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Fluid Abilities and Rule Learning: Patterning and Biconditional Discriminations

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Journal J Intell
Date 2019 Jun 5
PMID 31162434
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Abstract

Previous experience with discrimination problems that can only be solved by learning about stimulus configurations enhances performance on new configural discriminations. Some of these effects can be explained by a shift toward increased configural processing (learning about combinations of cues rather than about individual elements), or by a tendency to generalize a learned rule to a new training set. We investigated whether fluid abilities influence the extent that previous experience with configural discriminations improves performance on subsequent discriminations. In Experiments 1 and 2 we used patterning discriminations that could be solved by applying a simple rule, whereas in Experiment 3 we used biconditional discriminations that could not be solved using a rule. Fluid abilities predicted the improvement on the second training set in all experiments, including Experiment 3 in which rule-based generalization could not explain the improvement on the second discrimination. This supports the idea that fluid abilities contribute to performance by inducing a shift toward configural processing rather than rule-based generalization. However, fluid abilities also predicted performance on a rule-based transfer test in Experiment 2. Taken together, these results suggest that fluid abilities contribute to both a flexible shift toward configural processing and to rule-based generalization.

References
1.
Wills A, Graham S, Koh Z, McLaren I, Rolland M . Effects of concurrent load on feature- and rule-based generalization in human contingency learning. J Exp Psychol Anim Behav Process. 2011; 37(3):308-16. DOI: 10.1037/a0023120. View

2.
Mehta R, Russell E . Effects of pretraining on acquisition of novel configural discriminations in human predictive learning. Learn Behav. 2009; 37(4):311-24. DOI: 10.3758/LB.37.4.311. View

3.
Melchers K, Shanks D, Lachnit H . Stimulus coding in human associative learning: flexible representations of parts and wholes. Behav Processes. 2007; 77(3):413-27. DOI: 10.1016/j.beproc.2007.09.013. View

4.
Don H, Goldwater M, Otto A, Livesey E . Rule abstraction, model-based choice, and cognitive reflection. Psychon Bull Rev. 2016; 23(5):1615-1623. DOI: 10.3758/s13423-016-1012-y. View

5.
Kruschke J . ALCOVE: an exemplar-based connectionist model of category learning. Psychol Rev. 1992; 99(1):22-44. DOI: 10.1037/0033-295x.99.1.22. View