The learning ability of individuals within the schizophrenia spectrum is crucial for their psychosocial rehabilitation. When selecting a treatment, it is thus essential to consider the impact of medications on practice effects, an important type of learning ability. To achieve this end goal, a pre-treatment test has to be developed and tested in healthy participants first. This is the aim of the current work, which takes advantage of the schizotypal traits present in these participants to preliminary assess the test's validity for use among patients. In this study, 47 healthy participants completed the Schizotypal Personality Questionnaire (SPQ) and performed a semantic categorization task twice, with a 1.5-hour gap between sessions. Practice was found to reduce reaction times (RTs) in both low- and high-SPQ scorers. Additionally, practice decreased the amplitudes of the N400 event-related brain potentials elicited by semantically matching words in low SPQ scorers only, which shows the sensitivity of the task to schizotypy. Across the two sessions, both RTs and N400 amplitudes had good test-retest reliability. This task could thus be a valuable tool. Ongoing studies are currently evaluating the impact of fully deceptive placebos and of real antipsychotic medications on these practice effects. This round of research should subsequently assist psychiatrists in making informed decisions about selecting the most suitable medication for the psychosocial rehabilitation of a patient.
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http://dx.doi.org/10.1038/s41598-024-53468-4 | DOI Listing |
Discov Oncol
January 2025
Department of Thyroid Breast Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
Objective: Despite the identification of various prognostic factors for anaplastic thyroid carcinoma (ATC) patients over the years, a precise prognostic tool for these patients is still lacking. This study aimed to develop and validate a prognostic model for predicting survival outcomes for ATC patients using random survival forests (RSF), a machine learning algorithm.
Methods: A total of 1222 ATC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into a training set of 855 patients and a validation set of 367 patients.
Dev Psychol
January 2025
Department of Psychology, University of California, San Diego.
Numerate adults know that when two sets are equal, they should be labeled by the same number word. We explored the development of this principle-sometimes called "cardinal extension"-and how it relates to children's other numerical abilities. Experiment 1 revealed that 2- to 5-year-old children who could accurately count large sets often inferred that two equal sets should be labeled with the same number word, unlike children who could not accurately count large sets.
View Article and Find Full Text PDFJ Exp Psychol Gen
January 2025
Department of Psychology, Yale University.
Our ability to maintain a consistent attentional state is essential to many aspects of daily life. Still, despite our best efforts, attention naturally fluctuates between more and less vigilant states. Previous work has shown that offering performance-based rewards or incentives can help to buffer against attentional lapses.
View Article and Find Full Text PDFJ Exp Psychol Gen
January 2025
Department of Psychology, New York University.
The ability to discover patterns or rules from our experiences is critical to science, engineering, and art. In this article, we examine how much people's discovery of patterns can be incentivized by financial rewards. In particular, we investigate a classic category learning task for which the effect of financial incentives is unknown (Shepard et al.
View Article and Find Full Text PDFRadiology
January 2025
Stanford University School of Medicine, Department of Radiation Oncology, Stanford, CA, US.
Background Detection and segmentation of lung tumors on CT scans are critical for monitoring cancer progression, evaluating treatment responses, and planning radiation therapy; however, manual delineation is labor-intensive and subject to physician variability. Purpose To develop and evaluate an ensemble deep learning model for automating identification and segmentation of lung tumors on CT scans. Materials and Methods A retrospective study was conducted between July 2019 and November 2024 using a large dataset of CT simulation scans and clinical lung tumor segmentations from radiotherapy plans.
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