A symbolic-connectionist theory of relational inference and generalization.
Abstract
The authors present a theory of how relational inference and generalization can be accomplished within a cognitive architecture that is psychologically and neurally realistic. Their proposal is a form of symbolic connectionism: a connectionist system based on distributed representations of concept meanings, using temporal synchrony to bind fillers and roles into relational structures. The authors present a specific instantiation of their theory in the form of a computer simulation model, Learning and Inference with Schemas and Analogies (LISA). By using a kind of self-supervised learning, LISA can make specific inferences and form new relational generalizations and can hence acquire new schemas by induction from examples. The authors demonstrate the sufficiency of the model by using it to simulate a body of empirical phenomena concerning analogical inference and relational generalization.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Relational reasoning supports flexible reorganization of schemas and elements to construct new schemas and provides structure and coherence to experience.
“relational reasoning facilitates the flexible reorganization of schemas and elements to construct new schemas (Holyoak, 2012;Hummel & Holyoak, 2003), and provides structure and coherence (e.g., temporal structure, causal structure, etc.) to our experience.”
From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Hummel & Holyoak, 2003
Relational reasoning operates under reasoning schemas that reference current goals, contextual constraints, social norms, and rules for reasoning such as cause-and-effect, preconditions, and covariations.
“These operations occur under the guidance of 'reasoning schemas' that reference current goals, contextual constraints and social norms, and rules for the reasoning process itself (e.g., cause-and-effect, preconditions, covariations; Hummel & Holyoak, 2003;Rumelhart & Ortony, 1977).”
From Mental Time Travel? A Neurocognitive Model of Event Simulation. (2020) · cites this paper as Hummel & Holyoak, 2003
Cites
The magical number 4 in short-term memory: A reconsideration of mental storage capacity
Cowan, N. (2001) · Behavioral and Brain Sciences · doi:10.1017/s0140525x01003922 · Cited by 6,779 · see citing works
Schema induction and analogical transfer
Gick, M. L., & Holyoak, K. J. (1983) · Cognitive Psychology · doi:10.1016/0010-0285(83)90002-6 · Cited by 2,955 · see citing works
Self-citationDuncker, K. (1945) · Psychological Monographs · doi:10.1037/h0093599 · Cited by 2,938 · see citing works
Gick, M. L., & Holyoak, K. J. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90013-4 · Cited by 2,552 · see citing works
Self-citationStructure-mapping: A theoretical framework for analogy
GENTNER, D. (1983) · Cognitive Science · doi:10.1016/s0364-0213(83)80009-3 · Cited by 816 · see citing works
The Algebraic Mind: Integrating Connectionism and Cognitive Science
Marcus, G. F. (2001) · doi:10.7551/mitpress/1187.001.0001 · Cited by 341 · see citing works
Learning and applying contextual constraints in sentence comprehension
St. John, M. F., & McClelland, J. L. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90008-n · Cited by 322 · see citing works
Development of Analogical Problem-Solving Skill
Holyoak, K. J., Junn, E. N., & Billman, D. O. (1984) · Child Development · doi:10.2307/1129778 · Cited by 284 · see citing works
Self-citationUsher, M., & Niebur, E. (1996) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1996.8.4.311 · Cited by 147 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Reflective and Impulsive Determinants of Social Behavior
Strack, F., & Deutsch, R. (2004) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr0803_1 · Cited by 3,984 · see citing works
The proactive brain: using analogies and associations to generate predictions
Bar, M. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.05.005 · Cited by 1,144 · see citing works
Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012) · Cognitive Science · doi:10.1111/j.1551-6709.2012.01245.x · Cited by 772 · see citing works
“…& Anderson, 1990), rule or schema induction and analogy (e.g., Frank & Gibson, 2011; Gentner et al., 2009; Gick & Holyoak, 1983), generalization (Hummel & Holyoak, 2003; Shepard, 1987), discrimination (e.g., Chang, 2006; McClelland, Fiez, & McCandliss, 2002; Richman, Staszewski, & Simon, 1995),…”
Chapter 2 Design for a Working Memory
Oberauer, K. (2009) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(09)51002-x · Cited by 636 · see citing works
Mental Time Travel? A Neurocognitive Model of Event Simulation.
Addis, D. R. (2020) · Review of Philosophy and Psychology · doi:10.1007/s13164-020-00470-0 · Cited by 225 · see citing works
Binding by random bursts: a computational model of cognitive control
Verguts, T. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01117 · Cited by 76 · see citing works
Emergence of analogy from relation learning
Lu, H., Wu, Y. N., & Holyoak, K. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1814779116 · Cited by 70 · see citing works
Self-citationThe contribution of working memory to fluid reasoning: Capacity, control, or both?
Chuderski, A., & Necka, E. (2012) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/a0028465 · Cited by 60 · see citing works
Distributed analogical idea generation: inventing with crowds
Yu, L., Kittur, A., & Kraut, R. E. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557371 · Cited by 60 · see citing works
- Authors
- Hummel, J. E., & Holyoak, K. J.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- Psychological Review
- Published
- 2003
- Volume
- 110
- Issue
- 2
- Pages
- 220-264
- ISSN
- 0033-295X (ISSN-L)
- 1939-1471
- OpenAlex
- W1986787497
- Semantic Scholar
- b8d9ed504f70e38f9674df05bf3aadf729b5d379
- Cited by
- 567
- References
- 9
- Citations by year
- 2026: 4 · 2025: 49 · 2024: 24 · 2023: 25 · 2022: 15 · 2021: 7 · 2020: 18 · 2019: 42 · 2018: 19 · 2017: 17 · 2016: 22 · 2015: 19 · 2014: 25 · 2013: 30 · 2012: 41
- FWCI
- 9.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Psychological Review (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- relational inference, temporal synchronization, relational structures, schemas, analogical inference, self-supervised learning, inductive learning, cognitive architecture, distributed representations
- MeSH terms
- Generalization, Psychological, Humans, Memory, Models, Psychological, Symbolism, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
