Clear evidence for item limits in visual working memory

Abstract

There is a consensus that visual working memory (WM) resources are sharply limited, but debate persists regarding the simple question of whether there is a limit to the total number of items that can be stored concurrently. Zhang and Luck (2008) advanced this debate with an analytic procedure that provided strong evidence for random guessing responses, but their findings can also be described by models that deny guessing while asserting a high prevalence of low precision memories. Here, we used a whole report memory procedure in which subjects reported all items in each trial, and indicated whether they were guessing with each response. Critically, this procedure allowed us to measure memory performance for all items in each trial. When subjects were asked to remember 6 items, the response error distributions for about 3 out of the 6 items were best fit by a parameter-free guessing model (i.e. a uniform distribution). In addition, subjects’ self-reports of guessing precisely tracked the guessing rate estimated with a mixture model. Control experiments determined that guessing behavior was not due to output interference, and that there was still a high prevalence of guessing when subjects were instructed not to guess. Our novel approach yielded evidence that guesses, not low-precision representations, best explain limitations in working memory. These guesses also corroborate a capacity-limited working memory system – we found evidence that subjects are able to report non-zero information for only 3–4 items. Thus, WM capacity is constrained by an item limit that precludes the storage of more than 3–4 individuated feature values.

Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation

“…This is consistent with observations that an increase in WMC load is accompanied with what look like random responses (e.g., Adam, Vogel, & Awh, 2017;Zhang & Luck, 2008), since we would then expect individuals with lower WMC (as well as higher WMC individuals under increased memory load) to guess more often because their capacity is lower. Here, the motivation was simply to improve model fit-at least some participants' behavior was more variable than even a severely resource-constrained particle filter (i.e., a single particle).…”

Stochastic sampling provides a unifying account of visual working memory limits

“…2B; ∆LL = 162 ± 13.6), indicating that stochasticity is critical for capturing behavioral performance. In contrast to previous interpretations [18], we did not find that the results of the whole report tasks support a slot-like mechanism with a fixed limit on the number of memorized items.…”

“…S3), indicating that stochasticity is critical for capturing behavioral performance. Contrary to previous interpretations [17], model comparison on whole-report data did not support a slot-like mechanism with a fixed item limit. Intermediate models in which a fixed number of samples were randomly allocated to items (random-fixed model) or a Poisson random number of samples was distributed as evenly as possible between items (even-stochastic model) produced intermediate qualities of fit overall (Fig.…”

“…We tested the ability of stochastic and fixed sampling models to capture response errors in analogue report tasks. We fit the models to a large dataset of single-report tasks originating from different labs (see SI) and also to a set of whole-report experiments [18], in which participants reported the feature values of all items in a sample array, either in a prescribed random order or in an order freely chosen by each participant on each trial. While only a single study, the whole-report results include information regarding correlations in errors between items represented simultaneously in WM that could differentiate the models.…”

“…One prediction of working memory models with a fixed number of samples [13,17] is the appearance of random "guesses" once the number of items exceeds that limit ( Fig. 4A-B).…”