Differing effects of gain and loss feedback on rule-based and information-integration category learning.

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 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.