Sequence Learning Task Research Articles

Motor memory consolidation processes in young female adults with ADHD may be less susceptible to interference

Young adults with Attention Deficit Hyperactivity Disorder (ADHD) may have an atypical procedural (“how to”) memory consolidation phase, after practicing a movement sequence, with smaller gains in speed and some costs in accuracy, compared to typical peers, at 24h post-training. Here we tested the susceptibility of performance gains retained after motor practice by young female adults with (N=16) and without (N=16) ADHD to post-training interference. Participants were trained on the finger-to-thumb opposition sequence learning task with performance speed and accuracy recorded before training, immediately after, and at 24h post-training. Two hour after the initial training, participants practiced a second, similarly constructed but differently ordered sequence of movements. Typical young adults showed a significant interference effect, with only the performance of the second sequence showing robust gains in speed, with no costs in accuracy, in the 24h post-training consolidation phase. Participants with ADHD showed only small additional speed gains in the post-training consolidation phase but for both sequences. Altogether these results suggest that motor memory consolidation processes in young adults with ADHD may be less susceptible to interference compared to typical peers.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a “Learning with privileged information” approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Age-related differences in practice-dependent resting-state functional connectivity related to motor sequence learning.

Decreased neural plasticity is observed with healthy ageing in the primary sensorimotor (SM1) cortex thought to participate in motor learning and memory consolidation processes. In the present magnetoencephalography study, the post-training reorganization of resting-state functional connectivity (rsFC) and its relation with motor learning and early consolidation in 14 young (19-30 years) and 14 old (66-70 years) healthy participants were investigated. At the behavioral level, participants were trained on a motor sequence learning task then retested 20-30 min later for transient offline gains in performance. Using a sensorimotor seed-based approach, rsFC relying on beta band power envelope correlation was estimated immediately before and 10 min after the learning episode. Post-training changes in rsFC (from before to after learning) were correlated with motor learning performance and with the offline improvement in performance within the hour after learning. Young and old participants exhibited differential patterns of sensorimotor-related rsFC, bearing specific relationships with motor learning and consolidation. Our findings suggest that rsFC changes following learning reflect the offline processing of the new motor skill and contribute to the early memory consolidation within the hour after learning. Furthermore, differences in post-training changes in rsFC between young and old participants support the hypothesis that ageing modulates the neural circuits underlying the learning of a new motor skill and the early subsequent consolidation stages. Hum Brain Mapp 38:923-937, 2017. © 2016 Wiley Periodicals, Inc.

Sequence learning in Parkinson's disease: Focusing on action dynamics and the role of dopaminergic medication

Previous studies on movement sequence learning in Parkinson's disease (PD) have produced mixed results. A possible explanation for the inconsistent findings is that some studies have taken dopaminergic medication into account while others have not. Additionally, in previous studies the response modalities did not allow for an investigation of the action dynamics of sequential movements as they unfold over time. In the current study we investigated sequence learning in PD by specifically considering the role of medication status in a sequence learning task where mouse movements were performed. The focus on mouse movements allowed us to examine the action dynamics of sequential movement in terms of initiation time, movement time, movement accuracy, and velocity. PD patients performed the sequence learning task once on their regular medication, and once after overnight withdrawal from their medication. Results showed that sequence learning as reflected in initiation times was impaired when PD patients performed the task ON medication compared to OFF medication. In contrast, sequence learning as reflected in the accuracy of movement trajectories was enhanced when performing the task ON compared to OFF medication. Our findings suggest that while medication enhances execution processes of movement sequence learning, it may at the same time impair planning processes that precede actual execution. Overall, the current study extends earlier findings on movement sequence learning in PD by differentiating between various components of performance, and further refines previous dopamine overdose effects in sequence learning.

Enhancement of motor consolidation by post-training transcranial direct current stimulation in older people

Consolidation, by which performance increments after a training intervention are secured and sometimes generated, is reduced in elderly humans. The present study addressed the question whether transcranial direct current stimulation (tDCS) applied after motor training improves consolidation of explicit motor sequence learning in healthy older humans. In the first experiment, anodal or cathodal tDCS to the left primary motor cortex, anodal tDCS to premotor cortex, or sham tDCS was applied immediately after completion of a finger sequence learning task. Performance was retested at 8 and 22 hours after the initial training session. Whereas all groups achieved similar performance at the end of training, off-line improvements differed between groups at later retesting, depending on the type of intervention. Relative to the other 3 interventions, anodal tDCS to primary motor cortex (M1) led to performance improvements already at retesting 8 hours after initial learning and were maintained on the next day. In the second experiment, effects of the timing of post-training anodal tDCS to M1 with respect to the end of training were studied. Participants received anodal tDCS of M1 immediately or 60 or 120 minutes after training and were retested on sequence performance 8 hours post training. Only application of tDCS immediately after the end of training, but not after 1 or 2 hours, enhanced off-line consolidation. These findings suggest that anodal tDCS applied off-line immediately post training to M1 interacts specifically with early processes promoting consolidation of motor sequence learning in healthy older individuals.

Mood induction effects on motor sequence learning and stop signal reaction time.

The neurobiological theory of positive affect proposes that positive mood states may benefit cognitive performance due to an increase of dopamine throughout the brain. However, the results of many positive affect studies are inconsistent; this may be due to individual differences. The relationship between dopamine and performance is not linear, but instead follows an inverted "U" shape. Given this, we hypothesized that individuals with high working memory capacity, a proxy measure for dopaminergic transmission, would not benefit from positive mood induction and in fact performance in dopamine-mediated tasks would decline. In contrast, we predicted that individuals with low working memory capacities would receive the most benefit after positive mood induction. Here, we explored the effect of positive affect on two dopamine-mediated tasks, an explicit serial reaction time sequence learning task and the stop signal task, predicting that an individual's performance is modulated not only by working memory capacity, but also on the type of mood. Improvements in explicit sequence learning from pre- to post-positive mood induction were associated with working memory capacity; performance declined in individuals with higher working memory capacities following positive mood induction, but improved in individuals with lower working memory capacities. This was not the case for negative or neutral mood induction. Moreover, there was no relationship between the change in stop signal reaction time with any of the mood inductions and individual differences in working memory capacity. These results provide partial support for the neurobiological theory of positive affect and highlight the importance of taking into account individual differences in working memory when examining the effects of positive mood induction.

Effect of transcranial direct current stimulation (tDCS) over the prefrontal cortex combined with cognitive training for treating schizophrenia: a sham-controlled randomized clinical trial

We report a transcranial direct current stimulation (tDCS) protocol over the dorsolateral prefrontal cortex (DLPFC) combined with cognitive training in schizophrenia. We assessed psychotic symptoms in nine patients using the Positive and Negative Syndrome Scale (PANSS). All evaluations were scored at baseline, at the end of the intervention protocol, and during a 4-week follow-up. The tDCS protocol consisted of 10 consecutive sessions over 5-day periods. We placed the cathode over the right and the anode over the left DLPFC. For sham stimulation, we turned the device off after 60 seconds. Cognitive training consisted of the administration of N-back and sequence learning tasks. We performed an analysis of covariance (ANCOVA) to adjust for the dependent variable PANSS, considering the interaction with baseline severity scores (p = 0.619). Mixed analysis of variance (ANOVA) showed no statistical significance between the groups regarding final PANSS scores. The results failed to demonstrate that the concomitant use of tDCS and cognitive training is effective to improve clinical outcomes in patients with schizophrenia. The present findings should be analyzed with care, considering the small sample size. Larger controlled trials on electric/cognitive stimulation should be produced in order to enhance therapeutic strategies in schizophrenia.

Age-related white-matter correlates of motor sequence learning and consolidation

Older adults show impaired consolidation in motor sequence learning (MSL) tasks, failing to demonstrate the sleep-dependent performance gains usually seen in young individuals. To date, few studies have investigated the white-matter substrates of MSL in healthy aging, and none have addressed how fiber pathways differences may contribute to the age-related consolidation deficit. Accordingly, we used diffusion-weighted magnetic resonance imaging to explore how white-matter characteristics relate to performance using an explicit MSL task in young and older participants. Analysis revealed that initial learning scores were correlated to white-matter microstructure in the corticospinal tract and within the corpus callosum regardless of age. Furthermore, sleep-dependent consolidation scores, in young adults only, were related to white-matter tract organization in a frontal area where several major fiber bundles cross each other. These findings further our understanding of the neural correlates of MSL in healthy aging and provide the first evidence that age-related white-matter differences in tract configuration may underlie the age-related motor memory consolidation deficit.

Neural changes associated to procedural learning and automatization process in Developmental Coordination Disorder and/or Developmental Dyslexia.

Recent theories hypothesize that procedural learning may support the frequent overlap between neurodevelopmental disorders. The neural circuitry supporting procedural learning includes, among others, cortico-cerebellar and cortico-striatal loops. Alteration of these loops may account for the frequent comorbidity between Developmental Coordination Disorder (DCD) and Developmental Dyslexia (DD). The aim of our study was to investigate cerebral changes due to the learning and automatization of a sequence learning task in children with DD, or DCD, or both disorders. fMRI on 48 children (aged 8-12) with DD, DCD or DD+DCD was used to explore their brain activity during procedural tasks, performed either after two weeks of training or in the early stage of learning. Firstly, our results indicate that all children were able to perform the task with the same level of automaticity, but recruit different brain processes to achieve the same performance. Secondly, our fMRI results do not appear to confirm Nicolson and Fawcett's model. The neural correlates recruited for procedural learning by the DD and the comorbid groups are very close, while the DCD group presents distinct characteristics. This provide a promising direction on the neural mechanisms associated with procedural learning in neurodevelopmental disorders and for understanding comorbidity.

The NMDA receptor partial agonist d-cycloserine does not enhance motor learning

Rationale:There has recently been increasing interest in pharmacological manipulations that could potentially enhance exposure-based cognitive behaviour therapy for anxiety disorders. One such medication is the partial NMDA agonist d-cycloserine. It has been suggested that d-cycloserine enhances cognitive behaviour therapy by making learning faster. While animal studies have supported this view of the drug accelerating learning, evidence in human studies has been mixed. We therefore designed an experiment to measure the effects of d-cycloserine on human motor learning.Methods:Fifty-four healthy human volunteers were randomly assigned to a single dose of 250mg d-cycloserine versus placebo in a double-blind design. They then performed a motor sequence learning task.Results:D-cycloserine did not increase the speed of motor learning or the overall amount learnt. However, we noted that participants on d-cycloserine tended to respond more carefully (shifting towards slower, but more correct responses).Conclusion:The results suggest that d-cycloserine does not exert beneficial effects on psychological treatments via mechanisms involved in motor learning. Further studies are needed to clarify the influence on other cognitive mechanisms.

Visual-spatial sequence learning and memory in trained musicians.

Previous research has shown that musicians have enhanced visual-spatial abilities and sensorymotor skills. As a result of their long-term musical training and their experience-dependent activities, musicians may learn to associate sensory information with fine motor movements. Playing a musical instrument requires musicians to rapidly translate musical symbols into specific sensory-motor actions while also simultaneously monitoring the auditory signals produced by their instrument. In this study, we assessed the visual-spatial sequence learning and memory abilities of long-term musicians. We recruited 24 highly trained musicians and 24 nonmusicians, individuals with little or no musical training experience. Participants completed a visual-spatial sequence learning task as well as receptive vocabulary, nonverbal reasoning, and short-term memory tasks. Results revealed that musicians have enhanced visual-spatial sequence learning abilities relative to nonmusicians. Musicians also performed better than nonmusicians on the vocabulary and nonverbal reasoning measures. Additional analyses revealed that the large group difference observed on the visualspatial sequencing task between musicians and nonmusicians remained even after controlling for vocabulary, nonverbal reasoning, and short-term memory abilities. Musicians' improved visualspatial sequence learning may stem from basic underlying differences in visual-spatial and sensorymotor skills resulting from long-term experience and activities associated with playing a musical instrument.

Sleep to the beat: A nap favours consolidation of timing.

Growing evidence suggests that sleep is important for procedural learning, but few studies have investigated the effect of sleep on the temporal aspects of motor skill learning. We assessed the effect of a 90-min day-time nap on learning a motor timing task, using 2 adaptations of a serial interception sequence learning (SISL) task. Forty-two right-handed participants performed the task before and after a 90-min period of sleep or wake. Electroencephalography (EEG) was recorded throughout. The motor task consisted of a sequential spatial pattern and was performed according to 2 different timing conditions, that is, either following a sequential or a random temporal pattern. The increase in accuracy was compared between groups using a mixed linear regression model. Within the sleep group, performance improvement was modeled based on sleep characteristics, including spindle- and slow-wave density. The sleep group, but not the wake group, showed improvement in the random temporal, but especially and significantly more strongly in the sequential temporal condition. None of the sleep characteristics predicted improvement on either general of the timing conditions. In conclusion, a daytime nap improves performance on a timing task. We show that performance on the task with a sequential timing sequence benefits more from sleep than motor timing. More important, the temporal sequence did not benefit initial learning, because differences arose only after an offline period and specifically when this period contained sleep. Sleep appears to aid in the extraction of regularities for optimal subsequent performance. (PsycINFO Database Record

Sleep quality influences subsequent motor skill acquisition.

While the influence of sleep on motor memory consolidation has been extensively investigated, its relation to initial skill acquisition is less well understood. The purpose of the present study was to investigate the influence of sleep quality and quantity on subsequent motor skill acquisition in young adults without sleep disorders. Fifty-five healthy adults (mean age = 23.8 years; 34 women) wore actigraph wristbands for 4 nights, which provided data on sleep patterns before the experiment, and then returned to the laboratory to engage in a motor sequence learning task (explicit 5-item finger sequence tapping task). Indicators of sleep quality and quantity were then regressed on a measure of motor skill acquisition (Gains Within Training, GWT). Wake After Sleep Onset (WASO; i.e., the total amount of time the participants spent awake after falling asleep) was significantly and negatively related to GWT. This effect was not because of general arousal level, which was measured immediately before the motor task. Conversely, there was no relationship between GWT and sleep duration or self-reported sleep quality. These results indicate that sleep quality, as assessed by WASO and objectively measured with actigraphy before the motor task, significantly impacts motor skill acquisition in young healthy adults without sleep disorders. (PsycINFO Database Record

Longitudinal Changes in the Motor Learning-Related Brain Activation Response in Presymptomatic Huntington's Disease.

Neurocognitive decline, including deficits in motor learning, occurs in the presymptomatic phase of Huntington’s disease (HD) and precedes the onset of motor symptoms. Findings from recent neuroimaging studies have linked these deficits to alterations in fronto-striatal and fronto-parietal brain networks. However, little is known about the temporal dynamics of these networks when subjects approach phenoconversion. Here, 10 subjects with presymptomatic HD were scanned with 15O-labeled water at baseline and again 1.5 years later while performing a motor sequence learning task and a kinematically matched control task. Spatial covariance analysis was utilized to characterize patterns of change in learning-related neural activation occurring over time in these individuals. Pattern expression was compared to corresponding values in 10 age-matched healthy control subjects. Spatial covariance analysis revealed significant longitudinal changes in the expression of a specific learning-related activation pattern characterized by increasing activity in the right orbitofrontal cortex, with concurrent reductions in the right medial prefrontal and posterior cingulate regions, the left insula, left precuneus, and left cerebellum. Changes in the expression of this pattern over time correlated with baseline measurements of disease burden and learning performance. The network changes were accompanied by modest improvement in learning performance that took place concurrently in the gene carriers. The presence of increased network activity in the setting of stable task performance is consistent with a discrete compensatory mechanism. The findings suggest that this effect is most pronounced in the late presymptomatic phase of HD, as subjects approach clinical onset.

Overnight Motor Skill Learning Outcomes in Obstructive Sleep Apnea: Effect of Continuous Positive Airway Pressure.

To determine the effectiveness of continuous positive airway pressure (CPAP) therapy in alleviating known impairments in the overnight consolidation of motor skill learning in patients with obstructive sleep apnea (OSA). Twenty-five patients with untreated moderate-severe OSA, 13 first-night CPAP users, 17 compliant CPAP users, and 14 healthy control patients were trained on a motor sequence learning task (Sequential Finger Tapping Task, SFTT) and were subsequently tested prior to and after polysomnographic recorded sleep. Measures of subjective sleepiness (Karolinska Sleepiness Scale) and sustained attention (Psychomotor Vigilance Task) were also completed before and after sleep. Typical analyses of overnight improvement on the SFTT show significantly greater overnight gains in motor task speed in controls (+11.6 ± 4.7%, p = 0.007) and compliant CPAP users (+8.9 ± 4.3%, p = 0.008) compared to patients with OSA (-4.86 ± 4.5%). Additional analyses suggest that these improvements in motor performance occurred prior to the sleep episode, as all groups significantly improved (15% to 22%) over a 10-min presleep rest period. Thereafter, performance in all groups significantly deteriorated over sleep (6% to 16%) with trends toward patients with OSA showing greater losses in performance compared to control patients and compliant CPAP users. No between-group differences in subjective sleepiness and sustained attention were found presleep and postsleep. The current data suggest impairments in overnight motor learning in patients with OSA may be a combination of deficient stabilization of memory over a sleep episode as well as increased vulnerability to time on task fatigue effects. Compliant CPAP usage possibly offsets both of these impediments to learning outcomes by improving both sleep quality and subsequent daytime function.

LATENT SUBSTRATES OF COMPULSIVITY AND BEHAVIOURAL FLEXIBILITY

Parallel decisional systems constantly compete for control of our day-to-day choices. Recent computational models propose two distinct forms of reinforcement learning, model-based and model free, which drive goal-directed and habitual behaviours, respectively. Although most of us seem to effortlessly alternate between the two systems, several pathological disorders have been associated with their imbalance. Thus the need to further characterize such systems is clear.To examine the neural networks of these parallel processes, we employ a two-step sequential learning task used previously to show simultaneous engagement of model-based and model-free learning in healthy volunteers [1] and their imbalance in pathological states [2]. We separately acquired fMRI data from 66 healthy volunteers during rest using a novel multi-echo planar sequence that greatly boosts signal-to-noise ratios compared to traditional single-echo sequences thus allowing higher spatial resolution of subcortical structures [3]. We quantified the connectivity between regions of interest (ROI’s) by calculating Pearson correlations coefficients (R) between a priori hypothesized regions (henceforth referred to as ‘connectivity’). These connectivity values were then correlated with behavioural measures. Higher model-based learning was positively correlated with medial orbitofrontal cortex and ventral striatum connectivity (R=0.32, p=0.01). Model-based scores also positively correlated with connectivity between dorsolateral prefrontal cortex and ventral striatum (R=0.27, p=0.03). In contrast, model-free learning was positively correlated with posterior putamen and supplementary motor area connectivity (R=0.266, p=0.033); and negatively with medial orbitofrontal cortex and left amygdala connectivity (R=-0.255, p=0.042). Model-free learning was also positively correlated with right ventral striatum and amygdala connectivity (R=0.274, p=0.028). We finally show that cognitive flexibility in the form of attention set shifting is associated with functional connectivity between dorsolateral prefrontal cortex and ventral striatum (R=-0.298, p=0.021).These findings suggest a neural network for model-based learning involving: integration of instrumental performance (ventral striatum); flexible, computationally-driven updating of outcome value based on changing internal motivational states and external feedback (medial orbitofrontal cortex); and computation of the relative advantage of alternative action values (dorsolateral prefrontal cortex). The current findings provide a more integrative network model of how previously implicated cortical regions organize to drive goal-directed model-based learning. Whereas most previous findings have focused on the neural correlates of model-based learning, we highlight a motoric frontal-striatal network associated with model-free learning. Together, we illustrate the sensitivity of the multi-echo sequence for the distinguishment of such elusive behavioural control systems.

Task-related functional connectivity of the caudate mediates the association between trait mindfulness and implicit learning in older adults.

Accumulating evidence shows a positive relationship between mindfulness and explicit cognitive functioning, i.e., that which occurs with conscious intent and awareness. However, recent evidence suggests that there may be a negative relationship between mindfulness and implicit types of learning, or those that occur without conscious awareness or intent. Here we examined the neural mechanisms underlying the recently reported negative relationship between dispositional mindfulness and implicit probabilistic sequence learning in both younger and older adults. We tested the hypothesis that the relationship is mediated by communication, or functional connectivity, of brain regions once traditionally considered to be central to dissociable learning systems: the caudate, medial temporal lobe (MTL), and prefrontal cortex (PFC). We first replicated the negative relationship between mindfulness and implicit learning in a sample of healthy older adults (60-90 years old) who completed three event-related runs of an implicit sequence learning task. Then, using a seed-based connectivity approach, we identified task-related connectivity associated with individual differences in both learning and mindfulness. The main finding was that caudate-MTL connectivity (bilaterally) was positively correlated with learning and negatively correlated with mindfulness. Further, the strength of task-related connectivity between these regions mediated the negative relationship between mindfulness and learning. This pattern of results was limited to the older adults. Thus, at least in healthy older adults, the functional communication between two interactive learning-relevant systems can account for the relationship between mindfulness and implicit probabilistic sequence learning.

NREM2 and Sleep Spindles Are Instrumental to the Consolidation of Motor Sequence Memories.

Although numerous studies have convincingly demonstrated that sleep plays a critical role in motor sequence learning (MSL) consolidation, the specific contribution of the different sleep stages in this type of memory consolidation is still contentious. To probe the role of stage 2 non-REM sleep (NREM2) in this process, we used a conditioning protocol in three different groups of participants who either received an odor during initial training on a motor sequence learning task and were re-exposed to this odor during different sleep stages of the post-training night (i.e., NREM2 sleep [Cond-NREM2], REM sleep [Cond-REM], or were not conditioned during learning but exposed to the odor during NREM2 [NoCond]). Results show that the Cond-NREM2 group had significantly higher gains in performance at retest than both the Cond-REM and NoCond groups. Also, only the Cond-NREM2 group yielded significant changes in sleep spindle characteristics during cueing. Finally, we found that a change in frequency of sleep spindles during cued-memory reactivation mediated the relationship between the experimental groups and gains in performance the next day. These findings strongly suggest that cued-memory reactivation during NREM2 sleep triggers an increase in sleep spindle activity that is then related to the consolidation of motor sequence memories.

Probabilistic Motor Sequence Yields Greater Offline and Less Online Learning than Fixed Sequence.

It is well acknowledged that motor sequences can be learned quickly through online learning. Subsequently, the initial acquisition of a motor sequence is boosted or consolidated by offline learning. However, little is known whether offline learning can drive the fast learning of motor sequences (i.e., initial sequence learning in the first training session). To examine offline learning in the fast learning stage, we asked four groups of young adults to perform the serial reaction time (SRT) task with either a fixed or probabilistic sequence and with or without preliminary knowledge (PK) of the presence of a sequence. The sequence and PK were manipulated to emphasize either procedural (probabilistic sequence; no preliminary knowledge (NPK)) or declarative (fixed sequence; with PK) memory that were found to either facilitate or inhibit offline learning. In the SRT task, there were six learning blocks with a 2 min break between each consecutive block. Throughout the session, stimuli followed the same fixed or probabilistic pattern except in Block 5, in which stimuli appeared in a random order. We found that PK facilitated the learning of a fixed sequence, but not a probabilistic sequence. In addition to overall learning measured by the mean reaction time (RT), we examined the progressive changes in RT within and between blocks (i.e., online and offline learning, respectively). It was found that the two groups who performed the fixed sequence, regardless of PK, showed greater online learning than the other two groups who performed the probabilistic sequence. The groups who performed the probabilistic sequence, regardless of PK, did not display online learning, as indicated by a decline in performance within the learning blocks. However, they did demonstrate remarkably greater offline improvement in RT, which suggests that they are learning the probabilistic sequence offline. These results suggest that in the SRT task, the fast acquisition of a motor sequence is driven by concurrent online and offline learning. In addition, as the acquisition of a probabilistic sequence requires greater procedural memory compared to the acquisition of a fixed sequence, our results suggest that offline learning is more likely to take place in a procedural sequence learning task.

Sleep-independent off-line enhancement and time of the day effects in three forms of skill learning.

The role of sleep in memory and skill-learning processes is an important and widely debated issue. The current study explores the nature of the relationship between sleep and off-line improvement in three tasks for measuring different aspects of skill learning: the serial reaction time (SRT) task, which is a motor sequence learning task; the artificial grammar learning (AGL) task, testing abstract verbal sequence learning; and the weather prediction (WP) task, which is a non-sequential categorization task. Each participant was tested on one of the three tasks twice, either in a Wake condition (with a 12-h off-line period without sleep), or in a Sleep condition (with sleep). Results showed no sleep-related off-line improvement throughout the three tasks in a two-session re-learning design, but a sleep-independent time-based effect was found on the SRT task. No performance boost was observed in the WP and AGL tasks. Performance on the SRT showed a time of the day effect: the Sleep group outperforming the Wake group; however, this effect was restricted to overall response latencies. Taken together, no evidence was found in favor of sleep-dependent off-line enhancement in skill learning, but methodological concerns warrant further investigations.