The valence of new information influences learning rates in humans: good news tends to receive more weight than bad news. We investigated this learning bias in four experiments, by systematically manipulating the source of required action (free versus forced choices), outcome contingencies (low versus high reward) and motor requirements (go versus no-go choices). Analysis of model-estimated learning rates showed that the confirmation bias in learning rates was specific to free choices, but was independent of outcome contingencies.
View Article and Find Full Text PDFPurpose: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from computed tomography (CT) examination.
Materials And Methods: A database of 515 harmonized thyroid CT examinations was used, of which information regarding cartilage abnormality was provided for 326. The process consisted of determining image abnormality and, from these preprocessed images, finding the best learning algorithm to appropriately characterize thyroid cartilage as normal or abnormal.
Q J Exp Psychol (Hove)
July 2018
The sense of agency refers to the feeling that we control our actions and, through them, effects in the outside world. Reinforcement learning provides an important theoretical framework for understanding why people choose to make particular actions. Few previous studies have considered how reinforcement and learning might influence the subjective experience of agency over actions and outcomes.
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