The neural basis of implicit perceptual sequence learning

The Hippocampus Represents Information about Movements in Their Temporal Position in a Learned Motor Sequence

Our repertoire of motor skills is filled with sequential movements that need to be performed in a specific order. Here, we used functional magnetic resonance imaging to investigate whether the human hippocampus, a region known to support temporal order in non-motor memory, represents information about the order of sequential motor actions in human participants (both sexes). We also examined such representations in other regions of the motor network (i.e., the premotor cortex, supplementary motor area, anterior superior parietal lobule, and striatum) already known for their critical role in motor sequence learning. Results showed that the hippocampus represents information about movements in their learned temporal position in the sequence, but not about movements or temporal positions in random movement patterns. Other regions of the motor network coded for movements in their learned temporal position, as well as movements and positions in random movement patterns. Importantly, movement coding contributed to sequence learning patterns in primary, supplementary, and premotor cortices but not in striatal and parietal regions. Our findings deepen our understanding of how striatal and cortical regions contribute to motor sequence learning and point to the capacity of the hippocampus to represent movements in their temporal context, an ability possibly explaining its contribution to motor learning.

Sleep does not influence schema-facilitated motor memory consolidation

STUDY OBJECTIVES

Novel information is rapidly learned when it is compatible with previous knowledge. This "schema" effect, initially described for declarative memories, was recently extended to the motor memory domain. Importantly, this beneficial effect was only observed 24 hours-but not immediately-following motor schema acquisition. Given the established role of sleep in memory consolidation, we hypothesized that sleep following the initial learning of a schema is necessary for the subsequent rapid integration of novel motor information.

METHODS

Two experiments were conducted to investigate the effect of diurnal and nocturnal sleep on schema-mediated motor sequence memory consolidation. In Experiment 1, participants first learned an 8-element motor sequence through repeated practice (Session 1). They were then afforded a 90-minute nap opportunity (N = 25) or remained awake (N = 25) before learning a second motor sequence (Session 2) which was highly compatible with that learned prior to the sleep/wake interval. Experiment 2 was similar; however, Sessions 1 and 2 were separated by a 12-hour interval that included nocturnal sleep (N = 28) or only wakefulness (N = 29).

RESULTS

For both experiments, we found no group differences in motor sequence performance (reaction time and accuracy) following the sleep/wake interval. Furthermore, in Experiment 1, we found no correlation between sleep features (non-REM sleep duration, spindle and slow wave activity) and post-sleep behavioral performance.

CONCLUSIONS

The results of this research suggest that integration of novel motor information into a cognitive-motor schema does not specifically benefit from post-learning sleep.

The role of the hippocampus in statistical learning and language recovery in persons with post stroke aphasia

Although several studies have aimed for accurate predictions of language recovery in post stroke aphasia, individual language outcomes remain hard to predict. Large-scale prediction models are built using data from patients mainly in the chronic phase after stroke, although it is clinically more relevant to consider data from the acute phase. Previous research has mainly focused on deficits, i.e., behavioral deficits or specific brain damage, rather than compensatory mechanisms, i.e., intact cognitive skills or undamaged brain regions. One such unexplored brain region that might support language (re)learning in aphasia is the hippocampus, a region that has commonly been associated with an individual's learning potential, including statistical learning. This refers to a set of mechanisms upon which we rely heavily in daily life to learn a range of regularities across cognitive domains. Against this background, thirty-three patients with aphasia (22 males and 11 females, M = 69.76 years, SD = 10.57 years) were followed for 1 year in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) post stroke. We evaluated the unique predictive value of early structural hippocampal measures for short-term and long-term language outcomes (measured by the ANELT). In addition, we investigated whether statistical learning abilities were intact in patients with aphasia using three different tasks: an auditory-linguistic and visual task based on the computation of transitional probabilities and a visuomotor serial reaction time task. Finally, we examined the association of individuals' statistical learning potential with acute measures of hippocampal gray and white matter. Using Bayesian statistics, we found moderate evidence for the contribution of left hippocampal gray matter in the acute phase to the prediction of long-term language outcomes, over and above information on the lesion and the initial language deficit (measured by the ScreeLing). Non-linguistic statistical learning in patients with aphasia, measured in the subacute phase, was intact at the group level compared to 23 healthy older controls (8 males and 15 females, M = 74.09 years, SD = 6.76 years). Visuomotor statistical learning correlated with acute hippocampal gray and white matter. These findings reveal that particularly left hippocampal gray matter in the acute phase is a potential marker of language recovery after stroke, possibly through its statistical learning ability.

Ideomotor learning: Time to generalize a longstanding principle

The ideomotor principle holds that anticipating the sensory consequences of a movement triggers an associated motor response. Even though this framework dates back to the 19th century, it continues to lie at the heart of many contemporary approaches to human action control. Here we specifically focus on the ideomotor learning mechanism that has to precede action initiation via effect anticipation. Traditional approaches to this learning mechanism focused on establishing novel action-effect (or response-effect) associations. Here we apply the theoretical concept of common coding for action and perception to argue that the same learning principle should result in response-response and stimulus-stimulus associations just as well. Generalizing ideomotor learning in such a way results in a powerful and general framework of ideomotor action control, and it allows for integrating the two seemingly separate fields of ideomotor approaches and hierarchical learning.

Reduced functional connectivity supports statistical learning of temporally distributed regularities

Statistical learning is a powerful ability that extracts regularities from our environment and makes predictions about future events. Using functional magnetic resonance imaging, we aimed to probe how a wide range of brain areas are intertwined to support statistical learning, characterising its architecture in the whole-brain functional connectivity (FC). Participants performed a statistical learning task of temporally distributed regularities. We used refined behavioural learning scores to associate individuals' learning performances with the FC changed by statistical learning. As a result, the learning performance was mediated by the activation strength in the lateral occipital cortex, angular gyrus, precuneus, anterior cingulate cortex, and superior frontal gyrus. Through a group independent component analysis, activations of the superior frontal network showed the largest correlation with the statistical learning performances. Seed-to-voxel whole-brain and seed-to-ROI FC analyses revealed that the FC between the superior frontal gyrus and the salience, language, and dorsal attention networks were reduced during statistical learning. We suggest that the weakened functional connections between the superior frontal gyrus and brain regions involved in top-down control processes serve a pivotal role in statistical learning, supporting better processing of novel information such as the extraction of new patterns from the environment.

Spatial and Temporal Context Jointly Modulate the Sensory Response within the Ventral Visual Stream

Both spatial and temporal context play an important role in visual perception and behavior. Humans can extract statistical regularities from both forms of context to help process the present and to construct expectations about the future. Numerous studies have found reduced neural responses to expected stimuli compared with unexpected stimuli, for both spatial and temporal regularities. However, it is largely unclear whether and how these forms of context interact. In the current fMRI study, 33 human volunteers were exposed to pairs of object stimuli that could be expected or surprising in terms of their spatial and temporal context. We found reliable independent contributions of both spatial and temporal context in modulating the neural response. Specifically, neural responses to stimuli in expected compared with unexpected contexts were suppressed throughout the ventral visual stream. These results suggest that both spatial and temporal context may aid sensory processing in a similar fashion, providing evidence on how different types of context jointly modulate perceptual processing.

From Counting to Retrieving: Neural Networks Underlying Alphabet Arithmetic Learning

This fMRI study aimed at unraveling the neural basis of learning alphabet arithmetic facts, as a proxy of the transition from slow and effortful procedural counting-based processing to fast and effortless processing as it occurs in learning addition arithmetic facts. Neural changes were tracked while participants solved alphabet arithmetic problems in a verification task (e.g., F + 4 = J). Problems were repeated across four learning blocks. Two neural networks with opposed learning-related changes were identified. Activity in a network consisting of basal ganglia and parieto-frontal areas decreased with learning, which is in line with a reduction of the involvement of procedure-based processing. Conversely, activity in a network involving the left angular gyrus and, to a lesser extent, the hippocampus gradually increases with learning, evidencing the gradual involvement of retrieval-based processing. Connectivity analyses gave insight in the functional relationship between the two networks. Despite the opposing learning-related trajectories, it was found that both networks become more integrated. Taking alphabet arithmetic as a proxy for learning arithmetic, the present results have implications for current theories of learning arithmetic facts and can give direction to future developments.

Neural processes underlying statistical learning for speech segmentation in dogs

To learn words, humans extract statistical regularities from speech. Multiple species use statistical learning also to process speech, but the neural underpinnings of speech segmentation in non-humans remain largely unknown. Here, we investigated computational and neural markers of speech segmentation in dogs, a phylogenetically distant mammal that efficiently navigates humans' social and linguistic environment. Using electroencephalography (EEG), we compared event-related responses (ERPs) for artificial words previously presented in a continuous speech stream with different distributional statistics. Results revealed an early effect (220-470 ms) of transitional probability and a late component (590-790 ms) modulated by both word frequency and transitional probability. Using fMRI, we searched for brain regions sensitive to statistical regularities in speech. Structured speech elicited lower activity in the basal ganglia, a region involved in sequence learning, and repetition enhancement in the auditory cortex. Speech segmentation in dogs, similar to that of humans, involves complex computations, engaging both domain-general and modality-specific brain areas. VIDEO ABSTRACT.

The posterior cerebellum supports implicit learning of social belief sequences

Recent studies have documented the involvement of the posterior cerebellar Crus (I & II) in social mentalizing, when sequences play a critical role. We investigated for the first time implicit learning of belief sequences. We created a novel task in which true and false beliefs of other persons were alternated in an adapted serial reaction time (SRT) paradigm (Belief SRT task). Participants observed two protagonists whose beliefs concerning reality were manipulated, depending on their orientation toward the scene (true belief: directly observing the situation) or away from it (false belief: knowing only the prior situation). Unbeknownst to the participants, a fixed sequence related to the two protagonists' belief orientations was repeated throughout the task (Training phase); and to test the acquisition of this fixed sequence, it was occasionally interrupted by random sequences (Test phase). As a nonsocial control, the two protagonists and their orientations were replaced by two different shapes of different colors respectively (Control SRT task). As predicted, the posterior cerebellar Crus I & II were activated during the Belief SRT task and not in the Control SRT task. The Belief SRT task revealed that Crus I was activated during the initial learning of the fixed sequence (Training phase) and when this learned sequence was interrupted by random sequences (Test phase). Moreover, Crus II was activated during occasional reappearance of the learned sequence in the context of sequence violations (Test phase). Our results demonstrate the contribution of the posterior cerebellar Crus during implicit learning and predicting new belief sequences.

Implicit learning of symmetry of human movement and gray matter density: Evidence against pure domain general and pure domain specific theories of implicit learning

Theories of the neural basis of implicit learning postulated that specific regions were responsible for specific structures (e.g., supra-finite state) regardless of domain (e.g., vision, movement); others assumed that implicit learning was the adaptation that occurred within neural regions dealing with each domain. We explored whether people could implicitly learn to detect symmetry in biological motion, and if so, based on voxel-based morphometry (VBM), whether the learning was associated with language-related regions involved with supra-finite state grammars (such as symmetry) or motor-related regions. To explore the relevance of motor-related regions, we investigated brain structural changes in athletes compared with non-athletes and the advantage of athletes in implicit learning of action symmetry. Further, we examined whether motor imagery ability could account for the role of motor-related regions in this learning. Participants passively observed and memorized a number of biological motion sequences instantiating a symmetry rule and then judged new sequences as grammatical or not. Behaviorally, the implicit acquisition of symmetry could extend to process biological motion. Athletes showed superior classification accuracy and kinesthetic imagery ability, and gave more familiarity attributions. VBM results showed that athletes exhibited greater gray matter density in the right cerebellum, as well as the left lingual gyrus, the left precuneus, the left calcarine gyrus, and the right thalamus. Correlation analysis showed that the cerebellar gray matter density was positively associated with classification accuracy, which was mediated by kinesthetic imagery ability. Moreover, gray matter density of the left inferior frontal cortex was also positively associated with classification accuracy, indicating the involvement of regions related to symmetry learning across domains. The study provides initial evidence that implicit learning involves both adaptation within brain regions responsible for the specific domain as well as brain regions processing the same structure across domains, at least in a case of supra-finite state grammars.

Learning predictive structure without a teacher: decision strategies and brain routes

Extracting the structure of complex environments is at the core of our ability to interpret the present and predict the future. This skill is important for a range of behaviours from navigating a new city to learning music and language. Classical approaches that investigate our ability to extract the principles of organisation that govern complex environments focus on reward-based learning. Yet, the human brain is shown to be expert at learning generative structure based on mere exposure and without explicit reward. Individuals are shown to adapt to-unbeknownst to them-changes in the environment's temporal statistics and predict future events. Further, we present evidence for a common brain architecture for unsupervised structure learning and reward-based learning, suggesting that the brain is built on the premise that 'learning is its own reward' to support adaptive behaviour.

Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning

Successful human behaviour depends on the brain's ability to extract meaningful structure from information streams and make predictions about future events. Individuals can differ markedly in the decision strategies they use to learn the environment's statistics, yet we have little idea why. Here, we investigate whether the brain networks involved in learning temporal sequences without explicit reward differ depending on the decision strategy that individuals adopt. We demonstrate that individuals alter their decision strategy in response to changes in temporal statistics and engage dissociable circuits: extracting the exact sequence statistics relates to plasticity in motor corticostriatal circuits, while selecting the most probable outcomes relates to plasticity in visual, motivational and executive corticostriatal circuits. Combining graph metrics of functional and structural connectivity, we provide evidence that learning-dependent changes in these circuits predict individual decision strategy. Our findings propose brain plasticity mechanisms that mediate individual ability for interpreting the structure of variable environments.

Implicit acoustic sequence learning recruits the hippocampus

The exclusive role of the medial temporal lobe in explicit memory has been questioned by several studies reporting medial temporal lobe involvement during implicit learning. Prior studies have demonstrated that hippocampal engagement is present during the implicit learning of perceptual associations, however, it is absent during learning response-related associations. Therefore, it was hypothesized that the function of the medial temporal lobe during implicit learning is related to the extraction of perceptual associations in general. While in most implicit learning tasks visual stimuli were used, the aim of the current functional magnetic resonance imaging (fMRI) study was to detect whether activations within medial temporal lobe structures are also found during implicit learning of auditory associations. In a modified version of the classical serial reaction time task, participants reacted to the presentation of five different tones. Unbeknownst to the participants, the tones were presented with an underlying sequential regularity that could be learned. To avoid an influence of response learning on acoustic associative learning, response buttons were remapped in every trial. After learning, two different tests were used to measure participants' conscious knowledge about the underlying sequence in order to assess the amount of implicit memory and to exclude participants with explicit knowledge acquired during learning. fMRI results revealed hippocampal activations for implicit learning of the acoustic sequence. When detecting a relation between implicit learning of acoustic associations and hippocampal activations, this study indicated a relation between hippocampal activations and memory formation of perceptual-based relational representation regardless of explicit knowledge. Thus, present findings suggest a general functional role for the formation of sequenced perceptual associations independent of the involvement of awareness.

Functional brain networks for learning predictive statistics

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics.

Learning of goal-relevant and -irrelevant complex visual sequences in human V1

Learning and memory are supported by a network involving the medial temporal lobe and linked neocortical regions. Emerging evidence indicates that primary visual cortex (i.e., V1) may contribute to recognition memory, but this has been tested only with a single visuospatial sequence as the target memorandum. The present study used functional magnetic resonance imaging to investigate whether human V1 can support the learning of multiple, concurrent complex visual sequences involving discontinous (second-order) associations. Two peripheral, goal-irrelevant but structured sequences of orientated gratings appeared simultaneously in fixed locations of the right and left visual fields alongside a central, goal-relevant sequence that was in the focus of spatial attention. Pseudorandom sequences were introduced at multiple intervals during the presentation of the three structured visual sequences to provide an online measure of sequence-specific knowledge at each retinotopic location. We found that a network involving the precuneus and V1 was involved in learning the structured sequence presented at central fixation, whereas right V1 was modulated by repeated exposure to the concurrent structured sequence presented in the left visual field. The same result was not found in left V1. These results indicate for the first time that human V1 can support the learning of multiple concurrent sequences involving complex discontinuous inter-item associations, even peripheral sequences that are goal-irrelevant.

White-Matter Pathways for Statistical Learning of Temporal Structures

Extracting the statistics of event streams in natural environments is critical for interpreting current events and predicting future ones. The brain is known to rapidly find structure and meaning in unfamiliar streams of sensory experience, often by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the brain pathways that support this type of statistical learning. Here, we test whether changes in white-matter (WM) connectivity due to training relate to our ability to extract temporal regularities. By combining behavioral training and diffusion tensor imaging (DTI), we demonstrate that humans adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. In particular, we show that learning relates to the decision strategy that individuals adopt when extracting temporal statistics. We next test for learning-dependent changes in WM connectivity and ask whether they relate to individual variability in decision strategy. Our DTI results provide evidence for dissociable WM pathways that relate to individual strategy: extracting the exact sequence statistics (i.e., matching) relates to connectivity changes between caudate and hippocampus, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to connectivity changes between prefrontal, cingulate and basal ganglia (caudate, putamen) regions. Thus, our findings provide evidence for distinct cortico-striatal circuits that show learning-dependent changes of WM connectivity and support individual ability to learn behaviorally-relevant statistics.

Neural Signatures of Spatial Statistical Learning: Characterizing the Extraction of Structure from Complex Visual Scenes

Behavioral evidence has shown that humans automatically develop internal representations adapted to the temporal and spatial statistics of the environment. Building on prior fMRI studies that have focused on statistical learning of temporal sequences, we investigated the neural substrates and mechanisms underlying statistical learning from scenes with a structured spatial layout. Our goals were twofold: (1) to determine discrete brain regions in which degree of learning (i.e., behavioral performance) was a significant predictor of neural activity during acquisition of spatial regularities and (2) to examine how connectivity between this set of areas and the rest of the brain changed over the course of learning. Univariate activity analyses indicated a diffuse set of dorsal striatal and occipitoparietal activations correlated with individual differences in participants' ability to acquire the underlying spatial structure of the scenes. In addition, bilateral medial-temporal activation was linked to participants' behavioral performance, suggesting that spatial statistical learning recruits additional resources from the limbic system. Connectivity analyses examined, across the time course of learning, psychophysiological interactions with peak regions defined by the initial univariate analysis. Generally, we find that task-based connectivity with these regions was significantly greater in early relative to later periods of learning. Moreover, in certain cases, decreased task-based connectivity between time points was predicted by overall posttest performance. Results suggest a narrowing mechanism whereby the brain, confronted with a novel structured environment, initially boosts overall functional integration and then reduces interregional coupling over time.

Visual Perceptual Echo Reflects Learning of Regularities in Rapid Luminance Sequences

A novel neural signature of active visual processing has recently been described in the form of the "perceptual echo", in which the cross-correlation between a sequence of randomly fluctuating luminance values and occipital electrophysiological signals exhibits a long-lasting periodic (∼100 ms cycle) reverberation of the input stimulus (VanRullen and Macdonald, 2012). As yet, however, the mechanisms underlying the perceptual echo and its function remain unknown. Reasoning that natural visual signals often contain temporally predictable, though nonperiodic features, we hypothesized that the perceptual echo may reflect a periodic process associated with regularity learning. To test this hypothesis, we presented subjects with successive repetitions of a rapid nonperiodic luminance sequence, and examined the effects on the perceptual echo, finding that echo amplitude linearly increased with the number of presentations of a given luminance sequence. These data suggest that the perceptual echo reflects a neural signature of regularity learning.Furthermore, when a set of repeated sequences was followed by a sequence with inverted luminance polarities, the echo amplitude decreased to the same level evoked by a novel stimulus sequence. Crucially, when the original stimulus sequence was re-presented, the echo amplitude returned to a level consistent with the number of presentations of this sequence, indicating that the visual system retained sequence-specific information, for many seconds, even in the presence of intervening visual input. Altogether, our results reveal a previously undiscovered regularity learning mechanism within the human visual system, reflected by the perceptual echo.SIGNIFICANCE STATEMENT How the brain encodes and learns fast-changing but nonperiodic visual input remains unknown, even though such visual input characterizes natural scenes. We investigated whether the phenomenon of "perceptual echo" might index such learning. The perceptual echo is a long-lasting reverberation between a rapidly changing visual input and evoked neural activity, apparent in cross-correlations between occipital EEG and stimulus sequences, peaking in the alpha (∼10 Hz) range. We indeed found that perceptual echo is enhanced by repeatedly presenting the same visual sequence, indicating that the human visual system can rapidly and automatically learn regularities embedded within fast-changing dynamic sequences. These results point to a previously undiscovered regularity learning mechanism, operating at a rate defined by the alpha frequency.

Learning Predictive Statistics: Strategies and Brain Mechanisms

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions.SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to changes in the environment's statistics. We provide evidence for an alternate route for learning complex temporal statistics: extracting the most probable outcome in a given context is implemented by interactions between executive and motor corticostriatal mechanisms compared with visual corticostriatal circuits (including hippocampal cortex) that support learning of the exact temporal statistics.

Physical Activity Is Associated with Reduced Implicit Learning but Enhanced Relational Memory and Executive Functioning in Young Adults

Accumulating evidence suggests that physical activity improves explicit memory and executive cognitive functioning at the extreme ends of the lifespan (i.e., in older adults and children). However, it is unknown whether these associations hold for younger adults who are considered to be in their cognitive prime, or for implicit cognitive functions that do not depend on motor sequencing. Here we report the results of a study in which we examine the relationship between objectively measured physical activity and (1) explicit relational memory, (2) executive control, and (3) implicit probabilistic sequence learning in a sample of healthy, college-aged adults. The main finding was that physical activity was positively associated with explicit relational memory and executive control (replicating previous research), but negatively associated with implicit learning, particularly in females. These results raise the intriguing possibility that physical activity upregulates some cognitive processes, but downregulates others. Possible implications of this pattern of results for physical health and health habits are discussed.

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.

Learning Temporal Statistics for Sensory Predictions in Aging

Predicting future events based on previous knowledge about the environment is critical for successful everyday interactions. Here, we ask which brain regions support our ability to predict the future based on implicit knowledge about the past in young and older age. Combining behavioral and fMRI measurements, we test whether training on structured temporal sequences improves the ability to predict upcoming sensory events; we then compare brain regions involved in learning predictive structures between young and older adults. Our behavioral results demonstrate that exposure to temporal sequences without feedback facilitates the ability of young and older adults to predict the orientation of an upcoming stimulus. Our fMRI results provide evidence for the involvement of corticostriatal regions in learning predictive structures in both young and older learners. In particular, we showed learning-dependent fMRI responses for structured sequences in frontoparietal regions and the striatum (putamen) for young adults. However, for older adults, learning-dependent activations were observed mainly in subcortical (putamen, thalamus) regions but were weaker in frontoparietal regions. Significant correlations of learning-dependent behavioral and fMRI changes in these regions suggest a strong link between brain activations and behavioral improvement rather than general overactivation. Thus, our findings suggest that predicting future events based on knowledge of temporal statistics engages brain regions involved in implicit learning in both young and older adults.

Multisession Anodal tDCS Protocol Improves Motor System Function in an Aging Population

OBJECTIVES

The primary objective of this study was to investigate the effects of five consecutive, daily 20-minute sessions of M1 a-tDCS on motor learning in healthy, cognitively intact, aging adults.

DESIGN

A total of 23 participants (51 to 69 years old) performed five consecutive, daily 20-minute sessions of a serial reaction time task (SRT task) concomitant with either anodal (n = 12) or sham (n = 11) M1 a-tDCS.

RESULTS

We found a significant group × training sessions interaction, indicating that whereas aging adults in the sham group exhibited little-to-no sequence-specific learning improvements beyond the first day of training, reproducible improvements in the ability to learn new motor sequences over 5 consecutive sessions were the net result in age-equivalent participants from the M1 a-tDCS group. A significant main effect of group on sequence-specific learning revealed greater motor learning for the M1 a-tDCS group when the five learning sessions were averaged.

CONCLUSION

These findings raise into prominence the utility of multisession anodal TDCS protocols in combination with motor training to help prevent/alleviate age-associated motor function decline.

Basal ganglia and cortical networks for sequential ordering and rhythm of complex movements

Voluntary actions require the concurrent engagement and coordinated control of complex temporal (e.g., rhythm) and ordinal motor processes. Using high-resolution functional magnetic resonance imaging (fMRI) and multi-voxel pattern analysis (MVPA), we sought to determine the degree to which these complex motor processes are dissociable in basal ganglia and cortical networks. We employed three different finger-tapping tasks that differed in the demand on the sequential temporal rhythm or sequential ordering of submovements. Our results demonstrate that sequential rhythm and sequential order tasks were partially dissociable based on activation differences. The sequential rhythm task activated a widespread network centered around the supplementary motor area (SMA) and basal-ganglia regions including the dorsomedial putamen and caudate nucleus, while the sequential order task preferentially activated a fronto-parietal network. There was also extensive overlap between sequential rhythm and sequential order tasks, with both tasks commonly activating bilateral premotor, supplementary motor, and superior/inferior parietal cortical regions, as well as regions of the caudate/putamen of the basal ganglia and the ventro-lateral thalamus. Importantly, within the cortical regions that were active for both complex movements, MVPA could accurately classify different patterns of activation for the sequential rhythm and sequential order tasks. In the basal ganglia, however, overlapping activation for the sequential rhythm and sequential order tasks, which was found in classic motor circuits of the putamen and ventro-lateral thalamus, could not be accurately differentiated by MVPA. Overall, our results highlight the convergent architecture of the motor system, where complex motor information that is spatially distributed in the cortex converges into a more compact representation in the basal ganglia.

Learning to predict is spared in mild cognitive impairment due to Alzheimer's disease

Learning the statistics of the environment is critical for predicting upcoming events. However, little is known about how we translate previous knowledge about scene regularities to sensory predictions. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are known to have spared implicit but impaired explicit recognition memory are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards oriented gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. Further, we show that executive cognitive control may account for individual variability in predictive learning. That is, we observed significant positive correlations of performance in attentional and working memory tasks with post-training performance in the prediction task. Taken together, these results suggest a mediating role of circuits involved in cognitive control (i.e. frontal circuits) that may support the ability for predictive learning in MCI-AD.

Learning temporal statistics for sensory predictions in mild cognitive impairment

Training is known to improve performance in a variety of perceptual and cognitive skills. However, there is accumulating evidence that mere exposure (i.e. without supervised training) to regularities (i.e. patterns that co-occur in the environment) facilitates our ability to learn contingencies that allow us to interpret the current scene and make predictions about future events. Recent neuroimaging studies have implicated fronto-striatal and medial temporal lobe brain regions in the learning of spatial and temporal statistics. Here, we ask whether patients with mild cognitive impairment due to Alzheimer's disease (MCI-AD) that are characterized by hippocampal dysfunction are able to learn temporal regularities and predict upcoming events. We tested the ability of MCI-AD patients and age-matched controls to predict the orientation of a test stimulus following exposure to sequences of leftwards or rightwards orientated gratings. Our results demonstrate that exposure to temporal sequences without feedback facilitates the ability to predict an upcoming stimulus in both MCI-AD patients and controls. However, our fMRI results demonstrate that MCI-AD patients recruit an alternate circuit to hippocampus to succeed in learning of predictive structures. In particular, we observed stronger learning-dependent activations for structured sequences in frontal, subcortical and cerebellar regions for patients compared to age-matched controls. Thus, our findings suggest a cortico-striatal-cerebellar network that may mediate the ability for predictive learning despite hippocampal dysfunction in MCI-AD.

Decoding the future from past experience: learning shapes predictions in early visual cortex

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

Maintaining vs. enhancing motor sequence memories: respective roles of striatal and hippocampal systems

It is now accepted that hippocampal- and striatal-dependent memory systems do not act independently, but rather interact during both memory acquisition and consolidation. However, the respective functional roles of the hippocampus and the striatum in these processes remain unknown. Here, functional magnetic resonance imaging (fMRI) was used in a daytime sleep/wake protocol to investigate this knowledge gap. Using a protocol developed earlier in our lab (Albouy et al., 2013a), the manipulation of an explicit sequential finger-tapping task, allowed us to isolate allocentric (spatial) and egocentric (motor) representations of the sequence, which were supported by distinct hippocampo- and striato-cortical networks, respectively. Importantly, a sleep-dependent performance enhancement emerged for the hippocampal-dependent memory trace, whereas performance was maintained for the striatal-dependent memory trace, irrespective of the sleep condition. Regression analyses indicated that the interaction between these two systems influenced subsequent performance improvements. While striatal activity was negatively correlated with performance enhancement after both sleep and wakefulness in the allocentric representation, hippocampal activity was positively related to performance improvement for the egocentric representation, but only if sleep was allowed after training. Our results provide the first direct evidence of a functional dissociation in consolidation processes whereby memory stabilization seems supported by the striatum in a time-dependent manner whereas memory enhancement seems linked to hippocampal activity and sleep-dependent processes.

Comparison of effects of transcranial magnetic stimulation on primary motor cortex and supplementary motor area in motor skill learning (randomized, cross over study)

Motor skills require quick visuomotor reaction time, fast movement time, and accurate performance. Primary motor cortex (M1) and supplementary motor area (SMA) are closely related in learning motor skills. Also, it is well known that high frequency repeated transcranial magnetic stimulation (rTMS) on these sites has a facilitating effect. The aim of this study was to compare the effects of high frequency rTMS activation of these two brain sites on learning of motor skills. Twenty three normal volunteers participated. Subjects were randomly stimulated on either brain area, SMA or M1. The motor task required the learning of sequential finger movements, explicitly or implicitly. It consisted of pressing the keyboard sequentially with their right hand on seeing 7 digits on the monitor explicitly, and then tapping the 7 digits by memorization, implicitly. Subjects were instructed to hit the keyboard as fast and accurately as possible. Using Musical Instrument Digital Interface (MIDI), the keyboard pressing task was measured before and after high frequency rTMS for motor performance, which was measured by response time (RT), movement time, and accuracy (AC). A week later, the same task was repeated by cross-over study design. At this time, rTMS was applied on the other brain area. Two-way ANOVA was used to assess the carry over time effect and stimulation sites (M1 and SMA), as factors. Results indicated that no carry-over effect was observed. The AC and RT were not different between the two stimulating sites (M1 and SMA). But movement time was significantly decreased after rTMS on both SMA and M1. The amount of shortened movement time after rTMS on SMA was significantly increased as compared to the movement time after rTMS on M1 (p < 0.05), especially for implicit learning of motor tasks. The coefficient of variation was lower in implicit trial than in explicit trial. In conclusion, this finding indicated an important role of SMA compared to M1, in implicit motor learning.

Reduced repetition suppression in the occipital visual cortex during repeated negative Chinese personality-trait word processing

Reduced neural activation have been consistently observed during repeated items processing, a phenomenon termed repetition suppression. The present study used functional magnetic resonance imaging (fMRI) to investigate whether and how stimuli of emotional valence affects repetition suppression by adopting Chinese personality-trait words as materials. Seventeen participants were required to read the negative and neutral Chinese personality-trait words silently. And then they were presented with repeated and novel items during scanning. Results showed significant repetition suppression in the inferior occipital gyrus only for neutral personality-trait words, whereas similar repetition suppression in the left inferior temporal gyrus and left middle temporal gyrus was revealed for both the word types. These results indicated common and distinct neural substrates during processing Chinese repeated negative and neutral personality-trait words.

Learned spatiotemporal sequence recognition and prediction in primary visual cortex

Learning to recognize and predict temporal sequences is fundamental to sensory perception, and is impaired in several neuropsychiatric disorders, but little is known about where and how this occurs in the brain. We discovered that repeated presentations of a visual sequence over a course of days causes evoked response potentiation in mouse V1 that is highly specific for stimulus order and timing. Remarkably, after V1 is trained to recognize a sequence, cortical activity regenerates the full sequence even when individual stimulus elements are omitted. This novel neurophysiological report of sequence learning advances the understanding of how the brain makes “intelligent guesses” based on limited information to form visual percepts and suggests that it is possible to study the mechanistic basis of this high–level cognitive ability by studying low–level sensory systems.

Combining fMRI and behavioral measures to examine the process of human learning

Prior to the advent of fMRI, the primary means of examining the mechanisms underlying learning were restricted to studying human behavior and non-human neural systems. However, recent advances in neuroimaging technology have enabled the concurrent study of human behavior and neural activity. We propose that the integration of behavioral response with brain activity provides a powerful method of investigating the process through which internal representations are formed or changed. Nevertheless, a review of the literature reveals that many fMRI studies of learning either (1) focus on outcome rather than process or (2) are built on the untested assumption that learning unfolds uniformly over time. We discuss here various challenges faced by the field and highlight studies that have begun to address them. In doing so, we aim to encourage more research that examines the process of learning by considering the interrelation of behavioral measures and fMRI recording during learning.

Caudate resting connectivity predicts implicit probabilistic sequence learning

Abstract Implicit probabilistic sequence learning (IPSL) involves extracting statistical regularities from sequences of events without awareness, and is thought to underlie learning of language and behavioral repertoires of everyday life. We examined whether resting-state functional connectivity networks of the caudate predicted individual differences in IPSL performance measured on a separate day. Whole-brain connectivity maps of a bilateral dorsal caudate (DC) seed were created for each subject and examined for voxelwise correlations with sequence learning performance, as well as with overall response speed. Higher learning scores (but not overall response speed) were associated with stronger resting-state connectivity between the DC and right medial temporal lobe, as well as with lower resting-state connectivity between the DC and premotor regions involved in motor planning. Thus, how well one learns probabilistic regularities without awareness is predicted by the strength of a striato-cortical network in the resting brain.

Impaired implicit sequence learning in children with developmental dyslexia

It has been proposed that an impairment of procedural memory underlies a range of linguistic, cognitive and motor impairments observed in developmental dyslexia (DD). However, studies designed to test this hypothesis using the implicit sequence learning paradigm have yielded inconsistent results. A fundamental aspect of procedural learning is that it takes place over an extended time-period that may be divided into distinct stages based on both behavioural characteristics and neural correlates of performance. Yet, no study of implicit sequence learning in children with DD has included learning stages beyond a single practice session. The present study was designed to fill this important gap by extending the investigation to include the effects of overnight consolidation as well as those of further practice on a subsequent day. The results suggest that the most pronounced procedural learning impairment in DD may emerge only after extended practice, in learning stages beyond a single practice session.

Early visual learning induces long-lasting connectivity changes during rest in the human brain

Spontaneous fluctuations in resting state activity can change in response to experience-dependent plasticity and learning. Visual learning is fast and can be elicited in an MRI scanner. Here, we showed that a random dot motion coherence task can be learned within one training session. While the task activated primarily visual and parietal brain areas, learning related changes in neural activity were observed in the hippocampus. Crucially, even this rapid learning affected resting state dynamics both immediately after the learning and 24 h later. Specifically, the hippocampus changed its coupling with the striatum, in a way that was best explained as a consolidation of early learning related changes. Our findings suggest that long-lasting changes in neuronal coupling are accompanied by changes in resting state activity.

•

Early learning of sensory task changes hippocampal activity.

•

Coupling changes between hippocampus and striatum.

•

Resting-state changes are consolidated during sleep.

•

Stochastic DCM is a tool for resting state analysis.

Daytime sleep enhances consolidation of the spatial but not motoric representation of motor sequence memory

Motor sequence learning is known to rely on more than a single process. As the skill develops with practice, two different representations of the sequence are formed: a goal representation built under spatial allocentric coordinates and a movement representation mediated through egocentric motor coordinates. This study aimed to explore the influence of daytime sleep (nap) on consolidation of these two representations. Through the manipulation of an explicit finger sequence learning task and a transfer protocol, we show that both allocentric (spatial) and egocentric (motor) representations of the sequence can be isolated after initial training. Our results also demonstrate that nap favors the emergence of offline gains in performance for the allocentric, but not the egocentric representation, even after accounting for fatigue effects. Furthermore, sleep-dependent gains in performance observed for the allocentric representation are correlated with spindle density during non-rapid eye movement (NREM) sleep of the post-training nap. In contrast, performance on the egocentric representation is only maintained, but not improved, regardless of the sleep/wake condition. These results suggest that motor sequence memory acquisition and consolidation involve distinct mechanisms that rely on sleep (and specifically, spindle) or simple passage of time, depending respectively on whether the sequence is performed under allocentric or egocentric coordinates.

Dissociable neural systems of sequence learning

Although current theories all point to distinct neural systems for sequence learning, no consensus has been reached on which factors crucially define this distinction. Dissociable judgment-linked versus motor-linked and implicit versus explicit neural systems have been proposed. This paper reviews these two distinctions, yet concludes that these traditional dichotomies prove insufficient to account for all data on sequence learning and its neural organization. Instead, a broader theoretical framework is necessary providing a more continuous means of dissociating sequence learning systems. We argue that a more recent theory, dissociating multidimensional versus unidimensional neural systems, might provide such framework, and we discuss this theory in relation to more general principles of associative learning and recent imaging findings.