Team s are important units of organizational work because they bring diverse expertise, skills, and resources to complex tasks that may be too large or complex for a single individual to undertake. However, as projects and teams grow in size and complexity, tasks and member dependencies become more numerous and more complex, thus increasing the need for team coordination. Effective teams manage these dependencies using a number of explicit and implicit coordination mechanisms and processes. Teams coordinate explicitly using task programming mechanisms (e.g., schedules, plans, procedures) or by communicating (e.g., orally, in writing, formally, informally, interpersonally, in groups). We call these mechanisms explicit because team members use them purposely to coordinate. However, teams can also coordinate implicitly (i.e., without consciously trying to coordinate) through team cognition, or knowledge that team members share about the task and
This research examines how decision makers manage their attentional resources when making a series of interdependent decisions in a real-time environment. Decision strategies for real-time dynamic tasks consist of two main overlapping cognitive activities: monitoring and control. Monitoring refers to decision makers' tracking of key system variables as they work toward arriving at a decision. Control refers to the decision maker's generation, evaluation, and selection of alternative actions. In real-time tasks, these two activities compete for the same attentional resources. The questions that motivate the two studies presented here are: (1) can decision making be improved by increasing individuals' attentional resources, thereby enhancing their ability to monitor the system, and (2) can decision making be improved by providing individuals with feedback and/or feedforward control support? Our findings show that some kinds of cognitive support degrade performance, rather than enhance it. These results indicate that providing support for real-time dynamic decision making may be very difficult, and that designing effective decision aids requires a detailed understanding of the underlying cognitive processes.
Background: Unsupervised machine-learned analysis of cluster structures, applied using the emergent self-organizing feature maps (ESOM) combined with the unified distance matrix (U-matrix) has been shown to provide an unbiased method to identify true clusters. It outperforms classical hierarchical clustering algorithms that carry a considerable tendency to produce erroneous results. To facilitate the application of the ESOM/U-matrix method in biomedical research, we introduce the interactive R-based bioinformatics tool "Umatrix", which enables valid identification of a biologically meaningful cluster structure in the data by training a Kohonen-type self-organizing map followed by interface-guided interactive clustering on the emergent U-matrix map. Results: The ability to detect clinical relevant subgroups was applied to a data set comprising plasma concentrations of d = 25 lipid markers including endocannabinoids, lysophosphatidic acids, ceramides and sphingolipids acquired from n = 100 patients with Parkinson's disease and n = 100 controls. Following ESOM training, clear data structures in the high-dimensional data space were observed on the U-matrix, allowing separation of patients from controls almost perfectly. When the data structure was destroyed by Monte-Carlo random resampling, the U-matrix became unstructured and patients and controls were mixed. Obtained results are biologically plausible and supported by empirical evidence of a regulation of several classes of lipids in Parkinson's disease. Conclusions: Sophisticated analysis of structures in biomedical data provides a basis for the mechanistic interpretation of the observations and facilitates subsequent analyses focusing on hypothesis testing. The freely available R library "Umatrix" provides an interactive tool for broader application of unsupervised machine learning on complex biomedical data.
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