The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Wayne D. Gray - One of the best experts on this subject based on the ideXlab platform.
-
The functional Task Environment
2020Co-Authors: Wayne D. Gray, Hansjörg Neth, Michael J. SchoellesAbstract:floatation tanks that are used to encourage meditative states, in by far the majority of instances thought occurs in the context of some physical Task Environment. The physical Environment can be as simple as a light and book. It can be as complex as the face of a mountain and the equipment of the climber. It may be as dynamic as the cockpit of an F-16 in supersonic flight and as reactive as a firefight in Iraq or as heated as an argument between lovers. An emphasis on the Environment in cognitive science research is not new. The Environment was of prime concern to Simon in his famous “Ant on the Beach” parable (Simon, 1996), in which he warned of the perils of mistaking limits imposed by the Environment for limits inherent to human cognition. However, the Environment can include an infinity of detail. To be at all useful to understanding human cognition requires a focus on the Environment from the perspective of the to-be-accomplished Task; that is, it is the Task that “allows an Environment to be delimited” (Newell & Simon, 1972, p. 55). The Task delimited Environment, or more simply the Task Environment, forms the first blade in Newell and Simon’s (1972) oft-quoted scissors analogy:
-
Sage: Five Powerful Ideas for Studying and Transforming the Intelligence Analyst's Task Environment
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2020Co-Authors: Wayne D. Gray, Michael J. Schoelles, Selmer Bringsjord, Kirk Burrows, Brian ColderAbstract:Sage provides a scaled world in which real and simulated intelligence analysts work to solve realistic scenarios in innovative Task Environments. All aspects of Sage are instrumented for data collection and Sage itself is built to facilitate the swapping in and out of prototypes of innovative tools for information search, hypothesis generation, and hypothesis testing. Sage focuses not simply on the promised functionality of these tools, but the way in which the design of the interface supports or hinders the realization of this functionality. Beyond the individual tool and its interface, Sage supports the cognitive engineering of integrated Task Environments by the use of simulated cyborgs (simBorgs). SimBorgs combine high-fidelity computational cognitive models with low-fidelity artificial intelligence (AI) based reasoning components. This combination of cognitive modeling with AI enables the creation of intelligent agents, simBorgs, that will work tirelessly to perform usability testing on various combinations of Tasks and interfaces. NIMD supports innovative, creative, and high-risk research with the goal of supporting the intelligence analyst (IA) in discovering “novel intelligence from massive data”. In advancing towards this goal, most of the funded projects focus on building software tools that can be used to augment the intelligence analyst’s (IA’s) Task Environment. Sage 1 takes a different path toward this goal, one that may support some of the other efforts while contributing innovative and creative research of its own. Sage is the vision of Booz Allen Hamilton (Booz Allen) which teamed with Planet 9 Studios and Rensselaer Polytechnic Institute to help make the vision happen. Although the current report encompasses the full scope of Sage it emphasizes the work done by Rensselaer with a strong emphasis on the cognitive engineering focus provided by Rensselaer’s CogWorks Laboratory. Sage is built around five powerful ideas. Each of the following sections emphasizes one of these powerful ideas. The final section summarizes these ideas and accesses our likelihood of achieving them. Powerful Idea #1: Sage as a Scaled World
-
Simulated Task Environments: The role of high-fidelity simulations, scaled worlds, synthetic Environments, and laboratory Tasks in basic and applied cognitive research.
2020Co-Authors: Wayne D. GrayAbstract:Simulated Task Environments provide a setting that adds controlled complexity to experimental Tasks performed by human subjects in laboratory research. Researchers whose problems are mostly applied may find that their problems are easier to study in a simulated Task Environment than in the actual Task Environment. Researchers whose theories have been nurtured in the simple Environments of the typical laboratory study may find that adding controlled complexity will allow them to study how the theoretical constructs they have studied in isolation interact with other constructs in a more complex Task Environment. In this article I define a taxonomy and three dimensions of simulated Task Environments. The dimensions are based on viewing simulated Task Environments from the perspectives of the researcher, the Task, and the participants. Research on complex systems is inherently complex. It is my hope that the terms and distinctions introduced in this article will further the scientific enterprise by enabling us to spend less time explaining our paradigms and more time communicating our results.
-
Meta-T: Tetris^Ⓡ as an experimental paradigm for cognitive skills research
Behavior Research Methods, 2015Co-Authors: John K. Lindstedt, Wayne D. GrayAbstract:Studies of human performance in complex Tasks using video games are an attractive prospect, but many existing games lack a comprehensive way to modify the game and track performance beyond basic levels of analysis. Meta-T provides experimenters a tool to study behavior in a dynamic Task Environment with time-stressed decision-making and strong perceptual-motor elements, offering a host of experimental manipulations with a robust and detailed logging system for all user events, system events, and screen objects. Its experimenter-friendly interface provides control over detailed parameters of the Task Environment without need for programming expertise. Support for eye-tracking and computational cognitive modeling extend the paradigm’s scope.
-
cognitive architectures choreographing the dance of mental operations with the Task Environment
Human Factors, 2008Co-Authors: Wayne D. GrayAbstract:Objective: In this article, I present the ideas and trends that have given rise to the use of cognitive architectures in human factors and provide a cognitive engineering—oriented taxonomy of these architectures and a snapshot of their use for cognitive engineering. Background: Architectures of cognition have had a long history in human factors but a brief past. The long history entails a 50-year preamble, whereas the explosion of work in the current decade reflects the brief past. Understanding this history is key to understanding the current and future prospects for applying cognitive science theory to human factors practice. Method: The review defines three formative eras in cognitive engineering research: the 1950s, 1980s, and now. Results: In the first era, the fledging fields of cognitive science and human factors emphasized characteristics of the dancer, the limited capacity or bounded rationality view of the mind, and the ballroom, the Task Environment. The second era emphasized the dance (i.e., t...
Ivo D. Tafkov - One of the best experts on this subject based on the ideXlab platform.
-
Designing a Performance Feedback System in a Multi-Task Environment: Relative Performance Information Detail Level and Temporal Aggregation in a Multi-Task Environment
2017Co-Authors: R. Lynn Hannan, Gregory P. Mcphee, Andrew H. Newman, Ivo D. TafkovAbstract:When using relative performance information (RPI) in a multi-period setting, firms have discretion in selecting RPI’s detail level and temporal aggregation. With respect to the detail level, firms can only reveal the ranking of each employee (rank-score RPI) or provide the actual scores of each employee (actual-score RPI). Regarding temporal aggregation, firms can provide RPI which is re-set each period (re-set RPI) or provide RPI that captures cumulative performance across all periods completed thus far (cumulative RPI). Using an experiment, we investigate how RPI detail level and temporal aggregation affect employee effort allocation and performance in a multi-Task Environment. Consistent with the behavioral theories underlying our predictions, we find that compared to re-set RPI, cumulative RPI leads to greater distortion of effort allocations across Tasks away from firm-preferred allocations. We also find that to the extent that cumulative RPI increases effort distortion, the effect is greater when RPI provides actual score rather than rank score. Finally, we find that when effort distortion is highest (i.e., when cumulative RPI is combined with actual-score RPI), overall performance is lowest, thereby demonstrating the potentially detrimental effect of effort distortion on performance. Results of our study contribute to both theory and practice by enhancing our understanding of the effect of RPI in a multi-Task Environment, and particularly, how two of its characteristics, detail level and temporal aggregation, can affect effort allocation and performance in a multi-Task Environment.
-
the effect of relative performance information on performance and effort allocation in a multi Task Environment
The Accounting Review, 2013Co-Authors: Lynn R Hannan, Gregory P. Mcphee, Andrew H. Newman, Ivo D. TafkovAbstract:ABSTRACT : This study investigates how relative performance information (RPI) affects employee performance and allocation of effort across Tasks in a multi-Task Environment. Based on behavioral theories, we predict that the social comparison process inherent in RPI induces both a motivation effect that results in increased effort as well as an effort distortion effect that results in the distortion of effort allocations across Tasks away from the firm-preferred allocations. We also predict that both effects are magnified when the RPI is public compared to private. We argue that although the motivation effect will generally benefit performance, the effort distortion effect may be detrimental to performance. We design an experiment that isolates these two effects. Consistent with our predictions, we find that RPI induces both motivation and effort distortion effects and that both effects are magnified when the RPI is public rather than private. Although the motivation effect increases performance, we demons...
Joséh H. Kerstholt - One of the best experts on this subject based on the ideXlab platform.
-
The effect of time pressure on decision-making behaviour in a dynamic Task Environment
Acta Psychologica, 1994Co-Authors: Joséh H. KerstholtAbstract:Decision-making behaviour is considerably affected by dynamic aspects of the Task Environment. First of all, as a dynamic situation continuously changes, a decision maker has to take time into consideration. Second, a decision maker can use feedback providing information on the effect of own actions on system change, elaborating the set of strategies that can be used to cope with the decision problem. Third, in dealing with uncertainty a trade-off has to be made between costs of action, for example information search, versus the risks involved in doing nothing. The present paper describes an experiment in which subjects had to control a system that changed over time. Deteriorations in system performance could either result from changes in system parameters or from false alarms. Of major interest was decision behaviour as a function of time pressure, in this case, speed of system decline. Decision strategies are described in terms of the time allocated to decision phases and in terms of behavioural indices related to information requests and actions. The results showed a general speedup of information processing as time pressure increased. The decision strategy remained constant across all experimental conditions: subjects waited until a specific value of overall system performance was reached, then requested information on the underlying cause, and subsequently executed an action. Under high levels of time pressure, however, this strategy led to a significant increase in system crashes. The findings indicate that people do not optimally react to the time dimension of decision problems. It is concluded that future research should investigate the effects of a priori probabilities of false alarms, and of the costs involved in the decision-making process. © 1994.
Lynn R Hannan - One of the best experts on this subject based on the ideXlab platform.
-
the effect of relative performance information on performance and effort allocation in a multi Task Environment
The Accounting Review, 2013Co-Authors: Lynn R Hannan, Gregory P. Mcphee, Andrew H. Newman, Ivo D. TafkovAbstract:ABSTRACT : This study investigates how relative performance information (RPI) affects employee performance and allocation of effort across Tasks in a multi-Task Environment. Based on behavioral theories, we predict that the social comparison process inherent in RPI induces both a motivation effect that results in increased effort as well as an effort distortion effect that results in the distortion of effort allocations across Tasks away from the firm-preferred allocations. We also predict that both effects are magnified when the RPI is public compared to private. We argue that although the motivation effect will generally benefit performance, the effort distortion effect may be detrimental to performance. We design an experiment that isolates these two effects. Consistent with our predictions, we find that RPI induces both motivation and effort distortion effects and that both effects are magnified when the RPI is public rather than private. Although the motivation effect increases performance, we demons...
Paul Ward - One of the best experts on this subject based on the ideXlab platform.
-
complex perceptual cognitive expertise in a simulated Task Environment
Journal of Cognitive Engineering and Decision Making, 2013Co-Authors: Paul Ward, Anders K Ericsson, Mark A WilliamsAbstract:In popular models of expertise and decision making in complex Environments, such as the recognition-primed decision (RPD) model and take-the-first (TTF) heuristic, expert and skilled decision makers are described as generating few response options and typically choose the best option first. To explain these behaviors, proponents of TTF have suggested that a negative relationship exists between the number of options generated and decision quality. In the current article, we use a prediction and situational option generation paradigm to assess perceptual-cognitive skill in the complex domain of soccer to determine whether these claims explain how decision makers make predictions about others in the Environment. In three experiments we provide evidence to show that superior prediction performance was supported by a situation model-type mechanism as proposed by long-term working memory (LTWM) theory rather than simpler heuristics, such as TTF or RPD. The similarity between LTWM mechanisms and relevant macroco...
-
A study of the relationship of nursing interventions and cognitions to the physiologic outcomes of care in a simulated Task Environment.
Applied Nursing Research, 2009Co-Authors: James Whyte, Roxanne Pickett-hauber, Eileen Cormier, Laurie Grubbs, Paul WardAbstract:This study, based on the Expert Performance Approach, examined the clinical nursing performance of participants who were introduced into a simulated Task Environment requiring them to administer care to a client experiencing an exacerbation of Congestive Heart Failure. This was undertaken to identify cognitive and physiologic variables that differentiate performance levels among participants. Data on participant actions and verbal reports were coded to characterize their relationship with physiologic responses of the Human Patient Simulator. The results demonstrated that physiologic responses to nursing interventions reflect a reliable pattern that can be used to differentiate performance levels.
