Strategy shifts in classification skill acquisition: Does memory retrieval dominate rule use?

Abstract

Strategies adopted by participants during skill acquisition do not necessarily remain stable over training or practice trials, but rather often shift in the direction of speeding up responses and easing the cognitive load (Schunn & Reder, 2001). A number of formal models, notably by Logan (1988) and Rickard (1997), have been proposed to account for strategy shifts. These models have focused on a shift from performance based on a rule (algorithm, calculation) to performance based on the retrieval of previously experienced items from memory. In these models, retrieval from long-term memory is viewed as a singlestep, possibly automatic solution procedure for problems that might otherwise require multiple rule-based computations. In tasks to which these models have been applied, participants are first instructed in the rule or algorithm that governs responses. Thus, although there are certain fundamental differences between them, both the Logan and Rickard models assume that performance is under control of an algorithm-based strategy at the outset and that, at some point in practice, there is a transition in control from a strategy based on the algorithm to a strategy based on instance-memory retrieval.The Logan (1988) and Rickard (1997) models may be limited by the tasks to which they have been applied (e.g., alphabet arithmetic). In these tasks, only two strategies are possible. Participants are given the rule (or algorithm) in advance and told to use the rule-based strategy at the outset. Under these conditions, guessing is unlikely and a transition from memory retrieval to rule use is virtually impossible. In a binary classification task, using a simple self-report procedure to identify strategies, Bourne, Healy, Parker, and Rickard (1999) found that strategy shifts are not always in the rule-to-memory direction. When participants were not preinstructed in the rule or algorithm for a task, most of them reported guessing on early trials. After some significant number of repetitions of individual stimuli, most participants adopted a preferred strategy, which might or might not be memory based. Specifically, most participants preferred a rule-based strategy in what was called a natural task condition. This task was called natural because it involved a linguistic rule for pronunciation of English words (i.e., participants had to choose one of two pronunciations, "thee" or "thuh," for the definite article the, depending on a given noun or adjective; see Raymond, Fisher, & Healy, 2002, for evidence that college students are not fully knowledgeable about this rule despite its presence in all English dictionaries). In contrast, most participants eventually preferred a memory-based strategy in an artificial task condition (in which participants had to base their response on whether meaningless letter strings conformed to an alphabetical sequence). Overall measures (accuracy and response time [RT]) showed that performance was better under the control of the preferred strategy-either rule or memory-than under the nonprefer...

Language proficiency and metacognition as predictors of spontaneous rehearsal in children.

“…For example, Baddeley and colleagues (Baddeley, 1992;Baddeley, Thomson, & Buchanan, 1975), as well as more recent studies (Bourne, Healy, Kole, & Graham, 2006;Hulme, Thomson, Muir, & Lawrence, 1984) found that the faster stimuli were named, the greater the number recalled. The present study extends those findings by identifying a key role played by developing rehearsal skills in this process.…”

Bending the Rules: Strategic Behavioral Differences Are Reflected in the Brain

“…Following up on that, it might actually be the case that the rule integration strategy is not the most efficient one, given that it is actually slowing the participants down. Moreover, given that rule-based and item-based strategies are not mutually exclusive in the long run (Bourne, Healy, Kole, & Graham, 2006), it can be assumed that with even more task practice, the majority of the participants might shift to an item-based strategy.…”

Strategy-effects in prefrontal cortex during learning of higher-order S–R rules

“…More specifically, the objective is to elucidate the neural correlates of quantitative and qualitative strategy differences. Findings from categorization learning literature (Bourne et al, 2006;Bourne et al, 2010) as well as our own behavioral pilot studies (Wolfensteller and von Cramon, unpublished data) suggest that the vast majority of participants begin with a rule-based strategy and only later gradually switch to a more item-based strategy. In line with these findings, it was hypothesized that the RLPFC would be engaged in both subgroups of participants but more so in participants who adhere to the rule-based strategy.…”

“…However, behavioral pilot studies (Wolfensteller & von Cramon, unpublished data) and findings from categorization learning (Bourne et al, 2006(Bourne et al, , 2010 suggested that most people initially start out with rule-based strategies. Based on that we hypothesized a gradual transition from rule integration to itembased strategy and hence a quantitative strategy effect at an earlier stage of learning.…”

“…For instance, research on categorization learning has shown that when faced with to-be-categorized stimuli people customarily start out by adopting rule-based strategies but switch to memory-based strategies after practice (Bourne et al, 2006(Bourne et al, , 2010Gluck et al, 2002). A recent fMRI study revealed differential brain activation patterns for the implementation of well established higherorder stimulus-response (S-R) rules depending on inter-individual strategy differences (Wolfensteller and von Cramon, 2010).…”