Understanding the effectiveness of learning within learning communities Learning communities (LCs) are in vogue to promote organizational learning. Yet, a theory that explains whether, when and why LCs work is missing. To guide effective LCs, we develop and test an innovative ‘bottom-up’ model of how learning takes shape within LCs and how learning transfers from LCs to organizations.

Organizations spend annually billions on diversity and inclusion initiatives to attract and nurture talent from underrepresented groups. These initiatives, however, rarely reach their intended goals. This research examines covert employee resistance as a possible overlooked mechanism that explains the failure of these initiatives and explores possible interventions to limit (the impact of) such resistance.

Unemployment represents one of the worst stressors that people encounter in their working lives, having negative financial and mental-health consequences for individuals and being a costly burden for society. Due to volatile economic development, unemployment spells are increasingly inevitable for many workers. Research aimed at understanding barriers to successful job search and high-quality reemployment is important to individuals, to professionals providing job-search services, and to governments. Job search is a demanding act of self-regulation. It requires the setting and pursuit of goals, the regulation of emotion and behavior, and the ability to persist when things get difficult. Extant research documents structural, process, and content approaches to self-regulation, but these approaches have rarely been integrated. Further, self-regulation has been mostly studied as a static phenomenon. Essential knowledge about the evolution of self-regulation over time is lacking. Also, prior research has paid little attention to external/contextual ? as opposed to intra-individual ? factors that could impact the initiation and development of self-regulation. Our proposed research addresses these key shortfalls in studies on self-regulation during unemployment. We build and test theory on self-regulation during unemployment in four integrated subprojects: (1) a cross-national investigation of the impact of system-based unemployment insurance benefits on initiation, development, and consequences of job-search behaviors; (2) research on self-regulatory responses to setbacks and rejections; (3) the study of career adaptability as a source for self-regulation and reemployment quality; and (4) examining the causes, content, dynamics, and outcomes of procrastination as a form of self-regulation failure. We combine innovative methods such as experience-sampling and diary designs with lab and field experiments. Besides scientific products we will provide interventions that advance self-regulation during unemployment. This WORKOUT-project will deliver a step change in scientific knowledge of self-regulatory processes and effects, contribute to the effectiveness of employment counseling, and ultimately improve unemployed individuals? lives.

Women and racial-ethnic minorities are underrepresented in (higher) leadership positions. Research on the psychological underpinnings of this underrepresentation traditionally focuses on stereotypicality bias explanations (i.e., women and minorities do not ?fit? the typical image of leaders). The present research introduces an additional, prototypicality bias explanation (i.e., women and minorities may not be perceived as ideal representatives of groups). Besides more elaborately specifying the underlying psychological mechanisms, the introduced Dual Pathway to Leadership Model also focuses on how the context (crisis and diversity policy) affects female and minority leadership through its influence on these mechanisms. Addressing shortcomings in the literature and novel research-questions, my main goal is to illuminate how biases in social-cognitive and social-identity processes simultaneously affect female and minority leadership-emergence and -evaluations under differing contexts. In order to attain this goal, I will perform four elaborate experiments which have important theoretical and practical implications for both glass ceiling researcher and work organizations. KEYWORDS: the glass ceiling, leadership, diversity

The human brain contains several visual processing regions that selectively activate when we view real-world scenes, such as landscapes or city streets, and are therefore thought to be critical for behavioural interactions with scenes, such as navigation and action planning. It is poorly understood, however, how the visual system extracts information relevant for actions from retinal inputs: how it determines what actions are afforded by the visual environment that is currently in view. I will study this process by capitalizing on recent technological advances in both computer vision and cognitive neuroscience. First, I will quantify the visual features that are necessary to solve different scene tasks using deep neural networks (DNNs). These state-of-the-art computer vision models have recently led to great improvement in automatic recognition of objects, but can be trained on other tasks. I will use these networks as computational tools to measure scene information relevant for actions, by training different DNNs on navigational (“is there a path?”) and action-related (“can I walk here?”) tasks. Second, I will measure the neural representations underlying these tasks using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) recordings. Using a known pattern analysis method called representational similarity analysis, these measurements will be combined (spatiotemporal fusion) to achieve neural measurements of visual processing with high spatial and temporal resolution. Crucially, by comparing the visual features learned by different DNNs against the neural representations evoked under different scene recognition tasks, I will identify the visual features that our brains extract for the purpose of determining the action affordances of a scene. As a result, we will learn how the retinal input is transformed to arrive at behaviourally relevant neural representations of real-world environments.