University of New South Wales.
J Exp Psychol Anim Learn Cogn. 2021 Apr;47(2):211-215. doi: 10.1037/xan0000271.
Rescorla (2001) used the compound test procedure to compare associative changes to cues located at different points on a performance scale. He found that associative changes to cues conditioned in compound are not necessarily equal, as predicted by common error term theories like Rescorla and Wagner (1972), but instead are larger for the poorer predictor of a trial outcome. Hence, Rescorla proposed a modification to the Rescorla-Wagner model whereby associative change is calculated as the product of 2 error terms: a common error term, as in the original model, and a unique error term for each cue present, which accounts for his findings that the poorer predictor of a trial outcome undergoes more associative change. In a recent study, Spicer, Mitchell, Wills, and Jones (2020) reported findings that appear to be inconsistent with Rescorla's proposal. These authors compared associative changes to cues that differed in associative strength as well as the certainty with which they predicted a trial outcome: One cue had greater strength than did the other, but its prediction of the trial outcome was less certain. Spicer et al. found that the cue that evoked a larger prediction error (the more certain cue) underwent less (not more) associative change and, thereby, concluded that associative change in people is not primarily determined by prediction error. Instead, they argued that cues that predict certain outcomes are somewhat protected from further associative change (theory protection), resulting in greater change to cues that predict uncertain outcomes. In this article, we offer an alternative explanation for the Spicer et al. findings using an approach described by Holmes, Chan, and Westbrook (2019). We show that if the learning-to-performance mapping function is a double sigmoid across the full range of associative strength, the Rescorla-Wagner model accommodates Rescorla's compound test results, as well as those reported by Spicer et al. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
雷斯考拉(2001)使用复合测试程序比较位于不同表现尺度上的线索的关联变化。他发现,复合条件下线索的关联变化不一定与雷斯考拉和瓦格纳(1972)等常见错误项理论所预测的那样相等,而是与试验结果的较差预测者相比更大。因此,雷斯考拉对雷斯考拉-瓦格纳模型进行了修改,其中关联变化被计算为两个错误项的乘积:一个是原始模型中的常见错误项,另一个是每个线索的独特错误项,这解释了他的发现,即试验结果的较差预测者经历了更多的关联变化。在最近的一项研究中,斯派塞、米切尔、威尔斯和琼斯(2020)报告了与雷斯考拉的提议似乎不一致的发现。这些作者比较了在关联强度以及对试验结果的预测确定性方面不同的线索的关联变化:一个线索的强度比另一个线索大,但它对试验结果的预测不太确定。斯派塞等人发现,引起较大预测误差(较确定的线索)的线索经历的关联变化较小(而不是较大),因此得出结论,人的关联变化主要不是由预测误差决定的。相反,他们认为,预测确定结果的线索在某种程度上受到进一步关联变化的保护(理论保护),从而导致对预测不确定结果的线索发生更大的变化。在本文中,我们使用霍尔姆斯、陈和韦斯特布鲁克(2019)描述的方法为斯派塞等人的研究结果提供了另一种解释。我们表明,如果学习-表现映射函数在整个关联强度范围内是一个双 sigmoid,则雷斯考拉-瓦格纳模型既可以容纳雷斯考拉的复合测试结果,也可以容纳斯派塞等人报告的结果(心理学信息数据库记录(c)2021 APA,保留所有权利)。