There Is no Theory-Free Measure of "Swaps" in Visual Working Memory Experiments.

Visual working memory is highly limited, and its capacity is tied to many indices of cognitive function. For this reason, there is much interest in understanding its architecture and the sources of its limited capacity. As part of this research effort, researchers often attempt to decompose visual working memory errors into different kinds of errors, with different origins. One of the most common kinds of memory error is referred to as a "swap," where people report a value that closely resembles an item that was not probed (e.g., an incorrect, non-target item). This is typically assumed to reflect confusions, like location binding errors, which result in the wrong item being reported. Capturing swap rates reliably and validly is of great importance because it permits researchers to accurately decompose different sources of memory errors and elucidate the processes that give rise to them. Here, we ask whether different visual working memory models yield robust and consistent estimates of swap rates. This is a major gap in the literature because in both empirical and modeling work, researchers measure swaps without motivating their choice of swap model. Therefore, we use extensive parameter recovery simulations with three mainstream swap models to demonstrate how the choice of measurement model can result in very large differences in estimated swap rates. We find that these choices can have major implications for how swap rates are estimated to change across conditions. In particular, each of the three models we consider can lead to differential quantitative and qualitative interpretations of the data. Our work serves as a cautionary note to researchers as well as a guide for model-based measurement of visual working memory processes.

Measuring memory is harder than you think: How to avoid problematic measurement practices in memory research.

We argue that critical areas of memory research rely on problematic measurement practices and provide concrete suggestions to improve the situation. In particular, we highlight the prevalence of memory studies that use tasks (like the "old/new" task: "have you seen this item before? yes/no") where quantifying performance is deeply dependent on counterfactual reasoning that depends on the (unknowable) distribution of underlying memory signals. As a result of this difficulty, different literatures in memory research (e.g., visual working memory, eyewitness identification, picture memory, etc.) have settled on a variety of fundamentally different metrics to get performance measures from such tasks (e.g., A', corrected hit rate, percent correct, d', diagnosticity ratios, K values, etc.), even though these metrics make different, contradictory assumptions about the distribution of latent memory signals, and even though all of their assumptions are frequently incorrect. We suggest that in order for the psychology and neuroscience of memory to become a more cumulative, theory-driven science, more attention must be given to measurement issues. We make a concrete suggestion: The default memory task for those simply interested in performance should change from old/new ("did you see this item'?") to two-alternative forced-choice ("which of these two items did you see?"). In situations where old/new variants are preferred (e.g., eyewitness identification; theoretical investigations of the nature of memory signals), receiver operating characteristic (ROC) analysis should be performed rather than a binary old/new task.

You cannot "count" how many items people remember in visual working memory: The importance of signal detection-based measures for understanding change detection performance.

Change detection tasks are commonly used to measure and understand the nature of visual working memory capacity. Across three experiments, we examine whether the nature of the memory signals used to perform change detection are continuous or all-or-none and consider the implications for proper measurement of performance. In Experiment 1, we find evidence from confidence reports that visual working memory is continuous in strength, with strong support for an equal variance signal detection model with no guesses or lapses. Experiments 2 and 3 test an implication of this, which is that K should confound response criteria and memory. We found K values increased by roughly 30% when criteria are shifted despite no change in the underlying memory signals. Overall, our data call into question a large body of work using threshold measures, like K, to analyze change detection data. This metric confounds response bias with memory performance and is inconsistent with the vast majority of visual working memory models, which propose variations in precision or strength are present in working memory. Instead, our data indicate an equal variance signal detection model (and thus, d')-without need for lapses or guesses-is sufficient to explain change detection performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

What You See Is What You Hear: Sounds Alter the Contents of Visual Perception.

Visual object recognition is not performed in isolation but depends on prior knowledge and context. Here, we found that auditory context plays a critical role in visual object perception. Using a psychophysical task in which naturalistic sounds were paired with noisy visual inputs, we demonstrated across two experiments (young adults; ns = 18-40 in Experiments 1 and 2, respectively) that the representations of ambiguous visual objects were shifted toward the visual features of an object that were related to the incidental sound. In a series of control experiments, we found that these effects were not driven by decision or response biases (ns = 40-85) nor were they due to top-down expectations (n = 40). Instead, these effects were driven by the continuous integration of audiovisual inputs during perception itself. Together, our results demonstrate that the perceptual experience of visual objects is directly shaped by naturalistic auditory context, which provides independent and diagnostic information about the visual world.

Guidance of attention by working memory is a matter of representational fidelity.

Items that are held in visual working memory can guide attention toward matching features in the environment. Predominant theories propose that to guide attention, a memory item must be internally prioritized and given a special template status, which builds on the assumption that there are qualitatively distinct states in working memory. Here, we propose that no distinct states in working memory are necessary to explain why some items guide attention and others do not. Instead, we propose variations in attentional guidance arise because individual memories naturally vary in their representational fidelity, and only highly accurate memories automatically guide attention. Across a series of experiments and a simulation we show that (a) items in working memory vary naturally in representational fidelity; (b) attention is guided by all well-represented items, though frequently only one item is represented well enough to guide; and (c) no special working memory state for prioritized items is necessary to explain guidance. These findings challenge current models of attentional guidance and working memory and instead support a simpler account for how working memory and attention interact: Only the representational fidelity of memories, which varies naturally between items, determines whether and how strongly a memory representation guides attention. (PsycInfo Database Record (c) 2022 APA, all rights reserved).