Planification de l’âge du départ à la retraite professionnelle : contribution de l’ouverture aux expériences

The present research examines mechanisms underlying planned retirement age by applying Beehr’Model (1986). The contribution of personality traits was investigated among 300 active employees. Multiple regression analyses, performed at both broad and facet-level, revealed that Openness to experiences and ideas positively contribute to planned retirement age beyond variables of Beehr’Model (i.e., personal, contextual and psychological variables). This study contributes to an in-depth understand of planning retirement mechanisms and insists on the major role of dispositional variables.     

The multidimensionality of test anxiety: A test of different models

Abstract

Confirmatory factor analysis was employed to test the multidimensional structure of test anxiety. Starting with the traditional two-factor model, it was of interest whether additional dimensions could still be represented by a single higher-order factor of test anxiety or whether the additional dimensions represented correlates of test anxiety. The Revised Test Anxiety (RTA) scale and the German Test Anxiety Inventory (TAI-G) were combined collecting data from a binational sample of 218 American and 218 German university students. Considering the statistical and theoretical aspects of model fit, they indicated that a model consisting of three primary factors (worry, emotionality, and lack of confidence) fit the data best. While distraction and self-efficacy may be regarded as correlates of test anxiety, lack of confidence was substantiated as a component of test anxiety. Implications for the conceptualization of the content domain and hence area of test anxiety were discussed.     

Dissociation of sensory facilitation and decision bias in statistical learning

ABSTRACT

The detection of regularities in the sensory environment, known as statistical learning, is an important brain function that has been observed in many experimental contexts. In these experiments, statistical learning of patterned sensory stimulation leads to improvements in the speed and/or accuracy with which subsequent stimuli are recognized. That is, statistical learning facilitates the transformation of sensory stimuli into motor responses, but the mechanism by which this occurs is unclear. Statistical learning could improve the efficiency of sensory processing, or it could bias responses toward particular outcomes. The distinction is important, as these different hypotheses imply different functions and different neural substrates for statistical learning. Here we address this problem by studying statistical learning as a decision-making process, which allows us to leverage the extensive computational literature on this topic. Specifically we describe a method for applying the Diffusion Decision Model (DDM) to isolate different sensory and cognitive processes associated with decision-making. The results indicate that statistical learning improves performance on a visual learning task in two distinct ways: by altering the efficiency of sensory processing and by introducing biases in the decision-making process. By fitting the parameters of the DDM to data from individual subjects, we find that the prominence of these two factors differed substantially across the population, and that these differences were predictive of individual performance on the psychophysical task. Overall, these results indicate that different cognitive processes can be recruited by statistical learning, and that the DDM is a powerful framework for detecting these influences.