Early in this century, most empirically oriented psychologists believed that all motivation was based in the physiology of a set of non-nervous system tissue needs. The theories of that era reflected this belief and used it in an attempt to explain an increasing number of phenomena. It was not until the 1950s that it became irrefutably clear that much of human motivation is based not in these drives, but rather in a set of innate psychological needs. Their physiological basis is (...) less understood; and as concepts, these needs lend themselves more easily to psycho logical than to physiological theorizing. The convergence of evidence from a variety of scholarly efforts suggests that there are three such needs: self-determination, competence, and interpersonal relatedness. This book is primarily about self-determination and competence (with particular emphasis on the former), and about the processes and structures that relate to these needs. The need for interpersonal relat edness, while no less important, remains to be explored, and the findings from those explorations will need to be integrated with the present theory to develop a broad, organismic theory of human motivation. Thus far, we have articulated self-determination theory, which is offered as a working theory-a theory in the making. To stimulate the research that will allow it to evolve further, we have stated self-determination theory in the form of minitheories that relate to more circumscribed domains, and we have developed paradigms for testing predictions from the various minitheories. (shrink)

Recent controversies about the level of replicability of behavioral research analyzed using statistical inference have cast interest in developing more efficient techniques for analyzing the results of psychological experiments. Here we claim that complementing the analytical workflow of psychological experiments with Machine Learning-based analysis will both maximize accuracy and minimize replicability issues. As compared to statistical inference, ML analysis of experimental data is model agnostic and primarily focused on prediction rather than inference. We also highlight some potential pitfalls resulting from (...) adoption of Machine Learning based experiment analysis. If not properly used it can lead to over-optimistic accuracy estimates similarly observed using statistical inference. Remedies to such pitfalls are also presented such and building model based on cross validation and the use of ensemble models. ML models are typically regarded as black boxes and we will discuss strategies aimed at rendering more transparent the predictions. (shrink)

Teaching requires much emotion work which takes its toll on teachers. Emotion work is usually studied from one of two perspectives, a job or an individual perspective. In this study, we assessed the relative importance of these two perspectives in predicting emotional exhaustion. More than 200 teachers completed a questionnaire comprising the DISQ , the Dutch Questionnaire on Emotional Labour , and the UBOS . In line with previous studies, our findings indicated that emotional exhaustion is positively associated with emotional (...) job demands and surface acting. The relative importance of the two operationalisations of emotion work was assessed by comparing the results of two regression analyses. Whereas the model with job demands explained 18% of the variance, the model with emotional labour explained only 5%. In understanding what might contribute to emotional exhaustion in teachers, the emotional job demands might be much more important than the self‐regulation perspective that is measured with emotional labour. (shrink)