Dmitry V. Ushakov

Contemporary psychology and cognitive neuroscience create many opportunities for studying the brain functions, but also generate numerous challenges. To date, scientists face common conceptual problems which are relevant to almost every research study/ case such as: classification of functions, unified methodological approaches, explanation of the psychological phenomenon etc. The Sino-German Workshop which took place in Hamburg in 2013 aimed to address unasked questions and unused answers, attracting scientists from different countries and different fields of psychology, neuroscience, medicine, history, and philosophy. The present discussion on the 9 unasked questions was initiated by Professor Ernst Poeppel and was held on by Russian participants from various academic institutions.

Over the past several years, the possibilities for Russia’s economic growth have been discussed widely. This problem is unquestionably topical. However, do material factors alone determine the well-being of a society and its people? Since the mid-1980s, the overwhelming majority of indicators of the psychological state of Russian society have been demonstrating a negative tendency, and this trend not only reflects on the citizens’ sense of self but also creates obstacles on the way to the country’s innovative development.

The authors give the quantitative estimation of the macropsychological state of modern Russian society based on its political, social and economic characteristics. For these purposes the composite index of the macropsychological state of society is developed. It combines two secondary indexes such as a societal psychological stability index and a societal socio-psychological well-being index.

Background. Spearman’s law of diminishing returns (SLODR) states that intercorrelations between scores on tests of intellectual abilities were higher when the data set was comprised of subjects with lower intellectual abilities and vice versa. After almost a hundred years of research, this trend has only been detected on average.

Objective. To determine whether the very different results were obtained due to variations in scaling and the selection of subjects.

Design. We used three methods for SLODR detection based on moderated factor analysis (MFCA) to test real data and three sets of simulated data. Of the latter group, the first one simulated a real SLODR effect. The second one simulated the case of a different density of tasks of varying difficulty; it did not have a real SLODR effect. The third one simulated a skewed selection of respondents with different abilities and also did not have a real SLODR effect. We selected the simulation parameters so that the correlation matrix of the simulated data was similar to the matrix created from the real data, and all distributions had similar skewness parameters (about -0.3).

Results. The results of MFCA are contradictory and we cannot clearly distinguish by this method the dataset with real SLODR from datasets with similar correlation structure and skewness, but without a real SLODR effect. Theresults allow us to conclude that when effects like SLODR are very subtle and can be identified only with a large sample, then features of the psychometric scale become very important, because small variations of scale metrics may lead either to masking of real SLODR or to false identification of SLODR.