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Abstract We report 10 experiments exploring the proposition that memory retrieval is perceptual attention turned inward. The experiments adapt the Eriksen and Eriksen perceptual flanker effect to a memory task in which subjects must decide whether a cued item in a probe display appeared in the same position in a memory list. Previous research with thisepisodic flanker taskfound distance and compatibility effects like those in the perceptual flanker task, suggesting that the same attentional spotlight is turned inward in memory retrieval. The previous experiments used lists of six consonants. The experiments reported here were designed to generalize the results to a broader range of conditions, from letters to words, colors, and pictures, and from set size 6 to set sizes of 4 and 5. Experiments 1–4 varied distance and set size with lists of four, five, or six letters, words, colors, and pictures, respectively. The distance effect was observed with all materials and all set sizes. Experiments 5–8 varied compatibility by presenting context items in the probe that were either the same as the memory list (and therefore compatible with “yes” responses and incompatible with “no” responses) or different from the memory list (and therefore incompatible with “yes” responses and compatible with “no” responses). We found compatibility effects with all materials and all set sizes. These results support the proposition that memory retrieval is attention turned inward. Turned inward or outward, attention is a general process that applies the same computations to different kinds of materials. 

Position-specific intrusions of items from prior lists are rare but important phenomena that distinguish broad classes of theory in serial memory. They are uniquely predicted by position coding theories, which assume items on all lists are associated with the same set of codes representing their positions. Activating a position code activates items associated with it in current and prior lists in proportion to their distance from the activated position. Thus, prior list intrusions are most likely to come from the coded position. Alternative “item dependent” theories based on associations between items and contexts built from items have difficulty accounting for the position specificity of prior list intrusions. We tested the position coding account with a position-cued recognition task designed to produce prior list interference. Cuing a position should activate a position code, which should activate items in nearby positions in the current and prior lists. We presented lures from the prior list to test for position-specific activation in response time and error rate; lures from nearby positions should interfere more. We found no evidence for such interference in 10 experiments, falsifying the position coding prediction. We ran two serial recall experiments with the same materials and found position-specific prior list intrusions. These results challenge all theories of serial memory: Position coding theories can explain the prior list intrusions in serial recall and but not the absence of prior list interference in cued recognition. Item dependent theories can explain the absence of prior list interference in cued recognition but cannot explain the occurrence of prior list intrusions in serial recall. 

Evidence-accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behavior. EAMs have generated significant theoretical advances in psychology, behavioral economics, and cognitive neuroscience and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues and inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, relating experimental manipulations to EAM parameters, planning appropriate sample sizes, and preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the our substantial collective experience with EAMs. By encouraging good task-design practices and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications. 

Evidence accumulation models (EAMs) are powerful tools for making sense of human and animal decision-making behaviour. EAMs have generated significant theoretical advances in psychology, behavioural economics, and cognitive neuroscience, and are increasingly used as a measurement tool in clinical research and other applied settings. Obtaining valid and reliable inferences from EAMs depends on knowing how to establish a close match between model assumptions and features of the task/data to which the model is applied. However, this knowledge is rarely articulated in the EAM literature, leaving beginners to rely on the private advice of mentors and colleagues, and on inefficient trial-and-error learning. In this article, we provide practical guidance for designing tasks appropriate for EAMs, for relating experimental manipulations to EAM parameters, for planning appropriate sample sizes, and for preparing data and conducting an EAM analysis. Our advice is based on prior methodological studies and the authors’ substantial collective experience with EAMs. By encouraging good task design practices, and warning of potential pitfalls, we hope to improve the quality and trustworthiness of future EAM research and applications.