Models of free recall describe free recall initiation as a decision-making process in which items compete to be retrieved. Recently, Osth and Farrell (Psychological Review, 126, 578–609, 2019) applied evidence accumulation models to complete RT distributions and serial positions of participants’ first recalls in free recall, which resulted in some novel conclusions about primacy and recency effects. Specifically, the results of the modeling favored an account in which primacy was due to reinstatement of the start-of-the-list, and recency was found to be exponential in shape. In this work, we examine what happens when participants are given alternative recall instructions. Prior work has demonstrated weaker primacy and greater recency when fewer items are required to report (Ward & Tan, Memory & Cognition, 2019), and a key question is whether this change in instructions qualitatively changes the nature of the recall process, or merely changes the parameters of the recall competition. We conducted an experiment where participants studied six- or 12-item lists and were post-cued as to whether to retrieve a single item, or as many items as possible. Subsequently, we applied LBA models with various assumptions about primacy and recency, implemented using hierarchical Bayesian techniques. While greater recency was observed when only one item was required for output, the model selection did not suggest that there were qualitative differences between the two conditions. Specifically, start-of-list reinstatement and exponential recency functions were favored in both conditions.

自由回忆模型将自由回忆的发起描述为一个决策过程，其中项目竞争检索。最近，Osth 和 Farrell（心理评论，126, 578–609, 2019) 应用证据积累模型来完成参与者在自由回忆中第一次回忆的 RT 分布和序列位置，从而得出了一些关于首要效应和近因效应的新结论。具体来说，建模的结果有利于这样一种帐户，其中首要是由于恢复了列表的开始，并且发现新近度是指数形式的。在这项工作中，我们研究了当参与者被给予替代回忆指令时会发生什么。当需要报告的项目较少时，先前的工作表现出较弱的首要性和较高的新近性（Ward & Tan、Memory & Cognition, 2019)，而一个关键问题是，这种指令的变化是否会从质上改变召回过程的性质，或者仅仅是改变召回竞争的参数。我们进行了一项实验，参与者研究了 6 个或 12 个项目的列表，并被告知是检索单个项目还是尽可能多的项目。随后，我们应用了具有关于首要性和新近性的各种假设的 LBA 模型，并使用分层贝叶斯技术实现。虽然当只需要一个项目输出时观察到更大的新近度，但模型选择并未表明这两种条件之间存在质的差异。具体来说，在这两种情况下，列表开始恢复和指数新近函数都受到青睐。