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Automatically detecting mental state such as stress from video images of the face could support evaluating stress responses in applicants for high risk jobs or contribute to timely stress detection in challenging operational settings (e.g., aircrew, command center operators). Challenges in automatically estimating mental state include the generalization of models across contexts and across participants. We here aim to create robust models by training them using data from different contexts and including physiological features. Fifty-one participants were exposed to different types of stressors (cognitive, social evaluative and startle) and baseline variants of the stressors. Video, electrocardiogram (ECG), electrodermal activity (EDA) and self-reports (arousal and valence) were recorded. Logistic regression models aimed to classify between high and low arousal and valence across participants, where “high” and “low” were defined relative to the center of the rating scale. Accuracy scores of different models were evaluated: models trained and tested within a specific context (either a baseline or stressor variant of a task), intermediate context (baseline and stressor variant of a task), or general context (all conditions together). Furthermore, for these different model variants, only the video data was included, only the physiological data, or both video and physiological data. We found that all (video, physiological and video-physio) models could successfully distinguish between high- and low-rated arousal and valence, though performance tended to be better for (1) arousal than valence, (2) specific context than intermediate and general contexts, (3) video-physio data than video or physiological data alone. Automatic feature selection resulted in inclusion of 3–20 features, where the models based on video-physio data usually included features from video, ECG and EDA. Still, performance of video-only models approached the performance of video-physio models. Arous

Military personnel needs to be resilient to be able to remain effective, motivated, and in good mental and physical health. Military organizations select on resilience to determine whether candidates are psychologically fit for their job. The INSPIRE Resilience Scale (IRS) is such a selection instrument that aims to assess the psychological resilience potential of candidates in high-risk professions. A longitudinal predictive validity study was conducted in five European Defense organizations and in the Dutch National Police. The IRS was submitted in selection (N = 11,404), and criterion data about performance and health were collected in the second half of the first training year (N = 726). The results based on correlational and regression analyses showed that the IRS scores significantly predicted the criterion measures. Emotional stability, part of the IRS, turned out to be the best predictor. Results also showed that candidates who dropped out of training had significantly lower means on the IRS in selection than candidates who were still in training in the second half of the first training year. Overall, the IRS proved to be a valid instrument to assess resilience potential in candidates for high-risk professions. Selecting on resilience may therefore contribute to training success and reduction of health problems.

In complex endeavors, characterized by multiple interdependent participants with different functions and objectives, it is difficult for an entity to determine how to cooperate with other entities. Simply striving to cooperate at the highest level possible comes at high costs. But how should an entity determine what cooperation approach is appropriate? The present paper describes the newly developed LOCI-method (Levels of Chosen Interdependence) that can be used to determine ones position in the field of other participants, to map out existing dependencies, and to make choices about potential cooperation partners and cooperation approaches. It focuses on interdependence as the most important determinant and consequence of choices for cooperation partners and approaches. The method centers around the Interdependence Space, a three dimensional space with axes that correspond to three properties of interdependence: degree of dependence, mutuality of dependence, and degree of correspondence. An entity that employs the method fills the interdependence space with relevant parties according to their positions on the axes, by means of a questionnaire. Subsequently, the entity is guided in "reading" the Interdependence Space to better understand the field of interdependencies. Finally, a set of generic principles facilitates in choosing the appropriate cooperation partners and cooperation approaches., Presented at the International Command and Control Research and Technology Symposium (16th), ICCRTS 2011, Quebec City, Canada, June 21-23, 2011 and published in proceedings of the same. Briefing charts begin p24.