Differing effects of gain and loss feedback on rule-based and information-integration category learning
- PDF / 1,164,208 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 52 Downloads / 187 Views
BRIEF REPORT
Differing effects of gain and loss feedback on rule-based and information-integration category learning Zhiya Liu 1 & Yitao Zhang 1 & Ding Ma 1 & Qunfang Xu 1 & Carol A. Seger 1,2 Accepted: 8 September 2020 # The Psychonomic Society, Inc. 2020
Abstract Although most category-learning studies use feedback for training, little attention has been paid to how individuals use feedback value and framing of feedback as gains or losses to support learning. We compared learning of rule-based (RB) and informationintegration (II) categories with point-valued feedback in which participants gained or lost higher point values for more difficult category members (those closer to the decision bound). We implemented point-valued feedback in four different conditions: Gain (earn points for correct answers), Loss (lose points for incorrect answers), Gain+Loss (earn points for correct answers and lose points for incorrect answers), and Control (accuracy feedback only without point gain or loss). Participants were trained until they reached criterion. Overall, point-valued feedback led to better learning than control conditions. However, the patterns differed across category-learning tasks. In the II task participants reached learning criterion fastest when they received both Gains and Losses. This is consistent with the reliance of II learning on reinforcement-based mechanisms and research showing common coding of gains and losses in neural regions underlying II learning. In contrast, in the RB task, participants reached criterion most rapidly when they received either Gains or Losses, but not both Gains and Losses together. The relative impairment in the Gain+ Loss condition in RB learning may be due to conflicting executive function demands for interpreting and using the separate Gain and Loss information, and is consistent with reliance of RB learning on explicit hypothesis-testing mechanisms. Keywords Category learning . Rule-based . Information integration . Feedback
Introduction Feedback plays an important role in category learning. Most category-learning tasks train participants via trial and error with feedback (Ashby & Valentin, 2017). The mechanisms through which feedback enables learning depend on the type of category learning strategy. For example, when learning via a rule-based strategy, feedback can help participants eliminate alternative hypotheses and improve the precision of the
* Carol A. Seger [email protected] 1
Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 55 Zhongshan Avenue West, Guangzhou 510631, China
2
Department of Psychology, Molecular, Cellular and Integrative Neurosciences Program, Colorado State University, 1876 Campus Delivery, Fort Collins, CO 80523, USA
current rule. In reinforcement-based instrumental learning, feedback serves as reinforcement similar to explici
Data Loading...
