Individual differences in learning and transfer: Stable tendencies for learning exemplars versus abstracting rules.

Abstract

We hypothesize that during training some learners may focus on acquiring the particular exemplars and responses associated with the exemplars (termed exemplar learners), whereas other learners attempt to abstract underlying regularities reflected in the particular exemplars linked to an appropriate response (termed rule learners). Supporting this distinction, after training (on a function-learning task), participants either displayed an extrapolation profile reflecting acquisition of the trained cue-criterion associations (exemplar learners) or abstraction of the function rule (rule learners; Studies 1a and 1b). Further, working memory capacity (measured by Ospan) was associated with the tendency to rely on rule versus exemplar processes. Studies 1c and 2 examined the persistence of these learning tendencies on several categorization tasks. Study 1c showed that rule learners were more likely than exemplar learners (indexed a priori by extrapolation profiles) to resist using idiosyncratic features (exemplar similarity) in generalization (transfer) of the trained category. Study 2 showed that the rule learners but not the exemplar learners performed well on a novel categorization task (transfer) after training on an abstract coherent category. These patterns suggest that in complex conceptual tasks, (a) individuals tend to either focus on exemplars during learning or on extracting some abstraction of the concept, (b) this tendency might be a relatively stable characteristic of the individual, and (c) transfer patterns are determined by that tendency.

When high working memory capacity is and is not beneficial for predicting nonlinear processes

“…Intuitively, higher memory capacity should improve prediction accuracy because people need to consider information about the past of a process in order to forecast its future. And, indeed, research has shown that higher WMC is related to higher prediction accuracy for continuous processes (function learning) and categorization tasks (Bröder, Newell, & Platzer, 2010;Lewandowsky, Yang, Newell, & Kalish, 2012;McDaniel, Cahill, Robbins, & Wiener, 2014). One explanation for this observation is that higher WMC is associated with an improved ability to actively maintain and manipulate information, which is needed to calibrate cognitive prediction algorithms to the learning data and to abstract systematic regularities.…”

“…WMC is also associated with accuracy in a prototypical rule-induction task-Raven's Progressive Matrices-particularly for items requiring complex rules (D. R. Little, Lewandowsky, & Craig, 2014;Wiley, Jarosz, Cushen, & Colflesh, 2011). Despite these compelling connections, there is only one study directly assessing how WMC affects the prediction of continuous processes: McDaniel et al (2014) suspected that higher WMC allows participants to actively maintain a range of cue-criterion values and to concurrently compare them over trials to abstract a functional rule. Higher WMC participants were indeed more likely to use rule-based as opposed to exemplar-based strategies and achieved higher prediction accuracy.…”

“…Instead, participants needed to rely on memory to make predictions. In contrast to typical function learning paradigms (e.g., McDaniel et al, 2014), we were interested in how participants predict the development of ongoing processes rather than learn an abstract functional relation. Data points were therefore presented in sequential order (rather than random), and each data point was presented only once (rather than multiple times).…”

“…One explanation for this observation is that higher WMC is associated with an improved ability to actively maintain and manipulate information, which is needed to calibrate cognitive prediction algorithms to the learning data and to abstract systematic regularities. These abstracted regularities, or Brules,^can then be used for prediction (McDaniel et al, 2014). However, numerous studies following the ecological rationality approach (e.g., Marewski, Gaissmaier, & Gigerenzer, 2010) have shown that in a surprising number of situations simple, information-frugal heuristics-which rely on minimal information and are not working-memory intensive-perform just as well as complex rules (Marewski & Schooler, 2011).…”

Naïve and Robust: Class‐Conditional Independence in Human Classification Learning

“…We build on recent demonstrations of interindividual differences in category learning (Bartlema, Lee, Wetzels, & Vanpaemel, 2014;McDaniel, Cahill, Robbins, & Wiener, 2014), by using a learning paradigm that terminates based on individual performance (like e.g., Homa, Dunbar, & Nohre, 1991;Medin & Smith, 1981), and model individual subjects' learning and beliefs. One limitation of previous studies that argued for shifts from simple to complex classification strategies is a focus on large, discrete bins of learning trials (56 in Smith & Minda, 1998;36 in Johansen & Palmeri, 2002), or on specific test trials interspersed during learning (Erickson & Kruschke, 1998;Nosofsky, Kruschke, & Mckinley, 1992;Nosofsky, Palmeri, & McKinley, 1994;Smith & Minda, 2002).…”