Objective: The aim of this study was to provide an analysis of the implications of the dominance of intuitive cognition in human reasoning and decision making for conceptualizing models and taxonomies of human-automation interaction, focusing on the Parasuraman et al. model and taxonomy.
Background: Knowledge about how humans reason and make decisions, which has been shown to be largely intuitive, has implications for the design of future human-machine systems.
Method: One hundred twenty articles and books cited in other works as well as those obtained from an Internet search were reviewed. Works were deemed eligible if they were published within the past 50 years and common to a given literature.
Results: Analysis shows that intuitive cognition dominates human reasoning and decision making in all situations examined. The implications of the dominance of intuitive cognition for the Parasuraman et al. model and taxonomy are discussed. A taxonomy of human-automation interaction that incorporates intuitive cognition is suggested.
Application: Understanding the ways in which human reasoning and decision making is intuitive can provide insight for future models and taxonomies of human-automation interaction.
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http://dx.doi.org/10.1177/0018720816659796 | DOI Listing |
Background: Clinical rating scales and neuropsychological tests are commonly used for assessing sign and disease severity, yet lack detail in the early stages Alzheimer's Disease (AD). Existing evaluation methods can be subjective, nonlinear, expensive, or reliant on anecdotal evidence making objective and consistent characterization and phenotyping of AD difficult. Multimodal analysis of patient behavior, rather than scoring of patient-generated output which can be skewed by compensation strategies, presents a unique opportunity to objectively quantify AD related changes.
View Article and Find Full Text PDFBackground: Postoperative delirium (POD) is characterized by fluctuating attention after surgery and is associated with increased risk of developing Alzheimer's Disease (AD). While the neurophysiological changes that underlie POD and increased risk of AD are unclear, recent data has raised the possibility that an exaggerated brain response to anesthetics may be a biomarker for POD risk and preclinical AD-like pathology. Thus, we examined whether anesthetic-dose-adjusted intraoperative brain activity is associated with POD or preoperative brain vulnerabilities (preclinical AD-like pathology, preoperative inattention) that may contribute to risk of POD (and later AD).
View Article and Find Full Text PDFBackground: Statistical network analysis has transformed neuroimaging research in recent years by enabling flexible and intuitive integration of multiple data types and preserving the topological brain connectivity structure while uncovering mechanism of degenerative aging. In this study, we apply a novel latent space joint network model to perform a case-control comparison using the functional connectivity data together with region-specific cortical volume, cortical thickness, surface area and PET information. By preserving complex network structures during imaging biomarker detection, we find sex-specific topological structures associated with dementia.
View Article and Find Full Text PDFDiagnosis (Berl)
January 2025
Scientific and Osteopathic Research Department, Institut de Formation en Ostéopathie du Grand Avignon IFO-GA, Avignon, France.
Objectives: Although cognitive biases are one of the most frequent causes of diagnostic errors, their influence remains underestimated in allied health professions, especially in osteopathy. Yet, a part of osteopathic clinical reasoning and diagnosis rely on the practitioner's intuition and subjective haptic perceptions. The aim of this study is to highlight links between the cognitive biases perceived by the practitioner to understand cognitive patterns during osteopathic diagnosis, and to suggest debiasing strategies.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, México, DF, Mexico.
Background: The World Health Organization forecasts a population of 2,000 million people over 60 years by the year 2050, with 7% of this population suffering from dementia. Making a constant clinical-technological evaluation of older adults allows early detection of the disease and provides a better quality of life for the patient. In this sense, the research and development of innovative technological systems for the early detection of the disease, its monitoring and management of the growing number of patients with cognitive diseases has increased in recent years, integrating data collection and its automatic processing based on geriatric metrics into these systems using artificial intelligence (AI) methods.
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