Promoting spontaneous analogical transfer by idealizing target representations.
Máximo Trench1,2, Lucía Micaela Tavernini3,4, Robert L Goldstone5
1Department of Psychology, Universidad Nacional del Comahue, Quintral 1250 (8400), Bariloche, Río Negro, Argentina. maximo.trench@crub.uncoma.edu.ar.
Memory & Cognition
|March 21, 2023
Summary
Creating an idealized model of a target problem improves problem-solving by aiding analog access. This target idealization strategy is more broadly applicable than prior methods for the late abstraction principle.
Area of Science:
- Cognitive Psychology
- Artificial Intelligence
Background:
- The late abstraction principle aids analogical reasoning by creating abstract representations.
- Current methods require external information or high cognitive load.
Purpose of the Study:
- To investigate the effectiveness of target idealization for analogical retrieval.
- To present a computational model explaining the observed effects.
Main Methods:
- Experiment 1: Compared idealized situation models, concrete simulations, and no simulations for problem-solving.
- Experiment 2: Confirmed the mechanism involved analog access.
- Developed the SampComp computational model based on feature sampling.
Main Results:
- Idealized situation models significantly increased correct solutions compared to other conditions.
- The benefit stemmed from improved access to base analogs.
- SampComp accurately predicted the interaction between base and target idealization.
Conclusions:
- Target idealization is an effective and broadly applicable strategy for enhancing analogical retrieval.
- The SampComp model provides a computational account of analogical reasoning through feature sampling.
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