Several attention-based models of associative learning are built upon the learned predictiveness principle, whereby learning is optimized by attending to the most predictive features and ignoring the least predictive features. Despite their functional similarity, these models differ in their formal mechanisms and thus may produce very different predictions in some circumstances. As we demonstrate, this is particularly evident in the inverse base-rate effect. Using simulations with a modified Mackintosh model and the EXIT model, we found that models based on the learned predictiveness principle can account for rare-outcome choice biases associated with the inverse base-rate effect, despite making opposite predictions for relative attention to rare versus common predictors. The models also make different predictions regarding changes in attention across training, and effects of context associations on attention to cues. Using a human causal learning task, we replicated the inverse base-rate effect and a recently reported reduction in this effect when the context is not predictive of the common outcome and used eye-tracking to test model predictions about changes in attention both prior to making a decision, and during feedback. The results support the predictions made by EXIT, where the rare predictor commands greater attention than the common predictor throughout training. In addition, patterns of attention prior to making a decision differed to those during feedback, where effects of using a partially predictive context were evident only prior to making a prediction. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

中文翻译：

---

习得的预测模型可以预测相反的基准偏向效应中的注意偏见。

在学习的预测性原理的基础上建立了几种基于注意力的联想学习模型，从而通过关注最多的预测功能而忽略了最少的预测功能来优化学习。尽管它们的功能相似，但是这些模型的形式机制不同，因此在某些情况下可能会产生完全不同的预测。正如我们所展示的，这在逆基频效应中尤为明显。使用修改后的Mackintosh模型和EXIT模型进行的仿真，我们发现基于学习的预测性原理的模型可以解释与逆基本率效应相关的稀有结果选择偏见，尽管对相对关注稀有与常见做出了相反的预测预测变量。该模型还对培训期间注意力的变化以及上下文关联对提示的注意力的影响做出了不同的预测。使用人为因果的学习任务，当上下文不能预测共同的结果时，我们复制了基本速率的反作用和最近报道的这种作用的降低，并使用眼动追踪来测试关于注意力变化的模型预测一个决定，并在反馈过程中。结果支持EXIT所做的预测，在整个训练过程中，罕见的预测变量比普通的预测变量要引起更大的关注。此外，做出决定之前的注意力模式与反馈过程中的注意力模式有所不同，在反馈过程中，仅在做出预测之前使用部分预测性上下文的效果才明显。（PsycINFO数据库记录（c）2019 APA，