Cognitive control of sequential behavior

Movement can be considered as a crucial aspect of any living being, and has sometimes been considered as the main reason for the actual coming into existence of cognition. Most actions we perform in everyday life consist of series (sequences) of simple movements, by which we are able to attain fluent execution of more complex movement patterns. In this thesis, the mechanisms underlying motor sequence learning, as studied with the discrete sequence production (DSP) task, were investigated by focusing on response times, error rates and measures derived from the electroencephalogram (EEG). Results show that sequence learning in the DSP task is initially based on stimulus-response learning, but with practice sequence learning in the DSP task becomes based on multiple representations, which develop with practice. These representations can be effector dependent and effector independent. Measured derived from the EEG suggest the involvement of a general motor representation during the preparation of sequences, which is effector independent. The activity of this general motor representation decreases with practice, which suggests that with unfamiliar sequences response specifications are unknown and have to be filled in, whereas with familiar and mirrored sequences more response specifications are fixed in the general motor representation. Finally, to learn more about sequence learning we studied the differences in sequence learning in people with dyslexia. Dyslexics are thought to have difficulties with skill automatization, such as motor sequence learning. In this thesis it was shown for the first time that dyslexics were slowed in discrete sequence learning, as compared with controls. This agrees with the automatization deficit in dyslexics suggested by the cerebellar-deficit hypothesis

Rapid acquisition of long spatial sequences in long-term memory

Learning complex movement sequences requires an active, attentional selection of the content that is learned. The selection mechanism can not be investigated in classical stimulus-guided sequence learning paradigms because it requires a movement sequence production that is not triggered by external stimuli. In deferred imitation learning the whole stimulus sequence is presented and reproduction is started only after the presentation has ended. In order to investigate how the selective control of the learning process proceeds in natural learning situations and to investigate all influencing parameters we developed a new paradigm in which long sequences were learned by deferred imitation learning. In this task a long sequence of stimuli was presented on a graphic tablet and reproduced by manual pointing after the stimulus presentation was finished. Since the sequence exceeded the capacity of working memory because of its length it had to be reproduced and learned in several trials. Therefore, an attentional selection was required during learning. In our first study a method for evaluating reproduction performance in the new learning paradigm was developed. The assignment of reproductions to target positions posed a major methodological difficulty. This problem was solved by introducing an assignment algorithm that takes the order of reproduction into account. The algorithm was explained, it was further compared to an algorithm that performs a nearest neighbor assignment and finally validated by a comparison to a human operator assignment. The results showed that the assignment algorithm is an appropriate method for analyzing long sequences of pointing movements and is suitable for evaluating reproduction performance and learning progress in deferred imitation learning of long sequences. In the second study we investigated further how long sequences of pointing movements are acquired. Long-term retention tests showed that the sequences were retained for at least two weeks in long-term memory. A transfer test showed that the sequences were represented in an effector independent representation. The distributions of pointing positions were analyzed in detail in order to characterize the control signal of the pointing movements. The analysis showed that position errors to successive target positions were not dependent on the movement direction and further, that directional error did not propagate to reproductions of successive target positions. These results suggest that end points rather than movement trajectories are memorized in this learning task. Our third study evaluated the organization and reorganization of the sequence representation in memory. The change in sequence reproduction without intermediate presentations showed that the remembered target positions drifted away from the initial representation, where the target drift saturated after about 5 trials. The analysis of the drift direction of representations of single target positions showed that there was no systematic drift direction for single subjects. Further it indicated that the representation did not drift to similar, but to different patterns across subjects. In order to investigate whether sequences are encoded in chunks or as single target positions we performed an experiment in which two target positions in a well learned sequence were exchanged. We analyzed the effect of the target exchange on target positions neighboring the exchanged target position. The target exchange effected neither the position nor the variance of neighboring memorized target positions. These results support the view that single target positions rather than chunks of target positions are memorized. Thus our study suggests that the sequence acquisition is guided by an active selection process which is able to quickly acquire abstract movement plans. Our findings further support the view that these movement plans are represented as strings of independent, absolute target positions

Fast social-like learning of complex behaviors based on motor motifs

Social learning is widely observed in many species. Less experienced agents copy successful behaviors, exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of $n$ motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for $(n-1)!$ possible sequences of motifs in a neural network, we employ the winner-less competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire ``on the fly'' its synaptic couplings in no more than $(n-1)$ learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that indeed the learner is capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware

Contactless privacy-preserving head movement recognition using deep learning for driver fatigue detection

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.</p

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

Dance training shapes action perception and its neural implementation within the young and older adult brain

How we perceive others in action is shaped by our prior experience. Many factors influence brain responses when observing others in action, including training in a particular physical skill, such as sport or dance, and also general development and aging processes. Here, we investigate how learning a complex motor skill shapes neural and behavioural responses among a dance-naïve sample of 20 young and 19 older adults. Across four days, participants physically rehearsed one set of dance sequences, observed a second set, and a third set remained untrained. Functional MRI was obtained prior to and immediately following training. Participants’ behavioural performance on motor and visual tasks improved across the training period, with younger adults showing steeper performance gains than older adults. At the brain level, both age groups demonstrated decreased sensorimotor cortical engagement after physical training, with younger adults showing more pronounced decreases in inferior parietal activity compared to older adults. Neural decoding results demonstrate that among both age groups, visual and motor regions contain experience-specific representations of new motor learning. By combining behavioural measures of performance with univariate and multivariate measures of brain activity, we can start to build a more complete picture of age-related changes in experience-dependent plasticity

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