Department of Computing Science, University of Alberta, 3-39 Athabasca Hall, Edmonton, AB, T6G 2E9, Canada.
Mem Cognit. 2019 Oct;47(7):1415-1430. doi: 10.3758/s13421-019-00942-4.
A vector-based model of discriminative learning is presented. It is demonstrated to learn association strengths identical to the Rescorla-Wagner model under certain parameter settings (Rescorla & Wagner, 1972, Classical Conditioning II: Current Research and Theory, 2, 64-99). For other parameter settings, it approximates the association strengths learned by the Rescorla-Wagner model. I argue that the Rescorla-Wagner model has conceptual details that exclude it as an algorithmically plausible model of learning. The vector learning model, however, does not suffer from the same conceptual issues. Finally, we demonstrate that the vector learning model provides insight into how animals might learn the semantics of stimuli rather than just their associations. Results for simulations of language processing experiments are reported.
提出了一种基于向量的辨别式学习模型。在某些参数设置下（Rescorla & Wagner，1972，经典条件作用 II：当前研究与理论，2，64-99），它被证明可以学习与 Rescorla-Wagner 模型相同的关联强度。在其他参数设置下，它近似于 Rescorla-Wagner 模型学习的关联强度。我认为 Rescorla-Wagner 模型具有一些概念细节，使其不能作为学习的算法合理模型。然而，向量学习模型没有受到相同的概念问题的影响。最后，我们证明了向量学习模型提供了一种了解动物如何学习刺激的语义而不仅仅是它们的关联的方法。报告了语言处理实验模拟的结果。
