Asking the right questions about the psychology of human inquiry: Nine open challenges

  • Anna CoenenEmail author
  • Jonathan D. Nelson
  • Todd M. Gureckis
Theoretical Review


The ability to act on the world with the goal of gaining information is core to human adaptability and intelligence. Perhaps the most successful and influential account of such abilities is the Optimal Experiment Design (OED) hypothesis, which argues that humans intuitively perform experiments on the world similar to the way an effective scientist plans an experiment. The widespread application of this theory within many areas of psychology calls for a critical evaluation of the theory’s core claims. Despite many successes, we argue that the OED hypothesis remains lacking as a theory of human inquiry and that research in the area often fails to confront some of the most interesting and important questions. In this critical review, we raise and discuss nine open questions about the psychology of human inquiry.


Inquiry Information search Information gain Optimal experiment design Active learning Question asking 



We thank Neil Bramley, Justine Hoch, Doug Markant, Greg Murphy, and Marjorie Rhodes for many helpful comments on a draft of this paper. We also thank Kylan Larson for assistance with illustrations. This work was supported by BCS-1255538 from the National Science Foundation, the John S. McDonnell Foundation Scholar Award, and a UNSW Sydney Visiting Scholar Fellow, to TMG; and by NE 1713/1-2 from the Deutsche Forschungsgemeinschaft (DFG) as part of the ”New Frameworks of Rationality” (SPP 1516) priority program, to JDN.


  1. Anderson, J. R. (1990) The adaptive character of thought. Hillsdale: Erlbaum.Google Scholar
  2. Anderson, J. R. (1991). The adaptive nature of human categorization. Psychological Review, 98(3), 409–429.CrossRefGoogle Scholar
  3. Atkinson, R. C. (1972). Optimizing the learning of a second-language vocabulary. Journal of Experimental Psychology, 96(1), 124.CrossRefGoogle Scholar
  4. Austerweil, J., & Griffiths, T. (2011). Seeking confirmation is rational for deterministic hypotheses. Cognitive Science, 35, 499–526.CrossRefGoogle Scholar
  5. Bachman, P., Sordoni, A., & Trischler, A. (2017). Towards information-seeking agents. In Iclr.Google Scholar
  6. Baron, J., Beattie, J., & Hershey, J. C. (1988). Heuristics and biases in diagnostic reasoning: Ii. congruence, information, and certainty. Organizational Behavior and Human Decision Processes, 42(1), 88–110.CrossRefGoogle Scholar
  7. Baron, J., & Hershey, J. C. (1988). Outcome bias in decision evaluation. Journal of Personality and Social Psychology, 54(4), 569.PubMedCrossRefGoogle Scholar
  8. Bartlett, F. C., & Burt, C. (1933). Remembering: a study in experimental and social psychology. British Journal of Educational Psychology, 3(2), 187–192.CrossRefGoogle Scholar
  9. Battaglia, P., Hamrick, J., & Tenenbaum, J. (2013). Simulation as an engine of physical scene understanding. Proceedings of the National Academy of Sciences, 110(45), 18327–18332.CrossRefGoogle Scholar
  10. Bayes, T. (1763). An essay towards solving a problem in the doctrine of chance. Philosophical Transactions of the Royal Society of London, 53, 370–418.CrossRefGoogle Scholar
  11. Berge, C. (1971) Principles of combinatorics. San Diego: Academic Press.Google Scholar
  12. Berlyne, D. E. (1966). Curiosity and exploration. Science, 153(3731), 25–33.PubMedCrossRefGoogle Scholar
  13. Blanchard, T. C., Hayden, B. Y., & Bromberg-Martin, E. S. (2015). Orbitofrontal cortex uses distinct codes for different choice attributes in decisions motivated by curiosity. Neuron, 85(3), 602–614.PubMedPubMedCentralCrossRefGoogle Scholar
  14. Blum, A. L., & Langley, P. (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1), 245–271.CrossRefGoogle Scholar
  15. Bonawitz, E. B., Ferranti, D., Saxe, R., Gopnik, A., Meltzoff, A. N., Woodward, J., & Schulz, L. (2010). Just do it? investigating the gap between prediction and action in toddlers causal inferences. Cognition, 115(1), 104–117.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Bonawitz, E. B., Shafto, P., Gweon, H., Goodman, N. D., Spelke, E., & Schulz, L. (2011). The double-edged sword of pedagogy: Instruction limits spontaneous exploration and discovery. Cognition, 120(3), 322–330.PubMedPubMedCentralCrossRefGoogle Scholar
  17. Bonawitz, E. B., van Schijndel, T. J., Friel, D., & Schulz, L. (2012). Children balance theories and evidence in exploration, explanation, and learning. Cognitive Psychology, 64(4), 215–234.PubMedCrossRefGoogle Scholar
  18. Bonawitz, E. B., 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.PubMedCrossRefGoogle Scholar
  19. Bonawitz, E. B., 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.PubMedCrossRefGoogle Scholar
  20. Borji, A., & Itti, L. (2013). State-of-the-art in visual attention modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1), 185–207.PubMedCrossRefGoogle Scholar
  21. Botvinick, M. M., Niv, Y., & Barto, A. C. (2009). Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective. Cognition, 113(3), 262–280.PubMedCrossRefGoogle Scholar
  22. Bramley, N. R., Dayan, P., & Lagnado, D. A. (2015). Staying afloat on neuraths boat–heuristics for sequential causal learning. In Proceedings of the 36th annual conference of the Cognitive Science Society (pp. 262–267).Google Scholar
  23. Bramley, N. R., Gerstenberg, T., & Tenenbaum, J. B. (2016). Natural science: Active learning in dynamic physical microworlds. In Papafragou, A., Grodner, D., Mirman, D., & J. C. Trueswell (Eds.) Proceedings of the 38th annual meeting of the Cognitive Science Society. Austin.Google Scholar
  24. Bramley, N. R., Lagnado, D., & Speekenbrink, M. (2015). Conservative forgetful scholars - how people learn causal structure through sequences of interventions. Journal of Experimental Psychology: Learning, Memory, and Cognition.Google Scholar
  25. Bromberg-Martin, E. S., & Hikosaka, O. (2011). Lateral habenula neurons signal errors in the prediction of reward information. Nature Neuroscience, 14(9), 1209–1216.PubMedPubMedCentralCrossRefGoogle Scholar
  26. Brown, S. D., & Steyvers, M. (2009). Detecting and predicting changes. Cognitive Psychology, 58(1), 49–67.PubMedCrossRefGoogle Scholar
  27. Buchsbaum, D., Bridgers, S., Skolnick-Weisberg, D., & Gopnik, A. (2012). The power of possibility: causal learning, counterfactual reasoning, and pretend play. Philosophical Transactions of the Royal Society of London, 367 (1599), 2202–2212.PubMedPubMedCentralCrossRefGoogle Scholar
  28. Cakmak, M., & Thomaz, A. L. (2012). Designing robot learners that ask good questions. In Proceedings of the seventh annual ACM/IEEE international conference on human–robot interaction (pp. 17–24).Google Scholar
  29. Carey, S., & Spelke, E. (1996). Science and core knowledge. Philosophy of Science, 63(4), 515–533.CrossRefGoogle Scholar
  30. Case, R. (1974). Structures and strictures: Some functional limitations on the course of cognitive growth. Cognitive Psychology, 6(4), 544–574.CrossRefGoogle Scholar
  31. Castro, R., Kalish, C., Nowak, R., Qian, R., Rogers, T., & Zhu, X. (2008) Human active learning. Advances in neural information processing systems Vol. 21. Cambridge: MIT Press.Google Scholar
  32. Catrambone, R., & Holyoak, K. (1989). Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 1147.Google Scholar
  33. Cavagnaro, D. R., Myung, J. I., Pitt, M. A., & Kujala, J. V. (2010). Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive science. Neural Computation, 22(4), 887–905.PubMedCrossRefGoogle Scholar
  34. Cavagnaro, D. R., Aranovich, G. J., Mcclure, S. M., Pitt, M. A., & Myung, J. I. (2014). On the functional form of temporal discounting: an optimized adaptive test. Journal of Risk and Uncertainty, 52(3), 233–254.CrossRefGoogle Scholar
  35. Chater, N., Crocker, M., & Pickering, M. (1998). The rational analysis of inquiry: The case of parsing. In M. Oaskford, & N. Chater (Eds.) Rational models of cognition (pp. 441–468): Oxford University Press.Google Scholar
  36. Chater, N., & Loewenstein, G. (2015). The under-appreciated drive for sense-making. Journal of Economic Behavior & Organization.Google Scholar
  37. Chen, Z., & Klahr, D. (1999). All other things being equal: Acquisition and transfer of the control of variables strategy. Child Development, 70(5), 1098–1120.PubMedCrossRefGoogle Scholar
  38. Chen, S. Y., Ross, B. H., & Murphy, G. L. (2014). Implicit and explicit processes in category-based induction: Is induction best when we don’t think?. Journal of Experimental Psychology: General, 143(1), 227.CrossRefGoogle Scholar
  39. Chin, C., & Brown, D. E. (2002). Student-generated questions: a meaningful aspect of learning in science. International Journal of Science Education, 24(5), 521–549.CrossRefGoogle Scholar
  40. Christie, S., & Genter, D. (2010). Where hypotheses come from: Learning new relations by structural alignment. Journal of Cognition and Development, 11, 3.CrossRefGoogle Scholar
  41. Coenen, A., Bramley, N. R., Ruggeri, A., & Gureckis, T. M. (2017). Beliefs about sparsity affect causal experimentation. In Proceedings of the 39th Annual Conference of the Cognitive Science Society. Austin.Google Scholar
  42. Coenen, A., & Gureckis, T. M. (2015). Are biases when making causal interventions related to biases in belief updating?. In R. D. Noelle, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (Eds.) Proceedings of the 37th annual conference of the Cognitive Science Society. Austin: Cognitive Science Society.Google Scholar
  43. Coenen, A., Rehder, B., & Gureckis, T. M. (2015). Strategies to intervene on causal systems are adaptively selected. Cognitive Psychology, 79, 102–133.PubMedCrossRefGoogle Scholar
  44. Cole, W., Robinson, S., & Adolph, K. (2016). Bouts of steps: The organization of infant exploration. Developmental Psychobiology, 58, 341–354.PubMedCrossRefGoogle Scholar
  45. Cook, C., Goodman, N. D., & Schulz, L. (2011). Where science starts: Spontaneous experiments in preschoolers exploratory play. Cognition, 120(3), 341–349.PubMedCrossRefGoogle Scholar
  46. Courville, A. C., & Daw, N. D. (2007). The rat as particle filter. In Advances in neural information processing systems (pp. 369–376).Google Scholar
  47. Crupi, V., & Tentori, K. (2014). State of the field: Measuring information and confirmation. Studies in History and Philosophy of Science, 47, 81–90.CrossRefGoogle Scholar
  48. Crupi, V., Nelson, J., Meder, B., Cevolani, G., & Tentori, K. (2018). Generalized information theory meets human cognition: Introducing a unified framework to model uncertainty and information search. Cognitive Science.Google Scholar
  49. Denison, S., Bonawitz, E., Gopnik, A., & Griffiths, T. L. (2013). Rational variability in children?s causal inferences: The sampling hypothesis. Cognition, 126(2), 285–300.PubMedCrossRefGoogle Scholar
  50. Denrell, J., & March, J. G. (2001). Adaptation as information restriction: The hot stove effect. Organization Science, 12(5), 523–538.CrossRefGoogle Scholar
  51. Denrell, J., & Le Mens, G. (2007). Interdependent sampling and social influence. Psychological Review, 114 (2), 398.PubMedCrossRefGoogle Scholar
  52. Doherty, M. E., Mynatt, C. R., Tweney, R. D., & Schiavo, M. D. (1979). Pseudodiagnosticity. Acta Psychologica, 43(2), 111–121.CrossRefGoogle Scholar
  53. Dougherty, M. R. P., & Hunter, J. (2003a). Probability judgment and subadditivity: the role of working memory capacity and constraining retrieval. Memory & Cognition, 31(6), 968–982. CrossRefGoogle Scholar
  54. Dougherty, M. R. P., & Hunter, J. E. (2003b). Hypothesis generation, probability judgment, and individual differences in working memory capacity. Acta Psychologica, 113(3), 263–282. PubMedCrossRefGoogle Scholar
  55. Dougherty, M. R. P., Thomas, R., & Lange, N. (2010). Toward an integrative theory of hypothesis generation, probability judgment, and hypothesis testing. Psychology of Learning and Motivation, 52, 299–342.CrossRefGoogle Scholar
  56. Edwards, W. (1965). Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing. Journal of Mathematical Psychology, 2(2), 312–329.CrossRefGoogle Scholar
  57. Edwards, W. (1968). Conservatism in human information processing. Formal Representation of Human Judgment, 17, 51.Google Scholar
  58. Elmore, J. G., Barton, M. B., Moceri, V. M., Polk, S., Arena, P. J., & Fletcher, S. W. (1998). Ten-year risk of false positive screening mammograms and clinical breast examinations. New England Journal of Medicine, 338 (16), 1089–1096.PubMedCrossRefGoogle Scholar
  59. Fedorov, V. V. (1972). Theory of optimal experiments. Access Online via Elsevier.Google Scholar
  60. Ferguson, T. S. (1989). Who solved the secretary problem?. Statistical Science, 4(3), 282–289.CrossRefGoogle Scholar
  61. Ferguson, T. S. (2012). Optimal stopping and applications. Electronic Text.∼tom/Stopping/Contents.html
  62. Fernbach, P. M., Darlow, A., & Sloman, S. A. (2010). Neglect of alternative causes in predictive but not diagnostic reasoning. Psychological Science, 21(3), 329–336.PubMedCrossRefGoogle Scholar
  63. Fernbach, P. M., Darlow, A., & Sloman, S. A. (2011). When good evidence goes bad: The weak evidence effect in judgment and decision-making. Cognition, 119(3), 459–467.PubMedCrossRefGoogle Scholar
  64. Fisac, J. F., Liu, C., Hamrick, J. B., Sastry, S., Hedrick, J. K., Griffiths, T. L., & Dragan, A. D. (2016). Generating plans that predict themselves. In Proceedings of WAFR.Google Scholar
  65. Frank, M. C., & Goodman, N. D. (2012). Predicting pragmatic reasoning in language games. Science, 336 (6084), 998–998.PubMedCrossRefGoogle Scholar
  66. Franke, M., & Degen, J. (2016). Reasoning in reference games: Individual-vs. population-level probabilistic modeling. PloS one, 11(5), e0154854.PubMedPubMedCentralCrossRefGoogle Scholar
  67. Friston, K. (2009). The free-energy principle: a rough guide to the brain?. Trends in Cognitive Sciences, 13 (7), 293–301.PubMedCrossRefGoogle Scholar
  68. Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015). Active inference and epistemic value. Cognitive Neuroscience, 6(4), 187–214.PubMedCrossRefGoogle Scholar
  69. Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., & Pezzulo, G. (2017). Active inference: a process theory. Neural Computation, 29(1), 1–49.PubMedCrossRefGoogle Scholar
  70. Gershman, S., Vul, E., & Tenenbaum, J. B. (2012). Multistability and perceptual inference. Neural Computation, 24(1), 1–24.PubMedCrossRefGoogle Scholar
  71. Gershman, S., & Daw, N. (2017). Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology, 68, 1–28.CrossRefGoogle Scholar
  72. Gick, M., & Holyoak, K. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1–38.CrossRefGoogle Scholar
  73. Gigerenzer, G., Mata, J., & Frank, R. (2009). Public knowledge of benefits of breast and prostate cancer screening in Europe. Journal of the National Cancer Institute, 101(17), 1216–1220.PubMedPubMedCentralCrossRefGoogle Scholar
  74. Ginsberg, M., & Smith, D. (1988). Reasoning about action I: a possible worlds approach. Artificial Intelligence, 35(2), 165–195.CrossRefGoogle Scholar
  75. Good, I. J. (1950) Probability and the weighting of evidence. New York: Charles Griffin.Google Scholar
  76. Goodman, N. D., & Stuhlmüller, A. (2013). Knowledge and implicature: Modeling language understanding as social cognition. Topics in Cognitive Science, 5(1), 173–184.PubMedCrossRefGoogle Scholar
  77. Goodman, N. D., Frank, M., Griffiths, T., & Tenenbaum, J. (2015). Relevant and robust. A response to Marcus and Davis. Psychological Science, 26(4), 539–541.PubMedCrossRefGoogle Scholar
  78. Goodman, N. D., & Frank, M. C. (2016). Pragmatic language interpretation as probabilistic inference. Trends in Cognitive Sciences, 20(11), 818–829.PubMedCrossRefGoogle Scholar
  79. Gopnik, A. (1996). The scientist as child. Philosophy of Science, 63(4), 485–514.CrossRefGoogle Scholar
  80. Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L., Kushnir, T., & Danks, D. (2004). A theory of causal learning in children: causal maps and Bayes nets. Psychological Review, 111(1), 3.PubMedCrossRefGoogle Scholar
  81. Gopnik, A. (2009). The philosophical baby: What children’s minds tell us about truth, love & the meaning of life. Random House.Google Scholar
  82. Gopnik, A. (2012). Scientific thinking in young children: Theoretical advances, empirical research, and policy implications. Science, 337(6102), 1623–1627.PubMedCrossRefGoogle Scholar
  83. Gopnik, A., & Wellman, H. M. (2012). Reconstructing constructivism: Causal models, Bayesian learning mechanisms, and the theory. Psychological Bulletin, 138(6), 1085.PubMedPubMedCentralCrossRefGoogle Scholar
  84. Gopnik, A., Griffiths, T., & Lucas, C. (2015). When younger learners can be better (or at least more open-minded) than older ones. Current Directions in Psychological Science, 24(2), 87–92.CrossRefGoogle Scholar
  85. Gottlieb, J. (2012). Attention, learning, and the value of information. Neuron, 76(2), 281–295.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 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), 585–593.PubMedPubMedCentralCrossRefGoogle Scholar
  87. Graesser, A., Langston, M., & Bagget, W. (1993). Exploring information about concepts by asking questions. In G. Nakamura, R. Taraban, & D. Medin (Eds.) The psychology of learning and motivation: Categorization by humans and machines, (Vol. 29 pp. 411–436): Academic Press.Google Scholar
  88. Graesser, A., & Person, N. K. (1994). Question asking during tutoring. American Educational Research Journal, 31(1), 104–137.CrossRefGoogle Scholar
  89. Gregg, L. W., & Simon, H. A. (1967). Process models and stochastic theories of simple concept formation. Journal of Mathematical Psychology, 4(2), 246–276.CrossRefGoogle Scholar
  90. Grice, H. P. (1975). Logic and conversation, 41–58.Google Scholar
  91. Griffiths, T. L., & Tenenbaum, J. B. (2006). Optimal predictions in everyday cognition. Psychological Science, 17(9), 767–773.PubMedCrossRefGoogle Scholar
  92. Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010). Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences, 14, 357–364.PubMedCrossRefGoogle Scholar
  93. Gruber, M. J., Gelman, B. D., & Ranganath, C. (2014). States of curiosity modulate hippocampus-dependent learning via the dopaminergic circuit. Neuron, 84(2), 486–496.PubMedPubMedCentralCrossRefGoogle Scholar
  94. Guez, A., Silver, D., & Dayan, P. (2012). Efficient Bayes-adaptive reinforcement learning using sample-based search. In Advances in neural information processing systems (pp. 1025–1033).Google Scholar
  95. Gureckis, T. M., & Love, B. C. (2003). Human unsupervised and supervised learning as a quantitative distinction. International Journal of Pattern Recognition and Artificial Intelligence, 17, 885–901.CrossRefGoogle Scholar
  96. Gureckis, T. M., & Markant, D. B. (2009). Active learning strategies in a spatial concept learning game. In Proceedings of the 31st annual conference of the Cognitive Science Society (pp. 3145–3150). Austin.Google Scholar
  97. Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning a cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464–481.PubMedCrossRefGoogle Scholar
  98. Gweon, H., Tenenbaum, J. B., & Schulz, L. (2010). Infants consider both the sample and the sampling process in inductive generalization. Proceedings of the National Academy of Sciences, 107(20), 9066–9071.CrossRefGoogle Scholar
  99. Gweon, H., Palton, H., Konopka, J., & Schulz, L. (2014). Sins of omission: Children selectively explore when teachers are under-informative. Cognition, 132, 335–341.PubMedCrossRefGoogle Scholar
  100. Hamrick, J., Smith, K., Griffiths, T., & Vul, E. (2015). Think again? the amount of mental simulation tracks uncertainty in the outcome. In R. D. Noelle, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (Eds.) Proceedings of the 37th annual conference of the Cognitive Science Society.Google Scholar
  101. Hawkins, R. X., Stuhlmüller, A., Degen, J., & Goodman, N. D. (2015). Why do you ask? good questions provoke informative answers. In R. D. Noelle, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings, & P. P. Maglio (Eds.) Proceedings of the 37th annual conference of the Cognitive Science Society. Austin: Cognitive Science Society.Google Scholar
  102. Hayes, B. K., Hawkins, G. E., & Newell, B. R. (2015). Consider the alternative: The effects of causal knowledge on representing and using alternative hypotheses in judgments under uncertainty. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(5), 723–739.PubMedGoogle Scholar
  103. Hendrickson, A. T., Navarro, D. J., & Perfors, A. (2016). Sensitivity to hypothesis size during information search. Decision, 3(1), 62.CrossRefGoogle Scholar
  104. Hoch, J., O’Grady, S., & Adolph, K. (in review). It’s the journey, not the destination: Locomotor exploration in infants.Google Scholar
  105. Hoch, J., Rachwani, J., & Adolph, K. E. (in review). Why do infants move? locomotor exploration in crawling and walking infants.Google Scholar
  106. Hodgins, H. S., & Zuckerman, M. (1993). Beyond selecting information: Biases in spontaneous questions and resultant conclusions . Journal of Experimental Social Psychology, 29(5), 387–407.CrossRefGoogle Scholar
  107. Horwich, P. (1982). Probability and evidence. CUP Archive.Google Scholar
  108. Huys, Q. J. M., Eshel, N., O’Nions, E., Sheridan, L., Dayan, P., & Roiser, J. P. (2012). Bonsai trees in your head: How the Pavlovian system sculpts goal-directed choices by pruning decision trees. PLoS Computational Biology, 8(3), e1002410.PubMedPubMedCentralCrossRefGoogle Scholar
  109. Inhelder, B., & Piaget, J. (1958) The growth of logical thinking. New York: Basic Books.Google Scholar
  110. Itti, L., & Baldi, P. (2005). A principled approach to detecting surprising events in video. In 2005. IEEE computer society conference on Computer vision and pattern recognition, (Vol. 1 pp. 631–637).Google Scholar
  111. Itti, L., & Baldi, P. (2006). Bayesian surprise attracts human attention. In B. Weiss, J. Schoelkopf, & Platt (Eds.) Advances in Neural Information Processing Systems, (Vol. 18 pp. 547–554).Google Scholar
  112. Jain, U., Zhang, Z., & Schwing, A. (2017). Creativity: Generating diverse questions using variational autoencoders. arXiv:
  113. Jones, M., & Love, B. C. (2011). Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences, 34(04), 169–188.PubMedCrossRefGoogle Scholar
  114. Kachergis, G., Rhodes, M., & Gureckis, T. M. (2016). Desirable difficulties in the development of active inquiry skills. In Proceedings of the 38th annual conference of the Cognitive Science Society.Google Scholar
  115. Kachergis, G., Rhodes, M., & Gureckis, T. (2017). Desirable difficulties in the development of active inquiry skills. Cognition, 166, 407–417.PubMedCrossRefGoogle Scholar
  116. Kahneman, D., & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237.CrossRefGoogle Scholar
  117. Kahneman, D., Slovic, P., & Tversky, A. (1982) Judgment under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  118. Kang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T.-y., & Camerer, C. F. (2009). The wick in the candle of learning epistemic curiosity activates reward circuitry and enhances memory. Psychological Science, 20(8), 963– 973.PubMedCrossRefGoogle Scholar
  119. Kelemen, D., & Rosset, E. (2009). The human function compunction: Teleological explanation in adults. Cognition, 111(1), 138–142.PubMedCrossRefGoogle Scholar
  120. Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2012). The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex. PloS one, 7(5), e36399.PubMedPubMedCentralCrossRefGoogle Scholar
  121. Kidd, C., Piantadosi, S. T., & Aslin, R. N. (2014). The Goldilocks effect in infant auditory attention. Child Development, 85(5), 1795–1804.PubMedPubMedCentralGoogle Scholar
  122. Kidd, C., & Hayden, B. Y. (2015). The psychology and neuroscience of curiosity. Neuron, 88(3), 449–460.PubMedPubMedCentralCrossRefGoogle Scholar
  123. Kim, W., Pitt, M. A., Lu, Z. L., Steyvers, M., & Myung, J. I. (2014). A hierarchical adaptive approach to optimal experimental design. Neural computation. Neural Computation, 26(11), 2465–2492.PubMedPubMedCentralCrossRefGoogle Scholar
  124. Kirkpatrick, S., Gelatt, C., & Vecchi, M. (1983). Optimization by simulated annealing. Science, 220, 671–680.PubMedCrossRefGoogle Scholar
  125. Klahr, D., Fay, A. L., & Dunbar, K. (1993). Heuristics for scientific experimentation: a developmental study. Cognitive Psychology, 25(1), 111–146.PubMedCrossRefGoogle Scholar
  126. Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction effects of direct instruction and discovery learning. Psychological Science, 15(10), 661–667.PubMedCrossRefGoogle Scholar
  127. Klayman, J., & Ha, Y.-W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94(2), 211.CrossRefGoogle Scholar
  128. Klayman, J., & Ha, Y.-W. (1989). Hypothesis testing in rule discovery: strategy, structure, and content. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(4), 596.Google Scholar
  129. Klayman, J. (1995). Varieties of confirmation bias. Psychology of Learning and Motivation, 32, 385–418.CrossRefGoogle Scholar
  130. Krynski, T. R., & Tenenbaum, J. B. (2007). The role of causality in judgment under uncertainty. Journal of Experimental Psychology: General, 136(3), 430.CrossRefGoogle Scholar
  131. Kuhn, D., Garcia-Mila, M., Zohar, A., Andersen, C., White, S. H., Klahr, D., & Carver, S. M. (1995). Strategies of knowledge acquisition. Monographs of the Society for Research in Child Development, 60(4), i+iii+v-vi+ 1-157.CrossRefGoogle Scholar
  132. Kuhn, D., Black, J., Keselman, A., & Kaplan, D. (2000). The development of cognitive skills to support inquiry learning. Cognition and Instruction, 18(4), 495–523.CrossRefGoogle Scholar
  133. Kushnir, T., & Gopnik, A. (2005). Young children infer causal strength from probabilities and interventions. Psychological Science, 16(9), 678–683.PubMedCrossRefGoogle Scholar
  134. Kushnir, T., Wellman, H. M., & Gelman, S. A. (2008). The role of preschoolersŠ social understanding in evaluating the informativeness of causal interventions. Cognition, 107(3), 1084–1092.PubMedCrossRefGoogle Scholar
  135. Lagnado, D. A., & Sloman, S. (2004). The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(4), 856.PubMedGoogle Scholar
  136. Legare, C. H. (2012). Exploring explanation: Explaining inconsistent evidence informs exploratory, hypothesis-testing behavior in young children. Child Development, 83(1), 173–185.PubMedCrossRefGoogle Scholar
  137. Le Mens, G., & Denrell, J. (2011). Rational learning and information sampling: on the ÂŞnaivetyÂŤ assumption in sampling explanations of judgment biases. Psychological Review, 118(2), 379.PubMedCrossRefGoogle Scholar
  138. Lewicki, M. S. (2002). Efficient coding of natural sounds. Nature Neuroscience, 5(4), 356.PubMedCrossRefGoogle Scholar
  139. Lewis, D. (1969) Convention: a philosophical study. Cambridge: Harvard University Press.Google Scholar
  140. Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N., & Griffiths, T. (2014). Algorithm selection by rational metareasoning as a model of human strategy selection. In Advances in neural information processing systems (pp. 2870–2878).Google Scholar
  141. Lindley, D. V. (1956). On a measure of the information provided by an experiment. The Annals of Mathematical Statistics, 27(4), 986– 1005.CrossRefGoogle Scholar
  142. Loewenstein, G. (1994). The psychology of curiosity: a review and reinterpretation. Psychological Bulletin, 116 (1), 75.CrossRefGoogle Scholar
  143. Lomasky, R., Brodley, C. E., Aernecke, M., Walt, D., & Friedl, M. (2007). Active class selection. In Machine learning: ECML 2007 (pp. 640–647). Berlin: Springer.Google Scholar
  144. Lombrozo, T., & Carey, S. (2006). Functional explanation and the function of explanation. Cognition, 99(2), 167–204.PubMedCrossRefGoogle Scholar
  145. MacDonald, K., & Frank, M. C. (2016). When does passive learning improve the effectiveness of active learning?. In Papafragou, A., Grodner, D., Mirman, D., & J. Trueswell (Eds.) Proceedings of the 38th annual conference of the Cognitive Science Society. Austin.Google Scholar
  146. Mackay, D. (1992). Information-based objective functions for active data selection. Neural Computation, 4, 590–604.CrossRefGoogle Scholar
  147. Malt, B. C., Ross, B. H., & Murphy, G. L. (1995). Predicting features for members of natural categories when categorization is uncertain. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(3), 646.PubMedGoogle Scholar
  148. Mandler, J. M. (2014). Stories, scripts, and scenes: Aspects of schema theory. Psychology Press.Google Scholar
  149. Marcus, G. F., & Davis, E. (2013). How robust are probabilistic models of higher-level cognition?. Psychological Science, 24(12), 2351–2360.PubMedCrossRefGoogle Scholar
  150. Marewski, J. N., & Schooler, L. J. (2011). Cognitive niches: an ecological model of strategy selection. Psychological Review, 118(3), 393.PubMedCrossRefGoogle Scholar
  151. Markant, D. B., & Gureckis, T. M. (2012). Does the utility of information influence sampling behavior?. In Miyake, N., Peebles, D., & R. P. Cooper (Eds.) Proceedings of the 34th annual conference of the Cognitive Science Society (pp. 719–724). Austin.Google Scholar
  152. Markant, D. B., & Gureckis, T. M. (2014). Is it better to select or to receive? Learning via active and passive hypothesis testing. Journal of Experimental Psychology-General, 143(1), 94–122.PubMedCrossRefGoogle Scholar
  153. Markant, D. B., Settles, B., & Gureckis, T. M. (2015). Self-directed learning favors local, rather than global, uncertainty. Cognitive Science, 40(1), 100–120.PubMedCrossRefGoogle Scholar
  154. Markant, D. B. (2016). The impact of biased hypothesis generation on self-directed learning. In Papafragou, A., Grodner, D., Mirman, D., & J. Trueswell (Eds.) Proceedings of the 38th annual conference of the Cognitive Science Society. Austin: Cognitive Science Society.Google Scholar
  155. Marvin, C., & Shohamy, D. (2016). Curiosity and reward: Valence predicts choice and information prediction errors enhance learning. Journal of Experimental Psychology: General, 145(3), 266–272.CrossRefGoogle Scholar
  156. McCormack, T., Bramley, N. R., Frosch, C., Patrick, F., & Lagnado, D. (2016). Children’s use of interventions to learn causal structure. Journal of Experimental Child Psychology, 141, 1–22.PubMedCrossRefGoogle Scholar
  157. McKenzie, C. R., Ferreira, V. S., Mikkelsen, L. A., McDermott, K. J., & Skrable, R. P. (2001). Do conditional hypotheses target rare events?. Organizational Behavior and Human Decision Processes, 85(2), 291–309.PubMedCrossRefGoogle Scholar
  158. Meder, B., & Nelson, J. D. (2012). Information search with situation-specific reward functions. Judgment and Decision Making, 7(2), 119–148.Google Scholar
  159. Meltzoff, A. N. (1995). Understanding the intentions of others: re-enactment of intended acts by 18-month-old children. Developmental Psychology, 31(5), 838.PubMedPubMedCentralCrossRefGoogle Scholar
  160. Metcalfe, J., & Kornell, N. (2003). The dynamics of learning and allocation of study time to a region of proximal learning. Journal of Experimental Psychology: General, 132(4), 530.CrossRefGoogle Scholar
  161. Minsky, M. (1974). A framework for representing knowledge. MIT-AI Laboratory Memo 306.Google Scholar
  162. Miyake, N., & Norman, D. (1979). To ask a question one must know enough to know what is not known. Journal of Verbal Learning and Verbal Behavior, 18, 357–364.CrossRefGoogle Scholar
  163. Montessori, M. (1912) The Montessori method. New York: Schocken.Google Scholar
  164. Mosher, F. A., & Hornsby, J. R. (1966) Studies in cognitive growth. New York: Wiley.Google Scholar
  165. Mozer, M., Pashler, H., & Homaei, H. (2008). Optimal predictions in everyday cognition: The wisdom of individuals or crowds?. Cognitive Science, 32(7), 1133–1147.PubMedCrossRefGoogle Scholar
  166. Muliere, P., & Parmigiani, G. (1993). Utility and means in the 1930s. Statistical Science, 8(4), 421–432.CrossRefGoogle Scholar
  167. Murphy, K. P. (2001) Active learning of causal Bayes net structure. U.C. Berkeley: Technical Report, Department of Computer Science.Google Scholar
  168. Murphy, G. L., Chen, S. Y., & Ross, B. H. (2012). Reasoning with uncertain categories. Thinking & Reasoning, 18(1), 81–117.CrossRefGoogle Scholar
  169. Myung, J. I., & Pitt, M. A. (2009). Optimal experimental design for model discrimination. Psychological Review, 116(3), 499.PubMedPubMedCentralCrossRefGoogle Scholar
  170. Najemnik, J., & Geisler, W. S. (2009). Simple summation rule for optimal fixation selection in visual search. Vision Research, 49, 1286–1294.PubMedCrossRefGoogle Scholar
  171. Najemnik, J., & Geisler, W. S. (2005). Optimal eye movement strategies in visual search. Nature, 434(7031), 387–391.PubMedCrossRefGoogle Scholar
  172. Navarro, D. J., & Perfors, A. F. (2011). Hypothesis generation, sparse categories, and the positive test strategy. Psychological Review, 118(1), 120.PubMedCrossRefGoogle Scholar
  173. Nelson, K. (1973). Structure and strategy in learning to talk. Monographs of the Society for Research in Child Development, 38(1-2, Serial No. 149), 1–135.CrossRefGoogle Scholar
  174. Nelson, J. D., Tenenbaum, J. B., & Movellan, J. R. (2001). Active inference in concept learning. In J. D. Moore, & K. Stenning (Eds.) Proceedings of the 23rd Conference of the Cognitive Science Society (pp. 692–697). Austin.Google Scholar
  175. Nelson, J. D. (2005). Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain. Psychological Review, 112(4), 979–999.PubMedCrossRefGoogle Scholar
  176. Nelson, J. D., McKenzie, C. R., Cottrell, G. W., & Sejnowski, T. J. (2010). Experience matters: information acquisition optimizes probability gain. Psychological Science, 21(7), 960–969.PubMedPubMedCentralCrossRefGoogle Scholar
  177. Nelson, J. D., Divjak, B., Gudmundsdottir, G., Martignon, L. F., & Meder, B. (2014). Children’s sequential information search is sensitive to environmental probabilities. Cognition, 130(1), 74–80.PubMedCrossRefGoogle Scholar
  178. Nickerson, R. S. (1998). Confirmation bias: a ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175.CrossRefGoogle Scholar
  179. Nosofsky, R. M., & Palmeri, T. J. (1998). A rule-plus-exception model for classifying objects in continuous-dimension spaces. Psychonomic Bulletin & Review, 5(3), 345–369.CrossRefGoogle Scholar
  180. Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101(4), 608.CrossRefGoogle Scholar
  181. Oaksford, M., & Chater, N. (1996). Rational explanation of the selection task. Psychological Review, 103(2), 381–391.CrossRefGoogle Scholar
  182. Oaksford, M., Chater, N., Grainger, B., & Larkin, J. (1997). Optimal data selection in the reduced array selection task (RAST). Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(2), 441.Google Scholar
  183. O’Brien, B., & Ellsworth, P. C. (2006). Confirmation bias in criminal investigations. In 1st annual conference on empirical legal studies paper.Google Scholar
  184. Otto, A. R., Raio, C. M., Chiang, A., Phelps, E. A., & Daw, N. D. (2013). Working-memory capacity protects model-based learning from stress. Proceedings of the National Academy of Sciences, 110(52), 20941–20946.CrossRefGoogle Scholar
  185. Oudeyer, P. Y., Kaplan, F., & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11(2), 265–286.CrossRefGoogle Scholar
  186. Oudeyer, P. Y., Gottlieb, J., & Lopes, M. (2016). Intrinsic motivation, curiosity, and learning: Theory and applications in educational technologies. Progress in Brain Research, 229, 257–284.PubMedCrossRefGoogle Scholar
  187. Pauker, S. G., & Kassirer, J. P. (1980). The threshold approach to clinical decision making. New England Journal of Medicine, 302, 1109–1117.PubMedCrossRefGoogle Scholar
  188. Pearl, J. (2009) Causality. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  189. Phillips, L. D., & Edwards, W. (1966). Conservatism in a simple probability inference task. Journal of Experimental Psychology, 72(3), 346.PubMedCrossRefGoogle Scholar
  190. Popper, K. R. (1968). Logik der forschung: zur erkenntnistheorie der moderner naturwissenschaft. Mohr Siebeck.Google Scholar
  191. Pothos, E., & Chater, N. (2005). Unsupervised categorization and category learning. The Quarterly Journal of Experimental Psychology, 58A(4), 733–752.CrossRefGoogle Scholar
  192. Rafferty, A. N., Zaharia, M., & Griffiths, T. L. (2014). Optimally designing games for behavioural research. Proceedings of the Royal Society A, 470(2167), 20130828.CrossRefGoogle Scholar
  193. Raghavan, H., Madani, O., & Jones, R. (2006). Active learning with feedback on features and instances. The Journal of Machine Learning Research, 7, 1655–1686.Google Scholar
  194. Raiffa, H., & Schlaifer, R. O. (1961) Applied statistical decision theory. New York: Wiley.Google Scholar
  195. Redgrave, P., & Gurney, K. (2006). The short-latency dopamine signal: a role in discovering novel actions?. Nature Reviews Neuroscience, 7(12), 967–975.PubMedCrossRefGoogle Scholar
  196. Ren, M., Kiros, R., & Zemel, R. (2015). Exploring models and data for image question answering. In Advances in neural information processing systems (pp. 2953–2961).Google Scholar
  197. Rich, A. S., & Gureckis, T. M. (2014). The value of approaching bad things. In Proceedings of the 36th annual conference of the Cognitive Science Society. Austin: Cognitive Science Society.Google Scholar
  198. Rich, A. S., & Gureckis, T. M. (2017). Exploratory choice reflects the future value of information. Decision.Google Scholar
  199. Rieskamp, J., & Otto, P. E. (2006). Ssl: a theory of how people learn to select strategies. Journal of Experimental Psychology: General, 135(2), 207.CrossRefGoogle Scholar
  200. Ross, B. H., & Murphy, G. L. (1996). Category-based predictions: influence of uncertainty and feature associations. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(3), 736.PubMedGoogle Scholar
  201. Rothe, A., Lake, B. M., & Gureckis, T. M. (2016). Asking and evaluating natural language questions. In Proceedings of the 38th annual conference of the Cognitive Science Society.Google Scholar
  202. Rothe, A., Lake, B., & Gureckis, T. (in review). Do people ask good questions? Computational Brain and Behavior.Google Scholar
  203. Ruderman, D. L. (1994). Designing receptive fields for highest fidelity. Network: Computation in Neural Systems, 5(2), 147–155.CrossRefGoogle Scholar
  204. Ruggeri, A., & Lombrozo, T. (2015). Children adapt their questions to achieve efficient search. Cognition, 143, 203–216.PubMedCrossRefGoogle Scholar
  205. Ruggeri, A., Lombrozo, T., Griffiths, T., & Xu, F. (2015). Children search for information as efficiently as adults, but seek additional confirmatory evidence. In D. C. Noelle (Ed.) Proceedings of CogSci 37.Google Scholar
  206. Rusconi, P., Marelli, M., D’Addario, M., Russo, S., & Cherubini, P. (2014). Evidence evaluation: Measure z corresponds to human utility judgments better than measure l and optimal-experimental-design models. Journal of Experimental Psychology: Learning, Memory, and Cognition, 40(3), 703–723.PubMedGoogle Scholar
  207. 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.PubMedCrossRefGoogle Scholar
  208. Schulz, L., & Bonawitz, E. B. (2007). Serious fun: preschoolers engage in more exploratory play when evidence is confounded. Developmental Psychology, 43(4), 1045.PubMedCrossRefGoogle Scholar
  209. Schulz, L., Gopnik, A., & Glymour, C. (2007a). Preschool children learn about causal structure from conditional interventions. Developmental Science, 10(3), 322–332.PubMedCrossRefGoogle Scholar
  210. Schulz, L., Kushnir, T., & Gopnik, A. (2007b). Learning from doing: Interventions and causal inference. In A. Gopnik, & L Schulz (Eds.) Causal learning: Psychology, philosophy, and computation: Oxford University Press.Google Scholar
  211. Schulz, L. (2012a). Finding new facts; thinking new thoughts. Advances in Child Development and Behavior, 43, 269–294.PubMedCrossRefGoogle Scholar
  212. Schulz, L. (2012b). The origins of inquiry: Inductive inference and exploration in early childhood. Trends in Cognitive Sciences, 16(7), 382–389.PubMedCrossRefGoogle Scholar
  213. Schulz, L. (2015). Infants explore the unexpected. Science, 348, 42–43.PubMedCrossRefGoogle Scholar
  214. Settles, B. (2010) Active learning literature survey. Madison: University of Wisconsin.Google Scholar
  215. Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others the consequences of psychological reasoning for human learning. Perspectives on Psychological Science, 7(4), 341–351.PubMedCrossRefGoogle Scholar
  216. Shafto, P., Goodman, N. D., & Griffiths, T. L. (2014). A rational account of pedagogical reasoning: Teaching by, and learning from, examples. Cognitive Psychology, 71, 55–89.PubMedCrossRefGoogle Scholar
  217. Shi, L., Griffiths, T., Feldman, N., & Sanborn, A. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17(4), 443–464.CrossRefGoogle Scholar
  218. Siegel, M., Magin, R., Tenenbaum, J., & Schulz, L. (2014). Black boxes: Hypothesis testing via indirect perceptual evidence. In A. Papafragou, D. Grodner, D. Mirman, & J. Trueswell (Eds.) Proceedings of the 36th annual conference of the Cognitive Science Society. Austin: Cognitive Science Society.Google Scholar
  219. Sim, Z., Tanner, M., Alpert, N., & Xu, F. (2015). Children learn better when they select their own data. In Proceedings of the 34th annual conference of the Cognitive Science Society. Austin.Google Scholar
  220. Simon, H. A. (1976). From substantive to procedural rationality. In 25 years of economic theory (pp. 65–86): Springer.Google Scholar
  221. Singh, S. P., Barto, A. G., & Chentanez, N. (2004). Intrinsically motivated reinforcement learning. In NIPS, (Vol. 17 pp. 1281–1288).Google Scholar
  222. Skov, R. B., & Sherman, S. J. (1986). Information-gathering processes: diagnosticity, hypothesis-confirmatory strategies, and perceived hypothesis confirmation. Journal of Experimental Social Psychology, 22(2), 93–121.CrossRefGoogle Scholar
  223. Slowiaczek, L. M., Klayman, J., Sherman, S. J., & Skov, R. B. (1992). Information selection and use in hypothesis testing: What is a good question, and what is a good answer?. Memory & Cognition, 20(4), 392–405.CrossRefGoogle Scholar
  224. Spelke, E. S., & Kinzler, K. D. (2007). Core knowledge. Developmental Science, 10(1), 89–96.PubMedCrossRefGoogle Scholar
  225. Stahl, A. E., & Feigenson, L. (2015). Observing the unexpected enhances infants learning and exploration. Science, 348(6230), 91–94.PubMedPubMedCentralCrossRefGoogle Scholar
  226. Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27(3), 453–489.CrossRefGoogle Scholar
  227. Sutton, R. S., & Barto, A. G. (1988) Reinforcement learning: An introduction. Cambridge: MIT Press.Google Scholar
  228. Tenenbaum, J. B. (1999) A (Unpublished doctoral dissertation). Cambridge: MIT.Google Scholar
  229. Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24(04), 629–640.PubMedGoogle Scholar
  230. Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10(7), 309–318.PubMedCrossRefGoogle Scholar
  231. Thomas, R. P., Dougherty, M. R. P., Sprenger, A. M., & Harbison, J. I. (2008). Diagnostic hypothesis generation and human judgment. Psychological Review, 115, 155–185.PubMedCrossRefGoogle Scholar
  232. Trueswell, J. C., Medina, T. N., Hafri, A., & Gleitman, L. R. (2013). Propose but verify: Fast mapping meets cross-situational word learning. Cognitive Psychology, 66(1), 126–156.PubMedCrossRefGoogle Scholar
  233. Tschirgi, J. E. (1980). Sensible reasoning: A hypothesis about hypotheses. Child Development, 51(1), 1–10.CrossRefGoogle Scholar
  234. Tversky, A., & Edwards, W. (1966). Information versus reward in binary choices. Journal of Experimental Psychology, 71(5), 680.PubMedCrossRefGoogle Scholar
  235. Ullman, S., Vidal-Naquet, M., & Sali, E. (2002). Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5(7), 682.PubMedCrossRefGoogle Scholar
  236. U. S. Department of Education (2017). Reimagining the Role of Technology in Education: 2017 National Education Technology Plan Update (Technical Report). Office of Educational Technology.Google Scholar
  237. van Schijndel, T., Visser, I., van Bers, B., & Raijmakers, M. (2015). Preschoolers perform more informative experiments after observing theory-violating evidence. Journal of Experimental Child Psychology, 131, 104–119.PubMedCrossRefGoogle Scholar
  238. 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. PubMedCrossRefGoogle Scholar
  239. Vygotsky, L. (1962) Thought and language. Cambridge: MIT Press.CrossRefGoogle Scholar
  240. Waldmann, M. R., & Hagmayer, Y. (2005). Seeing versus doing: two modes of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31(2), 216.PubMedGoogle Scholar
  241. Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12(3), 129–140.CrossRefGoogle Scholar
  242. Wason, P. C. (1966). Reasoning. In B. Foss (Ed.) New horizons in psychology. in Books. Pengu: Harmondsworth.Google Scholar
  243. Woodward, A. L. (1998). Infants selectively encode the goal object of an actor’s reach. Cognition, 69(1), 1–34.PubMedCrossRefGoogle Scholar
  244. Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114(2), 245.PubMedCrossRefGoogle Scholar
  245. Zhang, L., Tong, M. H., Marks, T. K., Shan, H., & Cottrell, G. W. (2008). Sun: a Bayesian framework for saliency using natural statistics. Journal of Vision, 8(7), 32–32.PubMedCrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2018

Authors and Affiliations

  • Anna Coenen
    • 1
    Email author
  • Jonathan D. Nelson
    • 2
    • 3
  • Todd M. Gureckis
    • 1
  1. 1.New York UniversityNew YorkUSA
  2. 2.Max Planck Institute for Human DevelopmentBerlinGermany
  3. 3.University of SurreyGuildfordUK

Personalised recommendations