论文标题
超越Rescorla-Wagner：学习的起伏
Beyond Rescorla-Wagner: the ups and downs of learning
论文作者
论文摘要
我们检查了最近提出的关联帕夫洛夫学习动力学模型的鲁棒性，该模型以自然方式扩展了Rescorla-Wagner（RW）模型，并预测受试者响应中逐渐减弱的振荡。使用两个实验的数据，我们将动力学振荡模型（DOM）与RW学习曲线和振荡的叠加制成的振荡模型进行比较。数据不仅清楚地显示出振荡模式，而且它们还偏爱DOM而不是增加的振荡模型，从而指出这些振荡是关联过程的表现。后者被解释为一个事实，即受试者对试验结果的预测比RW模型更延长，但不确定性更大。
We check the robustness of a recently proposed dynamical model of associative Pavlovian learning that extends the Rescorla-Wagner (RW) model in a natural way and predicts progressively damped oscillations in the response of the subjects. Using the data of two experiments, we compare the dynamical oscillatory model (DOM) with an oscillatory model made of the superposition of the RW learning curve and oscillations. Not only do data clearly show an oscillatory pattern, but they also favor the DOM over the added oscillation model, thus pointing out that these oscillations are the manifestation of an associative process. The latter is interpreted as the fact that subjects make predictions on trial outcomes more extended in time than in the RW model, but with more uncertainty.
