The geography of strain: organizational resilience as a function of intergroup relations

Organizational resilience is an organization’s ability to absorb strain and preserve or improve functioning, despite the presence of adversity. In existing scholarship there is the implicit assumption that organizations experience and respond holistically to acute forms of adversity. We challenge this assumption by theorizing about how adversity can create differential strain, affecting parts of an organization rather than the whole. We argue that relations among those parts fundamentally shape organizational resilience. We develop a theoretical model that maps how the differentiated emergence of strain in focal parts of an organization triggers the movements of adjoining parts to provide or withhold resources necessary for the focal parts to adapt effectively. Drawing on core principles of theories about intergroup relations, we theorize about three specific pathways—integration, disavowal, and reclamation—by which responses of adjoining parts to focal part strain shape organizational resilience. We further theorize about influences on whether and when adjoining parts are likely to select different pathways. The resulting theory reveals how the social processes among parts of organizations influence member responses to adversity and, ultimately, organizational resilience. We conclude by noting the implications for organizational resilience theory, research, and practice.Accepted manuscrip

Predictor Aided Tracking in a System with Time Delay - Performance Involving Flat Surface, Roll, and Pitch Conditions

Predictor aided human tracking performance with time delay control under flat surface, roll, pitch, and roll and pitch condition

The Formal Underpinnings of the Response Functions used in X-Ray Spectral Analysis

This work provides an in-depth mathematical description of the response functions that are used for spatial and spectral analysis of X-ray data. The use of such functions is well-known to anyone familiar with the analysis of X-ray data where they may be identified with the quantities contained in the Ancillary Response File (ARF), the Redistribution Matrix File (RMF), and the Exposure Map. Starting from first-principles, explicit mathematical expressions for these functions, for both imaging and dispersive modes, are arrived at in terms of the underlying instrumental characteristics of the telescope including the effects of pointing motion. The response functions are presented in the context of integral equations relating the expected detector count rate to the source spectrum incident upon the telescope. Their application to the analysis of several source distributions is considered. These include multiple, possibly overlapping, and spectrally distinct point sources, as well as extended sources. Assumptions and limitations behind the usage of these functions, as well as their practical computation are addressed.Comment: 22 pages, 3 figures (LaTeX

An analysis of astronaut performance capability in the lunar environment. Volume 1 - Performance problems and requirements for additional research

Analyzing data on expected astronaut performance in lunar environmen

An Analysis of Astronaut Performance Capability in the Lunar Environment. Volume 2 - Performance Capability Support Data

Astronaut performance capability in lunar environmen

Network constraints on learnability of probabilistic motor sequences

Human learners are adept at grasping the complex relationships underlying incoming sequential input. In the present work, we formalize complex relationships as graph structures derived from temporal associations in motor sequences. Next, we explore the extent to which learners are sensitive to key variations in the topological properties inherent to those graph structures. Participants performed a probabilistic motor sequence task in which the order of button presses was determined by the traversal of graphs with modular, lattice-like, or random organization. Graph nodes each represented a unique button press and edges represented a transition between button presses. Results indicate that learning, indexed here by participants' response times, was strongly mediated by the graph's meso-scale organization, with modular graphs being associated with shorter response times than random and lattice graphs. Moreover, variations in a node's number of connections (degree) and a node's role in mediating long-distance communication (betweenness centrality) impacted graph learning, even after accounting for level of practice on that node. These results demonstrate that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information.Comment: 29 pages, 4 figure

Functional brain network architecture supporting the learning of social networks in humans

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies