Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-24T18:28:12.384Z Has data issue: false hasContentIssue false

13 - Models of Inductive Reasoning

from Part III - Computational Modeling of Basic Cognitive Functionalities

Published online by Cambridge University Press:  21 April 2023

Ron Sun
Affiliation:
Rensselaer Polytechnic Institute, New York
Get access

Summary

Inductive reasoning involves using existing knowledge to make predictions about novel cases. This chapter reviews and evaluates computational models of this fundamental aspect of cognition, with a focus on work involving property induction. The review includes early induction models such as similarity coverage, and the feature-based induction model, as well as a detailed coverage of more recent Bayesian and connectionist approaches. Each model is examined against benchmark empirical phenomena. Model limitations are also identified. The chapter highlights the major advances that have been made in our understanding of the mechanisms that drive induction, as well as identifying challenges for future modeling. These include accounting for individual and developmental differences and applying induction models to explain other forms of reasoning.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2023

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

Anderson, J. R. (1991). The adaptive nature of human categorization, Psychological Review, 98, 409429.Google Scholar
Blok, S. V., Medin, D. L., & Osherson, D. N. (2007). Induction as conditional probability judgment. Memory & Cognition, 36(6), 13531364.Google Scholar
Bonawitz, E., & Shafto, P. (2016). Computational models of development, social influences. Current Opinion in Behavioral Sciences, 7, 95100.Google Scholar
Bowers, J. S., & Davis, C. J. (2012). Bayesian just-so stories in psychology and neuroscience. Psychological Bulletin, 138(3), 389414.Google Scholar
Bright, A. K., & Feeney, A. (2014). The engine of thought is a hybrid: roles of associative and structured knowledge in reasoning. Journal of Experimental Psychology: General, 143(6), 20822102.Google Scholar
Carey, S. (1985). Conceptual Change in Childhood. Cambridge, MA: Bradford Books.Google Scholar
Carnap, R. (1968). Inductive logic and inductive intuition. In Lakatos, I. (Ed.), Studies in Logic and the Foundations of Mathematics (vol. 51, pp. 258314). Amsterdam: Elsevier.Google Scholar
Cassey, P., Hawkins, G. E., Donkin, C., & Brown, S. D. (2016). Using alien coins to test whether simple inference is Bayesian. Journal of Experimental Psychology: Learning, Memory, and Cognition, 42(3), 497503.Google Scholar
Coley, J. D., & Vasilyeva, N. Y. (2010). Generating inductive inferences: premise relations and property effects. Psychology of Learning and Motivation: Advances in Research and Theory, 53, 183226.CrossRefGoogle Scholar
Collins, A. & Michalski, R. (1989). The logic of plausible reasoning: a core theory. Cognitive Science, 13(1), l49.Google Scholar
Dunsmoor, J. E., & Murphy, G. L. (2014). Stimulus typicality determines how broadly fear is generalized. Psychological Science, 25, 18161821.Google Scholar
Evans, J. St. B. T., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: advancing the debate. Perspectives on Psychological Science, 8, 223241.Google Scholar
Feeney, A. (2017). Forty years of progress on category-based inductive reasoning. In Ball, L. J. & Thompson, V. A. (Eds.), International Handbook of Thinking and Reasoning (pp. 167185). London: Routledge.Google Scholar
Feeney, A., & Heit, E. (2011). Properties of the diversity effect in category-based inductive reasoning. Thinking & Reasoning, 17, 156181.Google Scholar
Feeney, A., Shafto, P., & Dunning, D. (2007). Who is susceptible to conjunction fallacies in category-based induction? Psychonomic Bulletin & Review, 14, 884889.Google Scholar
Feiler, D., Tong, J., & Larrick, R. (2013). Biased judgment in censored environments. Management Science, 59, 573591.Google Scholar
Fisher, A. V. (2015). Development of inductive generalization. Child Development Perspectives, 9(3), 172177.CrossRefGoogle Scholar
Frank, M. C., Goldwater, S., Griffiths, T. L., & Tenenbaum, J. B. (2010). Modeling human performance in statistical word segmentation. Cognition, 117, 107125.Google Scholar
Gelman, S. A., & Markman, E. M. (1986). Categories and induction in young children. Cognition, 23(3), 183209.Google Scholar
Gershman, S. J., & Beck, J. M. (2018). Complex probabilistic inference. In Moustafa, A. A. (Ed). Computational Models of Brain and Behavior, (pp. 453466). Hoboken, NJ: Wiley.Google Scholar
Goodman, N. (1972). Seven strictures on similarity. In Goodman, N., Problems and Projects (pp. 437447). Indianapolis, IN: Bobbs-Merrill.Google Scholar
Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: level of analysis between computational and the algorithmic. Topics in Cognitive Science, 7, 217229.Google Scholar
Handley, S. J., & Trippas, D. (2015). Dual processes and the interplay between knowledge and structure: a new parallel processing model. Psychology of Learning and Motivation, 62, 3358.Google Scholar
Hayes, B. K., Banner, S., Forrester, S., & Navarro, D. J. (2019). Selective sampling and inductive inference: drawing inferences based on observed and missing evidence. Cognitive Psychology, 113, 101221.Google Scholar
Hayes, B. K., Banner, S., & Navarro, D. J. (2017). Sampling frames, Bayesian inference and inductive reasoning. In Gunzelmann, G., Howes, A., Tenbrink, T., & Davelaar, E. (Eds.), Proceedings of the 39th Annual Meeting of the Cognitive Science Society (pp. 488493). Austin, TX: Cognitive Science Society.Google Scholar
Hayes, B. K., & Heit, E. (2018). Inductive reasoning 2.0. Wiley Interdisciplinary Reviews Cognitive Science, 9(3), 113, e1459.Google Scholar
Hayes, B. K., Navarro, D. J., Stephens, R. G., Ransom, K., & Dilevski, N. (2019). The diversity effect in inductive reasoning depends on sampling assumptions. Psychonomic Bulletin & Review, 26, 10431050.CrossRefGoogle ScholarPubMed
Hayes, B. K. Stephens, R. G., Ngo, J., Dunn, J. C., (2018). The dimensionality of reasoning: evidence for a single process account of inductive and deductive inference. Journal of Experimental Psychology: Learning, Memory and Cognition, 44, 13331351.Google Scholar
Hayes, B. K., & Thompson, S. P. (2007). Causal relation and feature similarity in children’s inductive reasoning. Journal of Experimental Psychology: General, 136, 470484.Google Scholar
Hayes, B. K., Wei, P., Dunn, J. C., & Stephens, R. G. (2019). Why is logic so likeable? A single-process account of argument evaluation with logic and liking judgments. Journal of Experimental Psychology: Learning, Memory and Cognition. 46, 699719.Google Scholar
Heit, E. (1998). A Bayesian analysis of some forms of inductive reasoning. In Oaksford, M. & Chater, N. (Eds.), Rational Models of Cognition (pp. 248274). Oxford: Oxford University Press.Google Scholar
Heit, E., & Feeney, A. (2005). Relations between premise similarity and inductive strength. Psychonomic Bulletin & Review, 12(2), 340344.Google Scholar
Heit, E., & Rotello, C. M. (2010). Relations between inductive reasoning and deductive reasoning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 36, 805812.Google Scholar
Heit, E., & Rubinstein, J. (1994). Similarity and property effects in inductive reasoning. Journal of Experimental Psychology, 20(2), 411422.Google Scholar
Hendrickson, A. T., Perfors, A., Navarro, D. J., & Ransom, K. (2019). Sample size, number of categories and sampling assumptions: exploring some differences between categorization and generalization. Cognitive Psychology, 111, 80102.CrossRefGoogle ScholarPubMed
Hogarth, R., Lejarraga, T., & Soyer, E. (2015). The two settings of kind and wicked learning environments. Current Directions in Psychological Science, 24, 379385.Google Scholar
Kemp, C., & Jern, A. (2013). A taxonomy of inductive problems. Psychological Bulletin and Review, 21, 2346.Google Scholar
Kemp, C., & Tenenbaum, J. B. (2009). Structured statistical models of inductive reasoning. Psychological Review, 116, 2058.Google Scholar
Lawson, C. A., & Kalish, C. W. (2009). Sample selection and inductive generalization. Memory & Cognition, 37(5), 596607.Google Scholar
Le Mens, G., & Denrell, J. (2011). Rational learning and information sampling: on the “naivety” assumption in sampling explanations of judgment biases. Psychological Review, 118(2), 379392.Google Scholar
Lee, J. C., Lovibond, P. F., Hayes, B. K., & Navarro, D. (2019). Negative evidence and inductive reasoning in generalization of associative learning. Journal of Experimental Psychology: General, 148, 289303.Google Scholar
López, A., Gelman, S. A., Gutheil, G., & Smith, E. E. (1992). The development of category-based induction. Child Development, 63(5), 10701090.Google Scholar
Marr, D. (1982). Vision. San Francisco, CA: W. H. Freeman.Google Scholar
McClelland, J. L., & Rogers, T. T. (2003). The parallel distributed processing approach to semantic cognition. Nature Reviews Neuroscience, 4(4), 310322.Google Scholar
McKenzie, C. R. (2003). Rational models as theoriesnot standards – of behavior. Trends in Cognitive Sciences, 7, 403406.Google Scholar
Medin, D. L., Coley, J. D., Storms, G., & Hayes, B. K. (2003). A relevance theory of induction. Psychonomic Bulletin & Review, 10, 517532.Google Scholar
Medin, D. L., Wattenmaker, W. D., & Hampson, S. E. (1987). Family resemblance, conceptual cohesiveness, and category construction. Cognitive Psychology, 19, 242279.Google Scholar
Mitchell, T. (1997). Machine Learning. London: McGraw-Hill.Google Scholar
Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence. Psychological Review, 92(3), 289316.Google Scholar
Navarro, D. J., & Perfors, A. F. (2010). Similarity, feature discovery, and the size principle. Acta Psychologica, 133, 256268.CrossRefGoogle ScholarPubMed
Navarro, D. J., Dry, M. J., & Lee, M. D. (2012). Sampling assumptions in inductive generalization. Cognitive Science, 36(2), 187223.Google Scholar
Nisbett, R. E., Krantz, D. H., Jepson, C., & Kunda, Z. (1983). The use of statistical heuristics in everyday inductive reasoning. Psychological Review, 90, 339363.Google Scholar
Oaksford, M., & Chater, N. (2007). Bayesian Rationality: The Probabilistic Approach to Human Reasoning. Oxford: Oxford University Press.Google Scholar
Oaksford, M., & Chater, N. (2013). Dynamic inference and everyday conditional reasoning in the new paradigm. Thinking & Reasoning, 19, 346379.Google Scholar
Osherson, D. N., Smith, E. E., Wilkie, O., & Lopez, A. (1990). Category-based induction. Psychological Review, 97, 185200.Google Scholar
Ransom, K. J., Perfors, A., & Navarro, D. J. (2016). Leaping to conclusions: why premise relevance affects argument strength. Cognitive Science, 40, 17751796.Google Scholar
Rehder, B. (2009). Causal-based property generalization. Cognitive Science, 33, 301343.Google Scholar
Rips, L. J. (1975). Inductive judgements about natural categories. Journal of Verbal Learning & Verbal Behavior, 14, 665681.Google Scholar
Rogers, T. T., & McClelland, J. L. (2004). Semantic Cognition: A Parallel Distributed Processing Approach. Cambridge, MA: MIT Press.Google Scholar
Rogers, T. T., & McClelland, J. L. (2014). Parallel distributed processing at 25: further explorations in the microstructure of cognition. Cognitive Science, 38, 10241077.Google Scholar
Sanborn, A. N., & Chater, N. (2016). Bayesian brains without probabilities. Trends in Cognitive Sciences, 20(12), 883893.Google Scholar
Sanjana, N. E., & Tenenbaum, J. B. (2003). Bayesian models of inductive generalization. In Jordan, M. I., LeCun, Y., & Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (pp. 5966). Cambridge, MA: MIT Press.Google Scholar
Shafto, P., Coley, J. D., & Baldwin, D. (2007). Effects of time pressure on context-sensitive property induction. Psychonomic Bulletin & Review, 14, 890894.Google Scholar
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), 341351.CrossRefGoogle ScholarPubMed
Shafto, P., Kemp, C., Bonawitz, E. B., Coley, J. D., & Tenenbaum, J. B. (2008). Inductive reasoning about causally transmitted properties. Cognition, 109, 175192.CrossRefGoogle ScholarPubMed
Shi, L., Griffiths, T. L., Feldman, N. H., & Sanborn, A. N. (2010). Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17(4), 443464.Google Scholar
Sloman, S. A. (1993). Feature-based induction. Cognitive Psychology, 25, 231280.Google Scholar
Sloman, S. A. (1998). Categorical inference is not a tree: the myth of inheritance hierarchies. Cognitive Psychology, 35, 133.Google Scholar
Smith, E. E., Lopéz, A., & Osherson, D. (1992). Category membership, similarity, and naive induction. In Healy, A. F., Kosslyn, S. M., & Shiffrin, R. M. (Eds.), Essays in Honor of William K. Estes, Vol. 2. From Learning Processes to Cognitive Processes (pp. 181206). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Stephens, R. G., Dunn, J. C., & Hayes, B. K. (2018). Are there two processes in reasoning? The dimensionality of inductive and deductive inferences. Psychological Review, 125(2), 218244.Google Scholar
Stephens, R. G., Matzke, D., & Hayes, B. K. (2019). Disappearing dissociations in experimental psychology: using state-trace analysis to test for multiple processes. Journal of Mathematical Psychology, 90, 322.Google Scholar
Sun, R. (1995). Robust reasoning: integrating rule-based and similarity-based reasoning. Artificial Intelligence, 75, 241295.Google Scholar
Sun, R., & Zhang, X. (2006). Accounting for a variety of reasoning data within a cognitive architecture. Journal of Experimental and Theoretical Artificial Intelligence, 18(2), 169191.Google Scholar
Tauber, S., Navarro, D. J., Perfors, A., & Steyvers, M. (2017). Bayesian models of cognition revisited: setting optimality aside and letting data drive psychological theory. Psychological Review, 124, 410441.Google Scholar
Tenenbaum, J. B., & Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences, 24, 629640.Google Scholar
Voorspoels, W., Navarro, D. J., Perfors, A., Ransom, K., & Storms, G. (2015). How do people learn from negative evidence? Non-monotonic generalizations and sampling assumptions in inductive reasoning. Cognitive Psychology, 81, 125.Google Scholar
Xie, B., Hayes, B. K., & Navarro, D. J. (2018). Adding types, but not tokens, affects the breadth of property induction. In Rogers, T., Rau, M., Zhu, X., & Kalish, C. W. (Eds.), Proceedings of the 40th Annual Meeting of the Cognitive Science Society (pp. 11991204). Austin, TX: Cognitive Science Society.Google Scholar
Xu, F., & Tenenbaum, J. B. (2007). Word learning as Bayesian inference. Psychological Review, 114, 245275.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.

  • Models of Inductive Reasoning
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.017
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.

  • Models of Inductive Reasoning
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.017
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.

  • Models of Inductive Reasoning
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book: The Cambridge Handbook of Computational Cognitive Sciences
  • Online publication: 21 April 2023
  • Chapter DOI: https://doi.org/10.1017/9781108755610.017
Available formats
×