Reconstructing the Einstellung Effect

Computational Brain & Behavior - Tập 6 - Trang 526-542 - 2022
Marcel Binz1, Eric Schulz1
1MPRG Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Tübingen, Germany

Tóm tắt

The Einstellung effect was first described by Abraham Luchins in his doctoral thesis published in 1942. The effect occurs when a repeated solution to old problems is applied to a new problem even though a more appropriate response is available. In Luchins’ so-called water jar task, participants had to measure a specific amount of water using three jars of different capacities. Luchins found that subjects kept using methods they had applied in previous trials, even if a more efficient solution for the current trial was available: an Einstellung effect. Moreover, Luchins studied the different conditions that could possibly mediate this effect, including telling participants to pay more attention, changing the number of tasks, alternating between different types of tasks, as well as putting participants under time pressure. In the current work, we reconstruct and reanalyze the data of the various experimental conditions published in Luchins’ thesis. We furthermore show that a model of resource-rational decision-making can explain all of the observed effects. This model assumes that people transform prior preferences into a posterior policy to maximize rewards under time constraints. Taken together, our reconstructive and modeling results put the Einstellung effect under the lens of modern-day psychology and show how resource-rational models can explain effects that have historically been seen as deficiencies of human problem-solving.

Tài liệu tham khảo

Anderson, J.R. (2014). Rules of the mind. Psychology Press. Ariely, D., & Zakay, D. (2001). A timely account of the role of duration in decision making. Acta Psychologica, 108(2), 187–207. Atwood, M.E., & Polson, P.G. (1976). A process model for water jug problems. Cognitive Psychology, 8(2), 191–216. Bertelson, P. (1965). Serial choice reaction-time as a function of response versus signal-and-response repetition. Nature, 206(4980), 217–218. Betsch, T., Haberstroh, S., Molter, B., & Glöckner, A. (2004). Oops, I did it—Relapse errors in routinized decision making. Organizational Behavior and Human Decision Processes, 93(1), 62–74. Bhui, R., Lai, L., & Gershman, S.J. (2021). Resource-rational decision making. Current Opinion in Behavioral Sciences, 41, 15–21. Bilalić, M., McLeod, P., & Gobet, F. (2008a). Inflexibility of experts–Reality or myth? Quantifying the Einstellung effect in chess masters. Cognitive Psychology, 56(2), 73–102. Bilalić, M., McLeod, P., & Gobet, F. (2008). Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect. Cognition, 108(3), 652–661. Bilalić, M., McLeod, P., & Gobet, F. (2010). The mechanism of the Einstellung (set) effect: A pervasive source of cognitive bias. Current Directions in Psychological Science, 19(2), 111–115. Binz, M., Gershman, S.J., Schulz, E., & Endres, D. (2020). Heuristics from bounded meta-learned inference. Birch, H.G., & Rabinowitz, H.S. (1951). The negative effect of previous experience on productive thinking. Journal of Experimental Psychology, 41(2), 121. Blech, C., Gaschler, R., & Bilalić, M. (2020). Why do people fail to see simple solutions? Using think-aloud protocols to uncover the mechanism behind the Einstellung (mental set) effect. Thinking & Reasoning, 26(4), 552–580. Braun, D.A., Ortega, P.A., Theodorou, E., & Schaal, S. (2011). Path integral control and bounded rationality. In 2011 IEEE symposium on adaptive dynamic programming and reinforcement learning (ADPRL) (pp. 202–209). IEEE. Chrysikou, E.G., & Weisberg, R.W. (2005). Following the wrong footsteps: Fixation effects of pictorial examples in a design problem-solving task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(5), 1134. Crilly, N. (2015). Fixation and creativity in concept development: The attitudes and practices of expert designers. Design Studies, 38, 54–91. Croskerry, P. (2003). The importance of cognitive errors in diagnosis and strategies to minimize them. Academic medicine, 78(8), 775–780. Dantzer, R. (1986). Behavioral, physiological and functional aspects of stereotyped behavior: A review and a re-interpretation. Journal of Animal Science, 62(6), 1776–1786. Dasgupta, I., Schulz, E., Goodman, N.D., & Gershman, S.J. (2018). Remembrance of inferences past: Amortization in human hypothesis generation. Cognition, 178, 67–81. Dasgupta, I., Schulz, E., Tenenbaum, J.B., & Gershman, S.J. (2020). A theory of learning to infer. Psychological Review, 127(3), 412. Davidow, J.Y., Foerde, K., Galván, A., & Shohamy, D. (2016). An upside to reward sensitivity: The hippocampus supports enhanced reinforcement learning in adolescence. Neuron, 92(1), 93–99. Duncker, K., & Lees, L.S. (1945). On problem-solving. Psychological Monographs, 58(5), i. Ericsson, K.A., & Charness, N. (1994). Expert performance: Its structure and acquisition. American Psychologist, 49(8), 725. Ericsson, K.A., Prietula, M.J., & Cokely, E.T. (2007). The making of an expert. Harvard Business Review, 85, 114. Fischhoff, B., Slovic, P., & Lichtenstein, S. (1979). Subjective sensitivity analysis. Organizational Behavior and Human Performance, 23(3), 339–359. Flesch, T., Balaguer, J., Dekker, R., Nili, H., & Summerfield, C. (2018). Comparing continual task learning in minds and machines. Proceedings of the National Academy of Sciences, 115(44), E10313–E10322. Funke, J. (2012). Complex problem solving. Encyclopedia of the Sciences of Learning, (pp. 682–685). Heidelberg: Springer. Genewein, T., Leibfried, F., Grau-Moya, J., & Braun, D.A. (2015). Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle. Frontiers in Robotics and AI, 2, 27. German, T.P., & Defeyter, M.A. (2000). Immunity to functional fixedness in young children. Psychonomic Bulletin & Review, 7(4), 707–712. Gershman, S.J. (2020). Origin of perseveration in the trade-off between reward and complexity. Cognition, 204, 104394. Gershman, S.J., & Goodman, N. (2014). Amortized inference in probabilistic reasoning. In Proceedings of the annual meeting of the cognitive science society, Vol. 36. 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. Gigerenzer, G., & Selten, R. (2002). Bounded rationality: The adaptive toolbox. Cambridge: MIT Press. Gopnik, A., O’Grady, S., Lucas, C.G., Griffiths, T.L., Wente, A., Bridgers, S., Aboody, R., Fung, H., & Dahl, R.E. (2017). Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood. Proceedings of the National Academy of Sciences, 114(30), 7892– 7899. Graaf, E.D. (1989). A test of medical problem-solving scored by nurses and doctors: The handicap of expertise. Medical Education, 23(4), 381–386. Harsha, P., Jain, R., McAllester, D., & Radhakrishnan, J. (2007). The communication complexity of correlation. In Twenty-second annual IEEE conference on computational complexity (CCC’07) (pp. 10–23). IEEE. Havasi, M., Peharz, R., & Hernández-Lobato, J.M. (2018). Minimal random code learning: Getting bits back from compressed model parameters. arXiv:1810.00440. Kahneman, D., & Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. Heuristics and Biases: The Psychology of Intuitive Judgment, 49, 81. Klein, G. (1993). Sources of error in naturalistic decision making tasks. In Proceedings of the human factors and ergonomics society annual meeting, (Vol. 37 pp. 368–371). Los Angeles: SAGE Publications. Koehler, J.J. (1996). The base rate fallacy reconsidered: Normative, descriptive and methodological challenges. Behavioral & Brain Science, 19, 1. Kornell, N., & Bjork, R.A. (2008). Learning concepts and categories: Is spacing the “enemy of induction”? Psychological Science, 19(6), 585–592. Krechevsky, I., & Honzik, C.H. (1932). Fixation in the rat. University of California Publications in Psychology. Lane, D.M., & Jensen, D.G. (1993). Einstellung: Knowledge of the phenomenon facilitates problem solving. In Proceedings of the human factors and ergonomics society annual meeting, (Vol. 37 pp. 1277–1280). Los Angeles: SAGE Publications. Lieder, F., & Griffiths, T.L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762. Lieder, F., & Griffiths, T.L. (2019). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 1– 85. Lieder, F., & Griffiths, T.L. (2020). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43. Lovett, M.C., & Anderson, J.R. (1996). History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psychology, 31(2), 168–217. Luchins, A.S. (1942). Mechanization in problem solving: The effect of Einstellung. Psychological Monographs, 54(6), i. Luchins, A.S. (1951). On recent usage of the Einstellung-effect as a test of rigidity. Journal of Consulting Psychology, 15(2), 89. Luchins, A.S., & Luchins, E.H. (1959). Rigidity of behavior: A variational approach to the effect of Einstellung. Maier, N.R.F. (1930). Reasoning in humans. I. On direction. Journal of Comparative Psychology, 10(2), 115–143. https://doi.org/10.1037/h0073232. Maier, N.R. (1931). Reasoning in humans. II. The solution of a problem and its appearance in consciousness. Journal of Comparative Psychology, 12(2), 181. Master, S.L., Eckstein, M.K., Gotlieb, N., Dahl, R., Wilbrecht, L., & Collins, A.G. (2020). Distentangling the systems contributing to changes in learning during adolescence. Developmental Cognitive Neuroscience, 41, 100732. Meder, B., Wu, C.M., Schulz, E., & Ruggeri, A. (2021). Development of directed and random exploration in children. Developmental Science, e13095. https://doi.org/10.1111/desc.13095. Morewedge, C.K., Yoon, H., Scopelliti, I., Symborski, C.W., Korris, J.H., & Kassam, K.S. (2015). Debiasing decisions: Improved decision making with a single training intervention. Policy Insights from the Behavioral and Brain Sciences, 2(1), 129–140. Neroni, M.A., & Crilly, N. (2020). How to guard against fixation? Demonstrating individual vulnerability is more effective than warning of general risk. The Journal of Creative Behavior. Nussenbaum, K., & Hartley, C.A. (2019). Reinforcement learning across development: What insights can we draw from a decade of research? Developmental Cognitive Neuroscience, 40, 100733. Ortega, P.A., Braun, D.A., Dyer, J., Kim, K.-E., & Tishby, N. (2015). Information-theoretic bounded rationality. arXiv:1512.06789. Poggio, T., Fahle, M., & Edelman, S. (1992). Fast perceptual learning in visual hyperacuity. Science, 256(5059), 1018–1021. Reingold, E.M., Charness, N., Schultetus, R.S., & Stampe, D.M. (2001). Perceptual automaticity in expert chess players: Parallel encoding of chess relations. Psychonomic Bulletin & Review, 8(3), 504–510. Rock, I. (1957). The role of repetition in associative learning. The American Journal of Psychology, 70(2), 186–193. Rohrer, D., Dedrick, R.F., & Stershic, S. (2015). Interleaved practice improves mathematics learning. Journal of Educational Psychology, 107(3), 900. Ross, V.M. (1952). A comparison of the effect of Einstellung in different age groups. Russell, S., & Wefald, E. (1991). Principles of metareasoning. Artificial Intelligence, 49, 361–395. 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. Saxe, A.M. (2013). Precis of deep linear neural networks: A theory of learning in the brain and mind. Schultz, P.W., & Searleman, A. (2002). Rigidity of thought and behavior: 100 years of research. Genetic, Social, and General Psychology Monographs, 128(2), 165. Schulz, E., Wu, C.M., Ruggeri, A., & Meder, B. (2019). Searching for rewards like a child means less generalization and more directed exploration. Psychological Science, 30(11), 1561–1572. Simon, H.A. (1972). Theories of bounded rationality. Decision and Organization, 1(1), 161–176. Simon, H.A. (1990). Bounded rationality. In Utility and probability (pp. 15–18). Springer. Suzuki, S. (2020). Zen mind, beginner’s mind. Shambhala Publications. Tishby, N., Pereira, F.C., & Bialek, W. (2000). The information bottleneck method. arXiv:0004057. Tolman, E.C. (1934). Theories of learning. Vul, E., Goodman, N., Griffiths, T.L., & Tenenbaum, J.B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599–637. Wikipedia contributors. (2022). Einstellung effect — Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/Einstellung_effect. Accessed 20 July 2022. Wu, C., Schulz, E., Gerbaulet, K., Pleskac, T., & Speekenbrink, M. (2019). Under pressure: The influence of time limits on human exploration. In 41st annual conference of the cognitive science society (CogSci 2019) (pp. 1219–1225).