Optimizing attention and memory through expertise
Abstract
In most complex dynamic work environments, an operator must learn where and when to pay attention to the most critical elements in t,he workspace to effectively perform their jobs. In the current proposal, we seek to better understand how building expertise within, a given work domain may shape attention and working memory in an effort to optimize these core cognitive mechanisms for improving t,ask performance. We will apply a novel suite of AI and machine learning approaches to EEG recordings as subjects become experts on n,ovel complex tasks. The project will examine three core questions. First, are experts faster at tasks because they can orient attent,ion more quickly or are they faster because their domain knowledge allows them to better anticipate new events? Second, can experts, hold more domain information in working memory or are they better able to use chunking and other associative learning mechanisms to, bolster their working memory? Finally, are experts better able to handle distraction because they can suppress task-irrelevant info,rmation or are they faster at recovering attention following brief distraction?
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 08, 2022
- Source ID
- N000142212123
Entities
People
- Edward K Vogel
Organizations
- Office of Naval Research
- United States Navy
- University of Chicago