Investigation of sequence processing: A cognitive and computational neuroscience perspective

Serial order processing or sequence processing underlies many human activities such as speech, language, skill learning, planning, problem-solving, etc. Investigating the neural bases of sequence processing enables us to understand serial order in cognition and also helps in building intelligent devices. In this article, we review various cognitive issues related to sequence processing with examples. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, a theoretical approach based on statistical models and reinforcement learning paradigm is presented. These theoretical ideas are useful for studying sequence learning in a principled way. This article also suggests a two-way process diagram integrating experimentation (cognitive neuroscience) and theory/ computational modelling (computational neuroscience). This integrated framework is useful not only in the present study of serial order, but also for understanding many cognitive processes

Motor skill learning between selection and execution.

Learning motor skills evolves from the effortful selection of single movement elements to their combined fast and accurate production. We review recent trends in the study of skill learning which suggest a hierarchical organization of the representations that underlie such expert performance, with premotor areas encoding short sequential movement elements (chunks) or particular component features (timing/spatial organization). This hierarchical representation allows the system to utilize elements of well-learned skills in a flexible manner. One neural correlate of skill development is the emergence of specialized neural circuits that can produce the required elements in a stable and invariant fashion. We discuss the challenges in detecting these changes with fMRI

The role of planning in motor learning

Humans can learn a remarkable diversity of motor skills. While these skills are sometimes long lasting, they may also be subject to interference. For example, people can learn to reach in the presence of a dynamic (force-field) perturbation generated by a robotic interface. However, when two force fields that act in opposing directions are presented alternately, there is substantial interference, preventing learning of either. Here we examine the role of motor planning in motor memory formation and interference. We challenge a predominant view of motor learning, which suggests that multiple perturbations can only be learned when each is associated (closely in time) with a different physical (or perceived) state of the body. Instead, we show that two opposing perturbations which interfere when experienced over the same movement, can be learned if each is associated with a different neural state (i.e. motor plan). That is, distinct motor memories can be formed by planning each movement through the perturbation as part of a different, wider motor sequence, even if not executed. Exploring the implications of this result, we subsequently show that like planning, motor imagery of different future movements can change the neural state to affect the separation of motor memories. These results lead us to propose that situations which generate different neural responses in motor-related regions will naturally act as different contexts for learning. Interestingly however, we show that the same principle does not appear to underlie motor memory decay. Finally, having established the importance of planning in motor adaptation, we attempt to predict how motor plans should be divided and recombined when task sets become more complex. We simulate normative control policies under the hypothesis that motor chunking may arise from the need to efficiently represent motor commands, and test the implications for concurrent field learning. Together, these results highlight that the actions that humans plan are critical to the representation of motor skills that are learned. This suggests a key role for motor planning in the broad control repertoire humans develop.PhD funding by a Cambridge-Rutherford Memorial Scholarship, awarded by the Rutherford Foundatio

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.Defense Advanced Research Projects Agency/Office of Naval Research (N00014-95-1-0409); National Institute of Mental Health (R01 DC02852

Cognitive Contributions to Motor Learning

Transcranial direct current stimulation (tDCS) can facilitate motor learning. However, the tDCS literature scarcely addresses whether stimulation to prefrontal brain regions affects motor learning, whether chunking together of individual actions can be influenced by tDCS, and whether there are age differences in how stimulation affects sequence learning. Here we completed a series of studies that examined the application of tDCS to the prefrontal cortex (PFC), motor cortex (M1), or the presupplementary motor area (preSMA) and its impact on motor sequence learning to understand the neural bases of motor learning. First, we found both left and right PFC stimulation slowed reaction time decreases and chunking. Stimulation to the preSMA lowered reaction time but came at the expense of a higher number of chunks. and tDCS over M1 helped with reaction time decreases and chunking. Further, contrasts revealed the M1 group had overall faster reaction times and fewer chunks. In order to understand the sequence learning impairment of left PFC anodal tDCS group, we added a left PFC cathodal montage. The left PFC cathodal group demonstrated impaired learning, with longer reaction time and a greater number of chunks, results similar to the left PFC anodal montage. In experiment two, participants from the left PFC, M1, and sham tDCS groups returned for a fourth session to assess long-term effects of tDCS. Participants completed a single session of practice without tDCS on the same sequences assigned to them the year before. We found the M1 tDCS group reduced reaction time at a faster rate relative to sham and the left PFC group demonstrated less forgetting over the course of a year, but overall slower reaction times. Finally, we determined how tDCS applied to the same four brain regions as in the first study affected sequence learning and chunking in older adults. We found no age differences regarding stimulation effects on reaction time reductions; both age groups benefited from M1 stimulation, whereas stimulation to the prefrontal cortices impaired learning. However, we did find age-group differences in chunking. Stimulation to M1 helped chunking processes for both age groups and to a greater extent for older adults. Thus, our findings suggest that regardless of age, stimulation to prefrontal cortices impairs learning, likely interfering with the automatization of sequence, whereas stimulation to M1 facilitates learning, especially in chunk formation. In light of our findings, we suggest the Cognitive framework for Sequential Motor Behavior (C-SMB), a framework that accounts for motor sequence learning should be modified to account for our findings.PHDKines & Psychology PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144056/1/bgreeley_1.pd

EU accession and Poland's external trade policy

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Chunking dynamics: heteroclinics in mind

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permissionRecent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components—brain modes—participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles—metastable states—connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics.Mikhail I. Rabinovich acknowledges support from ONR grant N00014310205. Pablo Varona was supported by MINECO TIN2012-30883. Irma Tristan acknowledges support from the UC-MEXUS-CONACYT Fellowship. Valentin S. Afraimovich was partially supported by Ohio University Glidden Professorship program

Explaining the neural activity distribution associated with discrete movement sequences:Evidence for parallel functional systems

To explore the effects of practice we scanned participants with fMRI while they were performing four-key unfamiliar and familiar sequences, and compared the associated activities relative to simple control sequences. On the basis of a recent cognitive model of sequential motor behavior (C-SMB), we propose that the observed neural activity would be associated with three functional networks that can operate in parallel and that allow (a) responding to stimuli in a reaction mode, (b) sequence execution using spatial sequence representations in a central-symbolic mode, and (c) sequence execution using motor chunk representations in a chunking mode. On the basis of this model and findings in the literature, we predicted which neural areas would be active during execution of the unfamiliar and familiar keying sequences. The observed neural activities were largely in line with our predictions, and allowed functions to be attributed to the active brain areas that fit the three above functional systems. The results corroborate C-SMB’s assumption that at advanced skill levels the systems executing motor chunks and translating key-specific stimuli are racing to trigger individual responses. They further support recent behavioral indications that spatial sequence representations continue to be used

Learning of chunking sequences in cognition and behavior

We often learn and recall long sequences in smaller segments, such as a phone number 858 534 22 30 memorized as four segments. Behavioral experiments suggest that humans and some animals employ this strategy of breaking down cognitive or behavioral sequences into chunks in a wide variety of tasks, but the dynamical principles of how this is achieved remains unknown. Here, we study the temporal dynamics of chunking for learning cognitive sequences in a chunking representation using a dynamical model of competing modes arranged to evoke hierarchical Winnerless Competition (WLC) dynamics. Sequential memory is represented as trajectories along a chain of metastable fixed points at each level of the hierarchy, and bistable Hebbian dynamics enables the learning of such trajectories in an unsupervised fashion. Using computer simulations, we demonstrate the learning of a chunking representation of sequences and their robust recall. During learning, the dynamics associates a set of modes to each information-carrying item in the sequence and encodes their relative order. During recall, hierarchical WLC guarantees the robustness of the sequence order when the sequence is not too long. The resulting patterns of activities share several features observed in behavioral experiments, such as the pauses between boundaries of chunks, their size and their duration. Failures in learning chunking sequences provide new insights into the dynamical causes of neurological disorders such as Parkinson's disease and Schizophrenia

Practice Makes Imperfect: Restorative Effects of Sleep on Motor Learning

Emerging evidence suggests that sleep plays a key role in procedural learning, particularly in the continued development of motor skill learning following initial acquisition. We argue that a detailed examination of the time course of performance across sleep on the finger-tapping task, established as the paradigm for studying the effect of sleep on motor learning, will help distinguish a restorative role of sleep in motor skill learning from a proactive one. Healthy subjects rehearsed for 12 trials and, following a night of sleep, were tested. Early training rapidly improved speed as well as accuracy on pre-sleep training. Additional rehearsal caused a marked slow-down in further improvement or partial reversal in performance to observed levels below theoretical upper limits derived on the basis of early pre-sleep rehearsal. This decrement in learning efficacy does not occur always, but if and only if it does, overnight sleep has an effect in fully or partly restoring the efficacy and actual performance to the optimal theoretically achieveable level. Our findings re-interpret the sleep-dependent memory enhancement in motor learning reported in the literature as a restoration of fatigued circuitry specialized for the skill. In providing restitution to the fatigued brain, sleep eliminates the rehearsal-induced synaptic fatigue of the circuitry specialized for the task and restores the benefit of early pre-sleep rehearsal. The present findings lend support to the notion that latent sleep-dependent enhancement of performance is a behavioral expression of the brain's restitution in sleep