Although people often have to learn from environments with scarce and highly selective outcome feedback, the question of how nonfeedback trials are represented in memory and affect later performance has received little attention in models of learning and decision making. In this article, the authors use the generalized context model (Nosofsky, 1986) as a vehicle to test contrasting hypotheses about the coding of nonfeedback trials. Data across 3 experiments with selective decision-contingent and selective outcome-contingent feedback provide support for the hypothesis of constructivist coding (Elwin, Juslin, Olsson, & Enkvist, 2007), according to which the outcomes on nonfeedback trials are coded with the most likely outcome, as inferred by the individual. The relation to sampling-based approaches to judgment, and the adaptive significance of constructivist coding, are discussed. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
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Front Hum Neurosci
August 2024
School of Software, Jiangxi Agricultural University, Nanchang, China.
For the electroencephalogram- (EEG-) based motor imagery (MI) brain-computer interface (BCI) system, more attention has been paid to the advanced machine learning algorithms rather than the effective MI training protocols over past two decades. However, it is crucial to assist the subjects in modulating their active brains to fulfill the endogenous MI tasks during the calibration process, which will facilitate signal processing using various machine learning algorithms. Therefore, we propose a trial-feedback paradigm to improve MI training and introduce a non-feedback paradigm for comparison.
View Article and Find Full Text PDFJMIR Mhealth Uhealth
January 2024
Virgen Macarena University Hospital, Seville, Spain.
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View Article and Find Full Text PDFBMC Med Res Methodol
June 2023
Institute for Social and Economic Research, University of Essex, Colchester, UK.
Background: While medical studies generally provide health feedback to participants, in observational studies this is not always the case due to logistical and financial difficulties, or concerns about changing observed behaviours. However, evidence suggests that lack of feedback may deter participants from providing biological samples. This paper investigates the effect of offering feedback of blood results on participation in biomeasure sample collection.
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View Article and Find Full Text PDFInt J Environ Res Public Health
May 2021
Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Korea.
This study aimed to assess the effectiveness of a novel chest compression (CC) smart-ring-based feedback system in a manikin simulation. In this randomized, crossover, controlled study, we evaluated the effect of smart-ring CC feedback on cardiopulmonary resuscitation (CPR). The learnability and usability of the tool were evaluated with the System Usability Scale (SUS).
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