Cognitive and neuronal bases of expertise

This thesis examines the cognitive and neural bases of expertise. In so doing, several psychological phenomena were investigated-imagery. memory and thinking-using different tasks, and a variety of techniques of data gathering, including standard behavioural experiments, questionnaires, eye-movement recording, and functional magnetic resonance imaging (fMRI). Chess players participated in all the studies, and chess tasks were used. The data confirmed the versatility and power of chess as a task environment, since the results provided fruitful information for the understanding of different human cognitive processes. The role of practice in this domain of expertise was examined. The strong view that extended deliberate practice is a necessary and sufficient condition for the acquisition of expert performance, did not receive support in this thesis. Alternatively, a less extreme position was adopted: extended practice is a necessary, but not a sufficient condition for the acquisition of expert performance. A search for individual differences in factors unrelated to chess practice was carried out. The sources of these individual differences, as well as the cognitive abilities in which individual differences may exist, were considered. One of the sources-the age at which serious practice starts-was a good predictor of chess skill. Handedness, which is supposed to be determined by environmental factors in utero, slightly differentiated chess players from non-players, but no differences in this variable were found between strong and the weak players. Regarding the cognitive abilities, chess players performed slightly better than the non-chess players in a spatial task. Individual differences were also considered within a single leyel of expertise-master level. Differences in forgetting rate in long-term memory and reaction time were observed for one of the masters. These results contributed to the improvement of an extant theory of expertise-template/CHREST [CHunks and REtrieval STructures] theory-by estimating values for some of its parameters based on the empirical data obtained, and by proposing the addition of a spatial short-term memory

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

Modelling and analysis of amplitude, phase and synchrony in human brain activity patterns

The critical brain hypothesis provides a framework for viewing the human brain as a critical system, which may transmit information, reorganise itself and react to external stimuli efficiently. A critical system incorporates structures at a range of spatial and temporal scales, and may be associated with power law distributions of neuronal avalanches and power law scaling functions. In the temporal domain, the critical brain hypothesis is supported by a power law decay of the autocorrelation function of neurophysiological signals, which indicates the presence of long-range temporal correlations (LRTCs). LRTCs have been found to exist in the amplitude envelope of neurophysiological signals such as EEG, EMG and MEG, which reveal patterns of local synchronisation within neuronal pools. Synchronisation is an important tool for communication in the nervous system and can also exist between disparate regions of the nervous system. In this thesis, inter-regional synchronisation is characterised by the rate of change of phase difference between neurophysiological time series at different neuronal regions and investigated using the novel phase synchrony analysis method. The phase synchrony analysis method is shown to recover the DFA exponents in time series where these are known. The method indicates that LRTCs are present in the rate of change of phase difference between time series derived from classical models of criticality at critical parameters, and in particular the Ising model of ferromagnetism and the Kuramoto model of coupled oscillators. The method is also applied to the Cabral model, in which Kuramoto oscillators with natural frequencies close to those of cortical rhythms are embedded in a network based on brain connectivity. It is shown that LRTCs in the rate of change of phase difference are disrupted when the network properties of the system are reorganised. The presence of LRTCs is assessed using detrended fluctuation analysis (DFA), which assumes the linearity of a log-log plot of detrended fluctuation magnitude. In this thesis it is demonstrated that this assumption does not always hold, and a novel heuristic technique, ML-DFA, is introduced for validating DFA results. Finally, the phase synchrony analysis method is applied to EEG, EMG and MEG time series. The presence of LRTCs in the rate of change of phase difference between time series recorded from the left and right motor cortices are shown to exist during resting state, but to be disrupted by a finger tapping task. The findings of this thesis are interpreted in the light of the critical brain hypothesis, and shown to provide motivation for future research in this area