Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-03T08:51:07.419Z Has data issue: false hasContentIssue false
  • English
  • Français

Relational priming plays a supporting but not leading role in adult analogy-making

Published online by Cambridge University Press:  29 July 2008

Alexander A. Petrov
Alexander A. Petrov
Affiliation:
Department of Psychology, Ohio State University, Columbus, OH 43210. [email protected]://alexpetrov.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Leech et al.'s analysis adds to an emerging consensus of the role of priming in analogy-making. However, their model cannot scale up to adult-level performance because not all relations can be cast as functions. One-size-fits-all accounts cannot capture the richness of analogy. Proportional analogies and transitive inferences can be made by nonstructural mechanisms. Therefore, these tasks do not generalize to tasks that require structure mapping.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 31 , Issue 4 , August 2008 , pp. 392 - 393
Copyright
Copyright © Cambridge University Press 2008

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

Cer, D. & O'Reilly, R. (2006) Neural mechanisms of binding in the hippocampus and neocortex: Insights from computational models. In: Handbook of binding and memory: Perspectives from cognitive neuroscience, ed. Zimmer, H., Mecklinger, A. & Lindenberger, U., pp. 193–220. Oxford University Press.Google Scholar
Davis, H. (1992) Transitive inference in rats (Rattus norvegicus). Journal of Comparative Psychology 106:342–49.CrossRefGoogle ScholarPubMed
Frank, M., O'Reilly, R. & Curran, T. (2006) When memory fails, intuition reigns: Midazolam enhances implicit inference in humans. Psychological Science 17(8):700–707.CrossRefGoogle ScholarPubMed
Frank, M., Rudy, J., Levy, W. & O'Reilly, R. (2005) When logic fails: Implicit transitive inference in humans. Memory and Cognition 33(4):742–50.Google Scholar
Frank, M., Rudy, J. & O'Reilly, R. (2003) Transitivity, flexibility, conjunctive representations, and the hippocampus: II. A computational analysis. Hippocampus 13:299–312.Google Scholar
French, R. M. (1995) The subtlety of sameness. Bradford Books/MIT Press.Google Scholar
French, R. M. (2002) The computational modeling of analogy-making. Trends in Cognitive Sciences 6:200–205.Google Scholar
Gentner, D. (1983) Structure-mapping: A theoretical framework for analogy. Cognitive Science 7:155–70.Google Scholar
Goswami, U. (2001) Analogical reasoning in children. In: The analogical mind: Perspectives from cognitive science, ed. Holyoak, K., Gentner, D., & Kokinov, B., pp. 437–70. MIT Press.CrossRefGoogle Scholar
Goswami, U. & Brown, A. L. (1989) Melting chocolate and melting snowmen: Analogical reasoning and causal relations. Cognition 35:69–95.CrossRefGoogle Scholar
Goswami, U. & Brown, A. L. (1990) Higher-order structure and relational reasoning: Contrasting analogical and thematic relations. Cognition 36:207–26.Google Scholar
Halford, G. S., Wilson, W. H. & Phillips, W. (1998) Processing capacity defined by relational complexity: Implications for comparative, developmental, and cognitive psychology. Behavioral and Brain Sciences 21:803–31; discussion pp. 831–65.CrossRefGoogle ScholarPubMed
Hinton, G. (1990) Mapping part-whole hierarchies into connectionist networks. Artificial Intelligence 46:47–75.Google Scholar
Hinton, G. & Sejnowski, T. (1986) Learning and relearning in Boltzmann machines. In: Parallel distributed processing: Explorations in the microstructure of cognition. Vol. 1: Foundations, ed. Rumelhart, D. & McClelland, J., pp. 282–317. MIT Press.Google Scholar
Hofstadter, D. R. (1984) The Copycat project: An experiment in nondeterminism and creative analogies. MIT AI Memo, No. 755.Google Scholar
Kokinov, B. (1990) Associative memory-based reasoning: Some experimental results. In: Proceedings of the Twelfth Annual Conference of the Cognitive Science Society, pp. 741–49. Erlbaum.Google Scholar
Kokinov, B. & Petrov, A. (2001) Integration of memory and reasoning in analogy-making: The AMBR model. In: The analogical mind: Perspectives from cognitive science, ed. Gentner, D., Holyoak, K. & Kokinov, B., pp. 59–124. MIT Press.Google Scholar
Maybery, M., Bain, J. & Halford, G. (1986) Information processing demands of transitive inferences. Journal of Experimental Psychology: Learning, Memory, and Cognition 12:600–13.Google Scholar
McClelland, J., McNaughton, B. & O'Reilly, R. (1995) Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review 102:419–57.Google Scholar
Mitchell, M. (1993) Analogy-making as perception: A computer model. Bradford Books/MIT Press.Google Scholar
Oden, D., Thompson, K. R. & Premack, D. (2001) Can an ape reason analogically? Comprehension and production of analogical problems by Sarah, a chimpanzee (Pan troglodytes). In: The analogical mind: Perspectives from cognitive science, ed. Gentner, D., Holyoak, K. & Kokinov, B., pp. 471–97. MIT Press.CrossRefGoogle Scholar
O'Reilly, R. C. (2006) Biologically based computational Models of high-level cognition. Science 314:91–94.Google Scholar
Rogers, T. T. & McClelland, J. L. (2004) Semantic cognition: A parallel distributed processing approach. MIT Press.CrossRefGoogle Scholar
Rougier, N. P., Noelle, D., Braver, T. S., Cohen, J. D. & O'Reilly, R. C. (2005) Prefrontal cortex and the flexibility of cognitive control: Rules without symbols. Proceedings of the National Academy of Sciences 102:7338–43.Google Scholar
Schunn, C. D. & Dunbar, K. (1996) Priming, analogy, and awareness in complex reasoning. Memory and Cognition 24:271–84.Google Scholar
Smolensky, P. & Legendre, G. (2006) The harmonic mind: From neural computation to optimality-theoretic grammar. MIT Press.Google Scholar
St. John, M. & McClelland, J. (1990) Learning and applying contextual constraints in sentence comprehension. Artificial Intelligence 46:217–57.Google Scholar
Van Elzakker, M., O'Reilly, R. & Rudy, J. (2003) Transitivity, flexibility, conjunctive representations, and the hippocampus: I. An empirical analysis. Hippocampus 13:292–98.CrossRefGoogle ScholarPubMed
von Fersen, L., Wynne, C., Delius, J. & Staddon, J. (1991) Transitive inference in pigeons. Journal of Experimental Psychology: Animal Behavior Processes 17:334–41.Google Scholar