How do recall requirements affect decision-making in free recall initiation? A linear ballistic accumulator approach

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.

Testing the primary and convergent retrieval model of recall: Recall practice produces faster recall success but also faster recall failure

The primary and convergent retrieval (PCR) model assumes that the act of successful recall not only boosts associations between the item and retrieval cues but additionally strengthens associations within the item (i.e., between the features of an item), speeding the rate of information retrieval from memory. The latter effect is termed intra-item learning and is a unique benefit of recall practice (i.e., the "testing effect"). Prior work confirmed the prediction that recall practice produces faster subsequent recall than restudy practice even if accuracy is higher following restudy. The current study replicated this result, but also examined the downside of recall practice: that after a failure to recall during practice, participants will be faster in their failure to recall on a subsequent recall test. This prediction was confirmed in a multisession cued recall experiment that collected accuracy and recall latency measurements for no practice, recall practice, or restudy, with an immediate or delayed final test. The linear ballistic accumulator model was fit to latency distributions, and model comparison determined that these effects reflect differences in drift rates, as predicted by the PCR model.

Getting satisfied with "satisfaction of search": How to measure errors during multiple-target visual search

Visual search studies are common in cognitive psychology, and the results generally focus upon accuracy, response times, or both. Most research has focused upon search scenarios where no more than 1 target will be present for any single trial. However, if multiple targets can be present on a single trial, it introduces an additional source of error because the found target can interfere with subsequent search performance. These errors have been studied thoroughly in radiology for decades, although their emphasis in cognitive psychology studies has been more recent. One particular issue with multiple-target search is that these subsequent search errors (i.e., specific errors which occur following a found target) are measured differently by different studies. There is currently no guidance as to which measurement method is best or what impact different measurement methods could have upon various results and conclusions. The current investigation provides two efforts to address these issues. First, the existing literature is reviewed to clarify the appropriate scenarios where subsequent search errors could be observed. Second, several different measurement methods are used with several existing datasets to contrast and compare how each method would have affected the results and conclusions of those studies. The evidence is then used to provide appropriate guidelines for measuring multiple-target search errors in future studies.