Implicit Sequence Learning Research Articles

Age differences in symbolic representations of motor sequence learning

We recently reported that young adults (YA) preferentially recruit cerebellar lobule HVI for symbolic motor sequence learning [3]. Learning magnitude in the symbolic condition was correlated with activation level in lobule HVI. Here, we evaluated age differences in the symbolic representation of motor sequence learning. Fourteen YA and 14 older adults (OA) performed the alternating serial reaction time task (ASRT) under conditions in which the spatial processing component was selectively eliminated from stimulus presentation (spatial versus symbolic), response execution (manual versus vocal), or both. Results showed that OA had reduced learning magnitudes relative to YA. Using the cerebellum lobule HVI as a region-of-interest, we found that OA had significantly lower activation in this region than YA during the symbolic learning conditions (FWE, P < 0.05). Similar to YA, OA also showed a significant correlation between learning magnitude and cerebellar activation in the symbolic conditions. These results suggest that although YA and OA recruit similar neural networks during implicit learning, OA under-recruit relevant brain areas which may partially explain their implicit sequence learning deficits.

Spatial and nonspatial implicit motor learning in Korsakoff's amnesia: evidence for selective deficits.

Patients with amnesia have deficits in declarative memory but intact memory for motor and perceptual skills, which suggests that explicit memory and implicit memory are distinct. However, the evidence that implicit motor learning is intact in amnesic patients is contradictory. This study investigated implicit sequence learning in amnesic patients with Korsakoff’s syndrome (N = 20) and matched controls (N = 14), using the classical Serial Reaction Time Task and a newly developed Pattern Learning Task in which the planning and execution of the responses are more spatially demanding. Results showed that implicit motor learning occurred in both groups of participants; however, on the Pattern Learning Task, the percentage of errors did not increase in the Korsakoff group in the random test phase, which is indicative of less implicit learning. Thus, our findings show that the performance of patients with Korsakoff’s syndrome is compromised on an implicit learning task with a strong spatial response component.

On the automaticity of pure perceptual sequence learning

We investigated the automaticity of implicit sequence learning by varying perceptual load in a pure perceptual sequence learning paradigm. Participants responded to the randomly changing identity of a target, while the irrelevant target location was structured. In Experiment 1, the target was presented under low or high perceptual load during training, whereas testing occurred without load. Unexpectedly, no sequence learning was observed. In Experiment 2, perceptual load was introduced during the test phase to determine whether load is required to express perceptual knowledge. Learning itself was unaffected by visuospatial demands, but more learning was expressed under high load test conditions. In Experiment 3, we demonstrated that perceptual load is not required for the acquisition of perceptual sequence knowledge. These findings suggest that perceptual load does not mediate the perceptual sequence learning process itself, supporting the automaticity of implicit learning, but is mandatory for the expression of pure perceptual sequence knowledge.

Enhanced Implicit Sequence Learning in College‐age Video Game Players and Musicians

SUMMARYWe examined whether college‐age video game players and musicians are better than controls at implicit sequence learning in the Alternating Serial Reaction Time Task. People learn to use subtle sequence regularities to respond more accurately and quickly to predictable versus non‐predictable events. Although previous studies have shown experts' enhanced processing speed and perception, this is the first to demonstrate that people who regularly play video games or a musical instrument showed greater implicit sequence learning, suggesting that experience playing games or music may improve the efficiency with which people learn sequential regularities in the environment. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Interference between Sentence Processing and Probabilistic Implicit Sequence Learning

BackgroundDuring sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.Methodology/Principal FindingsIn the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.Conclusions/SignificanceOur findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations.

Implicit sequence learning and contextual cueing do not compete for central cognitive resources.

Sequence learning and contextual cueing explore different forms of implicit learning, arising from practice with a structured serial task, or with a search task with informative contexts. We assess whether these two learning effects arise simultaneously when both remain implicit. Experiments 1 and 2 confirm that a cueing effect can be observed under a continuous setting and that there is no interference between contextual cueing and sequence learning. Experiments 3a and 3b tested whether an interference arises specifically when the sequence becomes explicit. Results show that the expression of contextual cueing disappeared in those conditions but that context information is still acquired, and it affects performance when the sequence is removed. The results are discussed in relation to the current debates about the automaticity of implicit learning, and about the role of attention in the acquisition and expression of contextual cueing.

The Neural Basis of Implicit Perceptual Sequence Learning

The present fMRI study investigated the neural areas involved in implicit perceptual sequence learning. To obtain more insight in the functional contributions of the brain areas, we tracked both the behavioral and neural time course of the learning process, using a perceptual serial color matching task. Next, to investigate whether the neural time course was specific for perceptual information, imaging results were compared to the results of implicit motor sequence learning, previously investigated using an identical serial color matching task (Gheysen et al., 2010). Results indicated that implicit sequences can be acquired by at least two neural systems: the caudate nucleus and the hippocampus, having different operating principles. The caudate nucleus contributed to the implicit sequence learning process for perceptual as well as motor information in a similar and gradual way. The hippocampus, on the other hand, was engaged in a much faster learning process which was more pronounced for the motor compared to the perceptual task. Interestingly, the perceptual and motor learning process occurred on a comparable implicit level, suggesting that consciousness is not the main determinant factor dissociating the hippocampal from the caudate learning system. This study is not only the first to successfully and unambiguously compare brain activation between perceptual and motor levels of implicit sequence learning, it also provides new insights into the specific hippocampal and caudate learning function.

Oral cortisol impairs implicit sequence learning

Glucocorticoids have been shown to affect declarative memory, an explicit form of memory for facts and events operated by medial temporal lobe structures. Recent neuroimaging data suggest that the medial temporal lobe (including the hippocampus) is also active in implicit sequence learning. The aim of the present study was to investigate whether implicit sequence learning may also be affected by glucocorticoid administration. Oral cortisol (30 mg) was given to 29 healthy subjects whereas 31 control subjects received placebo. One hour after treatment all volunteers performed five consecutive blocks of a five-choice serial reaction time task by responding to colored lights by pressing buttons of the same color. The subjects responded without knowing to a quasi-randomized stimulus sequence, including higher-order sequential regularities (a combination of two colors that predicted the following target color). The reaction speed of every button-press (100 per block) was determined and difference scores were calculated as a proof of learning. Both groups showed significant implicit sequence learning throughout the experiment. However, we found an impaired learning performance of the cortisol group compared with the placebo group. Further analysis revealed that a delayed learning in the cortisol group occurred at the very beginning of the task. This study is the first human investigation indicating impaired implicit memory function after exogenous administration of the stress hormone cortisol. This effect may depend on hippocampus engagement in implicit sequence learning, but the involvement of other brain structures is also discussed.

Dyslexic children show deficits in implicit sequence learning, but not in explicit sequence learning or contextual cueing

Dyslexia is a specific learning disability characterized by difficulties with accurate and/or fluent word recognition and by poor spelling abilities. The absence of other high level cognitive deficits in the dyslexic population has led some authors to propose that non-strategical processes like implicit learning could be impaired in this population. Most studies have addressed this issue by using sequence learning tasks, but so far the results have not been conclusive. We test this hypothesis by comparing the performance of dyslexic children and good readers in both implicit and explicit versions of the sequence learning task, as well as in another implicit learning task not involving sequential information. The results showed that dyslexic children failed to learn the sequence when they were not informed about its presence (implicit condition). In contrast, they learned without significant differences in relation to the good readers group when they were encouraged to discover the sequence and to use it in order to improve their performance (explicit condition). Moreover, we observed that this implicit learning deficit was not extended to other forms of non-sequential, implicit learning such as contextual cueing. In this case, both groups showed similar implicit learning about the information provided by the visual context. These results help to clarify previous contradictory data, and they are discussed in relation to how the implicit sequence learning deficit could contribute to the understanding of dyslexia.

Special Issue Editorial: Advances in Computational Intelligence and Applications

The Seventh International Symposium on Neural Networks (ISNN 2010) was held on June 6–9, 2010 in Shanghai, China. The ISNN 2010 was a great success and provided a high-level international forum for scientists and engineers to present the latest research in neural networks, computational intelligence, cognitive systems, and related fields. To highlight the success of this conference, we edited this special issue for Cognitive Computation. We chose nine papers from over 591 papers submitted to ISNN 2010, which reflect the high quality of the presentations at the conference while capturing the spirit of our theme, ‘‘Advances in Computational Intelligence and Applications,’’ for this special issue. Our goal for this special issue is to present the latest research developments in computational intelligence with a focus on cognitive computation and their applications across a wide range of domains. Over the past decades, we have witnessed tremendous interest and developments in all aspects of computational intelligence research, ranging from both theoretical foundations, principles, to practical applications in different domains. To reflect a flavor of recent research activities in the community, we carefully selected these nine papers for this special issue. The selected papers can be organized into the following four coherent sections. The first section is directly related to neural network models and applications. The paper by Deng presents a new control strategy for the chaotic neural network, in which the refractoriness is tuned by using feedback control based on online averaging of network states. Simulation results demonstrate that the proposed model can achieve favorable performance in handling noisy, incomplete, and composite patterns, while also achieving either enhanced or comparable memory capacity compared with the classic Hopfield net and other chaotic neural network models. In the other paper, Gluge et al. discuss recurrent networks for implicit learning of temporal sequences, a very important area to understand high-level cognitive intelligence. The authors propose a recurrent network with backpropagation training algorithm that is adapted to the reinforcement learning scheme. The simulation results fit quantitatively as well as qualitatively to the behavioral results, suggesting the role of temporal context in associative learning scenarios. The second section is related to language processing and understanding, an important topic in cognitive science and computational intelligence. The paper by Murata et al. investigates the maximum entropy (ME) approach for natural language processing (NLP) problems including machine translation and information extraction. Comparative studies of the proposed approach with the existing techniques are illustrated in this paper. In related work to this field, the paper by Kaznatcheev investigates connectionist models on the interplay of nouns and pronouns in personal pronoun acquisition, a very interesting and important problem in psychology. For instance, the author discusses the learning in the shifting reference situation for children. It is reported that learning of two different noun-and-pronoun addressee patterns is consistent with Z. Zeng (&) Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P. R. China e-mail: zgzeng@gmail.com

Are implicit learning abilities sensitive to the type of material to be processed? Study on typical readers and children with dyslexia

This study aims to examine the impact of the linguistic nature of the material to be tracked in a serial reaction time task on the performance of typical readers and children with dyslexia. In doing so, we wished to detect eventual differences in the mobilisation of implicit learning skills between typical readers according to their experience with the written word (8- vs 10-year-olds) on the one hand, and between typical readers and children with dyslexia, on the other hand. Experiment 1 confirms the efficiency of implicit sequence learning in typical readers regardless of the nature of the item being tracked. Experiment 2 indicates that the sequence learning of children with dyslexia is sensitive to the nature of the target. Children with dyslexia show differences in the evolution of response times according to the nature of the item to be tracked.

Implicit learning of fifth- and sixth-order sequential probabilities

Serial reaction time (SRT) task studies have established that people can implicitly learn sequential contingencies as complex as fourth-order probabilities. The present study examined people's ability to learn fifth-order (Experiment 1) and sixth-order (Experiment 2) probabilities. Remarkably, people learned fifth- and sixth-order probabilities. This suggests that the implicit sequence learning mechanism can operate over a range of at least seven sequence elements.

Dopamine transporter genotype predicts implicit sequence learning

Implicit learning, the non-conscious acquisition of sequential and spatial environmental regularities, underlies skills such as language, social intuition, or detecting a target in a complex scene. We examined relationships between a variation of the dopamine transporter (DAT1) gene (SLC6A3), which influences dopamine transporter expression in the striatum, and two forms of implicit learning that differ in the regularity to be learned and in striatal involvement. Participants, grouped as 9-repeat carriers or 10/10 homozygotes, completed the triplets learning task (TLT) and the spatial contextual cueing task (SCCT). The TLT assesses sequence learning, recruiting the striatal system, particularly as training continues. In contrast, the SCCT assesses spatial context learning, recruiting medial temporal brain networks. For both tasks, participants demonstrated learning in faster and/or more accurate responses to repeating patterns or spatial arrays. As predicted, TLT learning was greater for the 9-repeat carriers than the 10/10 group (despite equal overall accuracy and response speed) whereas there were no significant group differences in SCCT. Thus, presence of the DAT1 9-repeat allele was beneficial only for implicit sequence learning, indicating the influence of DAT1 genotype on one form of implicit learning and supporting evidence that implicit learning of sequential dependencies and spatial layouts recruit different neural systems.

Implicit sequence learning is represented by stimulus—response rules

For nearly two decades, researchers have investigated spatial sequence learning in an attempt to identify what specifically is learned during sequential tasks (e.g., stimulus order, response order, etc.). Despite extensive research, controversy remains concerning the information-processing locus of this learning effect. There are three main theories concerning the nature of spatial sequence learning, corresponding to the perceptual, motor, or response selection (i.e., central mechanisms underlying the association between stimulus and response pairs) processes required for successful task performance. The present data investigate this controversy and support the theory that stimulus-response (S-R) rules are critical for sequence learning. The results from two experiments demonstrate that sequence learning is disrupted only when the S-R rules for the task are altered. When the S-R rules remain constant or involve only a minor transformation, significant sequence learning occurs. These data implicate spatial response selection as a likely mechanism mediating spatial sequential learning.

Two Forms of Implicit Learning in Childhood ADHD

Attention deficit hyperactivity disorder (ADHD) is characterized by inattention, impulsivity, and hyperactivity mediated by frontal-striatal-cerebellar dysfunction. These circuits support implicit learning of perceptual-motor sequences but not visual-spatial context. ADHD and control children performed the Alternating Serial Reaction Time (ASRT) task, a measure of sequence learning, and the Contextual Cueing (CC) task, a measure of spatial contextual learning. Relative to controls, children with ADHD showed inconsistent ASRT learning but did not differ on CC learning. Thus, implicit sequence learning, a cognitive process mediated by frontal-striatal-cerebellar circuitry that is not under executive control, was atypical in ADHD.

Symbolic representations in motor sequence learning

It has been shown that varying the spatial versus symbolic nature of stimulus presentation and response production, which affects stimulus–response (S–R) mapping requirements, influences the magnitude of implicit sequence learning (Koch and Hoffman, 2000). Here, we evaluated how spatial and symbolic stimuli and responses affect the neural bases of sequence learning. We selectively eliminated the spatial component of stimulus presentation (spatial vs. symbolic), response execution (manual vs. vocal), or both. Fourteen participants performed the alternating serial reaction time task under these conditions in an MRI scanner, with interleaved acquisition to allow for recording of vocal response reaction times. Nine regions of interest (ROIs) were selected to test the hypothesis that the dorsolateral prefrontal cortex (DLPFC) was preferentially engaged for spatially cued conditions and cerebellum lobule HVI, crus I and II were associated with symbolically cued learning. We found that the left cerebellum lobule HVI was selectively recruited for symbolic learning and the percent signal change in this region was correlated with learning magnitude under the symbolic conditions. In contrast, the DLPFC did not exhibit selective activation for learning under spatial conditions. The inferior parietal lobule exhibited increased activation during learning regardless of the condition, supporting its role in forming an abstract representation of learned sequences. These findings reveal different brain networks that are flexibly engaged depending on the conditions of sequence learning.

Visuospatial Sequence Learning without Seeing

BackgroundThe ability to detect and integrate associations between unrelated items that are close in space and time is a key feature of human learning and memory. Learning sequential associations between non-adjacent visual stimuli (higher-order visuospatial dependencies) can occur either with or without awareness (explicit vs. implicit learning) of the products of learning. Existing behavioural and neurocognitive studies of explicit and implicit sequence learning, however, are based on conscious access to the sequence of target locations and, typically, on conditions where the locations for orienting, or motor, responses coincide with the locations of the target sequence.Methodology/Principal FindingsDichoptic stimuli were presented on a novel sequence learning task using a mirror stereoscope to mask the eye-of-origin of visual input from conscious awareness. We demonstrate that conscious access to the sequence of target locations and responses that coincide with structure of the target sequence are dispensable features when learning higher-order visuospatial associations. Sequence knowledge was expressed in the ability of participants to identify the trained higher-order visuospatial sequence on a recognition test, even though the trained and untrained recognition sequences were identical when viewed at a conscious binocular level, and differed only at the level of the masked sequential associations.Conclusions/SignificanceThese results demonstrate that unconscious processing can support perceptual learning of higher-order sequential associations through interocular integration of retinotopic-based codes stemming from monocular eye-of-origin information. Furthermore, unlike other forms of perceptual associative learning, visuospatial attention did not need to be directed to the locations of the target sequence. More generally, the results pose a challenge to neural models of learning to account for a previously unknown capacity of the human visual system to support the detection, learning and recognition of higher-order sequential associations under conditions where observers are unable to see the target sequence or perform responses that coincide with structure of the target sequence.

Learning in Autism: Implicitly Superb

BackgroundAlthough autistic people have shown impairments in various learning and memory tasks, recent studies have reported mixed findings concerning implicit learning in ASD. Implicit skill learning, with its unconscious and statistical properties, underlies not only motor but also cognitive and social skills, and it therefore plays an important role from infancy to old age.Methodology/Principal FindingsWe investigated probabilistic implicit sequence learning and its consolidation in Autism Spectrum Disorder (ASD). Three groups of children participated: thirteen with high-functioning ASD, 14 age-matched controls, and 13 IQ-matched controls. All were tested on the Alternating Serial Reaction Time Task (ASRT), making it possible to separate general skill learning from sequence-specific learning. The ASRT task was repeated after 16 hours. We found that control and ASD children showed similar sequence-specific and general skill learning in the learning phase. Consolidation of skill learning and sequence-specific learning were also intact in the ASD compared to the control groups.Conclusions/SignificanceThese results suggest that autistic children can use the effects/results of implicit learning not only for a short period, but also for a longer stretch of time. Using these findings, therapists can design more effective educational and rehabilitation programs.

The mechanics of implicit learning of contingencies: A commentary on Custers &amp; Aarts’ paper

In their paper: "Learning of Predictive Relations Between Events Depends on Attention, Not on Awareness" Custers & Aarts demonstrate that when one is first exposed to a clear predictive relationship - a consequent predictive relationship will be represented as a unidirectional association ("predictor" to "predicted") in the percievers' minds regardless of their awareness of that relationship. Furthermore, a conscious intention to learn the relationship leads to the formation of a bidirectional (non-predictive) association. While these findings may prove to be a significant step in understanding other forms of implicit learning such as implicit artificial grammar learning and implicit sequence learning and why they are affected by intentional learning; Custers & Aarts' postulation that "top-down" regulation is at work here is debatable as their experimental manipulation can be understood as "bottom-up" activation of implicit learning processes.

Implicit Motor Learning in Patients with Parkinson’s and Alzheimer’s Disease: Differences in Learning Abilities?

Experimental studies show intact implicit motor learning in patients with Alzheimer's disease (AD) but the results for patients with Parkinson's disease (PD) are inconclusive. This study tests implicit sequence learning in AD and PD patients, and healthy controls, using the classical Serial Reaction Time Task (SRTT), and a somewhat similar Pattern Learning Task (PLT), which involves stylus movements in different directions, and which allows detailed movement analysis. As expected, the time measures showed less implicit motor learning in the PD patients relative to the other groups in both tasks, but their error percentages increased when the sequence changed from a fixed to a random order, which is indicative of implicit learning. The AD patients showed a reversed pattern of results. Arguably, errors and time measures may reflect the involvement of separate processes, e.g., spatial and motor components, which could be differently affected in AD and PD.