scholarly journals Psychophysiology and high-performance cognition - a brief review of the literature

2015 ◽  
Author(s):  
Benjamin Cowley
Keyword(s):  
Decision Making ◽  
Executive Control ◽  
High Performance ◽  
Time Pressure ◽  
Mental Workload ◽  
Knowledge Work ◽  
Review Of The Literature ◽  
Cognitive States ◽  
Decision Making Problem ◽  
High Level

The psychophysiological method can be used to detect some simple cognitive states such as arousal, attentiveness, or mental workload. This approach can be especially interesting when cognition has some productive purpose, as in knowledge work, and tends to be related to human-computer interaction (HCI). However more interesting for applied purposes are acts of coordinated high-level cognition. High- level (or higher-order) cognition (HLC) is typically associated with decision making, problem solving, and executive control of cognition and action. Further, an intuitive approach for assessing whether someone is engaged in HLC is to measure their performance of a known task. Given this, it is reasonable to define high-performance cognition (HPC) as HLC under some performance restriction, such as real-time pressure or expert skill level. Such states are also interesting for HCI in work, and their detection represents an ambitious aim for using the psychophysiological method. We report a brief review of the literature on the topic.

scholarly journals Psychophysiology and high-performance cognition - a brief review of the literature

2015 ◽  
Author(s):  
Benjamin Cowley
Keyword(s):  
Decision Making ◽  
Executive Control ◽  
High Performance ◽  
Time Pressure ◽  
Mental Workload ◽  
Knowledge Work ◽  
Review Of The Literature ◽  
Cognitive States ◽  
Decision Making Problem ◽  
High Level

The psychophysiological method can be used to detect some simple cognitive states such as arousal, attentiveness, or mental workload. This approach can be especially interesting when cognition has some productive purpose, as in knowledge work, and tends to be related to human-computer interaction (HCI). However more interesting for applied purposes are acts of coordinated high-level cognition. High- level (or higher-order) cognition (HLC) is typically associated with decision making, problem solving, and executive control of cognition and action. Further, an intuitive approach for assessing whether someone is engaged in HLC is to measure their performance of a known task. Given this, it is reasonable to define high-performance cognition (HPC) as HLC under some performance restriction, such as real-time pressure or expert skill level. Such states are also interesting for HCI in work, and their detection represents an ambitious aim for using the psychophysiological method. We report a brief review of the literature on the topic.

High Performance Work Systems and Organizational Outcomes: The Mediating Role of Information Quality

ILR Review ◽  
2003 ◽  
Vol 56 (4) ◽  
pp. 590-605 ◽  
Author(s):  
Gil A. Preuss
Keyword(s):  
Decision Making ◽  
High Performance ◽  
Information Quality ◽  
Knowledge Work ◽  
Care Hospital ◽  
Quality Of Information ◽  
Work Systems ◽  
High Performance Work Systems ◽  
Performance Quality

Using data on registered nurses and nursing assistants in 50 acute-care hospital units, the author explores the relationships among high performance work systems, information quality, and performance quality within a context shaped by equivocal information—information that can be interpreted in multiple and sometimes conflicting ways. He finds that the quality of information available for decision-making, which largely depends on the interpretative skills of the workers who are exposed to important equivocal information, partially mediates how employee knowledge, work design, and total quality management systems affect organizational performance (which is measured as the inverse of medication error incidence). Providing employees with extensive relevant knowledge and enabling them to use their skills during even seemingly routine tasks improves the effective quality of information they bring to decision-making, and thereby promotes high performance quality.

scholarly journals Cognitive States Matter: Design Guidelines for Driving Situation Awareness in Smart Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2978 ◽  
Author(s):  
Daehee Park ◽  
Wan Chul Yoon ◽  
Uichin Lee
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
Time Pressure ◽  
Design Guidelines ◽  
Cognitive State ◽  
Cognitive States ◽  
The Future ◽  
Situational Information ◽  
Pressure State ◽  
Resource State

Situation awareness (SA) is crucial for safe driving. It is all about perception, comprehension of current situations and projection of the future status. It is demanding for drivers to constantly maintain SA by checking for potential hazards while performing the primary driving tasks. As vehicles in the future will be equipped with more sensors, it is likely that an SA aiding system will present complex situational information to drivers. Although drivers have difficulty to process a variety of complex situational information due to limited cognitive capabilities and perceive the information differently depending upon their cognitive states, the well-known SA design principles by Endsley only provide general guidelines. The principles lack detailed guidelines for dealing with limited human cognitive capabilities. Cognitive capability is a mental capability including planning, complex idea comprehension, and learning from experience. A cognitive state can be regarded as a condition of being (e.g., the state of being aware of the situation). In this paper, we investigate the key cognitive attributes related to SA in driving contexts (i.e., attention focus, mental model, workload, and memory). Endsley proposed that those key cognitive attributes are the main factors that influence SA. In those with higher levels of attributes, we found eight cognitive states which mainly influence a human driver in achieving SA. These are the focused attention state, inattentional blindness state, unfamiliar situation state, familiar situation state, insufficient mental resource state, sufficient mental resource state, high time pressure state, and low time pressure state. We then propose cognitive state aware SA design guidelines that can help designers to effectively convey situation information to drivers. As a case study, we demonstrated the usefulness of our cognitive state aware SA design guidelines by conducting controlled experiments where an existing SA interface is compared with a new SA interface designed following the key guidelines. We used the Situation Awareness Global Assessment Technique (SAGAT) and Decision-Making Questionnaire (DMQ) to measure the SA and decision-making style scores, respectively. Our results show that the new guidelines allowed participants to achieve significantly higher SA and exhibit better decision making performance.

scholarly journals Human-Machine Cooperative Trajectory Planning for Semi-Autonomous Driving Based on the Understanding of Behavioral Semantics

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 946
Author(s):  
Bohan Jiang ◽  
Xiaohui Li ◽  
Yujun Zeng ◽  
Daxue Liu
Keyword(s):  
Decision Making ◽  
Trajectory Planning ◽  
Autonomous Driving ◽  
Planning Problem ◽  
Decision Level ◽  
Planning Approach ◽  
Decision Making Problem ◽  
Generation Level ◽  
High Level ◽  
Real Vehicle

This paper presents a novel cooperative trajectory planning approach for semi-autonomous driving. The machine interacts with the driver at the decision level and the trajectory generation level. To minimize conflicts between the machine and the human, the trajectory planning problem is decomposed into a high-level behavior decision-making problem and a low-level trajectory planning problem. The approach infers the driver’s behavioral semantics according to the driving context and the driver’s input. The trajectories are generated based on the behavioral semantics and driver’s input. The feasibility of the proposed approach is validated by real vehicle experiments. The results prove that the proposed human–machine cooperative trajectory planning approach can successfully help the driver to avoid collisions while respecting the driver’s behavior.

scholarly journals AI support for ethical decision-making around resuscitation: proceed with care

2021 ◽  
pp. medethics-2020-106786
Author(s):  
Nikola Biller-Andorno ◽  
Andrea Ferrario ◽  
Susanne Joebges ◽  
Tanja Krones ◽  
Federico Massini ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Preferences ◽  
Time Pressure ◽  
Ethical Decision Making ◽  
University Hospital ◽  
Design Development ◽  
Code Status ◽  
Personal Bias ◽  
The Status ◽  
High Level

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient’s Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions—conceptual, methodological and procedural—that need to be considered in further development and implementation efforts.

scholarly journals OPTIMAL ALTERNATIVE SELECTION MODELS IN A MULTI-STAGE DECISION-MAKING PROCESS

2019 ◽  
Vol 6 ◽  
pp. 43-50
Author(s):  
Oksana Mulesa ◽  
Vitaliy Snytyuk ◽  
Ivan Myronyuk
Keyword(s):  
Decision Making ◽  
Effective Alternative ◽  
Management Decision ◽  
Decision Making Process ◽  
Multi Stage ◽  
Decision Making Problem ◽  
Management Decision Making ◽  
The Face ◽  
Decision Making Models ◽  
High Level

Management decision-making tasks are usually characterized by a high level of uncertainty. When solving this class of problems, it is necessary to take into account the environmental conditions for the implementation of the decisions made and the consequences that may arise in this case. The decision-making task in the face of uncertainty can be represented in the form of a “game with nature”, in which the optimal player strategy is sought. A two-stage decision-making process is considered, in which at each stage the decision-making problem is solved in conditions of risk. The case is supposed in which, after making a decision at the first stage, choosing an effective alternative and the onset of a certain state of nature, it is necessary to solve the decision-making problem of the second stage. Decision-making models based on well-known decision models of the “game with nature” are proposed. The developed models allow in the process of choosing an effective alternative to the first stage to assess the possible consequences of such a choice, taking into account the expectations of the decision maker. In the course of experimental verification, it is shown that the developed decision-making models can be used to solve such multi-stage problems, the phased solution of which is incorrect. This may occur due to the fact that some of their stages are associated with certain losses, and others – with profit. In such situations, it is advisable to consider the problem as a whole and at each stage, take into account all available information as much as possible.

scholarly journals AI support for ethical decision-making around resuscitation: proceed with care

2020 ◽  
Author(s):  
Nikola Biller-Andorno ◽  
Andrea Ferrario ◽  
Susanne Joebges ◽  
Tanja Krones ◽  
Federico Massini ◽  
...  
Keyword(s):  
Decision Making ◽  
Patient Preferences ◽  
Time Pressure ◽  
Ethical Decision Making ◽  
University Hospital ◽  
Design Development ◽  
Code Status ◽  
Cardio Pulmonary Resuscitation ◽  
The Status ◽  
High Level

Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardio-pulmonary resuscitation leading to the determination of a patient's Do Not Attempt to Resuscitate (DNAR) status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that 1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and 2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions - conceptual, methodological and procedural - that need to be considered in further development and implementation efforts.

Observational Heuristics in a Group of High Level Paddle Sports Coaches

2017 ◽  
Vol 4 (2) ◽  
pp. 235-245 ◽  
Author(s):  
Scott Simon ◽  
Loel Collins ◽  
Dave Collins
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Preliminary Investigation ◽  
Small Scale ◽  
Professional Judgment ◽  
Limited Information ◽  
Coaching Process ◽  
Observation Process ◽  
High Level ◽  
Sports Coaches

Observation of performance forms a critical part of the complex coaching process. A professional judgment and decision making (PJDM) framework enables optimum decisions to be made under time pressure and with limited information that derive from that observation. Observation and the associated decision making can be particularly affected by heuristic bias. We extend the work on PJDM via a greater focus on its relationship with observation within the coaching process. After revisiting PJDM and observation, we introduce and explore heuristics as a “tool” within the observation process. Specifically, we propose that observation is prone to heuristics built on a coach’s experience and understanding. We report on a small scale preliminary investigation with a group of high-level paddle sport coaches. We identify heuristics that both restrict and enhance the effectiveness of the observation in an effort to promote discussion and further research.

Deciding the Adequacy of a Set of Tests for an Specific Assessment Situation. Decision-making Strategies

1996 ◽  
Vol 12 (1) ◽  
pp. 14-22
Author(s):  
R. Esteve ◽  
A. Godoy
Keyword(s):  
Decision Making ◽  
Time Pressure ◽  
Response Mode ◽  
Information Load ◽  
Variable Pattern ◽  
Mode Effects ◽  
Moderator Effects ◽  
Specific Assessment ◽  
Information Board ◽  
Decision Importance

The aim of the present paper was to test the effects of response mode (choice vs. judgment) on decision-making strategies when subjects were faced with the task of deciding the adequacy of a set of tests for a specific assessment situation. Compared with choice, judgment was predicted to lead to more information sought, more time spent on the task, a less variable pattern of search, and a greater amount of interdimensional search. Three variables hypothesized as potential moderators of the response mode effects are also studied: time pressure, information load and decision importance. Using an information board, 300 subjects made decisions (choices and judgments) on tests for a concrete assessment situation, under high or low time pressure, high or low information load, and high or low decision importance. Response mode produced strong effects on all measures of decision behavior except for pattern of search. Moderator effects occurred for time pressure and information load.

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