When "capacity" changes with set size: Ensemble representations support the detection of across-category changes in visual working memory.

Is there a fixed limit on how many objects we can hold actively in mind? Generally, researchers have found participants are worse at remembering a small number of objects if those objects are more complex, suggesting a limited resource rather than a fixed number of objects best explains working memory performance. However, some evidence has suggested that stimulus similarity better accounts for these effects and that, after accounting for such similarity, the data support a slot-based fixed item limit for working memory. Much of the evidence used to support the latter claim relies on working memory displays containing different categories of items. It has been found that, for large, across-category changes, performance does not differ for different kinds of complex stimuli. However, many of these studies fail to adequately control for the potential use of ensemble information in discriminating such large changes. Here, we sought to identify how much ensemble representations may explain performance across these tasks. In Experiment 1, we observed that, as set size increased from four to 12 items, capacity estimates for across-category changes increased linearly as well, providing evidence against the claim of a fixed capacity. In Experiment 2, we controlled for stimulus complexity and similarity but varied the utility of ensemble representations for the change-detection task. We observed significantly greater capacity when ensemble information could be used. Altogether, these results are contrary to a slot-like, fixed-object constraint on working memory capacity and consistent with object complexity and ensemble representations strongly affecting working memory performance

When "capacity" changes with set size: Ensemble representations support the detection of across-category changes in visual working memory.

Is there a fixed limit on how many objects we can hold actively in mind? Generally, researchers have found participants are worse at remembering a small number of objects if those objects are more complex, suggesting a limited resource rather than a fixed number of objects best explains working memory performance. However, some evidence has suggested that stimulus similarity better accounts for these effects and that, after accounting for such similarity, the data support a slot-based fixed item limit for working memory. Much of the evidence used to support the latter claim relies on working memory displays containing different categories of items. It has been found that, for large, across-category changes, performance does not differ for different kinds of complex stimuli. However, many of these studies fail to adequately control for the potential use of ensemble information in discriminating such large changes. Here, we sought to identify how much ensemble representations may explain performance across these tasks. In Experiment 1, we observed that, as set size increased from four to 12 items, capacity estimates for across-category changes increased linearly as well, providing evidence against the claim of a fixed capacity. In Experiment 2, we controlled for stimulus complexity and similarity but varied the utility of ensemble representations for the change-detection task. We observed significantly greater capacity when ensemble information could be used. Altogether, these results are contrary to a slot-like, fixed-object constraint on working memory capacity and consistent with object complexity and ensemble representations strongly affecting working memory performance

Is working memory inherently more "precise" than long-term memory? Extremely high fidelity visual long-term memories for frequently encountered objects.

Long-term memory is often considered easily corruptible, imprecise, and inaccurate, especially in comparison to working memory. However, most research used to support these findings relies on weak long-term memories: those where people have had only one brief exposure to an item. Here we investigated the fidelity of visual long-term memory in more naturalistic setting, with repeated exposures, and ask how it compares to visual working memory fidelity. Using psychophysical methods designed to precisely measure the fidelity of visual memory, we demonstrate that long-term memory for the color of frequently seen objects is as accurate as working memory for the color of a single item seen 1 s ago. In particular, we show that repetition greatly improves long-term memory, including the ability to discriminate an item from a very similar item (fidelity), in both a lab setting (Experiments 1-3) and a naturalistic setting (brand logos, Experiment 4). Overall, our results demonstrate the impressive nature of visual long-term memory fidelity, which we find is even higher fidelity than previously indicated in situations involving repetitions. Furthermore, our results suggest that there is no distinction between the fidelity of visual working memory and visual long-term memory, but instead both memory systems are capable of storing similar incredibly high-fidelity memories under the right circumstances. Our results also provide further evidence that there is no fundamental distinction between the "precision" of memory and the "likelihood of retrieving a memory," instead suggesting a single continuous measure of memory strength best accounts for working and long-term memory. (PsycInfo Database Record (c) 2020 APA, all rights reserved)

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)