During the 2010s, various fields of empirical research, including psychological science, have experienced a crisis of confidence regarding the trustworthiness of published research findings. Inter alia, these replication (or reproducibility) debates have led to a method-reform movement (the “new statistics“), large-scale replication initiatives, and changes in journal policies and scientific publishing (Open Science). One argument central to the broader context of these debates is the fact that there always are numerous flexibilities in data-analytic decisions (interchangeably termed as: researcher degrees of freedom, p-hacking, or the garden of forking paths). More specifically, researchers often seem to disagree which data to analyze, and how to analyze them. I present, compare, and discuss a variety of novel, not yet widely known, approaches to this general problem of empirical research, such as combinatorial meta-analysis, crowdsourcing data analysis, multiverse analysis, and specification-curve analysis. I illustrate the rationale, applicability, and potential of these approaches with real examples from current social, personality, and biological psychology, and by drawing upon both primary analysis and meta-analysis of research data.

Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological construct of g. Few attempts have been made to develop an ability construct exclusively based on brain characteristics. The best neuro-anatomical predictor of intelligence is brain volume as it shows a modest positive correlation with g and explains between 9 and 16% of variance. Larger brains contain more neurons thus they have greater computational power that presumably allows for more complex cognitive processing. Correlations with brain surface, thickness, convolution and callosal shape show less consistent patterns. The development of diffusion tensor imaging has allowed researchers to examine the microstructure of brain tissue. Positive correlations between white matter integrity and intelligence have been consistently observed, supporting the idea that efficient information transfer between hemispheres and brain areas is crucial for higher intellectual competence. Based on functional studies of the brain-intelligence relationship, three theories have been put forward: the neural efficiency hypothesis, the P-FIT and the multi demand (MD) system theory. The best consensus based on the diversity of results reported is that g is predominantly determined by lateral prefrontal attentional control of structured sensory episodes in posterior brain areas, a model that is also in line with contemporary cognitive neuroscience of working memory. Discussed will be also some recent attempts to use of rTMS or tACS and fcMRI to determine the causality between brain function and intelligence: (1) In the first step connections essential for intelligence (fcMRI) are identified, next (2) the identified areas are targeted in rTMS/tACS stimulation. (3) Possible changes in behavioral measures as well as changes in brain networks are established.

Throughout their lives people are adapting to the demands of their changing and dynamic environment. The potential modifiability of one’s cognitive and neural system, which stems from these adaptations, is referred to as cognitive or neural plasticity. Plasticity thus represents the ability to change, remodel and create neural pathways, and to adjust activity in response to new situations and changes in the environment. It mediates the acquisition of knowledge and skill, and understanding of neural plasticity could help in promoting it when it is needed and useful. Applying training interventions and measuring their scope and effects in order to identify the mechanisms underlying the plasticity of mind and brain is the soundest psychological pathway to understanding plasticity. The literature on cognitive training interventions has been growing rapidly, and demonstrates extreme boost of publication in the past decade. Several issues characterise the current state in the field: 1) heterogeneity of interventions in terms of research designs, methods, and training protocols, 2) relative lack of theoretical models describing the mechanisms underlying training and its effects, 3) the question of transferability of training-related gains to untrained tasks and abilities. The usual finding in early studies was that, given adequate training, individuals can significantly improve on a specific task, yet weak or no gains of were found on new tasks. Still, recent studies suggest that cognitive training can actually produce broader, more generalizable effects. This holds particularly true if cognitive training is directed at basic processing capacities, such as working memory or executive functioning.

The presentation deals with Machiavellianism, a dark personality characteristic defined by interpersonal manipulative strategies, cynical world view, and moral disengagement. Traditional approaches treat Machiavellianism as an evolutionary strategy or as a pseudopathology – a trait beneficial for the individual but harmful for the society. According to this view, Machiavellian individuals are considered to be masters of the puppets, i.e. proficient manipulators.
Contrasted to the above described strength-based approaches, research of the relationship between Machiavellianism, developmental issues, and psychopathology gives a completely different picture of Machiavellian individuals. According to this line of research, Machiavellian people are immature in their personality organization, show a considerable amount of psychological symptoms, and their developmental history is full of adversities.
In my lecture I will present some research evidence for the second line of reasoning, namely a deficit-approach to Machiavellianism. In presenting this evidence I will argue that Machiavellian behaviour is not manipulative only as far as we attribute manipulative intentions to Machiavellian individuals.

The origins of the difficulties in mathematics has high relevance not only for the theoretical significance but also for the social and educational impacts. In the last years, studies on the foundations of mathematical abilities and disabilities have been receiving growing attention. Therefore, it is not surprising that intensive research has been devoted to the detection of the cognitive factors that prompt math proficiency or predict math learning disabilities. Similarly, a growing amount of studies has also highlighted the importance of the affective factors as determinants of math achievement, with a prevailing focus on the role of anxiety. However, only a few studies to date have jointly investigated the role of both cognitive and affective factors associated with math ability, and even fewer have focused on young children.
A clear understanding of cognitive and emotional processes at the basis of math learning can provide valuable information about the causes of math difficulties at early age. Furthermore, this knowledge can be applied to the development of appropriate compensatory programs.
We know from a wealth of literature that individual differences in working memory and executive functions are related to mathematical performance and academic success above and beyond socio- economic status and general intelligence. We will discuss about this large body of knowledge as well as about the possible malleability of working memory and the efficacy of numeracy and working memory training on math proficiency. Moreover, we will discuss the importance of developing a comprehensive theoretical model, which includes cognitive and emotional factors, such as general and math specific anxiety, as relevant factors that should be taken into consideration in math learning. The scientific and educational relevance will be discussed.