Ignat A. Dubynin

Background. The sense of agency (SoA) provides us with the experience of be­ing a physical agent with free will. On a phenomenological basis, SoA can be di­vided into sensory components (feeling of agency, FoA) and more cognitive com­ponents (judgment of agency, JoA). Both these components can be independently measured.

Objective and Method. A new method was developed to test the possibility of preserving SoA and its components in the atypical conditions of passive movements. Parameters of the participant’s movement in response to a visual stimulus (reaction time, speed, and amplitude) were measured and used to control a servo that simu­lated the movement (executed passive movements). The scores on the psychometric scale of the agency and the event-related potentials (ERPs) were recorded for variable movement delays relative to the stimulus onset.

Results. It was found that the FoA was not present under passive movement con­ditions. At the same time, participants associated these movements with their own activity (JoA), even when their delay after the stimulus onset was too short to be ac­tively reproduced. The somatosensory ERPs’ amplitude decreased for the expected movements, demonstrating an inverse relationship with the agency scores. The lowest amplitude was observed when movements were actuated by another hand. The results can be explained using a predictive forward model, since the FoA was not observed in the absence of active movements. On the other hand, the ERPs’ data and the presence of JoA with various delays between the stimulus and movement support the postdic­tive model of agency, where the leading role is assigned to prejudice and contextual knowledge related to the action.

Conclusion. It seems that the “context pressure” of the situation, demanding a mandatory response to the stimulus, forms a cognitive prediction of movements without firm sensory representation.

The original experimental scheme was developed to investigate athletes’ functional states (FS) dynamics. The procedure allowed modeling various FS important for predicting the professional success of athletes: psychological and physiological stress, fatigue, and optimal FS (OFS). There were two main criteria for differentiation of the FS under study: efficiency rates and the psychological and physiological costs of the achieved efficiency level. Analysis of the FS-dependent psychophysiological changes showed significant interindividual differences on a number of parameters. Thus, no single indicator could be used as effective diagnostics for the FS criteria. A minimum number of indicators need to be recorded included cardiovascular indicators (heart rate, ECG), respiration, muscle tension (EMG), and brain activity (EEG) in the range of alpha and beta waves. The main problem can be artifacts induced by movement and muscle tension. The special procedure for artifact rejection and reduction of the artifacts was developed. It allowed recording EEG, ECG, and EOG signals simultaneously. Another problem was related to the development of the mathematical algorithm to analyze individual data and differentiate patterns of the signals recorded from the athletes. An original approach to differentiate the FS – the k-means clustering algorithm – was offered based on seven psychophysiological indicators. Results of clustering showed that the k- means algorithm for seven-component vectors allows one with confidence to differentiate state of quiet wakefulness, states of psychological and physiological stress. As the number of parameters used is attenuated from seven to four (without the EEG parameters) the accuracy of distinguishing FS is significantly reduced. To construct a complete and accurate differentiation of an athlete’s FS one should collect some statistical data on the dynamics of each FS in different time periods of the person’s life – in the process of training, after successful competition, and after losing competition.