Intra-EU Migration: Legal and Economic View on Jobseekers’ Welfare Rights

International migration influences economies of both the home and the host country. Some countries perceive immigration as a threat and consider limitations to their social security system to protect the domestic economy. Within the European Union, both the economic and the legal side of the issue must be taken into account. The paper presents an empirical analysis of natives’ and immigrants’ unemployment rates in EU15 countries and discusses a possible use of the findings in intra-EU migration policy. Using Labour Force Survey Data for 2012, we estimate a series of logistic regressions in order to compare the immigrant/native unemployment rates, considering the differences in the structure of both subpopulations (in terms of age, sex, education and types of jobs). We conclude that a substantial part of the gap between natives’ and immigrants’ unemployment rates can be explained by these additional characteristics in most of the EU15 countries.     

Take a risk or proceed with caution: Prevention motivation moderates responses to near-loss events

A persistently pressing issue identified in the decision-making literature is that people not only fail to recognize the risks entailed in a near-loss event but also subsequently engage in further risk-taking. We contend in this paper that prevention motivation critically regulates decision-makers' responses to a near-loss. Across three studies, we found that high prevention-focused individuals perceive greater risks in a near-loss event and are subsequently less likely to take risks, compared with low prevention-focused individuals. This finding highlights the significance of applying a motivational perspective to studying decision-making and risk.     

Evidence for a Data-Based Environmental Policy: Induction of a Behavior-Based Decision Support System

To advise policy‐makers about possible courses of action in the environmental domain, psychological science should employ a support system that allows for evidence‐based decisions with respect to the three generic policy questions: what, where, and how. The key to such a system is a measurement instrument in which environmental motivation becomes tangible in individual actions. In this article, we provide empirical examples of such a decision support system in the environmental domain. It consists of (a) evidence about environmental motivation of persons, (b) evidence about motivation's spatial distribution, (c) knowledge about the socio‐cultural conditions that affect individuals when they translate motivation into action (i.e. structural information), and (d) a forecast of the environmental impact—the concrete conservation potential of various behaviors. Pour conseiller les décideurs à propos de lignes de conduite possibles dans le domaine de l’environnement, la science psychologique devrait utiliser un système qui tienne compte des décisions provenant des trois questions génériques suivantes: quoi, ou et comment? La clé d’un tel système consiste en un instrument de mesure dans lequel la motivation environnementale devient tangible dans les actions individuelles. Dans cet article, nous prenons des exemples empiriques de telles aides à la décision dans le domaine de l’environnement. Peut être ainsi montré a) la motivation environnementale des personnes, b) la distribution spatiale de la motivation, c) l’influence de la connaissance des conditions socioculturelles des individus quand ils transforment motivation en action (i.e. information structurale) et (d) la prévision de l’impact environnemental sur le maintien potentiel de divers comportements.     

Time and decision making in humans

Decision making requires evaluating alternatives that differ on a number of attributes. During this evaluation process, selection of options depends on the duration of the options, the duration of the expected delay for realizing the options, and the time available to reach a decision. This article reviews the relationship between time and decision making in humans with respect to this evaluation process. Moreover, the role of psychological time, as compared with physical time, is accentuated. Five topics have been selected that illustrate how time and mental representations of time affect decision making. These are (1) the duration of options, (2) temporal decision making, (3) the time between having made a decision and experiencing the consequences of that decision, (4) the temporal perspective of decision makers, and (5) the duration of the decision process. The discussion of each topic is supplemented by suggestions for further research. It is shown that psychological time is often neglected in human decision making but seems to play an important role in the making of choices.     

The Center for Lifespan Psychology at the Max Planck Institute for Human Development: Overview of conceptual agenda and illustration of research activities

Founded in 1981 by the late Paul B. Baltes, the Center for Lifespan Psychology at the Max Planck Institute for Human Development has helped to establish lifespan psychology as a distinct conceptual approach within developmental psychology. Recently, the Center has extended its research programme into developmental behavioural neuroscience. Here, we provide an overview of the Center's conceptual agenda, and present two of its seven research projects in greater detail. Work at the Center is guided by three propositions: (1) to study lifespan changes in behaviour as interactions among maturation, learning, and senescence; (2) to develop theories and methods that integrate empirical evidence across domains of functioning, timescales, as well as behavioural and neuronal levels of analysis; (3) to identify mechanisms of development by exploring age-graded differences in plasticity. The Intra-Person Dynamics Project studies the organization of cognitive abilities within individuals of different ages, and investigates lifespan age differences in the plasticity and components of episodic memory performance. The Sensorimotor–Cognitive Couplings Project examines lifespan differences in dynamic dependencies between sensorimotor and cognitive performance. Both projects combine behavioural assessments with methods from developmental neuroscience to delineate age-graded changes in brain–behaviour mappings. Current research in other projects includes: (1) behavioural development in very old age, as assessed in the Berlin Aging Study; (2) the interplay of motivation, affect, and cognition in developmental regulation; (3) behavioural and electrophysiological mechanisms of social interaction from infancy to adulthood; and (4) formal and statistical issues in structural equation modelling, with an emphasis on latent growth curve modelling. Graduate education and research at the Center profit greatly from cooperation with other institutions in Berlin and Potsdam as well as from national and international collaboration.     

Process components of the Implicit Association Test: a diffusion-model analysis

The authors present a diffusion-model analysis of the Implicit Association Test (IAT). In Study 1, the IAT effect was decomposed into 3 dissociable components: Relative to the compatible phase, (a) ease and speed of information accumulation are lowered in the incompatible phase, (b) more cautious speed-accuracy settings are adopted, and (c) nondecision components of processing require more time. Studies 2 and 3 assessed the nature of interindividual differences in these components. Construct-specific variance in the IAT relating to the construct to be measured (such as implicit attitudes) was concentrated in the compatibility effect on information accumulation (Studies 2 and 3), whereas systematic method variance in the IAT was mapped on differential speed-accuracy settings (Study 3). Implications of these dissociations for process theories of the IAT and for applications are discussed.     

Personality Research and Assessment in the Era of Machine Learning

The increasing availability of high-dimensional, fine-grained data about human behaviour, gathered from mobile sensing studies and in the form of digital footprints, is poised to drastically alter the way personality psychologists perform research and undertake personality assessment. These new kinds and quantities of data raise important questions about how to analyse the data and interpret the results appropriately. Machine learning models are well suited to these kinds of data, allowing researchers to model highly complex relationships and to evaluate the generalizability and robustness of their results using resampling methods. The correct usage of machine learning models requires specialized methodological training that considers issues specific to this type of modelling. Here, we first provide a brief overview of past studies using machine learning in personality psychology. Second, we illustrate the main challenges that researchers face when building, interpreting, and validating machine learning models. Third, we discuss the evaluation of personality scales, derived using machine learning methods. Fourth, we highlight some key issues that arise from the use of latent variables in the modelling process. We conclude with an outlook on the future role of machine learning models in personality research and assessment.     

Clinical Ethics Consultation in the Transition Countries of Central and Eastern Europe

Science and Engineering Ethics - Since 1989, clinical ethics consultation in form of hospital ethics committees (HECs) was established in most of the transition countries of Central and Eastern...