Psychophysiology

The problem of lie detection has a long history. Main achievements in this field are concerned with registration of peripheral nervous system indicators. Our experiment provides possibility for development of a new lie detection technology, based on neurophysiologic correlates of cognitive processes diagnostics that underlie deception. The experiments were conducted by “Audio-Visual Slider” software (by Medicom MTD), which performed synchronized stimuli presentation and electrophysiological recording.

Achievement motivation is a psychological category which implies a desire to achieve significant (powerful) results in certain sphere. According to the results of psychophysiological research people who are motivated for success are very active before they are instructed by the researcher which proves that they aimed at the perception of the referent situation and the intense level of expectations. One of the vital issues today is a problem how genes influence human behaviour. Thus on the basis of contemporary researches we can conclude that such influence is regulated by brain processes.

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.