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Purpose: Sequential decision-making often involves a combination of simple trial-and-error learning (i.e., model-free learning), and more sophisticated learning where an abstract representation of the environment is formed, thereby facilitating prospective predictions about likely outcomes based on different choices (i.e., model-based learning). As such, the utilization of a model-based approach is thought to be advantageous in many contexts as it provides a more informed cognitive map. Emerging research suggests that trauma exposure may have a detrimental effect on model-based learning, which suggests that there may be clinical utility in examining pharmacological and/or behavioral approaches that boost model-based behavior. Although greater habitual physical activity (PA) is associated with enhanced cognitive function, no prior studies have examined the specific domain of model-based decision-making. This study aimed to examine whether greater PA is associated with greater model-based decision-making in pursuit of reward among trauma-exposed adults (N = 84).
Methods: Participants (62% women, 55% white, M ± SD age = 28 ± 9 y) completed the International Physical Activity Questionnaire-Short Form and a two-stage Markov task capable of quantifying model-free vs model-based decision-making. Mixed-effects logistic regression models were used to determine if PA volume (quartiles of MET-min/wk) promotes greater engagement in model-based behavioral strategies during the task.
Results: Participants from quartile 2 (β = 0.17, 95%CI = 0.11-0.23), quartile 3 (β = 0.27, 95%CI = 0.21-0.33), and quartile 4 (β = 0.23, 95%CI = 0.17-0.30) exhibited greater model-based decision-making compared to participants from quartile1 (β = 0.08, 95%CI = 0.02-0.14), with participants from quartile 3 exhibiting greater model-based decision-making compared to quartile 2.
Conclusions: PA volume is positively associated with a greater propensity to utilize model-based behavioral strategies during decision-making in pursuit of reward in trauma-exposed adults. Future research is needed to examine whether changes in PA behavior predict subsequent changes in model-based behavior.
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http://dx.doi.org/10.1249/MSS.0000000000003754 | DOI Listing |
Front Oncol
August 2025
Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Objective: The retrieval of 12 lymph nodes (LNs) remains a crucial criterion for accurate staging and prognosis evaluation in rectal cancer (RC). However, some patients fail to meet this threshold after surgery. This study developed a nomogram model based on clinical variables to predict the probability of retrieving 12 LNs postoperatively.
View Article and Find Full Text PDFClin Transl Gastroenterol
September 2025
Department of Internal Medicine, School of Medicine, University of Medicine and Pharmacy at Ho Cho Minh City, Vietnam.
Background: Severe acute pancreatitis (SAP) is a life-threatening condition requiring early risk stratification. While the Bedside Index for Severity in Acute Pancreatitis (BISAP) is widely used, its reliance on complex parameters limits its applicability in resource-constrained settings. This study introduces a decision tree model based on Classification and Regression Tree (CART) analysis, utilizing Neutrophil-to-Lymphocyte Ratio (NLR) and C-reactive Protein (CRP), as a simpler alternative for early SAP prediction.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Maths and Computer Science, Faculty of Science, University of Kinshasa, Kinshasa, The Democratic Republic of the Congo.
Reliable and timely fault diagnosis is critical for the safe and efficient operation of industrial systems. However, conventional diagnostic methods often struggle to handle uncertainties, vague data, and interdependent multi-criteria parameters, which can lead to incomplete or inaccurate results. Existing techniques are limited in their ability to manage hierarchical decision structures and overlapping information under real-world conditions.
View Article and Find Full Text PDFMedicine (Baltimore)
September 2025
Department of Critical Care Medicine, Nantong First People's Hospital, Nantong, Jiangsu Province, China.
Background: This study investigates the clinical value of a structured team approach incorporating shared decision-making in managing critically ill pregnant patients within an obstetrics intensive care unit (ICU).
Methods: A randomized controlled trial was conducted with 100 critically ill pregnant women admitted to our hospital's obstetrics ICU between January 2023 and December 2024. Participants were allocated via random number table to either the control group receiving conventional multidisciplinary resuscitation care (n = 50) or the observation group receiving the structured team model with shared decision-making (n = 50).
Front Plant Sci
August 2025
Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming, China.
Introduction: Rice is an important food crop but is susceptible to diseases. However, currently available spot segmentation models have high computational overhead and are difficult to deploy in field environments.
Methods: To address these limitations, a lightweight rice leaf spot segmentation model (MV3L-MSDE-PGFF-CA-DeepLabv3+, MMPC-DeepLabv3+) was developed for three common rice leaf diseases: rice blast, brown spot and bacterial leaf blight.