Alexey N. Gusev

This article describes techniques and procedures that are used to research the changeblindness phenomenon. The role of stimulus parameters in completing a visual task (detecting changes) was investigated. The following parameters of visual stimuli varied in a chronometric experiment: the number of objects, their location in the stimulus space, and the shape of the objects (including a new object that attracts attention as well as various changes of single objects, such as appearance/disappearance, location shifts, changes of color and shape). The results of this study indicate that change blindness can have a different intensity (the time of detecting changes in flickering images) depending on the number of objects, their location in the stimulus space (structured or randomized), and the type of change (the most complicated one was a change of color):

1. The number of objects has considerable influence on the intensity of change blindness and is the most powerful parameter. 
2. The shape of the objects within the image is not crucial for change-detection time.
3. The spatial organization of the objects is important for the successful detection of changes. The changes are detected quicker in images with regular rather than random organization. 
4. A distraction (in this case, a word that was substituted for an object) doesn’t have any considerable influence on change detection. 
5. Change-detection time increases as the interstimulus interval increases from 200 to 400 ms.
6. The detection of shifts and of appearance/disappearance is quicker than the detection of color change.
   

These results let us create stimulus patterns for change-blindness experiments that differ in complexity, and thus we could examine a wide range of hypotheses about the function of the psychological mechanisms of spatial attention that are used to explain this phenomenon.

The study was undertaken to find relationships between personality and temperamental traits (estimated with the help of the Adult Personality Traits Questionnaire by Manolova, Leonhard and the Russian version of the Structure of Temperament Questionnaire (STQ) by Rusalov V. & Trofimova I. (2007)) on the one hand, and parameters of intonation (mean ΔF0, tone span, speech rate, duration of speech and mean duration of syllables interval) on the other hand. The parameters of intonation were measured on sample recordings produced by 30 male and female participants. 60 recordings of natural monologues on proposed topics were obtained in situations of the presence and absence of a conversation partner. Demostrativity (as a personality trait according to Leonhard’s typology) was found to significantly affect mean ΔF0, tone span and speech rate in the presence of an interlocutor. Social Tempo (as a dimension of temperament according to Rusalov’s model) affects the speech rate. In the absence of an interlocutor, only an interaction effect of Demonstrativity and Communication Activity on the same group of vocal parameters was obtained. The presence of an interlocutor proved to be a special condition for the most explicit appearance of Demonstrativity. Temperamental indices that describe the Communication realm seem to moderate the appearance of Demonstrativity in different conditions. Most explicitly, the key feature of people with strong Demonstrativity is a high speech rate.

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.