10.1126/science.1218811.Google Scholar
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.Google Scholar
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.Google Scholar
Khaw, M. W., Li, Z. & Woodford, M. (2017) Risk aversion as a perceptual bias. NBER Working Paper No. 23294. National Bureau of Economic Research.Google Scholar
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.Google Scholar
Knill, D. C. & Richards, W. (1996) Perception as Bayesian inference. Cambridge University Press.Google Scholar
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.Google Scholar
Körding, K. P. & Wolpert, D. M. (2004) Bayesian integration in sensorimotor learning. Nature 427(6971):244–47. doi:10.1038/nature02169.Google Scholar
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.Google Scholar
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.Google Scholar
Larrick, R. P. (2004) Debiasing. In: Blackwell handbook of judgment and decision making, ed. Koehler, D. J. & Harvey, N., pp. 316–38. Blackwell Publishing.Google Scholar
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.Google Scholar
Lennie, P. (2003) The cost of cortical computation. Current Biology 13(6):493–97. doi:10.1016/S0960-9822(03)00135-0.Google Scholar
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.Google Scholar
Levy, W. B. & Baxter, R. A. (2002) Energy-efficient neuronal computation via quantal synaptic failures. Journal of Neuroscience 22(11):4746–55.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.Google Scholar
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.Google Scholar
Lieder, F. (2018) Beyond bounded rationality: Reverse-engineering and enhancing human intelligence (Doctoral dissertation). University of California, Berkeley.Google Scholar
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., Chen, O. X., Krueger, P. M. & Griffiths, T. L. (2019b) Cognitive prostheses for goal achievement. Nature Human Behavior 3:1096–1106.Google Scholar
Lieder, F. & Griffiths, T. L. (2017) Strategy selection as rational metareasoning. Psychological Review 124(6):762–94. doi:10.1037/rev0000075.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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. https://doi.org/10.1371/journal.pcbi.1006043.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Marcus, G. (2008) Kluge: The haphazard evolution of the human mind. Houghton Mifflin Harcourt.Google Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. MIT Press.Google Scholar
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.Google Scholar
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. 88–106. Cambridge University Press.Google Scholar
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):3–65. doi:10.1037/0033-295X.104.1.3.Google Scholar
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.Google Scholar
Mill, J. S. (1882) A system of logic, ratiocinative and inductive, 8th edition. Harper and Brothers.Google Scholar
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.Google Scholar
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.Google Scholar
Moore, D. A. & Healy, P. J. (2008) The trouble with overconfidence. Psychological Review 115(2):502–17.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
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.Google Scholar
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.Google Scholar
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.Google Scholar
Newell, A. & Simon, H. A. (1972) Human problem solving. Prentice-Hall.Google Scholar
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: https://doi.org/10.1242/jeb.017574.Google Scholar
Nobandegani, A. (2017) The minimalist mind: On minimality in learning, reasoning. McGill-Queen's University Press.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning (Oxford cognitive science). Oxford University Press.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Pashler, H. E. & Sutherland, S. (1998) The psychology of attention, vol. 15. MIT Press.Google Scholar
Payne, J. W., Bettman, J. R. & Johnson, E. J. (1993) The adaptive decision maker. Cambridge University Press.Google Scholar
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.Google Scholar
Polania, R., Woodford, M. & Ruff, C. C. (2019) Efficient coding of subjective value. Nature Neuroscience 22(1):134.Google Scholar
Ratcliff, R. (1978) A theory of memory retrieval. Psychological Review 85(2):59–108. doi:10.1037/0033-295X.85.2.59.Google Scholar
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.Google Scholar
Reis, R. (2006) Inattentive consumers. Journal of Monetary Economics 53(8):1761–1800. doi:10.3386/w10883.Google Scholar
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.Google Scholar
Rumelhart, D. E. & McClelland, J. L. (1987) Parallel distributed processing, vol. 1. MITPress.Google Scholar
Russell, S. J. (1997) Rationality and intelligence. Artificial Intelligence 94(1–2):57–77. doi:10.1016/S0004-3702(97)00026-X.Google Scholar
Russell, S. J. & Subramanian, D. (1995) Provably bounded-optimal agents. Journal of Artificial Intelligence Research 2(1):575–609. doi: 10.1613/jair.133.Google Scholar
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.Google Scholar
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.Google Scholar
Sanjurjo, A. (2017) Search with multiple attributes: Theory and empirics. Games and Economic Behavior 104:535–62. doi:10.2139/ssrn.2460129.Google Scholar
Sedlmeier, P. & Gigerenzer, G. (2001) Teaching Bayesian reasoning in less than two hours. Journal of Experimental Psychology: General 130(3):380–400. doi:10.1037//0096-3445.130.3.380.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
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.Google Scholar
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.Google Scholar
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.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. doi:10.1146/annurev-neuro-072116-031526.Google Scholar
Shrager, J. & Siegler, R. S. (1998) SCADS: A model of children's strategy choices and strategy discoveries. Psychological Science 9(5):405–10.Google Scholar
Shugan, S. M. (1980) The cost of thinking. Journal of Consumer Research 7(2):99–111. doi:10.1086/208799.Google Scholar
Siegler, R. & Jenkins, E. A. (1989) How children discover new strategies. Psychology Press.Google Scholar
Simon, H. A. (1955) A behavioral model of rational choice. The Quarterly Journal of Economics 69(1):99–118. doi:10.2307/1884852.Google Scholar
Simon, H. A. (1956) Rational choice and the structure of the environment. Psychological Review 63(2):129–38. doi:10.1037/h0042769.Google Scholar
Simon, H. A. (1982) Models of bounded rationality: Empirically grounded economic reason, vol. 3. MIT Press.Google Scholar
Sims, C. A. (2003) Implications of rational inattention. Journal of Monetary Economics 50(3):665–90. doi:10.1016/S0304-3932(03)00029-1.Google Scholar
Sims, C. A. (2006) Rational inattention: Beyond the linear-quadratic case. American Economic Review 96(2):158–63. doi:10.1257/000282806777212431.Google Scholar
Sims, C. R. (2016) Rate-distortion theory and human perception. Cognition 152:181–98. doi:10.1016/j.cognition.2016.03.020.Google Scholar
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.Google Scholar
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.Google Scholar
Sosis, C. & Bishop, M. (2014) Rationality. Wiley Interdisciplinary Reviews: Cognitive Science 5(1):27–37. doi:10.1002/wcs.1263.Google Scholar
Stanovich, K. E. (2011) Rationality and the reflective mind. Oxford University Press.Google Scholar
Sterling, P. & Laughlin, S. (2015) Principles of neural design. MIT Press.Google Scholar
Sternberg, S. (1966) High-speed scanning in human memory. Science 153(3736):652–54. doi:10.1126/science.153.3736.652.Google Scholar
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.Google Scholar
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53(1):1–26. doi:10.1016/j.cogpsych.2005.10.003.Google Scholar
Stigler, G. J. (1961) The economics of information. Journal of Political Economy 69(3):213–25.Google Scholar
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.Google Scholar
Suchow, J. W. (2014) Measuring, monitoring, and maintaining memories in a partially observable mind (Doctoral dissertation). Harvard University.Google Scholar
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.Google Scholar
Sutherland, S. (2013) Irrationality: The enemy within. Pinter & Martin Ltd.Google Scholar
Tajima, S., Drugowitsch, J. & Pouget, A. (2016) Optimal policy for value-based decision-making. Nature Communications 7:12400–11. doi:10.1038/ncomms12400.Google Scholar
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.Google Scholar
Todd, P. M. & Brighton, H. (2016) Building the theory of ecological rationality. Minds and Machines 26(1–2):9–30. doi:10.1007/s11023-015-9371-0.Google Scholar
Todd, P. M. & Gigerenzer, G. (2012) Ecological rationality: Intelligence in the world. Oxford University Press.Google Scholar
Todorov, E. (2004) Optimality principles in sensorimotor control. Nature Neuroscience 7(9):907–15. doi:10.1038/nn1309.Google Scholar
Treisman, A. M. & Gelade, G. (1980) A feature-integration theory of attention. Cognitive Psychology 12(1):97–136. doi:10.1016/0010-0285(80)90005-5.Google Scholar
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.Google Scholar
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.Google Scholar
Tversky, A. & Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185(4157):1124–31. doi:10.1126/science.185.4157.1124.Google Scholar
Tversky, A. & Kahneman, D. (1992) Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4):297–323. doi:10.1007/BF00122574.Google Scholar
van den Berg, R. & Ma, W. J. (2018) A resource-rational theory of set size effects in human visual working memory. ELife 7:e34963.Google Scholar
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.Google Scholar
Van Rooij, I. (2008) The tractable cognition thesis. Cognitive Science 32(6):939–84. doi:10.1080/03640210801897856.Google Scholar
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.Google Scholar
Von Neumann, J. & Morgenstern, O. (1944) The theory of games and economic behavior. Princeton University Press.Google Scholar
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):599–637. doi:10.1111/cogs.12101.Google Scholar
Vulkan, N. (2000) An economist's perspective on probability matching. Journal of Economic Surveys 14(1):101–18. doi:10.1111/1467-6419.00106.Google Scholar
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.Google Scholar
Wason, P. C. (1968) Reasoning about a rule. Quarterly Journal of Experimental Psychology 20(3):273–81. doi:10.1080/14640746808400161.Google Scholar
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.Google Scholar
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.Google Scholar
Wilson, M. (2002) Six views of embodied cognition. Psychonomic Bulletin & Review 9(4):625–36.Google Scholar
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.Google Scholar
Wolpert, D. M. & Ghahramani, Z. (2000) Computational principles of movement neuroscience. Nature Neuroscience 3(11):1212–17. doi:10.1038/81497.Google Scholar
Woodford, M. (2014) Stochastic choice: An optimizing neuroeconomic model. American Economic Review 104(5):495–500. doi:10.1257/aer.104.5.495.Google Scholar
Woodford, M. (2016) Optimal evidence accumulation and stochastic choice (Technical report). Columbia University.Google Scholar
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.Google Scholar
Zipf, G. K. (1949) Human behavior and the principle of least effort: An introduction to human ecology. Addison-Wesley Press.Google Scholar
Dukas, R. (2004) Evolutionary biology of animal cognition. Annual Review of Ecology, Evolution, and Systematics 35:347–74.Google Scholar