The dynamics underlying the rise of star performers

Across different domains, there are ‘star performers’ who are able to generate disproportionate levels of performance output. To date, little is known about the model principles underlying the rise of star performers. Here, we propose that star performers' abilities develop according to a multi-dimensional, multiplicative and dynamical process. Based on existing literature, we defined a dynamic network model, including different parameters functioning as enhancers or inhibitors of star performance. The enhancers were multiplicity of productivity, monopolistic productivity, job autonomy, and job complexity, whereas productivity ceiling was an inhibitor. These enhancers and inhibitors were expected to influence the tail-heaviness of the performance distribution. We therefore simulated several samples of performers, thereby including the assumed enhancers and inhibitors in the dynamic networks, and compared their tail-heaviness. Results showed that the dynamic network model resulted in heavier and lighter tail distributions, when including the enhancer- and inhibitor-parameters, respectively. Together, these results provide novel insights into the dynamical principles that give rise to star performers in the population

Conceptualizing and measuring psychological resilience:What can we learn from physics?

The number of resilience conceptualizations in psychology has rapidly grown, which confuses what resilience actually means. This is problematic, because the conceptualization typically guides the measurements, analyses, and practical interventions employed. The most popular conceptualizations of psychological resilience equate it with the ability to (1) resist negative effects of stressors, (2) “bounce back” from stressors, and/or (3) grow from stressors. In this paper, we review these three conceptualizations and argue that they reflect different concepts. This is supported by important lessons from engineering physics, where such concepts are clearly differentiated with precise mathematical underpinnings. Against this background, we outline why psychological resilience should be conceptualized and measured in terms of the process of returning to the previous state following a stressor (i.e., bouncing back). By establishing a clearer language of resilience and related processes, measurements and interventions in psychological research and practice can be targeted more precisely

Injury Prediction in Competitive Runners with Machine Learning

Purpose: Staying injury-free is a major factor for success in sports. Although injuries are difficult to forecast, novel technologies and data science applications could provide important insights. Our purpose is to use machine learning for the prediction of injuries in runners, based on detailed training logs. Methods: Prediction of injuries was evaluated on a new data set of 77 high-level middle and long distance runners, over a period of seven years. Two analytic approaches were applied. First, the training load from the previous seven days were expressed as a time series, with each day’s training being described by ten features. These features were a combination of objective data from a GPS watch (e.g., duration, distance), together with subjective data about the exertion and success of the training. Second, a training week was summarized by 22 aggregate features, and a time window of three weeks before the injury was considered. Results: A predictive system based on bagged XGBoost machine learning models, resulted in Receiver Operating Characteristic curves with average Areas Under the Curves of 0.724 and 0.678 for the day and week approach, respectively. Especially the results of the day approach reflect a reasonably high probability that our system makes correct injury predictions. Conclusions: Our machine learning-based approach predicts a sizable portion of the injuries, in particular when the model is based on training load-data in the days preceding an injury. Overall, these results demonstrate the possible merits of using machine learning to predict injuries and tailor training programs for athletes

Psychological Momentum

Psychological Momentum (PM) is a positive or negative dynamics of cognitive, affective, motivational, physiological, and behavioral responses to the perception of movement toward or away from either a desired or an undesired outcome. Such a perception can be fostered by any event or series of events that alters the perceived rate at which one is moving regarding the outcome in question. The history and the context in which such events are embedded determine the occurrence and intensity of PM more determining than the events per se. Therefore, PM is a process of extrapolation that builds upon experiences and extends to anticipated future outcomes (e.g., Hubbard, 2015). PM should not be confused with the “hot/cold hand” phenomenon, which refers to the belief that streaks of success/failure breed future success/failure. The occurrence of streaks is neither sufficient nor necessary to entail a perception of movement toward or away from a final outcome

Facing Repeated Stressors in a Motor Task:Does it Enhance or Diminish Resilience?

The aim of the present research is to test whether resilience in a motor task enhances or diminishes when encountering stressors. We conducted a lateral movement task during which we induced stressors and tracked the movement accuracy of each participant over time. Stressors corresponded to organismic constraints (i.e., visual occlusion), task constraints (i.e., movement sensitivity), or both types of constraints in an alternating pattern. In order to determine resilience, we introduced a measure combining the strength of a stressor and the relaxation time. Across the three conditions, we found that resilience was enhanced rather than diminished over time. This supports the notion that stressors in the form of constraint alterations can be beneficial to human motor performance

The relation between complexity and resilient motor performance, and the effects of differential learning

Complex systems typically demonstrate a mixture of regularity and flexibility in their behavior, which would make them adaptive. At the same time, adapting to perturbations is a core characteristic of resilience. The first aim of the current research was therefore to test the possible relation between complexity and resilient motor performance (i.e., performance while being perturbed). The second aim was to test whether complexity and resilient performance improve through differential learning. To address our aims, we designed two parallel experiments involving a motor task, in which participants moved a stick with their non-dominant hand along a slider. Participants could score points by moving a cursor as fast and accurately as possible between two boxes, positioned on the right- and left side of the screen in front of them. In a first session, we determined the complexity by analyzing the temporal structure of variation in the box-to-box movement intervals with a Detrended Fluctuation Analysis. Then, we introduced perturbations to the task: We altered the tracking speed of the cursor relative to the stick-movements briefly (i.e., 4 seconds) at intervals of 1 minute (Experiment 1), or we induced a prolonged change of the tracking speed each minute (Experiment 2). Subsequently, participants had three sessions of either classical learning or differential learning. Participants in the classical learning condition were trained to perform the ideal movement pattern, whereas those in the differential learning condition had to perform additional and irrelevant movements. Finally, we conducted a posttest that was the same as the first session. In both experiments, results showed moderate positive correlations between complexity and points scored (i.e., box touches) in the perturbation-period of the first session. Across the two experiments, only differential learning led to a higher complexity index (i.e., more prominent patterns of pink noise) from baseline to post-test. Unexpectedly, the classical learning group improved more in their resilient performance than the differential learning group. Together, this research provides empirical support for the relation between complexity and resilience, and between complexity and differential learning in human motor performance, which should be examined further