Is imitational learning a driving factor for the population bias in human hand preference?

Lateral preference is a widespread organizational principle in human and nonhuman animals. In humans, the most apparent lateralized trait (handedness) is unique in the animal kingdom because of a very pronounced bias towards right-handedness on a population level. In this study, based on previous experiments, we test the hypothesis that this bias was-among other factors-shaped by evolution through the facilitation of social learning. We exposed 134 subjects to footage of right- or left-handed knot making and analyzed whether concordant handedness between instructor and student facilitated quicker and more successful imitation. We used a set of nautical knots of different difficulty levels in order to test whether the potential effect of concordance became stronger with increasing knot difficulty. For all three performance measures (time until correct completion, number of attempts needed and correct imitation), we found hand congruency and difficulty level to be significant predictors but not the interaction of the two. We conclude that concordance of handedness between teacher and student of a motor skill enhances the speed and accuracy of imitation, which may have been a beneficial trait for selection to act upon, thereby shaping the human population bias in handedness

Testing the Darwinian function of lateralization:Does separation of workload between brain hemispheres increase cognitive performance?

Brain lateralization is a fundamental aspect of the organization of brain and behavior in the animal kingdom, begging the question about its Darwinian function. We tested the possibility that lateralization enhances cognitive performance in single- and dual-tasks. Previous studies reported mixed results on this topic and only a handful of studies have measured functional brain lateralization and performance independently and simultaneously. We therefore examined a possible positive effect of the strength and direction of lateralization on two demanding cognitive tasks: A visuospatial task (mental rotation MR), and a language task (word generation WG), executed either as a singletask or as dual-task. Participants (n = 72) performed these tasks while their single-task brain lateralization was assessed with functional Transcranial Doppler for both tasks. From these measurements we determined strength and direction of lateralization for both tasks and the individual pattern of lateralization (contralateral or ipsilateral) was derived. These factors, along with sex, were used in a GLM analysis to determine if they predicted the respective performance measure of the tasks. We found that for MR there was a significant medium effect of direction of lateralization on performance with better performance in left-lateralized (atypical) participants (partial eta squared 0.061; p = .039). After correction for outliers, there was a significant effect for strength (p = .049). For the dual-task, there was a significant positive medium effect of strength of lateralization on performance (partial eta squared 0.062; p = .038, respectively) No other association between direction or strength in either tests were found. We conclude that there is no evidence for hemispheric crowding, and that strength of lateralization may be a factor that contributes to the evolutionary selection of functional brain lateralization. Pattern of lateralization does not, explaining the large inter-individual variation in these traits

A Review and Classification of Approaches for Dealing with Uncertainty in Multi-Criteria Decision Analysis for Healthcare Decisions

Multi-criteria decision analysis (MCDA) is increasingly used to support decisions in healthcare involving multiple and conflicting criteria. Although uncertainty is usually carefully addressed in health economic evaluations, whether and how the different sources of uncertainty are dealt with and with what methods in MCDA is less known. The objective of this study is to review how uncertainty can be explicitly taken into account in MCDA and to discuss which approach may be appropriate for healthcare decision makers. A literature review was conducted in the Scopus and PubMed databases. Two reviewers independently categorized studies according to research areas, the type of MCDA used, and the approach used to quantify uncertainty. Selected full text articles were read for methodological details. The search strategy identified 569 studies. The five approaches most identified were fuzzy set theory (45 % of studies), probabilistic sensitivity analysis (15 %), deterministic sensitivity analysis (31 %), Bayesian framework (6 %), and grey theory (3 %). A large number of papers considered the analytic hierarchy process in combination with fuzzy set theory (31 %). Only 3 % of studies were published in healthcare-related journals. In conclusion, our review identified five different approaches to take uncertainty into account in MCDA. The deterministic approach is most likely sufficient for most healthcare policy decisions because of its low complexity and straightforward implementation. However, more complex approaches may be needed when multiple sources of uncertainty must be considered simultaneousl