Stimulus discriminability and induction as independent components of generalization.

Abstract

Generalization of learning can arise from 2 distinct sources: failure to discriminate a novel test stimulus from the trained stimulus and active extrapolation from the trained stimulus to the test stimulus despite them being discriminable. We investigated these 2 processes in a predictive learning task by testing stimulus discriminability (identification of the trained stimulus) as well as generalization of learning (outcome expectancy). Generalization gradients were broader for expectancy than for identification, in both single cue and differential (discrimination) designs, implying a substantial extrapolation component for the most dissimilar stimuli. The shapes of the expectancy gradients were strongly determined by the training design (single cue vs. differential) and by the rules inferred by participants (similarity vs. linear). By contrast, the identification gradients were unaffected by the training design or inferred rules and were equivalent for predictive and nonpredictive stimuli. These results indicate that perceptual discriminability plays a substantial role in generalization, but it is largely unaffected by associative learning. Instead, learning appears to impact on generalization via an independent extrapolation component which involves cognitive processes such as inductive reasoning.

To perceive or not to perceive? Individual differences in perception predict rule induction and response generalization

“…Testing the above hypothesis requires the assessment of generalization, perception, and rules in the same participants. A recent study by Lovibond, Lee, and Hayes (2020) provides just this. Lovibond et al investigated the extent to which gradients of stimulus perception and US expectancy ratings were influenced by the type of rule that participants adopted (linear vs. similar) and the type of conditioning procedure administered in training (simple vs. differential).…”

“…In total, six different datasets are reported, four of which have been previously published in Lovibond et al (2020), and were retrieved from OSF: https://osf.io/xnva4/. The full details of the protocol used to generate those datasets (1, 2, 5, and 6) can be found in Lovibond et al (2020). We only report the main procedural details needed to interpret our analyses below.…”

“…Despite abundant examples of response generalization within the laboratory and real-life (Enquist & Ghirlanda, 2005;Ghirlanda & Enquist, 2003;Mednick & Freedman, 1960), insight into the underlying mechanisms remains unclear for several reasons (Struyf, Zaman, Vervliet, & Van Diest, 2015;Zaman, Chalkia, et al, 2020). The first issue is that current evidence suggests that there are multiple mechanisms through which generalized response can occur, including a failure to discriminate or inductive extrapolation (Lovibond, Lee, & Hayes, 2020;Zaman, Chalkia, et al, 2020). In other words, response generalization may be an interaction between perceptual and cognitive processes.…”

“…The identification and differentiation regarding these and other mechanisms based solely on patterns of generalized responding, however, remains difficult as different mechanisms can yield the same behavioral phenotype (Struyf, Zaman, Vervliet, & Van Diest, 2015). The second complexity arises from the fact that response generalization in humans displays a high amount of variability (Lee, Mills, Hayes, & Livesey, 2020;Zaman, Chalkia, et al, 2020), with large individual differences in inductive rules (Lee et al, 2018;Lovibond et al, 2020;Wong & Lovibond, 2017) and perceptual ability (Struyf, Zaman, Hermans, & Vervliet, 2017;Struyf et al, 2015;Zaman, Ceulemans, Hermans, & Beckers, 2019;Zaman, Struyf, Ceulemans, Beckers, & Vervliet, 2019;Zaman, Chalkia, et al, 2020;Zaman, stimulus features. This leads to the prediction that more accurate stimulus perception should lead to a higher likelihood of deriving a similarity over a linear generalization rule.…”

“…The overall aim of this study was to test whether differences in stimulus perception predict patterns of response generalization, and also, the type of reported rule. We achieved this aim by performing a series of cluster analyses to identify individual differences in stimulus perception using six datasets, four of which were reported in Lovibond et al (2020), and two of which are unpublished.…”