Post-error slowing (PES) - a relative increase in response time for a decision on trialtgiven an error on trialt - 1 - is a well-known effect in studies of human decision-making. Post-error processing is reflected in neural signatures such as reduced activity in sensorimotor regions and increased activity in medial prefrontal cortex. PES is thought to reflect the deployment of executive resources to get task performance back on track. This provides a general account of PES that cuts across perceptual decision-making, memory, and learning tasks. With respect to PES and learning, things are complicated by the fact that learning often reflectsmultiple qualitatively different processes with distinct neural correlates. It is unclear if multiple processes shape PES during learning, or if PES reflects a policy for reacting to errors generated by one particular process (e.g., cortico-striatal reinforcement learning). Here we provide behavioral and computational evidence that PES is influenced by the operation of multiple distinct processes. Human subjects learned a simple visuomotor skill (arbitrary visuomotor association learning) under low load conditionsmore amenable to simple working memory-based strategies, and high load conditions that were putatively more reliant on trial-by-trial reinforcement learning. PES decreased withload, even when the progress of learning (i.e., reinforcement history) was accounted for. This result suggested that PES during learning is influenced by the recruitment of working memory. Indeed, observed PES effects were approximated by a computational model with parallel working memory and reinforcement learning systems that are differentially recruited according to cognitive load.
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http://dx.doi.org/10.1016/j.neuroscience.2021.10.016 | DOI Listing |
Biomed Eng Lett
January 2025
Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Unlabelled: A weight-bearing lateral radiograph (WBLR) of the foot is a gold standard for diagnosing adult-acquired flatfoot deformity. However, it is difficult to measure the major axis of bones in WBLR without using auxiliary lines. Herein, we develop semantic segmentation with a deep learning model (DLm) on the WBLR of the foot for enhanced diagnosis of pes planus and pes cavus.
View Article and Find Full Text PDFWorld J Orthop
December 2024
Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Background: Pes planus (flatfoot) and pes cavus (high arch foot) are common foot deformities, often requiring clinical and radiographic assessment for diagnosis and potential subsequent management. Traditional diagnostic methods, while effective, pose limitations such as cost, radiation exposure, and accessibility, particularly in underserved areas.
Aim: To develop deep learning algorithms that detect and classify such deformities using smartphone cameras.
Neural Netw
December 2024
Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China. Electronic address:
Document-level event causality identification (ECI) aims to detect causal relations in between event mentions in a document. Some recent approaches model diverse connections in between events, such as syntactic dependency and etc., with a graph neural network for event node representation learning.
View Article and Find Full Text PDFPsychophysiology
January 2025
Department of Psychology, Goethe University Frankfurt, Frankfurt Am Main, Germany.
According to the predictive processing framework, our brain constantly generates predictions based on past experiences and compares these predictions with incoming sensory information. When an event contradicts these predictions, it results in a prediction error (PE), which has been shown to enhance subsequent memory. However, the neural mechanisms underlying the influence of PEs on subsequent memory remain unclear.
View Article and Find Full Text PDFBMC Psychol
December 2024
UMR7267 Ecology and Biology of Interactions (EBI), University of Poitiers, University Hospital Center of Poitiers, Poitiers, France.
Background: After a literature review and interviews with patients living with obesity, key psychosocial determinants such as coping strategies, weight bias internalization, body dissatisfaction and self-efficacy were identified as critical to address obesity-related stigma. The intervention was tailored using evidence-based techniques and input from health professionals to ensure relevance and avoid redundancy. This randomized controlled trial (RCT) aims to evaluate the effect of an intervention specifically designed to address weight stigma among individuals living with obesity.
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