Eqpmt Grant in Support of Exploring Blunder by Individuals & Teams
Abstract
Real-time, dynamic environments in which event sequencing and/or timing is beyond the performer s control may present even the most skilled performers with nothing but bad choices.However, rather than accepting a fate designed by chance, individuals and teams in uncertain task environments invent, design, or discover methods that mitigate the e#ects of fate; simply put, they attempt to make the best of a bad situation. Understanding the interplay amongteam members in blunder recovery requires detailed analysis and modeling of how the dynamic allocation of shared visual and motor attention varies with overall team familiarity and with the expertise of each team member.This research program builds on past work that sampled individual expertise across a wide range of skill on dozens of measures collected from human performance on two, game-based,dynamic decision tasks. It extends that work by collecting longitudinal data on tasks per-formed by individuals and harvesting Big Data on a competitive team task. Funding provided by the DURIP will enable us to extend this work by collecting eye data on the point-of-gaze of single performers in both team and individual tasks. Most revolutionary, it will extend ourwork into the seldom (if ever) traversed realm of the diferences and similarities in instance-by-instance allocation of visual attention by di#erent pairs of team members. At present, team data tends to emphasize outcomes whereas individual data includes outcome data as well as detailed process data such as timestamped records of keystrokes, eye move-ments, point-of-gaze, hand movements, as well as all relevant external events. As teams arecomposed of individuals, strong theories of team behavior will not emerge until the differences and commonalities in individual behavior of team members are understood. This approach to individual and team performance combines empirical data with statistical and machine models. The models allow us to s"imulate what we believe is happening and also allows us to simplify the task environment by considering how subsets of factors ""play"" by themselves""before considering how all factors play together."" For example, in dynamic tasks, perception time and movement time" compete with decision outcomes in complex ways. Using machine models, we can eliminate perception and movement time to focus solely" on decision outcomes.Once we understand the ""optimal decision"" when time for perception and movement are notfactors, we can then i"nvestigate how individuals and teams satisfice when they must choose among the subset of outcomes that can be accomplished in the time available. In summary, we seek to break new ground for cross-domain generality in the development of extreme expertise in individuals and to augment theories of team performance to include the development of expertise of teams and the individual expertise of t"eam members. As many Navy team tasks take place in real-time at stationary or moving ""computer consoles"" (e.g.,three person Drone t"eams; submarine command teams, pilot-copilot aviation teams) the time is right to unite the study of team training and team performance with studies of individual cognition. The instrumentation requested of DURIP will enable us to collect detailed data on blunder recovery by teams and individuals to better understand how successful individuals and successful teams anticipate and recover from these inevitable consequences of performingin real-time, dynamic task environments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 15, 2019
- Source ID
- N000141912441
Entities
People
- Wayne D. Gray
Organizations
- Office of Naval Research
- Rensselaer Polytechnic Institute
- United States Navy