ye movement parameters while reading show cognitive processes of structural analysis of written speech

This paper gives an overview of the published data on eye movement parameters while reading sentences in different languages with both local and global syntactic ambiguity. A locally ambiguous sentence contains a syntactically problematic phrase that leads to only one interpretation, while a globally ambiguous sentence has more than one distinct interpretation. In the first case the ambiguity persists only to the end of the sentence, when it is successfully resolved; in the second case the ambiguity is still present after reading the whole sentence. The obvious difficulty in analyzing the structure of locally and globally ambiguous sentences leads to increased reading time compared with unambiguous sentences. The syntactic ambiguity increases two major parameters: the fixation duration when reading words critical for interpreting the sentence, and the frequency of regressive saccades to reread those words. The reading time for critical words, disambiguating the local ambiguity, depends on the principle of early/late closure (i.e., high/low attachment): preferring a recurrent pattern to associate the critical word with a distant or closer word, respectively (as determined by its position in the sentence), and differs across languages. The first study of eye movement parameters in reading globally syntactic ambiguous sentences in the Russian language is reported in this paper. Our findings open up the prospects of quantitative studies of syntactic disambiguation in Slavonic and Romano-Germanic languages

The Psychophysiological Diagnostics of the Functional State of the ATHLE TE. Preliminary Data

The original experimental scheme was developed to investigate athletes’ functionalstates (FS) dynamics. The procedure allowed modeling various FS importantfor predicting the professional success of athletes: psychological and physiologicalstress, fatigue, and optimal FS (OFS). There were two main criteria fordifferentiation of the FS under study: efficiency rates and the psychological andphysiological costs of the achieved efficiency level. Analysis of the FS-dependentpsychophysiological changes showed significant interindividual differences on anumber of parameters. Thus, no single indicator could be used as effective diagnosticsfor the FS criteria. A minimum number of indicators need to be recordedincluded cardiovascular indicators (heart rate, ECG), respiration, muscle tension(EMG), and brain activity (EEG) in the range of alpha and beta waves. The mainproblem can be artifacts induced by movement and muscle tension. The specialprocedure for artifact rejection and reduction of the artifacts was developed. Itallowed recording EEG, ECG, and EOG signals simultaneously. Another problemwas related to the development of the mathematical algorithm to analyze individualdata 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 clusteringshowed that the k-means algorithm for seven-component vectors allows onewith confidence to differentiate state of quiet wakefulness, states of psychologicaland physiological stress. As the number of parameters used is attenuatedfrom seven to four (without the EEG parameters) the accuracy of distinguishing FS is significantly reduced. To construct a complete and accurate differentiationof an athlete’s FS one should collect some statistical data on the dynamics ofeach FS in different time periods of the person’s life – in the process of training,after successful competition, and after losing competition

utomated real-time classification of functional states: the significance of individual tuning stage

Automated classification of a human functional state is an important problem, with applications including stress resistance evaluation, supervision over operators of critical infrastructure, teaching and phobia therapy. Such classification is particularly efficient in systems for teaching and phobia therapy that include a virtual reality module, and provide the capability for dynamic adjustment of task complexity. In this paper, a method for automated real-time binary classification of human functional states (calm wakefulness vs. stress) based on discrete wavelet transform of EEG data is considered. It is shown that an individual tuning stage of the classification algorithm — a stage that allows the involvement of certain information on individual peculiarities in the classification, using very short individual learning samples, significantly increases classification reliability. The experimental study that proved this assertion was based on a specialized scenario in which individuals solved the task of detecting objects with given properties in a dynamic set of flying objects