Predicting the future: from implicit learning to consolidation

Sequence learning can be differentiated according to phases (rapid and slower), modalities (perceptual and motor), and whether or not it is conscious (implicit and explicit). Implicit sequence learning occurs when information is acquired from an environment of complex stimuli without conscious access either to what was learned or to the fact that learning occurred. In everyday life, this learning mechanism is crucial for adapting to the environment and for predicting events unconsciously. Implicit sequence learning underlies not only motor, but also cognitive and social skills; it is therefore an important aspect of life from infancy to old age. Moreover, this kind of learning does not occur only during practice, in the so-called online periods, but also between practice periods, during the so-called offline periods. The process that occurs during the offline periods is referred to as consolidation, which denotes the stabilization of a memory trace after the initial acquisition; this can result in increased resistance to interference or even improvement in performance following an offline period. Understanding the multiple aspects and influencing factors of consolidation can help us to reveal the nature of memory and changes in brain plasticity. Our review focuses on how consolidation varies with factors such as awareness, the length of offline periods, the type of information to be learned, and the age of participants. We highlight that consolidation is not a single process; instead, there are multiple mechanisms in the offline period, which are differently influenced by these factors

Implicit sequence learning and working memory: correlated or complicated?

The relationship between implicit/incidental sequence learning and working memory motivated a series of research because it is plausible that higher working memory capacity opens a “larger window” to a sequence, allowing thereby the sequence learning process to be easier. Although the majority of studies found no relationship between implicit sequence learning and working memory capacity, in the past few years several studies have tried to demonstrate the shared or partly shared brain networks underlying these two systems. In order to help the interpretation of these and future results, in this mini-review we suggest the following factors to be taken into consideration before testing the relationship between sequence learning and working memory: 1) the explicitness of the sequence; 2) the method of measuring working memory capacity; 3) online and offline stages of sequence learning; and 4) general skill- and sequence-specific learning

Age-dependent and coordinated shift in performance between implicit and explicit skill learning

It has been reported recently that while general sequence learning across ages conforms to the typical inverted-U shape pattern, with best performance in early adulthood, surprisingly, the basic ability of picking up in an implicit manner triplets that occur with high vs. low probability in the sequence is best before 12 years of age and it significantly weakens afterwards. Based on these findings, it has been hypothesized that the cognitively controlled processes coming online at around 12 are useful for more targeted explicit learning at the cost of becoming relatively less sensitive to raw probabilities of events. To test this hypothesis, we collected data in a sequence learning task using probabilistic sequences in five age groups from 11 to 39 years of age (N=288), replicating the original implicit learning paradigm in an explicit task setting where subjects were guided to find repeating sequences. We found that in contrast to the implicit results, performance with the high- vs. low-probability triplets was at the same level in all age groups when subjects sought patterns in the sequence explicitly. Importantly, measurements of explicit knowledge about the identity of the sequences revealed a significant increase in ability to explicitly access the true sequences exactly around the age where the earlier study found the significant drop in ability to learn implicitly raw probabilities. These findings support the conjecture that the gradually increasing involvement of more complex internal models optimizes our skill learning abilities by compensating for the performance loss due to down-weighting the raw probabilities of the sensory input, while expanding our ability to acquire more sophisticated skills

Is there more room to improve? The lifespan trajectory of procedural learning and its relationship to the between-and within-group differences in average response times

Characterizing the developmental trajectories of cognitive functions such as learning, memory and decision making across the lifespan faces fundamental challenges. Cognitive functions typically encompass several processes that can be differentially affected by age. Methodological issues also arise when comparisons are made across age groups that differ in basic performance measures, such as in average response times (RTs). Here we focus on procedural learning – a fundamental cognitive function that underlies the acquisition of cognitive, social, and motor skills – and demonstrate how disentangling subprocesses of learning and controlling for differences in average RTs can reveal different developmental trajectories across the human lifespan. Two hundred-seventy participants aged between 7 and 85 years performed a probabilistic sequence learning task that enabled us to separately measure two processes of procedural learning, namely general skill learning and statistical learning. Using raw RT measures, in between-group comparisons, we found a U-shaped trajectory with children and older adults exhibiting greater general skill learning compared to adolescents and younger adults. However, when we controlled for differences in average RTs (either by using ratio scores or focusing on a subsample of participants with similar average speed), only children (but not older adults) demonstrated superior general skill learning consistently across analyses. Testing the relationship between average RTs and general skill learning within age groups shed light on further age-related differences, suggesting that general skill learning measures are more affected by average speed in some age groups. Consistent with previous studies of learning probabilistic regularities, statistical learning showed a gradual decline across the lifespan, and learning performance seemed to be independent of average speed, regardless of the age group. Overall, our results suggest that children are superior learners in various aspects of procedural learning, including both general skill and statistical learning. Our study also highlights the importance to test, and control for, the effect of average speed on other RT measures of cognitive functions, which can fundamentally affect the interpretation of group differences in developmental, aging and clinical psychology and neuroscience studies

Different levels of statistical learning-hidden potentials of sequence learning tasks

In this paper, we reexamined the typical analysis methods of a visuomotor sequence learning task, namely the ASRT task (J. H. Howard & Howard, 1997). We pointed out that the current analysis of data could be improved by paying more attention to pre-existing biases (i.e. by eliminating artifacts by using new filters) and by introducing a new data grouping that is more in line with the task’s inherent statistical structure. These suggestions result in more types of learning scores that can be quantified and also in purer measures. Importantly, the filtering method proposed in this paper also results in higher individual variability, possibly indicating that it had been masked previously with the usual methods. The implications of our findings relate to other sequence learning tasks as well, and opens up opportunities to study different types of implicit learning phenomena

How can the depressed mind extract and remember predictive relationships of the environment? evidence from implicit probabilistic sequence learning

A growing body of evidence suggests that emotion and cognition are fundamentally intertwined; impairments in explicit, more effortful and attention-dependent cognitive functions have widely been observed in negative mood. Here we aimed to test how negative mood affects implicit cognition that is less susceptible to motivational and attentional factors associated with negative mood. Therefore, we examined implicit learning and retention of predictive relationships in patients with major depressive episode (MDE). Additionally, we directly compared subgroups of patients with major depressive disorder (MDD) vs. bipolar disorder (BD) in order to gain a deeper understanding of how implicit cognition is affected by these conditions. Implicit probabilistic sequence learning was measured by the Alternating Serial Reaction Time Task. The acquired knowledge was retested after a 24-hour delay period. Consistent with the frontostriatal deficits frequently reported in depression, we found weaker learning in patients with MDE, with a more pronounced deficit in patients with MDD compared to BD. After the 24-hour delay, MDE patients (both subgroups) showed forgetting, while the controls retained the previously acquired knowledge. These results cannot be explained by alterations in motivation, attention and reward processing but suggest more profound impairments of implicit learning and retention of predictive relationships among neutral stimuli in depression. To the best of our knowledge, this is the first study investigating retention of implicitly acquired sequential knowledge and reporting deficits in this domain in MDE. Our findings not only contribute to a better understanding of the complex interplay between affect and cognition but can also help improve screening, diagnosis and treatment protocols of depression

Measuring statistical learning by eye-tracking

Statistical learning—the skill to pick up probability-based regularities of the environment—plays a crucial role in adapting to the environment and learning perceptual, motor, and language skills in healthy and clinical populations. Here, we developed a new method to measure statistical learning without any manual responses. We used the Alternating Serial Reaction Time (ASRT) task, adapted to eye-tracker, which, besides measuring reaction times (RTs), enabled us to track learning-dependent anticipatory eye movements. We found robust, interference-resistant learning on RT; moreover, learning-dependent anticipatory eye movements were even more sensitive measures of statistical learning on this task. Our method provides a way to apply the widely used ASRT task to operationalize statistical learning in clinical populations where the use of manual tasks is hindered, such as in Parkinson’s disease. Furthermore, it also enables future basic research to use a more sensitive version of this task to measure predictive processing

The relationship between subjective sleep quality and cognitive performance in healthy young adults: evidence from three empirical studies

The role of subjective sleep quality in cognitive performance has gained increasing attention in recent decades. In this paper, our aim was to test the relationship between subjective sleep quality and a wide range of cognitive functions in a healthy young adult sample combined across three studies. Sleep quality was assessed by the Pittsburgh Sleep Quality Index, the Athens Insomnia Scale, and a sleep diary to capture general subjective sleep quality, and the Groningen Sleep Quality Scale to capture prior night’s sleep quality. Within cognitive functions, we tested working memory, executive functions, and several sub-processes of procedural learning. To provide more reliable results, we included robust frequentist as well as Bayesian statistical analyses. Unequivocally across all analyses, we showed that there is no association between subjective sleep quality and cognitive performance in the domains of working memory, executive functions and procedural learning in healthy young adults. Our paper can contribute to a deeper understanding of subjective sleep quality and its measures, and we discuss various factors that may affect whether associations can be observed between subjective sleep quality and cognitive performance

Perceptual and motor factors of implicit skill learning

Implicit skill learning underlies not only motor but also cognitive and social skills, and represents an important aspect of life from infancy to old age. Earlier research examining this fundamental form of learning has demonstrated that learning relies on motor and perceptual skills, along with the possible role of oculomotor learning. The goals of the present study were to determine whether motor or perceptual cues provide better prompts to sequence learning and to remove the possibility of oculomotor learning during the task. We used a modified version of the probabilistic ASRT task, which allowed the separation of motor and perceptual factors. Our results demonstrated that motor and perceptual factors influenced skill learning to a similar extent

The differential consolidation of perceptual and motor learning in skill acquisition

Implicit skill learning is an unconscious way of learning which underlies not only motor but also cognitive and social skills. This form of learning is based on both motor and perceptual information. Although many studies have investigated the perceptual and motor components of “online” skill learning, the effect of consolidation on perceptual and motor characteristics of skill learning has not been studied to our knowledge. In our research we used a sequence learning task to determine if consolidation had the same or different effect on the perceptual and the motor components of skill acquisition. We introduced a 12-hour (including or not including sleep) and a 24-hour (diurnal control) delay between the learning and the testing phase with AM-PM, PM-AM, AM-AM and PM-PM groups, in order to examine whether the offline period had differential effects on perceptual and motor learning. Although both perceptual and motor learning were significant in the testing phase, results showed that motor knowledge transfers more effectively than perceptual knowledge during the offline period, irrespective of whether sleep occurred or not and whether there was a 12- or 24-hour delay period between the learning and the testing phase. These results have important implications for the debate concerning perceptual/motor learning and the role of sleep in skill acquisition