Journal of Cognitive Engineering and Decision Making
Scope & Guideline
Elevating decision-making through interdisciplinary collaboration.
Introduction
Aims and Scopes
- Cognitive Engineering in Automated Systems:
The journal extensively covers the design and evaluation of cognitive systems, particularly how human cognitive processes interact with automated technologies in fields such as transportation, healthcare, and military operations. - Human Factors and Decision-Making:
Research often examines human decision-making under various conditions, including high-stakes environments, highlighting cognitive biases, communication patterns, and the role of trust in automation. - Automation Failures and Human Response:
A consistent theme is the investigation of automation failures, focusing on understanding how humans respond to these failures, the implications for system design, and strategies for mitigating risks. - Interdisciplinary Approaches:
The journal promotes an interdisciplinary perspective, integrating insights from cognitive psychology, human factors, systems engineering, and artificial intelligence to enhance decision-making frameworks. - Training and Skill Development:
Research also delves into training methodologies and tools designed to improve cognitive skills and situation awareness, particularly in high-stress occupations.
Trending and Emerging
- Human-AI Teaming:
There is a growing emphasis on the dynamics of human-AI collaboration, particularly in high-stakes settings like healthcare and military operations, highlighting the need for better integration of AI capabilities with human decision-making. - Automation Failure and Recovery Strategies:
Recent publications increasingly address the nuances of automation failures and the strategies for recovery, focusing on how users can regain situation awareness and effectively manage unexpected system behavior. - Cognitive Biases and Decision-Making in Crisis Situations:
Emerging research is focusing on how cognitive biases affect decision-making during crises, with implications for training and system design to enhance resilience and adaptability. - Ecological Interface Design:
There is significant interest in ecological interface design principles, which aim to create interfaces that align with human cognitive processes, thereby improving usability and reducing errors in complex systems. - Augmented and Virtual Reality Applications:
The use of augmented and virtual reality for training and simulation purposes is gaining traction, particularly in high-risk environments, showcasing innovative approaches to enhance cognitive skills and situational awareness.
Declining or Waning
- Traditional Decision Support Systems (DSS):
There has been a noticeable decrease in articles focused on traditional DSS frameworks, as the field moves towards more integrated and adaptive systems that incorporate real-time data and AI. - Basic Human-Machine Interaction Studies:
Research that merely explores basic human-machine interaction without a focus on cognitive implications or advanced automation appears to be less prominent, as the field increasingly demands nuanced understanding of complex interactions. - Static Models of Trust in Automation:
The exploration of static models of trust, which do not account for dynamic changes in user perceptions or system capabilities, is diminishing as newer, more comprehensive models are developed.
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