Evidence accumulation models have enabled strong advances in our understanding of decision-making, yet their application to examining learning has not been common. Using data from participants completing a dynamic random dot-motion direction discrimination task across four days, we characterized alterations in two components of perceptual decision-making (Drift Diffusion Model drift rate and response boundary). Continuous-time learning models were applied to characterize trajectories of performance change, with different models allowing for varying dynamics. The best-fitting model included drift rate changing as a continuous, exponential function of cumulative trial number. In contrast, response boundary changed within each daily session, but in an independent manner across daily sessions. Our results highlight two different processes underlying the pattern of behavior observed across the entire learning trajectory, one involving a continuous tuning of perceptual sensitivity, and another more variable process describing participants' threshold of when enough evidence is present to act.
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http://dx.doi.org/10.1038/s41539-023-00168-9 | DOI Listing |
Diabetes Metab Res Rev
January 2025
Rush Alzheimer's Disease Centre, Rush University Medical Center, Chicago, Illinois, USA.
Diabetes increases the risk of dementia, and insulin resistance (IR) has emerged as a potential unifying feature. Here, we review published findings over the past 2 decades on the relation of diabetes and IR to brain health, including those related to cognition and neuropathology, in the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study (ROS/MAP/MARS), three harmonised cohort studies of ageing and dementia at the Rush Alzheimer's Disease Center (RADC). A wide range of participant data, including information on medical conditions such as diabetes and neuropsychological tests, as well as other clinical and laboratory-based data collected annually.
View Article and Find Full Text PDFDigit Health
January 2025
Independent Researcher, Calgary, Alberta, Canada.
Digital health (DH) and artificial intelligence (AI) in healthcare are rapidly evolving but were addressed synonymously by many healthcare authorities and practitioners. A deep understanding and clarification of these concepts are fundamental and a prerequisite for developing robust frameworks and practical guidelines to ensure the safety, efficacy, and effectiveness of DH solutions and AI-embedded technologies. Categorizing DH into technologies (DHTs) and services (DHSs) enables regulatory, HTA, and reimbursement bodies to develop category-specific frameworks and guidelines for evaluating these solutions effectively.
View Article and Find Full Text PDFAppl Nurs Res
February 2025
Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands; School of Health Sciences, Faculty of Environmental and Life Sciences, University of Southampton, UK.
Objectives: The extent to which healthcare professionals apply Shared Decision Making (SDM) on hospital wards is still unknown. The aim was to explore the current knowledge of SDM among healthcare professionals and the experienced factors influencing SDM on the wards of Dutch hospitals, regarding both treatment and care decisions.
Setting: Twelve hospital wards in two university medical centres and one teaching hospital.
Decision confidence plays a critical role in humans' ability to make adaptive decisions in a noisy perceptual world. Despite its importance, there is currently little consensus about the computations underlying confidence judgements in perceptual decisions. To better understand these mechanisms, we addressed the extent to which confidence is informed by a naturalistic prior distribution.
View Article and Find Full Text PDFSci Rep
January 2025
Centre for Brain, Mind and Markets, Faculty of Business and Economics, The University of Melbourne, Melbourne, Australia.
Metacognition, the ability to monitor and reflect on our own mental states, enables us to assess our performance at different levels - from confidence in individual decisions to overall self-performance estimates (SPEs). It plays a particularly important part in computationally complex decisions that require a high level of cognitive resources, as the allocation of such limited resources presumably is based on metacognitive evaluations. However, little is known about metacognition in complex decisions, in particular, how people construct SPEs.
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