Comparison of implicit and explicit learning processes in a probabilistic task

This experiment compared the performance with explicit (rule-application and rule-discovery) and implicit (nonrule-instructed) learning approaches on the performance of a probabilistic video game task requiring fine motor control. The task required visual tracking of a small ball of light and "catching" it by means of joystick manipulation. A general pattern of improvement with practice occurred for all conditions. All conditions showed use of predictive relations among stimulus events. However, task performance of the rule-application and rule-discovery conditions were inferior to the nonrule-instructed implicit condition, particularly during the early phases of rule acquisition and application. This pattern strongly suggests substantial performance costs associated with attempting to discover or apply probabilistic rules. Decrements are likely due to increased cognitive demands associated with attempting to remember and strategically apply provided probability rules or attempting to discover and apply potentially important and useful probability information from a complex visual display.     

The role of explicit and implicit learning processes in concept discovery

Analysis of individual learning curves and concurrent verbal protocols from three experiments concerning discovery of a non-salient verbal concept and a pictorial analogue (Chinese ideograph) of the concept show that a substantial transition phase occurs in which discrimination of exemplars from non-exemplars of the concept is above chance but not yet asymptotic. Under most conditions the ability to verbalize knowledge of the concept occurred almost simultaneously with the onset of the transition phase. However, the addition of noise in the form of false feedback (Experiment 3) created a temporary dissociation between task performance and verbalizable knowledge. Additional results suggest that individual hypothesis revision/rejection strategies affect the length of the transition phase of learning, whereas the size of the domain of hypotheses being sampled affects the number of trial blocks before the transition phase begins. The effect of feedback error on the relation between early rates of hypothesis generation and subsequent transition phase length also suggests that a strategy of quick rejection of falsified hypotheses becomes less adaptive in noisy task environments (e.g. when there are many exceptions to a rule or the concept is probabilistic). Finally, failure to find effects of variables known to affect implicit learning suggests that implicit learning processes do not play a large role in the discovery of this type of concept.     

Implicit and explicit processes in a second-language learning task

Abstract

Two experiments examined the learning of a set of Greek pronunciation rules through explicit and implicit modes of rule presentation. Experiment 1 compared the effectiveness of implicit and explicit modes of presentation in two modalities, visual and auditory. Subjects in the explicit or rule group were presented with the rule set, and those in the implicit or natural group were shown a set of Greek words, composed of letters from the rule set, linked to their pronunciations. Subjects learned the Greek words to criterion and were then given a series of tests which aimed to tap different types of knowledge. The results showed an advantage of explicit study of the rules. In addition, an interaction was found between mode of presentation and modality. Explicit instruction was more effective in the visual than in the auditory modality, whereas there was no modality effect for implicit instruction. Experiment 2 examined a possible reason for the advantage of the rule groups by comparing different combinations of explicit and implicit presentation in the study and learning phases. The results suggested that explicit presentation of the rules is only beneficial when it is followed by practice at applying them.     

Distinguishing implicit and explicit learning

I agree with Dienes and Berry’s (1997) and Neal and Hesketh’s (1997) call for investigations of the qualitative differences between implicit and explicit learning and note that such investigations must be guided by a workable definition of what is implicit and by theories that predict what the qualitative differences might be. Following Schacter, Bowers, and Booker’s (1989) retrieval intentionality criterion, I propose using anencoding intentionality criterion to distinguish implicit from explicit learning; we can reasonably infer that implicit learning has occurred when a variable known to influence explicit learning has no effect in a comparable implicit learning condition. I then suggest that implicit learning depends on noncognitive, nonhierarchical associations, whereas explicit learning depends on cognitive, hierarchical associations, and briefly describe an experiment that confirms a qualitative difference between implicit and explicit learning predicted by this hypothesis.     

The emergence of explicit knowledge from implicit learning

Substantial evidence has highlighted the ability of observers to incidentally extract statistical contingencies present in visual environments. This study examined whether the knowledge extracted regarding statistical contingencies is unconscious initially, even when it becomes fully accessible to conscious awareness after extensive training. Using a “typical” contextual cuing procedure adapted to real-world scenes, we first observed that, after extensive training in searching for a target within repeated scenes, knowledge about regularities was associated with conscious awareness (Experiment 1). However, both subjective and objective measures of consciousness revealed that in the early phase of training, learning of regular structures first takes place at an unconscious level (Experiments 2 and 3). These results are discussed in the light of the causal relationships between learning and consciousness.     

Development of implicit and explicit category learning

We present two studies that examined developmental differences in the implicit and explicit acquisition of category knowledge. College-attending adults consistently outperformed school-age children on two separate information-integration paradigms due to children’s more frequent use of an explicit rule-based strategy. Accuracy rates were also higher for adults on a unidimensional rule-based task due to children’s more frequent use of the irrelevant dimension to guide their behavior. Results across these two studies suggest that the ability to learn categorization structures may be dependent on a child’s ability to inhibit output from the explicit system.     

Effects of learning contexts on implicit and explicit learning

Two parallel tasks involving rule learning were identified in Experiment 1A and were used to assess implicit and explicit learning. In both tasks, subjects had to input numbers in order to reach the target values of outputs. The relationship between inputs and outputs was either simple (in the simple task) or complex (in the complex task), and the way in which target values were presented could be in the form of either numbers (in the simple task) or lines (in the complex task). Experiment 1B examined the validity of the explicit measure in the complex task. Experiments 2–4 investigated the interaction between learning contexts and the simple/complex learning tasks. Verbalization and instructions to search for the rules facilitated the simple-task learning and hurt or have no effect on the complex-task learning. In the observational-learning condition, no learning occurred for the simple task, and the complex task learning was impaired. These results suggest that the complex task and simple task involve two distinct learning systems. Other implications are also discussed.     

Distinguishing the contributions of implicit and explicit processes to performance of the weather prediction task

Examinations of the cognitive neuroscience of category learning frequently rely on probabilistic classificationlearning Tasks—namely, the weather prediction task (WPT)—to study the neural mechanisms of implicit learning. Accumulating evidence suggests that the task also depends on explicit-learning processes. The present investigation manipulated the WPT to assess the specific contributions of implicit- and explicit-learning processes to performance, with a particular focus on how the contributions of these processes change as the task progresses. In Experiment 1, a manipulation designed to disrupt implicit-learning processes had no effect on classification accuracy or the distribution of individual response strategies. In Experiment 2, by contrast, a manipulation designed to disrupt explicit-learning processes substantially reduced classification accuracy and reduced the number of participants who relied on a correct response strategy. The present findings suggest that WPT learning is not an effective tool for investigating nondeclarative learning processes     

Psychometric intelligence dissociates implicit and explicit learning

The hypothesis that performance on implicit learning tasks is unrelated to psychometric intelligence was examined in a sample of 605 German pupils. Performance in artificial grammar learning, process control, and serial learning did not correlate with various measures of intelligence when participants were given standard implicit instructions. Under an explicit rule discovery instruction, however, a significant relationship between performance on the learning tasks and intelligence appeared. This finding provides support for Reber's hypothesis that implicit learning, in contrast to explicit learning, is independent of intelligence, and confirms thereby the distinction between the 2 modes of learning. However, because there were virtually no correlations among the 3 learning tasks, the assumption of a unitary ability of implicit learning was not supported.     

Contextual interference in implicit and explicit motor learning

The purposes of this study were to examine the contextual interference effect in implicit learning and to compare implicit and explicit learning. Thirty-two participants performed a pursuit-tracking task for 60 acquisition and ten retention trials. The middle segment of target pathways had only two patterns whereas other segments had random patterns. A combination of awareness and practice order for the middle segment created four acquisition conditions: implicit-blocked, implicit-serial, explicit-blocked and explicit-serial. A questionnaire and a recognition test revealed that implicit groups were unaware of the repetition. Results showed no contextual interference effect in either implicit or explicit learning. Acquisition and retention performances were better for the middle segment than the other segments regardless of awareness. No difference between the implicit and explicit groups was found, suggesting that the implicit learning condition led to learning equivalent to the explicit learning condition.     

Implicit versus explicit learning processes in a probabilistic, continuous fine-motor catching task

We compared the influences of explicit instruction and uninstructed implicit learning of correlations among visual events, in a fine-motor task. The task required visual tracking of a small "ball" of light and "catching" it by means of joystick manipulation. A general pattern of improvement with practice for instructed and "noninstructed" conditions was found. Additionally, both instructed and noninstructed conditions evidenced use of the predictive relationships among stimulus events. The improvement in overall performance caused by explicit verbal instructions was less than the improvement seen with the implicit learning condition. Further, instructed and noninstructed conditions showed different patterns of joystick activity. These findings suggest a qualitatively different approach to visual information processing and task performance for the instructional conditions     

Qualitative differences between implicit and explicit sequence learning

Four experiments investigate the differences between implicit and explicit sequence learning concerning their resilience to structural and superficial task changes. A superficial change that embedded the SRT task in the context of a selection task, while maintaining the sequence, did selectively hinder the expression of implicit learning. In contrast, a manipulation that maintained the task surface, but decreased the sequence validity, affected the expression of learning specifically when it was explicit. These results are discussed in the context of a dynamic framework (Cleeremans & Jiménez, 2002), which assumes that implicit knowledge is specially affected by contextual factors and that, as knowledge becomes explicit, it allows for the development of relevant metaknowledge that modulates the expression of explicit knowledge.     

The impact of response mode on implicit and explicit sequence learning

Evidence regarding the influence of response mode on sequence learning in serial reaction time (SRT) tasks has been mixed so far. In the present study, sequence learning was investigated under two different response conditions: manual (button presses) versus verbal (pronunciation of digits). Additionally, participants were divided post hoc into subgroups differing in their degree of explicit knowledge about the sequence. Results showed an interaction between response mode and type of learning (implicit vs. explicit), with explicit learning functioning more effectively under verbal than under manual conditions, whereas implicit learning was unaffected by the variation of the response mode. Implications concerning different underlying learning mechanisms (R-R learning vs. R-S learning) are discussed. Specifically, we suggest that the high response-effect distinctiveness of the verbal responses facilitated R-R learning.     

Electrophysiological correlates of exemplar-specific processes in implicit and explicit memory

The present ERP study investigated the retrieval of task-irrelevant exemplar-specific information under implicit and explicit memory conditions. Subjects completed either an indirect memory test (a natural/artificial judgment) or a direct recognition memory test. Both test groups were presented with new items, identical repetitions, and perceptually different but conceptually similar exemplars of previously seen study objects. Implicit and explicit memory retrieval elicited clearly dissociable ERP components that were differentially affected by exemplar changes from study to test. In the indirect test, identical repetitions, but not different exemplars, elicited a significant ERP repetition priming effect. In contrast, both types of repeated objects gave rise to a reliable old/new effect in the direct test. The results corroborate that implicit and explicit memory fall back on distinct cognitive representation and, more importantly, indicate that these representations differ in the type of stimulus information stored. Implicit retrieval entailed obligatory access to exemplar-specific perceptual information, despite its being task irrelevant. In contrast, explicit retrieval proved to be more flexible with conceptual and perceptual information accessed according to task demands.     

The construction of emotional experience requires the integration of implicit and explicit emotional processes

Although we agree that a constructivist approach to emotional experience makes sense, we propose that implicit (visceromotor and somatomotor) emotional processes are dissociable from explicit (attention and reflection) emotional processes, and that the conscious experience of emotion requires an integration of the two. Assessments of implicit emotion and emotional awareness can be helpful in the neuroscientific investigation of emotion.     

The interaction of the explicit and the implicit in skill learning: a dual-process approach

This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning.     

Nonintentional task set activation: Evidence from implicit task sequence learning

Studies have shown that task sets could be configured endogenously (i.e., on the basis of memory) according to an explicit sequence or exogenously according to a task cue. In two experiments, we examined whether an implicitly learned sequence could facilitate task set configuration without participants’ intention. These experiments led to opposite conclusions regarding this question, but their methodology made it impossible to distinguish between the interpretations. We altered the task-switching paradigm by embedding a hidden task sequence, while randomizing all other aspects, including perceptual (i.e., task cues) and motor elements. We found that a sequence of tasks, proper, was learned implicitly and that the memory of that sequence endogenously facilitated task decision processes without the participants’ explicit knowledge.     

Intentional control and implicit sequence learning

Sequence knowledge acquired by repeated exposure to targets in a speeded localization task was studied in 3 experiments that sought to test A. Destrebecqz and A. Cleeremans's (2001, 2003) claim that, under certain circumstances, the expression of such sequence knowledge cannot be brought under intentional control. In Experiment 1 participants were trained on either a deterministic or a probabilistic sequence and then performed a free-generation test under either inclusion or exclusion instructions. Participants were found to be capable of both expressing (inclusion) and avoiding expressing (exclusion) sequence knowledge. These results were confirmed in Experiment 2 with a more exact replication of Destrebecqz and Cleeremans's methodology. In Experiment 3 participants performed a trial-by-trial generation test under both inclusion and exclusion conditions after a much longer period of training. All the findings are consistent with the proposal that information acquired during sequence learning is explicit in nature.     

The projection of implicit and explicit goals

In 3 studies, the authors analyzed whether projection occurs for both conscious and nonconscious goals. In Experiment 1, participants who were predisposed to hold a learning goal over a performance goal rated others as possessing more of a learning goal. In Experiment 2, participants who were either implicitly primed with or explicitly assigned to have the goal to compete perceived others as striving for competitive goals more than control participants. In Experiment 3, the authors demonstrated that it was the actual goal to compete rather than the trait construct of competitiveness that was projected. The control of automatic goal projection effects is discussed, and interpersonal consequences of goal projection are delineated.