Three students with moderate mental retardation were taught a complex stimulus class with a two-choice conditional discrimination procedure applied across eight 10-member stimulus sets. Each set was composed of five age-appropriate and five age-inappropriate examples of clothing, accessories, and leisure items (e.g., a Walkman radio). Discrimination training was programmed serially across each set, and generalization probes were conducted concurrently among all sets. Generalization probes consisted of unreinforced conditional matching trials with comparison items being drawn from (a) the set undergoing training (within-set probes), (b) sets not undergoing training (between-set probes), and (c) both sample and comparison items from different sets (transitive stimulus control probes). Results indicate that within-set generalization, between-set generalization, and transitive stimulus relations controlled responding by all 3 students for items that had been contingently associated with reinforcement. However, items that gained control of responding through within-set and between-set generalization alone (i.e., not acquired through contingent reinforcement) remained at baseline levels during transitive stimulus control probes. Results are discussed in terms of a taxonomy of multiple sources of stimulus control that underlie socially defined and maintained stimulus classes.
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http://dx.doi.org/10.1901/jeab.1989.52-13 | DOI Listing |
J Med Imaging (Bellingham)
November 2024
The University of Chicago, Committee on Medical Physics, Department of Radiology, Chicago, Illinois, United States.
Purpose: This study aimed to investigate the impact of different model retraining schemes and data partitioning on model performance in the task of COVID-19 classification on standard chest radiographs (CXRs), in the context of model generalizability.
Approach: Two datasets from the same institution were used: Set A (9860 patients, collected from 02/20/2020 to 02/03/2021) and Set B (5893 patients, collected from 03/15/2020 to 01/01/2022). An original deep learning (DL) model trained and tested in the task of COVID-19 classification using the initial partition of Set A achieved an area under the curve (AUC) value of 0.
Sci Rep
October 2024
Perioperative and Intensive Care, Skåne University Hospital, Lund, Sweden.
Front Neural Circuits
February 2024
Department of Mental Health, Naval Hospital Camp Pendleton, Camp Pendleton, Oceanside, CA, United States.
Active inference (AIF) is a theory of the behavior of information-processing open dynamic systems. It describes them as generative models (GM) generating inferences on the causes of sensory input they receive from their environment. Based on these inferences, GMs generate predictions about sensory input.
View Article and Find Full Text PDFJ Clin Med
July 2023
Department of Health Administration, College of Business Administration, King Saud University, Riyadh 11587, Saudi Arabia.
This retrospective study aimed to compare the accuracy of the pediatric dental surgeon's estimated operative times for dental rehabilitation under general anesthesia (DRGA) in pediatric patients. This study population included 674 pediatric patients who underwent DRGA at the study facility between January 2022 and December 2022, using convenience sampling to select patients who met our inclusion criteria. Data were collected from electronic medical and anesthesia records based on several factors, including patient-related factors such as age and gender, surgeon-related factors such as rank and experience, and anesthesia-related factors such as induction and recovery time (in minutes).
View Article and Find Full Text PDFSet for variability (SfV) is an oral language task which requires an individual to disambiguate the mismatch between the decoded form of an irregular word and its actual lexical pronunciation. For example, in the task, the word is pronounced to rhyme with (i.e.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!