Implicit (Not Explicit) Learning Aptitude Predicts the Acquisition of Difficult (Not Easy) Structure

Dexterous finger movements are often characterized by highly coordinated movements. Such coordination might be derived from reorganization of the corticospinal system. In this study, we investigated 1) the manner in which finger movement covariation patterns are acquired, by examining the effects of the implicit and explicit learning of a serial reaction time task (SRTT), and 2) how such changes in finger coordination are represented in the corticospinal system. The subjects learned a button press sequence in both implicit and explicit learning conditions. In the implicit conditions, they were naive about what they were learning, whereas in the explicit conditions the subjects consciously learned the order of the sequence elements. Principal component analysis decomposed both the voluntary movements produced during the SRTT and the passive movements evoked by transcranial magnetic stimulation (TMS) over the primary motor cortex into a set of five finger joint covariation patterns. The structures of the voluntary and passive TMS-evoked movement patterns were reorganized by implicit learning but not explicit learning. Furthermore, in the implicit learning conditions the finger covariation patterns derived from the TMS-evoked and voluntary movements spanned similar movement subspaces. These results provide the first evidence that skilled sequential finger movements are acquired differently through implicit and explicit learning, i.e., the changes in finger coordination patterns induced by implicit learning are accompanied by functional reorganization of the corticospinal system, whereas explicit learning results in faster recruitment of individual finger movements without causing any changes in finger coordination. NEW & NOTEWORTHY Skilled sequential multifinger movements are characterized as highly coordinated movement patterns. These finger coordination patterns are represented in the corticospinal system, yet it still remains unclear how these patterns are acquired through implicit and explicit motor sequence learning. A direct comparison of learning-related changes between actively generated finger movements and passively evoked finger movements by TMS provided evidence that finger coordination patterns represented in the corticospinal system are reorganized through implicit, but not explicit, sequence learning.

Background/Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience against fatigue and secondary tasking after learning. It was also examined whether explicit learning in older adults can be promoted by alleviating time constraints during learning.Methods: The alternating serial reaction time task (ASRTT) was used. Experiment 1 compared the benefits of receiving full instructions about the stimulus sequence relative to receiving no instructions in young (20–25 years) and older (50–65 years) adults during retention and during transfer to fatigue and secondary task conditions. Experiment 2 alleviated time constraints during the initial bouts of practice with full instructions.Results: Experiment 1 indicated that the older adults learned on the ASRTT and achieved similar performance as young adults when no instructions were given. In contrast to the young adults, learning was not superior in older adults who received full instructions compared with those who did not. Experiment 2 indicated that alleviating time constraints allowed some of the older adults to gain from instruction but only under relatively low time constraints, but there was no retention with rigorous time constraints.Conclusion: Explicit learning, but not implicit learning, declines in older adults. This is partly due to older adults difficulties to apply explicit knowledge. Less rigorous time constraints can help to ameliorate some of these difficulties and may induce levels of explicit learning in older adults that will result in superior performance compared with implicit learning. Implicit learning did occur under time constraints that prevented explicit learning.

Introduction: The aim of this study was to assess of both implicit and explicit motor sequence learning in children with attention deficit hyper activity disorder (ADHD) and typical peers , by serial reaction time task. Materials and Methods: The sample size for each group was 12. The study sample was included 24 children with ADHD, in explicit learning group and implicit learning group. and 24 typical children was included in implicit learning group, and explicit learning group. ANOVA repetitive measured for comparing response time and errors in different blocks, and paired t-test was used for comparing regular and irregular blocks. Results: The comparision of implicit and explicit learning on, accuracy (error response) and speed (response time) in ADHD explicit group would indicate accuracy (P-value=0/012), speed (P-value=0/012) and in ADHD implicit groop would indicate accuracy (P-value=0/094), speed (P-value=0/954). so in typical explicit group would indicate accuracy (P-value=0/008), speed (P-value=0/05) and in typical implicit group showed that accuracy (P-value=0/011), response times (P-value=0/442). Conclusion: This study showed that explain the task in motor sequence learning is more better in ADHD children. so before performing a task should be considered. Keywords: Motor Sequence, Explicit Learning, Implicit Learning, ADHD

Reduced implicit and explicit sequence learning in first-episode schizophrenia

We routinely make fine motor adjustments to maintain optimal motor performance. These adaptations have been attributed to both implicit, error-based mechanisms, and explicit, strategy-based mechanisms. However, little is known about the neural basis of implicit vs. explicit learning. Here, we aimed to use anodal transcranial direct current stimulation (tDCS) to probe the relationship between different brain regions and learning mechanisms during a visuomotor adaptation task in humans. We hypothesized that anodal tDCS over the cerebellum (CB) should increase implicit learning while anodal tDCS over the dorsolateral prefrontal cortex (dlPFC), a region associated with higher-level cognition, should facilitate explicit learning. Using a horizontal visuomotor adaptation task that measures explicit/implicit contributions to learning (Taylor et al., 2014), we found that dlPFC stimulation significantly improved performance compared to the other groups, and weakly increased explicit learning. However, CB stimulation had no effects on either target error or implicit learning. Previous work showed variable CB stimulation effects only on a vertical visuomotor adaptation task (Jalali et al., 2017), so in Experiment 2, we conducted the same study using a vertical context to see if we could find effects of CB stimulation. We found only weak effects of CB stimulation on target error and implicit learning, and now the dlPFC effect did not replicate. To resolve this discrepancy, in Experiment 3, we examined the effect of context (vertical vs. horizontal) on implicit and explicit contributions and found that individuals performed significantly worse and used greater implicit learning in the vertical screen condition compared to the horizontal screen condition. Across all experiments, however, there was high inter-individual variability, with strong influences of a few individuals, suggesting that these effects are not consistent across individuals. Overall, this work provides preliminary support for the idea that different neural regions can be engaged to improve visuomotor adaptation, but shows that each region's effects are highly context-dependent and not clearly dissociable from one another. This holds implications especially in neurorehabilitation, where an intact neural region could be engaged to potentially compensate if another region is impaired. Future work should examine factors influencing interindividual variability during these processes.

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.

Prevailing theories of implicit or unaware learning propose a developmental invariance model, with implicit function maturing early in infancy or childhood despite prolonged improvements in explicit or intentional learning and memory systems across childhood. Neuroimaging studies of adult visuomotor sequence learning have associated fronto-striatal brain regions with implicit learning of spatial sequences. Given evidence of continued development in these brain regions during childhood, we compare implicit sequence learning in adults and 7- to 11-year-old children to examine potential developmental differences in the recruitment of fronto-striatal circuitry during implicit learning. Participants performed a standard serial reaction time task. Stimuli alternately followed a fixed 10-step sequence of locations or were presented in a pseudorandom order of locations. Adults outperformed children, achieving a significantly larger sequence learning effect and showing learning more quickly than children. Age-related differences in activity were observed in the premotor cortex, putamen, hippocampus, inferotemporal cortex, and parietal cortex. We observed differential recruitment of cortical and subcortical motor systems between groups, presumably reflecting age differences in motor response execution. Adults showed greater hippocampal activity for sequence trials, whereas children demonstrated greater signal during random trials. Activity in the right caudate correlated significantly with behavioral measures of implicit learning for both age groups, although adults showed greater signal change than children overall, as would be expected given developmental differences in sequence learning magnitude. These results challenge the idea of developmental invariance in implicit learning and instead support a view of parallel developments in implicit and explicit learning systems.

Learning symbols and their arrangement in language involves learning associations across and within modalities. Research on implicit learning and chunking within modalities (e.g. N. C. Ellis, 2002) has identified how language users are sensitive to the frequency of language forms and their sequential probabilities at all levels of granularity from phoneme to phrase. This knowledge allows efficient language processing and underpins acquisition by syntactic bootstrapping. Research on explicit learning (e.g. N. C. Ellis, 2005) has shown how conscious processing promotes the acquisition of novel explicit cross-modal form-meaning associations. These breathe meaning into the processing of language form and they underpin acquisition by semantic bootstrapping. This is particularly important in establishing novel processing routines in L2 acquisition. These representations are also then available as units of implicit learning in subsequent processing. Language systems emerge, both diachronically and ontogenetically, from the statistical abstraction of patterns latent within and across form and function in language usage. The complex adaptive system (N. C. Ellis & Larsen-Freeman, 2009b) of interactions within AND across form and function is far richer than that emergent from implicit or explicit learning alone.

Solving the Credit Assignment Problem: The interaction of Explicit and Implicit learning with Internal and External State Information Wai-Tat Fu (wfu@cmu.edu) Human Factors Division and Beckman Institute University of Illinois at Urbana-Champaign 1 Airport Road, Savoy, IL 61874, USA John R. Anderson (ja+@cmu.edu) Department of Psychology Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 USA recognition of external state information (signs on the walls). Indeed, many have argued that real-world skills often involve the interplay between cognition (internal), perception, and action (external) that the understanding of these interactive skills requires careful study of how internal (memory) and external information (cues in the environment) are processed in the learning processes (Ballard, 1997; Fu & Gray, 2000; 2004; Gray & Fu, 2004; Larkin, 1989; Gray, Sims, Fu, & Schoelles, in press). The navigation problem above is an example of one of the most difficult situations in skill learning: when the learner has to perform a sequence of actions but only gets feedback on their success at the end of the sequence (e.g., when the destination is reached). This creates a credit-assignment problem, in which the learner has to assign credits to earlier actions that are responsible for eventual success. When actions are interdependent, either memory of previous actions or recognition of the correct problem state in the external environment is required to properly assign credits to the appropriate actions. In this article, we present results from an experiment in which we study how people learn to solve the credit-assignment problem in a simple but challenging example of such a situation. Our focus is on the recent proposal that humans exhibit two distinct learning processes and we apply it to learning of action sequences with delayed feedback: an explicit process (with awareness) that requires memory for actions and outcomes, and an implicit process (without awareness) that does not require such memory. We will first review research in some related areas that informed the design of our experiment. Abstract In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to either remember past choices of actions (internal state information) or rely on external cues in the environment (external state information) to select the right actions. We investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without external state information. We found that when explicit memory encoding was dominant, subjects were faster to select the better option in their first choices than in the last choices; when implicit reinforcement learning was dominant subjects were faster to select the better option in their last choices than in their first choices. However, implicit reinforcement learning was only successful when distinct external state information was available. The results suggest the nature of learning in credit assignment: an explicit memory encoding process that keeps track of internal state information and a reinforcement-learning process that uses state information to propagate reinforcement backwards to previous choices. However, the implicit reinforcement learning process is effective only when the valences can be attributed to the appropriate states in the system – either internally generated states in the cognitive system or externally presented stimuli in the environment. Introduction Consider a person navigating in a large office building. The person has to decide when to turn left or right at various hallway intersections. The sequence of decisions is interdependent – e.g., turning left at a particular hallway intersection will affect the decisions at the next intersections. The person may therefore need to keep track of previous actions to inform what actions to take in the future. In reality, memory of previous actions (internal state information) may not be necessary as people can explicitly seek information in the environment (external state information) to know where one is located or which direction to go to reach a destination (Fu & Gray, 2006). Learning to navigate is therefore likely to involve both the retention of internal state information (memory) and the Explicit and Implicit Learning Probability Learning and Classification There have been numerous studies on the learning of the probabilistic relationship between choices and their consequences. The simplest situation is the probability- learning experiment in which subjects guess which of the alternatives occurs and then receives feedback on their guesses (e.g., Estes, 1964). One robust finding is that subjects often “probability match”; that is, they will choose a particular alternative with the same probability that it is reinforced (e.g., Friedman et al., 1964). This leads many to propose that probability matching is the result of an implicit

ABSTRACTRecent proposals on language aptitude have distinguished between cognitive aptitudes for implicit and explicit learning (e.g., Doughty et al., 2010; Linck et al., 2013). This methodological study extends recent work on aptitude by investigating whether aptitudes for implicit and explicit learning are differentially related to the two main information-processing cognitive styles proposed by dual-process theories in cognitive psychology: rational–analytical and experiential–intuitive (Pacini & Epstein, 1999). A relationship was found between a rational–analytical profile and explicit aptitude, as well as between an experiential–intuitive profile and implicit aptitude. Structural equation modeling further showed that the proposed model had acceptable fit. These results were interpreted as support for the qualitatively different types of cognitive processes underlying the two hypothesized types of aptitude.

Various studies have been conducted on implicit (unconscious) and explicit (conscious) motor skill sequence learning by means of serial reaction time tasks in order to understand the two learning systems in the human brain (Schimidtke & Heuer, 1996; Stickgold, 2003; Ivry, 2003). Although the effects of implicit learning, bimodal integration of tasks (task integration) and sleep on implicit motor skill learning have been explored in various combination, all three have yet to be studied in one research design. We conducted a pilot study in an attempt to investigate how these three mechanisms contribute to the implicit learning phenomenon. Results revealed that explicit sequence learners have overall quicker reaction times and more accurate responses. We also found a lower percentage of implicit learning during the less complex sequence tasks. Subsequent tests found that sleep had a significant effect on explicit sequence learning, but no significant effect on implicit sequence learning.

To report on the most recent evidence concerning the neuroanatomical correlates that distinguish conscious and unconscious sequence learning. Sequence-learning tasks are the most widely used test for evaluating learning that takes place with no intention to learn and without awareness of what has been learnt (implicit learning). In these tasks, participants respond to successive stimuli without being told that their run follows a sequence; with practice they improve their performance, even thought they are unable to generate the sequence when directly asked to do so. In this article we review the research that has been carried out to evaluate the performance of neurological patients or that uses neuroimaging techniques to pinpoint the neuroanatomical correlates of learning. Studies conducted using functional neuroimaging techniques reveal that conscious learning processes are linked to activity in the anterior cingulate and that there is an important overlap between the structures involved in implicit and explicit learning. Nevertheless, greater differences are seen depending on whether the learning takes place with distractions (in order to prevent subjects from discovering the sequence) or without distractions. These differences can be explained using models that draw a distinction between a dorsal pathway of non-attentional (implicit) learning and a more ventral attentional pathway (which is implicit, or explicit if the sequence is discovered). Studies with neurological patients confirm the involvement of a wide range of structures both in implicit and explicit learning, including the prefrontal lobe, the motor cortex, the temporal lobe, the basal ganglia and the cerebellum. Finally, we present evidence of the functions carried out by each structure.

This study examined implicit sequence learning in patients with obsessive-compulsive disorder (OCD) under dual-task conditions. Frontal-striatal networks support implicit learning and are implicated in the pathophysiology of OCD. Neuroimaging data suggest that during implicit learning, OCD patients use neural systems normally active during explicit learning to compensate for striatal dysfunction. The authors examined implicit sequence learning in 25 OCD patients and 25 healthy comparison subjects using a dual-task paradigm, with subjects simultaneously engaged in an explicit memory task and an implicit learning task. They predicted that implicit learning in OCD subjects would be disrupted because concurrent explicit information-processing demands would prevent use of compensatory processes. OCD patients failed to show evidence of implicit learning and exhibited a significant deficit in comparison with healthy subjects. These results are consistent with the hypothesis that concurrent explicit and implicit information-processing demands interfere with implicit learning in OCD patients.

Age affects chunk-based, but not rule-based learning in artificial grammar acquisition

When implicit learning fails: Amnesia and the problem of error elimination