Griffiths Oren, Holmes Nathan, Westbrook R Fred
School of Psychology, University of New South Wales, Sydney NSW, Australia.
Front Psychol. 2017 Feb 9;8:120. doi: 10.3389/fpsyg.2017.00120. eCollection 2017.
Models of associative learning have proposed that cue-outcome learning critically depends on the degree of prediction error encountered during training. Two experiments examined the role of error-driven extinction learning in a human causal learning task. Target cues underwent extinction in the presence of additional cues, which differed in the degree to which they predicted the outcome, thereby manipulating outcome expectancy and, in the absence of any change in reinforcement, prediction error. These prediction error manipulations have each been shown to modulate extinction learning in aversive conditioning studies. While both manipulations resulted in increased prediction error during training, neither enhanced extinction in the present human learning task (one manipulation resulted in less extinction at test). The results are discussed with reference to the types of associations that are regulated by prediction error, the types of error terms involved in their regulation, and how these interact with parameters involved in training.
联想学习模型提出，线索-结果学习关键取决于训练期间遇到的预测误差程度。两项实验考察了错误驱动的消退学习在人类因果学习任务中的作用。目标线索在存在其他线索的情况下进行消退，这些线索在预测结果的程度上有所不同，从而操纵结果预期，并且在强化无任何变化的情况下操纵预测误差。在厌恶条件作用研究中，这些预测误差操纵各自已被证明可调节消退学习。虽然两种操纵在训练期间均导致预测误差增加，但在当前人类学习任务中均未增强消退（一种操纵在测试时导致消退减少）。将参照由预测误差调节的联想类型、其调节中涉及的误差项类型以及这些如何与训练中涉及的参数相互作用来讨论这些结果。
