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Allocation of individuals' resources among multiple goals is an increasingly prominent theoretical and practical issue. Despite several theoretical perspectives that highlight the potential role of affect in this resource allocation process, empirical work on the topic is quite limited with little focus on the activation dimension of affect. This study aimed to provide further insight into this issue.

The current research explored the role of the activation dimension of affect in a multiple-goal environment. Specifically, 118 individuals participated in a 21-day longitudinal study in which they reported on affect and resource allocation related to two real-life goals.

Multilevel-modeling analyses indicated that activation positively relates to allocation of resources (effort, intended effort, and intended time devoted to a goal). The results also illustrate that task-related negative valence is a significant predictor for two of the three indicators of resource allocation (intended effort was the exception).

This research informs theory and practice at the intersection of emotion and work motivation by investigating a relatively understudied dimension of affect and provides results that help clarify the role of affect during the pursuit of multiple, competing goals.

Exercises are key components of assessment centers (ACs). However, little is known about the nature and determinants of AC exercise performance. The traditional exercise paradigm primarily emphasizes the need to simulate task, social, and organizational demands in AC exercises. This chapter draws on trait activation theory in proposing a new AC exercise paradigm. First, we develop a theoretical framework that addresses the complexity of situational characteristics of AC exercises as determinants of AC performance. Second, we argue for planting multiple stimuli within exercises as a structured means of eliciting candidate behavior. Third, we show how the new paradigm also has key insights for the rating part of ACs, namely, in selecting dimensions, designing behavioral checklists, screening assessors, and training assessors. Finally, the impact of this new AC exercise paradigm is anticipated on important AC outcomes such as reliability, internal/external construct-related validity, criterion-related validity, assessee perceptions, and feedback effectiveness.

Previous literature shows that activation requirements for welfare recipients reduce welfare participation. However, the effect of mandatory activation on welfare entry and exit rates has not been fully examined. In this article, we use a rich set of register data that covers the entire population of Stockholm to study how the introduction of activation programs aimed at unemployed welfare recipients in various city districts affects the probability of individuals entering and exiting social assistance (SA). Our results show that mandatory activation has no overall average effects on SA entry or SA exit. However, we do find a significant negative effect of mandatory activation on the SA entry rate for young individuals and for unmarried individuals without children. For unmarried individuals without children, we find a positive but statistically insignificant effect on the probability to leave SA. Thus, individuals with fewer family responsibilities seem to be more responsive to the reform.

While much research examines the consequences of deinstitutionalization for caregivers, few studies address support mobilization strategies used by patients themselves. We examine the relationship between mental health patients’ needs, their activation of network ties for health discussion, and network dynamics during the course of treatment. We hypothesize that patients strategically activate their network ties for support that matches their needs. Linking activation to network dynamics, we also propose that patients with greater needs exhaust their supportive relationships and experience more network turnover.

We draw on a dataset of new mental health patients (N=173) and their associated network members (N=4,144) observed over three years. Random-intercept regression models test the relationship between patients’ needs and (1) network tie activation for health discussion and (2) network turnover.

Although the overall level of need does not predict network tie activation, mental health patients are more likely to activate network ties who provide support that matches their expressed needs for discussion, emotional, and financial support (although not instrumental or informational support). In addition, patients with elevated needs experience increased network turnover. Strategic activation and its unintended consequence together suggest a revolving door of support for patients in crisis.

In the post-deinstitutionalization era, patients’ informal social safety nets must compensate for needs that are left unmet by deficits in the formal treatment system. We find that patients seek out network members who are well-equipped to help them cope with the onset of illness. At the same time, network activation may lead to instability as high-need patients churn through supportive relationships. Future research should examine the consequences of tie activation and support needs for network dynamics in different treatment contexts.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).