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.
Department of Psychology, Molecular, Cellular and Integrative Neurosciences Program, Colorado State University, 1876 Campus Delivery, Fort Collins, CO, 80523, USA.
Psychon Bull Rev. 2021 Feb;28(1):274-282. doi: 10.3758/s13423-020-01816-6. Epub 2020 Oct 1.
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 information-integration (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.
虽然大多数类别学习研究都使用反馈进行训练，但很少关注个体如何利用反馈值以及将反馈表示为收益或损失来支持学习。我们比较了基于规则 (RB) 和信息整合 (II) 类别与点值反馈的学习，其中参与者因更难的类别成员（更接近决策边界的成员）而获得或失去更高的点值。我们在四种不同的条件下实施了点值反馈：收益（正确回答可获得积分）、损失（错误回答会扣分）、收益+损失（正确回答可获得积分，错误回答会扣分）和控制（仅提供准确性反馈，无积分增减）。参与者在达到标准后继续接受训练。总体而言，点值反馈比控制条件更能促进学习。然而，不同的类别学习任务的模式有所不同。在 II 任务中，当参与者同时获得收益和损失时，他们最快达到学习标准。这与 II 学习依赖于基于强化的机制以及研究表明，在支持 II 学习的神经区域中，收益和损失的共同编码一致。相比之下，在 RB 任务中，当参与者获得收益或损失时，他们最快达到标准，但不是同时获得收益和损失。在 RB 学习中，收益+损失条件下的相对障碍可能是由于对解释和使用单独的收益和损失信息的执行功能需求之间存在冲突，这与 RB 学习依赖于明确的假设检验机制是一致的。
