Category Generalization After Entrenched Versus Probabilistic Erroneous Feedback

Abstract

Abstract The present study investigated the impact of occasional erroneous feedback on category learning. Subjects were presented with exemplars from multiple prototype categories, where the exemplars were novel on each learning block (nonrepeat) or were continuously recycled through learning (repeat). In training, selected training instances were associated with a label different from other members drawn from the same prototype category. After learning, subjects received a common transfer test requiring either the classification of novel instances (Experiment 1) or the discrimination of old from new instances (Experiments 2 and 3). The major results were that as expected, learning was more accurate in the repeat condition, with subjects learning to accurately classify both the correct and incorrect feedback patterns by the terminal learning block; learning in the nonrepeat condition was worse, with subjects misclassifying the erroneous feedback patterns at an increasing rate through learning; nonetheless, classification and recognition transfer were somewhat higher after nonrepeat learning; and subjects readily discriminated old from new after repetition training but not after nonrepetition training. Overall, the enhanced classification and recognition after nonrepeat training mirrored our results reported in a similar paradigm that used only correct feedback. Formal modeling suggested that a pure prototype model could not predict learning or transfer after repetition training, whereas a prototype model was superior at capturing results after nonrepetition learning.