Dependent measure and time constraints modulate the competition between conflicting feature-based and rule-based generalization processes.

Abstract

In our study, we tested the hypothesis that feature-based and rule-based generalization involve different types of processes that may affect each other producing different results depending on time constraints and on how generalization is measured. For this purpose, participants in our experiments learned cue-outcome relationships that followed the opposites rule: Single cues that signaled the same outcome (e.g., A-1/B-1) predicted the opposite outcome when presented in compound (e.g., AB-2). Some cues were only presented in compound during training (e.g., EF-1) to see if at test participants tended to generalize according to rule-based (i.e., E-2/F-2) or according to feature-based generalization (i.e., E-1/F-1). The generalization test used 2 different tasks: a predictive judgment task, and a cued-response priming task. In Experiment 1, participants' verbal ratings were consistent with rule-based generalization. However, participants' reaction times (RTs) in the cued-response priming task were consistent with feature-based generalization. Experiment 2 replicated the results from Experiment 1, and it also provided evidence consistent with feature-based or rule-based generalization depending on whether a short stimulus onset asynchrony (SOA; 200 ms) or a long SOA (1300 ms), respectively, was used in the priming task. Our results are interpreted as supporting the idea that feature-based generalization process relies on fast, associative processes, whereas rule-based generalization is slow and depends on executive control resources. The latter generalization process would inhibit the former when enough time and resources are available. Otherwise, feature-based generalization would take control of responses. (PsycINFO Database Record

Three Ways That Non-associative Knowledge May Affect Associative Learning Processes

“…One example is the Perruchet (1985, 2015) effect, where within the same experiment and indeed the same trial, diverging response patterns can be obtained in two behavioral systems (for example eye blink conditioning and causal rating; for details see below). Cobos et al (2016) observed diverging “associative” and “non-associative” response generalization in cued response times and verbal ratings, respectively and Morís et al (2014, Exp 4) found that non-associative knowledge, given by instruction, affects verbal judgements but not responses in a recognition priming-based test. But a related and in many ways more difficult question is how associative predictions might generate explicit judgements.…”

“…Associative networks generally anticipate this difference because the summation of associations formed to the single stimuli in negative patterning (C+ and D+ trials) provides a particularly strong and incorrect prediction for the compound (CD-). However, from an abstract relational perspective, positive and negative patterning possess the same complexity; they are perfect examples for a simple rule that the outcome of the compound is always the opposite of the outcome of the single cues (Shanks and Darby, 1998; Lachnit et al, 2001, 2002; Harris and Livesey, 2008; Cobos et al, 2016). Capitalizing on this simple relational property, Shanks and Darby also trained participants on a series of single cues (I+/J+/M-/N-) and compounds (KL-/OP+), and later tested how participants would predict the consequences of these cues in novel combinations (e.g., IJ?…”

“…For instance, the notion that associative learning can occur in the absence of awareness is still as contentious as ever (see Goujon et al, 2015; Colagiuri and Livesey, 2016; Vadillo et al, 2016 for a recent iteration of this debate concerning implicit learning in visual search). Nevertheless, a number of results (e.g., Morís et al, 2014; Perruchet, 2015; Cobos et al, 2016) suggest that associative learning mechanisms are separable from other cognitive sources of expectation in at least some circumstances and could represent the operation of an independent system. This possibility is certainly plausible enough to warrant a more in-depth consideration of how associative and non-associative sources of prediction might interact.…”

Rapid Top-Down Control of Behavior Due to Propositional Knowledge in Human Associative Learning

“…But requesting a verbal judgment may be less than ideal if the objective is to evaluate the intervention of associative processes. Usually, participants are provided with sufficient time to give their ratings and thus, goal-directed propositional processes can override responses from any competing associative processes [7, 13]. …”

Previously acquired cue–outcome structural knowledge guides new learning: Evidence from the retroactive-interference-between-cues effect

“…It is worth noting that both accounts are based on the operation of associative learning and associative retrieval mechanisms. Associative mechanisms are thought to be fast acting, automatic, and require little cognitive resources to operate (e.g., Cobos, Gutiérrez-Cobo, Morís, & Luque, 2017;Morís, Cobos, Luque, & López, 2014;Shanks, 2007;Wagner, 1981). Because of this, they are prototypic of the low-level processes proposed by two system theories, as opposed to high-level inference processes (see Darlow & Sloman, 2010, for a review).…”