Research Bytes: Brain complexity, predicting job success, neuroscience/creativity, fluid IQ and personality

Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15(5), 200-209.

Although the ultimate aim of neuroscientific enquiry is to gain an understanding of the brain and how its workings relate to the mind, the majority of current efforts are largely focused on small questions using increasingly detailed data. However, it might be possible to successfully address the larger question of mind–brain mechanisms if the cumulative findings from these neuroscientific studies are coupled with complementary approaches from physics and philosophy. The brain, we argue, can be understood as a complex system or network, in which mental states emerge from the interaction between multiple physical and functional levels. Achieving further conceptual progress will crucially depend on broad-scale discussions regarding the properties of cognition and the tools that are currently available or must be developed in order to study mind–brain mechanisms.
Article Outline



Ziegler, M., Dietl, E., Danay, E., Vogel, M., & Buhner, M. (2011). Predicting Training Success with General Mental Ability, Specific Ability Tests, and (Un)Structured Interviews: A meta-analysis with unique samples. International Journal of Selection and Assessment, 19(2), 170-182.


Several meta-analyses combine an extensive amount of research concerned with predicting training success. General mental ability is regarded as the best predictor with specific abilities or tests explaining little additional variance. However, only few studies measured all predictors within one sample. Thus, intercorrelations were often estimated based on other studies. Moreover, new methods for correcting range restriction are now available. The present meta-analyses used samples derived from a German company in which applicants for different apprenticeships were tested with an intelligence structure test, specific ability tests as well as a structured and an unstructured interview. Therefore, intercorrelations between different assessment tools did not have to be estimated from other data. Results in the final examination, taking place at least 2 years after the original assessment, served as criterion variable. The dominant role of general mental ability was confirmed. However, specific abilities were identified that can be used as valuable additions. Job complexity moderated some of the relationships. Structured interviews were found to have good incremental validity over and above general mental ability. Unstructured interviews, on the other hand, performed poorly. Practical implications are discussed.


Sawyer, K. (2011). The Cognitive Neuroscience of Creativity: A Critical Review. Creativity Research Journal, 23(2), 137-154.

Cognitive neuroscience studies of creativity have appeared with increasing frequently in recent years. Yet to date, no comprehensive and critical review of these studies has yet been published. The first part of this article presents a quick overview of the 3 primary methodologies used by cognitive neuroscientists: electroencephalography (EEG), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI). The second part provides a comprehensive review of cognitive neuroscience studies of creativity-related cognitive processes. The third part critically examines these studies; the goal is to be extremely clear about exactly what interpretations can appropriately be made of these studies. The conclusion provides recommendations for future research collaborations between creativity researchers and cognitive neuroscientists.


Djapo, N., KolenovicDjapo, J., Djokic, R., & Fako, I. (2011). Relationship between Cattell's 16PF and fluid and crystallized intelligence. Personality and Individual Differences, 51(1), 63-67.

The aim of the study was to explore the relationship between the five global factors and 16 dimensions of Cattell’s personality model and fluid and crystallized intelligence. A total of 105 third graders (45.7% males) of three high schools participated in the research. Fluid intelligence was measured by Raven’s Advanced Progressive Matrices and crystallized intelligence was measured by the Mill Hill Vocabulary Scale. Personality traits were measured by the Sixteen Personality Factor Questionnaire. Anxiety is correlated neither with fluid nor with crystallized intelligence. Extraversion and Self-Control are negatively correlated with fluid intelligence whereas Tough-Mindedness is positively correlated with it. Independence is positively correlated with crystallized intelligence and Tough-Mindedness is negatively correlated with it. Regression analysis reveals that all broad personality factors, except anxiety, are significant predictors of fluid intelligence. When combined together, these factors account for 25% of the variance of fluid intelligence scores. The regression model with crystallized intelligence as a criterion variable is not statistically significant. The study results are consistent with the Chamorro


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