Although the number of people with psychiatric disease has been increasing, some of them have not taken proper medical care. The psychiatric disease and developmental disorder are usually diagnosed by the interview and behavior observation by doctors and it takes long time to reach a final diagnosis result. Furthermore, the definitions of the disease are partly still uncertain and the diagnoses are likely to be ambiguous. It is therefore required that easy, quick and quantitative diagnosis method is developed. Because the mental characteristics such as psychiatric disease, developmental disorder and behavioral feature are supposed to be closely related to their mental activity such as concentration, it is expected that the measurement of concentration characteristics may help the diagnosis of the mental disorders and the estimation of the onset possibility.
The purpose of this study is, therefore, to measure the characteristics of concentration on intellectual work, to investigate their mental characteristics by questionnaire and to reveal the relationship between them. Concretely, an investigation experiment was conducted where cognitive tasks with unified difficulty are displayed on a tablet PC one by one and 36 feature values are extracted from the histogram of the response time, which express the degree and features of his/her intellectual concentration. In addition, 6 types of questionnaires are given to investigate his/her mental characteristics and another set of 36 parameters are extracted from the questionnaire answers. The questionnaires are Yatabe-Guilford Personality Inventory, Global Scale for Depression, General Health Questionnaire, BIS/BAS Scales, NEET/Hikikomori Risk Scale and Autism-Spectrum Quotient (AQ) Japanese version.
In this study, 226 participants joined the experiment and they conducted 30 minute cognitive tasks twice and answer the questionnaires.
When analyzing the results, a decision tree analysis tool was employed, where parameters of mental characteristics were set as objective variables, and feature values of concentration as explanatory variables. The latter were standardized and compressed to 5 main factors by principal component analysis in advance and then the combination of the factors were given to the tool. The main factors were answering time, deeper concentration, the growth of speed with a break, shallower concentration and the decline of concentration with a break.
As the analysis results of 226 sample data, though there was no obvious relationship revealed between them, some indirect tendencies were found such that the person who has high concentration time ratio is likely not to have autism-spectrum tendency. In the future, it is required not only to increase the number and the range of sample data but also to try various kinds of other analysis methods, such as time series analysis of answering time data.