Abbott, J.T., Austerweil, J.L., & Griffiths, T.L. (2015). Random walks on semantic networks can resemble optimal foraging. Psychological Review, 122(3), 558–569.
Article PubMed Google Scholar
Abbott, J.T., & Griffiths, T.L. (2011). Exploring the influence of particle filter parameters on order effects in causal learning, In Proceedings of the 33rd Annual Conference of the Cognitive Science Society. Austin, Texas: Cognitive Science Society.
Google Scholar
Anderson, J.R. (1983). A spreading activation theory of memory. Journal of verbal learning and verbal behavior, 22(3), 261– 295.
Article Google Scholar
Anderson, J.R. (1990). The adaptive character of thought. Hillsdale, NJ: Psychology Press.
Google Scholar
Anderson, J.R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14, 471–485.
Article Google Scholar
Ariely, D., Loewenstein, G., & Prelec, D. (2003). Coherent arbitrariness: Stable demand curves without stable preferences. The Quarterly Journal of Economics, 118(1), 73–106.
Article Google Scholar
Beach, L. R., & Mitchell, T. R. (1978). A contingency model for the selection of decision strategies. Academy of Management Review, 3(3), 439–449.
Google Scholar
Bonawitz, E., Denison, S., Gopnik, A., & Griffiths, T. L. (2014). Win-stay, lose-sample: A simple sequential algorithm for approximating Bayesian inference. Cognitive Psychology, 74, 35–65.
Article PubMed Google Scholar
Bonawitz, E., Denison, S., Griffiths, T. L., & Gopnik, A. (2014). Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences, 18(10), 497–500.
Article PubMed Google Scholar
Bourgin, D. D., Abbott, J. T., Griffiths, T. L., Smith, K. A., & Vul, E. (2014). Empirical evidence for markov chain monte carlo in memory search. In Proceedings of the 36th annual meeting of the cognitive science society, (pp. 224–229).
Braine, M. D. (1978). On the relation between the natural logic of reasoning and standard logic. Psychological Review, 85(1), 1.
Article Google Scholar
Brewer, N. T., & Chapman, G. B. (2002). The fragile basic anchoring effect. Journal of Behavioral Decision Making, 15, 65–77.
Article Google Scholar
Buesing, L., Bill, J., Nessler, B., & Maass, W. (2011). Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology, 7(11), e1002211.
Article PubMed PubMed Central Google Scholar
Chapman, G. B., & Johnson, E. J. (1994). The limits of anchoring. Journal of Behavioral Decision Making, 7(4), 223–242.
Article Google Scholar
Chapman, G. B., & Johnson, E. J. (2002). Incorporating the irrelevant: Anchors in judgments of belief and value. In Gilovich, T., Griffin, D., & Kahneman, D. (Eds.), Heuristics and biases: The psychology of intuitive judgment. Cambridge, U.K.: Cambridge University Press.
Google Scholar
Chater, N., & Oaksford, M. (2000). The rational analysis of mind and behavior. Synthese, 122(1), 93–131.
Article Google Scholar
Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological review, 82(6), 407.
Article Google Scholar
Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. (2013). Rational variability in children’s causal inferences: The sampling hypothesis. Cognition, 126(2), 285–300.
Article PubMed Google Scholar
Diamond, A. (2013). Executive functions. Annual review of psychology, 64, 135.
Article PubMed Google Scholar
Doucet, A., De Freitas, N., & Gordon, N. (2001). Sequential Monte Carlo methods in practice. New York: Springer.
Book Google Scholar
Englich, B., Mussweiler, T., & Strack, F. (2006). Playing dice with criminal sentences: The influence of irrelevant anchors on experts’ judicial decision making. Personality and Social Psychology Bulletin, 32(2), 188–200.
Article PubMed Google Scholar
Epley, N. (2004). A tale of tuned decks? Anchoring as accessibility and anchoring as adjustment. In Koehler, D. J., & Harvey, N. (Eds.), The Blackwell Handbook of Judgment and Decision Making (pp. 240–256). Oxford, UK: Blackwell.
Chapter Google Scholar
Epley, N., & Gilovich, T. (2004). Are adjustments insufficient? Personality and Social Psychology Bulletin, 30(4), 447–460.
Article PubMed Google Scholar
Epley, N., & Gilovich, T. (2005). When effortful thinking influences judgmental anchoring: Differential effects of forewarning and incentives on self-generated and externally provided anchors. Journal of Behavioral Decision Making, 18(3), 199–212.
Article Google Scholar
Epley, N., & Gilovich, T. (2006). The anchoring-and-adjustment heuristic. Psychological Science, 17(4), 311–318.
Article PubMed Google Scholar
Epley, N., Keysar, B., Van Boven, L., & Gilovich, T. (2004). Perspective taking as egocentric anchoring and adjustment. Journal of Personality and Social Psychology, 87(3), 327–339.
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), 119–130.
Article PubMed PubMed Central Google Scholar
Fodor, J. (1975). The language of thought. Cambridge, MA: Harvard University Press.
Google Scholar
Frank, M., & Goodman, N. (2012). Predicting pragmatic reasoning in language games. Science, 336(6084), 998.
Article PubMed Google Scholar
Friedman, M., & Savage, L. J. (1948). The utility analysis of choices involving risk. The Journal of Political Economy, 279–304.
Friston, K. (2009). The free-energy principle: A rough guide to the brain?. Trends in Cognitive Sciences, 13 (7), 293–301.
Article PubMed Google Scholar
Friston, K., & Kiebel, S. (2009). Predictive coding under the free-energy principle. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1521), 1211–1221.
Article Google Scholar
Galinsky, A. D., & Mussweiler, T. (2001). First offers as anchors: The role of perspective-taking and negotiator focus. Journal of Personality and Social Psychology, 81(4), 657.
Article PubMed 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), 273–278.
Article PubMed Google Scholar
Gershman, S. J., Vul, E., & Tenenbaum, J. B. (2012). Multistability and perceptual inference. Neural Computation, 24(1), 1–24.
Article PubMed Google Scholar
Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science, 3(1), 20–29.
Article PubMed Google Scholar
Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669.
Article PubMed Google Scholar
Gigerenzer, G., & Selten, R. (2002) In In Gigerenzer, G., & Selten, R. (Eds.), Bounded rationality: The adaptive toolbox. Cambridge, MA: The MIT Press.
Gilks, W., Richardson, S., & Spiegelhalter, D. (1996). Markov chain Monte Carlo in practice. London: Chapman and Hall.
Google Scholar
Good, I. J. (1983). Good thinking: The foundations of probability and its applications. USA: Univ Of Minnesota 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), 217–229.
Article PubMed Google Scholar
Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767–773.
Article PubMed Google Scholar
Griffiths, T. L., & Tenenbaum, J. B. (2011). Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent. Journal of Experimental Psychology: General, 140 (4), 725–743.
Article Google Scholar
Habenschuss, S., Jonke, Z., & Maass, W. (2013). Stochastic computations in cortical microcircuit models. PLoS Computational Biology, 9(11), e1003311.
Article PubMed PubMed Central Google Scholar
Hardt, O., & Pohl, R. (2003). Hindsight bias as a function of anchor distance and anchor plausibility. Memory, 11(4-5), 379–394.
Article PubMed Google Scholar
Harman, G. (2013). Rationality. In LaFollette, H., Deigh, J., & Stroud, S. (Eds.), International Encyclopedia of Ethics. Hoboken: Blackwell Publishing Ltd.
Google Scholar
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.
Article Google Scholar
Hedström, P., & Stern, C. (2008). Rational choice and sociology. In Durlauf, S., & Blume, L. (Eds.), The New Palgrave Dictionary of Economics. 2nd edn. Basingstoke, U.K.: Palgrave Macmillan.
Google Scholar
Horvitz, E., Suermondt, H., & Cooper, G. (1989). Bounded conditioning: Flexible inference for decisions under scarce resources, Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence (pp. 182–193). Mountain View: Association for Uncertainty in Artificial Intelligence.
Google Scholar
Jacowitz, K. E., & Kahneman, D. (1995). Measures of anchoring in estimation tasks. Personality and Social Psychology Bulletin, 21(11), 1161–1166.
Article Google Scholar
Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3(3), 430–454.
Article 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.
Article PubMed Google Scholar
Lieder, F., Goodman, N. D., & Huys, Q. J. M. (2013). Controllability and resource-rational planning. In Pillow, J., Rust, N., Cohen, M., & Latham, P. (Eds.), Cosyne Abstracts.
Google Scholar
Lieder, F., & Griffiths, T. L. (2015). When to use which heuristic: A rational solution to the strategy selection problem. In Noelle, D. C., & et al. (Eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society Austin. TX: Cognitive Science Society.
Google Scholar
Lieder, F., Griffiths, T. L., & Goodman, N. D. (2012). Burn-in, bias, and the rationality of anchoring. In Bartlett, P., Pereira, F. C. N., Bottou, L., Burges, C. J. C., & Weinberger, K. Q. (Eds.), Advances in Neural Information Processing Systems 26.
Google Scholar
Lieder, F., Griffiths, T. L., Huys, Q. J. M., & Goodman, N. D. (2017). Empirical evidence for resource-rational anchoring-and-adjustment.
Lieder, F., Hsu, M., & Griffiths, T. L. (2014). The high availability of extreme events serves resource-rational decision-making., In Proceedings of the 36th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Google Scholar
Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., & Griffiths, T. L. (2014). Algorithm selection by rational metareasoning as a model of human strategy selection. Advances in Neural Information Processing Systems 27.
Lohmann, S. (2008). Rational choice and political science. In Durlauf, S., & Blume, L. (Eds.), The New Palgrave Dictionary of Economics. 2nd edn. Basingstoke, U.K.: Palgrave Macmillan.
Google Scholar
Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman. Paperback.
McKenzie, C. R. (1994). The accuracy of intuitive judgment strategies: Covariation assessment and bayesian inference. Cognitive Psychology, 26(3), 209–239.
Article Google Scholar
Mengersen, K. L., & Tweedie, R. L. (1996). Rates of convergence of the Hastings and Metropolis algorithms. Annals of Statistics, 24(1), 101–121.
Article Google Scholar
Mill, J. S. (1882). A system of logic ratiocinative and inductive, 8th edn. New York: Harper and Brothers.
Google Scholar
Moreno-Bote, R., Knill, D. C., & Pouget, A. (2011). Bayesian sampling in visual perception. Proceedings of the National Academy of Sciences of the United States of America, 108(30), 12491– 12496.
Article PubMed PubMed Central Google Scholar
Mussweiler, T., & Strack, F. (1999). Hypothesis-consistent testing and semantic priming in the anchoring paradigm: A selective accessibility model. Journal of Experimental Social Psychology, 35(2), 136–164.
Article Google Scholar
Neal, R. (2011) In Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (Eds.), MCMC using Hamiltonian dynamics (Vol. 2, pp. 113–162). FL, USA: CRC Press.
Neely, J. H. (1977). Semantic priming and retrieval from lexical memory: Roles of inhibitionless spreading activation and limited-capacity attention. Journal of experimental psychology: General, 106(3), 226.
Article Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1958). Elements of a theory of human problem solving. Psychological Review, 65(3), 151–166.
Article Google Scholar
Nisbett, R. E., & Borgida, E. (1975). Attribution and the psychology of prediction. Journal of Personality and Social Psychology, 32(5), 932–943.
Article Google Scholar
Nisbett, R. E., & Ross, L. (1980). Human inference: Strategies and shortcomings of social judgment. Englewood Cliffs: Prentice-Hall.
Google Scholar
Northcraft, G. B., & Neale, M. A. (1987). Experts, amateurs, and real estate: An anchoring-and-adjustment perspective on property pricing decisions. Organizational Behavior and Human Decision Processes, 39(1), 84–97.
Article Google Scholar
Oaksford, M., & Chater, N. (2007). Bayesian rationality: The probabilistic approach to human reasoning (Oxford cognitive science series), 1st edn. Oxford: Oxford University Press.
Book Google Scholar
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker: Cambridge University Press.
Pohl, R. F. (1998). The effects of feedback source and plausibility of hindsight bias. European Journal of Cognitive Psychology, 10(2), 191–212.
Article Google Scholar
Russell, S. J. (1997). Rationality and intelligence. Artificial Intelligence, 94(1-2), 57–77.
Article Google Scholar
Russell, S. J., & Subramanian, D. (1995). Provably bounded-optimal agents. Journal of Articial Intelligence Research, 2, 575–609.
Google Scholar
Russell, S. J., & Wefald, E. (1991). Do the right thing: Studies in limited rationality. Cambridge, MA: The MIT Press.
Google Scholar
Russo, J. E., & Schoemaker, P. J. H. (1989). Decision traps: Ten barriers to brilliant decision-making and how to overcome them: Simon and Schuster.
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– 1167.
Article PubMed Google Scholar
Schwarz, N. (2014). Cognition and communication: Judgmental biases, research methods and the logic of conversation. New York: Psychology Press.
Google Scholar
Shafir, E., & LeBoeuf, R. A. (2002). Rationality. Annual Review of Psychology, 53(1), 491–517.
Article PubMed Google Scholar
Shugan, S. M. (1980). The cost of thinking. Journal of consumer Research, 7(2), 99–111.
Article Google Scholar
Simmons, J. P., LeBoeuf, R. A., & Nelson, L. D. (2010). The effect of accuracy motivation on anchoring and adjustment: Do people adjust from provided anchors? Journal of Personality and Social Psychology, 99(6), 917–932.
Article PubMed Google Scholar
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99–118.
Article Google Scholar
Simon, H. A. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129.
Article PubMed Google Scholar
Simon, H. A. (1972). Theories of bounded rationality. Decision and Organization, 1, 161–176.
Google Scholar
Simon, H. A. (1976). From substantive to procedural rationality. In Kastelein, T. J., Kuipers, S. K., Nijenhuis, W. A., & Wagenaar, G. R. (Eds.), 25 Years of Economic Theory (pp. 65–86). US: Springer.
Chapter Google Scholar
Simonson, I., & Drolet, A. (2004). Anchoring effects on consumers’ willingness-to-pay and willingness-to-accept. Journal of Consumer Research, 31(3), 681–690.
Article Google Scholar
Slovic, P., Fischhoff, B., & Lichtenstein, S. (1977). Cognitive processes and societal risk taking. In Jungermann, H., & De Zeeuw, G. (Eds.), Decision Making and Change in Human Affairs, (Vol. 16 pp. 7–36). Dordrecht, Netherlands: D. Reidel Publishing Company.
Speirs-Bridge, A., Fidler, F., McBride, M., Flander, L., Cumming, G., & Burgman, M. (2010). Reducing overconfidence in the interval judgments of experts. Risk Analysis, 30(3), 512–523.
Article PubMed Google Scholar
Stewart, N., Chater, N., & Brown, G. D. (2006). Decision by sampling. Cognitive Psychology, 53(1), 1–26.
Article PubMed Google Scholar
Strack, F., & Mussweiler, T. (1997). Explaining the enigmatic anchoring effect: Mechanisms of selective accessibility. Journal of Personality and Social Psychology, 73(3), 437.
Article Google Scholar
Sunnåker, M., Busetto, A. G., Numminen, E., Corander, J., Foll, M., & Dessimoz, C. (2013). Approximate bayesian computation. PLoS Computational Biology, 9(1), e1002803.
Article PubMed PubMed Central Google Scholar
Thorngate, W. (1980). Efficient decision heuristics. Behavioral Science, 25(3), 219–225.
Article Google Scholar
Turner, B. M., & Schley, D. R. (2016). The anchor integration model: A descriptive model of anchoring effects. Cognitive Psychology, 90, 1–47.
Article PubMed Google Scholar
Turner, B. M., & Sederberg, P. B. (2012). Approximate bayesian computation with differential evolution. Journal of Mathematical Psychology, 56(5), 375–385.
Article Google Scholar
Tversky, A. (1972). Elimination by aspects: A theory of choice. Psychological Review, 79(4), 281.
Article Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.
Article PubMed Google Scholar
Van Rooij, I. (2008). The tractable cognition thesis. Cognitive Science, 32(6), 939–984.
Von Neumann, J., & Morgenstern, O. (1944). The theory of games and economic behavior. Princeton: 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, 599–637.
Article PubMed Google Scholar
Wason, P. C. (1968). Reasoning about a rule. Quarterly Journal of Experimental Psychology, 20(3), 273–281.
Article PubMed Google Scholar
Wilson, T. D., Houston, C. E., Etling, K. M., & Brekke, N. (1996). A new look at anchoring effects: Basic anchoring and its antecedents. Journal of Experimental Psychology: General, 125(4), 387.
Article Google Scholar
Wright, W. F., & Anderson, U. (1989). Effects of situation familiarity and financial incentives on use of the anchoring and adjustment heuristic for probability assessment. Organizational Behavior and Human Decision Processes, 44(1), 68–82.
Article Google Scholar
Zhang, Y. C., & Schwarz, N. (2013). The power of precise numbers: A conversational logic analysis. Journal of Experimental Social Psychology, 49(5), 944–946.
Article Google Scholar
Page 2
From: The anchoring bias reflects rational use of cognitive resources
Anchoring effect
Simulated results
Resource-rational explanation
Insufficient adjustment from provided anchors
Jacowitz and Kahneman (1974), Tversky and Kahneman (1995)
Rational speed-accuracy tradeoff.
Insufficient adjustment from self-generated anchors
Epley and Gilovich (2006), Study 1
Rational speed-accuracy tradeoff.
Cognitive load, time pressure, and alcohol reduce adjustment.
Epley and Gilovich (2006), Study 2
Increased cost of adjustment reduces the resource-rational number of adjustments.
Anchoring bias increases with anchor extremity.
Russo and Schoemaker (1989)
Each adjustment reduces the bias by a constant factor (3). Since the resource-rational number of adjustments is insufficient, the bias is proportional to the distance from anchor to correct value.
Uncertainty increases anchoring.
Jacowitz and Kahneman (1995)
The expected change per adjustment is small when nearby values have similar plausibility.
Knowledge can reduce the anchoring bias.
Wilson et al. (1996), Study 1
High knowledge means low uncertainty. Low uncertainty leads to high adjustment (see above).
Accuracy motivation reduces anchoring bias when the anchor is self-generated but not when it is provided.
Tversky and Kahneman (1974), Epley and Gilovich (2005)
1. People are less uncertain about the quantities for which they generate their own anchors.
2. Accuracy motivation increases the number of adjustments but change per adjustment is lower when people are uncertain.
Telling people whether the correct value is larger or smaller than the anchor makes financial incentives more effective.
Simmons et al. (2010), Study 2
Being told the direction of adjustments makes adjustments more effective, because adjustments in the wrong direction will almost always be rejected.
Financial incentives are more effective when the anchor is extreme.
Simmons et al. (2010), Study 3
Values on the wrong side of an extreme anchor are much less plausible than values on the correct side. Therefore proposed adjustments in the wrong direction will almost always be rejected.