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

Making tools and making sense: complex, intentional behaviour in human evolution

Stone tool-making is an ancient and prototypically human skill characterized by multiple levels of intentional organization. In a formal sense, it displays surprising similarities to the multi-level organization of human language. Recent functional brain imaging studies of stone tool-making similarly demonstrate overlap with neural circuits involved in language processing. These observations consistent with the hypothesis that language and tool-making share key requirements for the construction of hierarchically structured action sequences and evolved together in a mutually reinforcing way

Neural Dynamics of Learning and Performance of Fixed Sequences: Latency Pattern Reorganizations and the N-STREAMS Model

Fixed sequences performed from memory play a key role in human cultural behavior, especially in music and in rapid communication through speaking, handwriting, and typing. Upon first performance, fixed sequences are often produced slowly, but extensive practice leads to performance that is both fluid and as rapid as allowed by constraints inherent in the task or the performer. The experimental study of fixed sequence learning and production has generated a large database with some challenging findings, including practice-related reorganizations of temporal properties of performance. In this paper, we analyze this literature and identify a coherent set of robust experimental effects. Among these are both the sequence length effect on latency, a dependence of reaction time on sequence length, and practice-dependent lost of the lengths effect on latency. We then introduce a neural network architecture capable of explaining these effects. Called the NSTREAMS model, this multi-module architecture embodies the hypothesis that the brain uses several substrates for serial order representation and learning. The theory describes three such substrates and how learning autonomously modifies their interaction over the course of practice. A key feature of the architecture is the co-operation of a 'competitive queuing' performance mechanism with both fundamentally parallel ('priority-tagged') and fundamentally sequential ('chain-like') representations of serial order. A neurobiological interpretation of the architecture suggests how different parts of the brain divide the labor for serial learning and performance. Rhodes (1999) presents a complete mathematical model as implementation of the architecture, and reports successful simulations of the major experimental effects. It also highlights how the network mechanisms incorporated in the architecture compare and contrast with earlier substrates proposed for competitive queuing, priority tagging and response chaining.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-1-0409); National Institute of Health (RO1 DC02852

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

Differential recruitment of the sensorimotor putamen and frontoparietal cortex during motor chunking in humans

Motor chunking facilitates movement production by combining motor elements into integrated units of behavior. Previous research suggests that chunking involves two processes: concatenation, aimed at the formation of motor-motor associations between elements or sets of elements, and segmentation, aimed at the parsing of multiple contiguous elements into shorter action sets. We used fMRI to measure the trial-wise recruitment of brain regions associated with these chunking processes as healthy subjects performed a cued-sequence production task. A dynamic network analysis identified chunking structure for a set of motor sequences acquired during fMRI and collected over 3 days of training. Activity in the bilateral sensorimotor putamen positively correlated with chunk concatenation, whereas a left-hemisphere frontoparietal network was correlated with chunk segmentation. Across subjects, there was an aggregate increase in chunk strength (concatenation) with training, suggesting that subcortical circuits play a direct role in the creation of fluid transitions across chunks

Neuronal bases of structural coherence in contemporary dance observation

The neuronal processes underlying dance observation have been the focus of an increasing number of brain imaging studies over the past decade. However, the existing literature mainly dealt with effects of motor and visual expertise, whereas the neural and cognitive mechanisms that underlie the interpretation of dance choreographies remained unexplored. Hence, much attention has been given to the Action Observation Network (AON) whereas the role of other potentially relevant neuro-cognitive mechanisms such as mentalizing (theory of mind) or language (narrative comprehension) in dance understanding is yet to be elucidated. We report the results of an fMRI study where the structural coherence of short contemporary dance choreographies was manipulated parametrically using the same taped movement material. Our participants were all trained dancers. The whole-brain analysis argues that the interpretation of structurally coherent dance phrases involves a subpart (Superior Parietal) of the AON as well as mentalizing regions in the dorsomedial Prefrontal Cortex. An ROI analysis based on a similar study using linguistic materials (Pallier et al. 2011) suggests that structural processing in language and dance might share certain neural mechanisms

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

Coherence and recurrency: maintenance, control and integration in working memory

Working memory (WM), including a ‘central executive’, is used to guide behavior by internal goals or intentions. We suggest that WM is best described as a set of three interdependent functions which are implemented in the prefrontal cortex (PFC). These functions are maintenance, control of attention and integration. A model for the maintenance function is presented, and we will argue that this model can be extended to incorporate the other functions as well. Maintenance is the capacity to briefly maintain information in the absence of corresponding input, and even in the face of distracting information. We will argue that maintenance is based on recurrent loops between PFC and posterior parts of the brain, and probably within PFC as well. In these loops information can be held temporarily in an active form. We show that a model based on these structural ideas is capable of maintaining a limited number of neural patterns. Not the size, but the coherence of patterns (i.e., a chunking principle based on synchronous firing of interconnected cell assemblies) determines the maintenance capacity. A mechanism that optimizes coherent pattern segregation, also poses a limit to the number of assemblies (about four) that can concurrently reverberate. Top-down attentional control (in perception, action and memory retrieval) can be modelled by the modulation and re-entry of top-down information to posterior parts of the brain. Hierarchically organized modules in PFC create the possibility for information integration. We argue that large-scale multimodal integration of information creates an ‘episodic buffer’, and may even suffice for implementing a central executive

Overnight consolidation aids the transfer of statistical knowledge from the medial temporal lobe to the striatum

Sleep is important for abstraction of the underlying principles (or gist) which bind together conceptually related stimuli, but little is known about the neural correlates of this process. Here, we investigate this issue using overnight sleep monitoring and functional magnetic resonance imaging (fMRI). Participants were exposed to a statistically structured sequence of auditory tones then tested immediately for recognition of short sequences which conformed to the learned statistical pattern. Subsequently, after consolidation over either 30min or 24h, they performed a delayed test session in which brain activity was monitored with fMRI. Behaviorally, there was greater improvement across 24h than across 30min, and this was predicted by the amount of slow wave sleep (SWS) obtained. Functionally, we observed weaker parahippocampal responses and stronger striatal responses after sleep. Like the behavioral result, these differences in functional response were predicted by the amount of SWS obtained. Furthermore, connectivity between striatum and parahippocampus was weaker after sleep, whereas connectivity between putamen and planum temporale was stronger. Taken together, these findings suggest that abstraction is associated with a gradual shift from the hippocampal to the striatal memory system and that this may be mediated by SWS

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts