Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-24T23:08:14.827Z Has data issue: false hasContentIssue false

Chapter 5 - What Makes a Good Query?

Prospects for a Comprehensive Theory of Human Information Acquisition

from Part II - How Do Humans Search for Information?

Published online by Cambridge University Press:  19 May 2022

By
Björn Meder ,
Vincenzo Crupi  and
Jonathan D. Nelson
Edited by
Irene Cogliati Dezza ,
Eric Schulz  and
Charley M. Wu
Irene Cogliati Dezza
Affiliation:
University College London
Eric Schulz
Affiliation:
Max-Planck-Institut für biologische Kybernetik, Tübingen
Charley M. Wu
Affiliation:
Eberhard-Karls-Universität Tübingen, Germany
Get access

Summary

Searching for information in a goal-directed manner is central for learning, diagnosis, and prediction. Children ask questions to learn new concepts, doctors conduct medical tests to diagnose their patients, and scientists perform experiments to test their theories. But what makes a good query? What principles govern human information acquisition and how do people decide which query to conduct to achieve their goals? What challenges need to be met to advance the theory and psychology of human inquiry? Addressing these issues, we introduce the conceptual and mathematical ideas underlying different models of the value of information, what purpose these models serve in psychological research, and how they can be integrated in a unified computational framework. We also discuss the conflict between short- and long-term efficiency of prominent methods for query selection, and the resulting normative and methodological implications for studying human sequential search. A final point of discussion concerns the relations between probabilistic (Bayesian) models of the value of information and heuristic search strategies, and the insights that can be gained from bridging different levels of analysis and types of models. We conclude by discussing open questions and challenges that research needs to address to build a comprehensive theory of human information acquisition.

Keywords

Information acquisitionhuman inquiryprobabilistic modelsheuristics
Type
Chapter
Information
The Drive for Knowledge
The Science of Human Information Seeking
 , pp. 101 - 123
Publisher: Cambridge University Press
Print publication year: 2022

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

Austerweil, J. L., & Griffiths, T. L. (2011). Seeking confirmation is rational for deterministic hypotheses. Cognitive Science, 35(3), 499–526. https://doi.org/10.1111/j.1551-6709.2010.01161.x.Google Scholar
Baron, J. (1985). Rationality and intelligence. Cambridge University Press.Google Scholar
Baron, J., & Hershey, J. C. (1988). Heuristics and biases in diagnostic reasoning: I. Priors, error costs, and test accuracy. Organizational Behavior and Human Decision Processes, 41(2), 259–279. https://doi.org/10.1016/0749-5978(88)90030-1.CrossRefGoogle Scholar
Beck, C. (2009). Generalised information and entropy measures in physics. Contemporary Physics, 50(4), 495–510. https://doi.org/10.1080/00107510902823517.CrossRefGoogle Scholar
Bellman, R. (1957). Dynamic programming. Princeton University Press.Google Scholar
Benish, W. A. (1999). Relative entropy as a measure of diagnostic information. Medical Decision Making, 19(2), 202–206. https://doi.org/10.1177/0272989X9901900211.CrossRefGoogle ScholarPubMed
Bramley, N. R., Lagnado, D. A., & Speekenbrink, M. (2015). Conservative forgetful scholars: How people learn causal structure through sequences of interventions. Journal of Experimental Psychology. Learning, Memory, and Cognition, 41(3), 708–731. https://doi.org/10.1037/xlm0000061.Google Scholar
Bruner, J. S., Goodnow, J. J., & Austin, G. A. (1956). A study of thinking. John Wiley and Sons.Google Scholar
Butko, N. J., & Movellan, J. R. (2010). Infomax control of eye movements. IEEE Transactions on Autonomous Mental Development, 2(2), 91–107. https://doi.org/10.1109/TAMD.2010.2051029.Google Scholar
Chamberlin, T. C. (1890). The method of multiple working hypotheses. Science, 15, 92–96. https://doi.org/10.1126/science.148.3671.754Google Scholar
Chater, N., Oaksford, M., Nakisa, R., & Redington, M. (2003). Fast, frugal, and rational: How rational norms explain behavior. Organizational Behavior and Human Decision Processes, 90(1), 63–86. https://doi.org/10.1016/S0749-5978(02)00508-3.Google Scholar
Coenen, A., Nelson, J. D. & Gureckis, T. M. (2019). Asking the right questions about the psychology of human inquiry: Nine open challenges. Psychonomic Bulletin & Review, 26, 1548–1587. https://doi.org/10.3758/s13423-018-1470-5.Google Scholar
Crupi, V. (2019). Measures of biological diversity: Overview and unified framework. In Casetta, E., Marques da Silva, J., and Vecchi, D. (Eds.), From assessing to conserving biodiversity (pp. 123–136). Springer.Google Scholar
Crupi, V., Nelson, J. D., 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, 42(5), 1410–1456. https://doi.org/10.1111/cogs.12613.Google Scholar
Crupi, V., & Tentori, K. (2014). State of the field: Measuring information and confirmation. Studies in History and Philosophy of Science Part A, 47, 81–90. https://doi.org/10.1016/j.shpsa.2014.05.002.Google Scholar
Crupi, V., Tentori, K., & Lombardi, L. (2009). Pseudodiagnosticity revisited. Psychological Review, 116(4), 971–985. https://doi.org/10.1037/a0017050.Google Scholar
Denney, D. R., & Denney, N. W. (1973). The use of classification for problem solving: A comparison of middle and old age. Developmental Psychology, 9(2), 275–278. https://doi.org/10.1037/h0035092.Google Scholar
Dubey, R., & Griffiths, T. L. (2020). Reconciling novelty and complexity through a rational analysis of curiosity. Psychological Review, 127(3), 455–476. https://doi.org/10.1037/rev0000175.Google Scholar
Filimon, F., Nelson, J. D., Sejnowski, T. J., Sereno, M. I., & Cottrell, G. W. (2020). The ventral striatum dissociates information expectation, reward anticipation, and reward receipt. Proceedings of the National Academy of Sciences, 117(26), 15200-15208. https://doi.org/10.1073/pnas.1911778117.Google Scholar
Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482. https://doi.org/10.1146/annurev-psych-120709-145346.Google Scholar
Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal, 31(121), 124–126. https://doi.org/10.2307/2223319.Google Scholar
Good, I. J. (1950). Probability and the weight of evidence. Charles Griffin & Co.Google Scholar
Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning: A cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464–481. https://doi.org/10.1177/1745691612454304.CrossRefGoogle ScholarPubMed
Hartley, R. V. (1928). Transmission of information. Bell System Technical Journal, 7(3), 535–563.Google Scholar
Hertwig, R., & Engel, C. (2016). Homo Ignorans: Deliberately choosing not to know. Perspectives on Psychological Science, 11(3), 359–372. https://doi.org/10.1177/1745691616635594.Google Scholar
Hyafil, L., & Rivest, R. L. (1976). Constructing optimal binary decision trees is NP-complete. Information Processing Letters, 5(1), 15–17. https://doi.org/10.1016/0020-0190(76)90095-8.Google Scholar
Kachergis, G., Berends, F., Kleijn, R. D., & Hommel, B. (2016). Human reinforcement learning of sequential action. In Papafragou, A, Grodner, D, & Mirman, D (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (CogSci 2016), pp. 193–198.Google Scholar
Kachergis, G., Rhodes, M., & Gureckis, T. (2017). Desirable difficulties during the development of active inquiry skills. Cognition, 166, 407–417. https://doi.org/10.1016/j.cognition.2017.05.021.Google Scholar
Keylock, C. J. (2005). Simpson diversity and the Shannon–Wiener index as special cases of a generalized entropy. Oikos, 109(1), 203–207. https://doi.org/10.1111/j.0030-1299.2005.13735.x.CrossRefGoogle Scholar
Klayman, J., & Ha, Y.-W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94(2), 211–228. https://doi.org/10.1037/0033-295X.94.2.211.Google Scholar
Kleinegesse, S. & Gutmann, M. U. (2020). Bayesian experimental design for implicit models by mutual information neural estimation. Proceedings of Machine Learning Research, 119, 5316–5326.Google Scholar
Kruschke, J.K. (2008). Bayesian approaches to associative learning: From passive to active learning. Learning & Behavior, 36, 210–226. https://doi.org/10.3758/LB.36.3.210.Google Scholar
Legge, G. E., Klitz, T. S., & Tjan, B. S. (1997). Mr. Chips: An ideal-observer model of reading. Psychological Review, 104(3), 524–553. https://doi.org/10.1037/0033-295X.104.3.524.Google Scholar
Lewontin, R.C. (1972) The apportionment of human diversity. In Dobzhansky, T., Hecht, M. K., and Steere, W. C. (Eds.), Evolutionary biology (pp. 381–398). New York, NY: Springer. https://doi.org/10.1007/978-1-4684-9063-3_14.Google Scholar
Li, S., Sun, Y., Liu, S., Sun, Y., Gureckis, T. M., & Bramley, N. R. (2019). Active physical inference via reinforcement learning. Proceedings of the Cognitive Science Society (pp. 2126–2132). Austin, TX: Cognitive Science Society.Google Scholar
Li, Z., Bramley, N. R., & Gureckis, T. M. (2020). Expectations about future learning influence moment-to-moment feelings of suspense. https://doi.org/10.31234/osf.io/532tw.Google Scholar
Lindley, D. V. (1956). On a measure of the information provided by an experiment. The Annals of Mathematical Statistics, 27(4), 986–1005.Google Scholar
Lookman, T., Balachandran, P. V., Xue, D., & Yuan, R. (2019). Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. NPJ Computational Materials, 5(1), 1–17. https://doi.org/10.1038/s41524-019-0153-8.Google Scholar
Markant, D., & Gureckis, T. M. (2012). Does the utility of information influence sampling behavior? Proceedings of the 34th Annual Conference of the Cognitive Science Society (pp. 719–724). Austin, TX: Cognitive Science Society.Google Scholar
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. https://doi.org/10.1037/a0032108.Google Scholar
Meder, B., & Nelson, J. D. (2012). Information search with situation-specific reward functions. Judgment and Decision Making, 7(2), 119–148.Google Scholar
Meder, B., Nelson, J. D., Jones, M., & Ruggeri, A. (2019). Stepwise versus globally optimal search in children and adults. Cognition, 191, Article 103965. https://doi.org/10.1016/j.cognition.2019.05.002.Google Scholar
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.Google Scholar
Meier, K. M., & Blair, M. R. (2013). Waiting and weighting: Information sampling is a balance between efficiency and error-reduction. Cognition, 126(2), 319–325. https://doi.org/10.1016/j.cognition.2012.09.014.CrossRefGoogle ScholarPubMed
Mohamed, T. P., Carbonell, J. G., & Ganapathiraju, M. K. (2010). Active learning for human protein-protein interaction prediction. BMC Bioinformatics, 11, S57. https://doi.org/10.1186/1471-2105-11-S1-S57.Google Scholar
Mosher, F. A., & Hornsby, J. R. (1966). On asking questions. In Bruner, J. S., Oliver, R. R., & Greenfield, P. M., et al. (Eds.), Studies in cognitive growth (pp. 86–102). Wiley.Google Scholar
Murphy, R. F. (2011). An active role for machine learning in drug development. Nature Chemical Biology, 7(6), 327–330. https://doi.org/10.1038/nchembio.576.CrossRefGoogle ScholarPubMed
Myung, J. I., & Pitt, M. A. (2009). Optimal experimental design for model discrimination. Psychological Review, 116(3), 499–518. https://doi.org/10.1037/a0016104.CrossRefGoogle ScholarPubMed
Najemnik, J., & Geisler, W. (2005). Optimal eye movement strategies in visual search. Nature, 434, 387–391. https://doi.org/10.1038/nature03390.Google Scholar
Nakamura, K. (2006). Neural representation of information measure in the primate premotor cortex. Journal of Neurophysiology, 96(1), 478–485. https://doi.org/10.1152/jn.01326.2005.Google Scholar
Navarro, D. J., & Perfors, A. F. (2011). Hypothesis generation, sparse categories, and the positive test strategy. Psychological Review, 118(1), 120–134. https://doi.org/10.1037/a0021110.Google Scholar
Nelson, J. D. (2005). Finding useful questions: On Bayesian diagnosticity, probability, impact, and information gain. Psychological Review, 112(4), 979–999. https://doi.org/10.1037/0033-295X.112.4.979.Google Scholar
Nelson, J. D., & Cottrell, G. W. (2007). A probabilistic model of eye movements in concept formation. Neurocomputing, 70, 2256–2272. https://doi.org/10.1016/j.neucom.2006.02.026.Google Scholar
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.Google Scholar
Nelson, J. D., & McKenzie, C. R. M. (2009). Confirmation bias. In Kattan, M. (Ed.), The Encyclopedia of Medical Decision Making (pp. 167–171). Sage.Google Scholar
Nelson, J. D., McKenzie, C. R. M., Cottrell, G. W., & Sejnowski, T. J. (2010). Experience matters: Information acquisition optimizes probability gain. Psychological Science, 21(7), 960–969. https://doi.org/10.1177/0956797610372637.Google Scholar
Nelson, J. D., Meder, B., & Jones, M. (2018). Towards a theory of heuristic and optimal planning for sequential information search. PsyArXiv.Google Scholar
Nelson, J. D., Rosenauer, C., Crupi, V., Tentori, K., & Meder, B. (2020). On the likelihood difference heuristic and the objective utility of possible medical tests. Manuscript submitted for publication.Google Scholar
Nelson, J. D., Tenenbaum, J. B., & Movellan, J. R. (2001). Active inference in concept learning. In Moore, J. D & Stenning, K (Eds.), Proceedings of the 23rd Conference of the Cognitive Science Society, 692–697.Google Scholar
Nickerson, R. S. (1996). Hempel’s paradox and Wason’s selection task: Logical and psychological puzzles of confirmation. Thinking & Reasoning, 2, 1–31. https://doi.org/10.1080/135467896394546.Google Scholar
Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101(4), 608–631. https://doi.org.10.1037/0033-295X.101.4.608.Google Scholar
Oaksford, M., & Chater, N. (1996). Rational explanation of the selection task. Psychological Review, 103(2), 381–391. https://doi.org/10.1037/0033-295X.103.2.381.Google Scholar
Patil, G. P., & Taillie, C. (1982) Diversity as a concept and its measurement. Journal of the American Statistical Association, 77(379), 548–561. https://doi.org/10.1080/01621459.1982.10477845.Google Scholar
Popper, K. R. (1959). The logic of scientific discovery. Hutchinson.Google Scholar
Raiffa, H., & Schlaifer, R. O. (1961). Applied statistical decision theory. Cambridge, MA: Division of Research, Graduate School of Business Administration, Harvard University.Google Scholar
Rényi, A. (1961). On measures of entropy and information. In Neyman, J. (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability I (pp. 547–556). University of California Press.Google Scholar
Ruggeri, A., & Lombrozo, T. (2015). Children adapt their questions to achieve efficient search. Cognition, 143, 203–216. https://doi.org/10.1016/j.cognition.2015.07.004.Google Scholar
Ruggeri, A., Lombrozo, T., Griffiths, T. L., & Xu, F. (2016). Sources of developmental change in the efficiency of information search. Developmental Psychology, 52(12), 2159–2173. https://doi.org/10.1037/dev0000240.Google Scholar
Ruggeri, A., Sim, Z. L., & Xu, F. (2017). “Why is Toma late to school again?” Preschoolers identify the most informative questions. Developmental Psychology, 53(9), 1620.Google Scholar
Savage, L. J. (1954). The foundations of statistics. Wiley.Google Scholar
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. https://doi.org/10.1177/0956797619863663.Google Scholar
Settles, B. (2010). Active learning literature survey. Technical Report, University of Wisconsin-Madison.Google Scholar
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27, 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.Google Scholar
Sharma, B., & Mittal, D. (1975). New non–additive measures of entropy for discrete probability distributions. Journal of Mathematical Sciences, 10, 28–40.Google Scholar
Sharot, T., & Sunstein, C. R. (2020). How people decide what they want to know. Nature Human Behaviour, 4, 14–19. https://doi.org/10.1038/s41562-019-0793-1.Google Scholar
Siegler, R. S. (1977). The twenty questions game as a form of problem solving. Child Development, 395–403.Google Scholar
Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688. https://doi.org/10.1038/163688a0Google Scholar
Skov, R. B., & Sherman, S. J. (1986). Information-gathering processes: Diagnosticity, hypothesis-confirmatory strategies, and perceived hypothesis confirmation. Journal of Experimental Social Psychology, 103, 278–282. https://doi.org/10.1016/j.beproc.2014.01.014.Google Scholar
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. https://doi.org/10.3758/BF03210923.Google Scholar
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., & Blum, B. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27, 453–489. https://doi.org/10.1016/S0364-0213(03)00010-7.Google Scholar
Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.Google Scholar
Tsallis, C. (1988). Possible generalization of Boltzmann-Gibbs statistics. Journal of Statistical Physics, 52(1–2), 479–487. https://doi.org/10.1007/BF01016429.Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124.Google Scholar
Vajda, I. & Zvárová, J. (2007). On generalized entropies, Bayesian decisions, and statistical diversity. Kybernetika, 43(5), 675–696.Google Scholar
Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology, 12, 129–140. https://doi.org/10.1080/17470216008416717.Google Scholar
Wason, P. C. (1968). Reasoning about a rule. Quarterly Journal of Experimental Psychology, 20, 273–281. https://doi.org/10.1080/14640746808400161.Google Scholar
Wells, G. L., & Lindsay, R. C. (1980). On estimating the diagnosticity of eyewitness nonidentifications. Psychological Bulletin, 88(3), 776–784. https://doi.org/10.1037/0033-2909.88.3.776.Google Scholar
Wu, C. M., Meder, B., Filimon, F., & Nelson, J. D. (2017). Asking better questions: How presentation formats influence information search. Journal of Experimental Psychology: Learning, Memory, and Cognition, 43(8), 1274–1297. https://doi.org/10.1037/xlm0000374.Google Scholar
Wu, C., Schulz, E., Speekenbrink, M., Nelson, J. D., & Meder, B. (2018). Generalization guides human exploration in vast decision spaces. Nature Human Behavior, 2, 915–924. https://doi.org/10.1038/s41562-018-0467-4.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×