Department of Computer Science, City, University of London.
Psychol Rev. 2019 Jul;126(4):506-549. doi: 10.1037/rev0000147. Epub 2019 Mar 14.
In this article a formal model of associative learning is presented that incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that, so far, have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: (a) a fully connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; (b) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; (c) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically (d) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
本文提出了一种形式化的联想学习模型，该模型结合了表示和计算机制，作为一个连贯的整体，使其能够准确预测到各种现象，到目前为止，这些现象在学习理论中还没有得到统一的解释。特别是，双误差动态渐近(DDA)模型引入了：(a)一种全连接的网络架构，其中刺激被表示为时间上聚类的元素，这些元素彼此关联，从而一个聚类的元素会在其他聚类上产生活动，这自然实现了中性刺激的关联和中介学习；(b)在传统的误差修正规则(双误差)中引入了预测误差项，这降低了对预期预测器的学习速度；(c)重新评估可联想性率，其假设是对结果的可预测性进行跟踪，从而学习到长时间的不确定性，降低对最初令人惊讶的结果的注意力水平；以及关键的(d)一个具有生物合理性的可变渐近值，它包含了赫布学习的原理，导致相似水平的聚类活动产生更强的关联。本文展示了 DDA 模型的一系列模拟的输出结果，并结合了文献中的实证结果。最后，讨论了模型的预测范围。(PsycINFO 数据库记录 (c) 2019 APA，保留所有权利)。
