Slot-like capacity and resource-like coding in a neural model of multiple-item working memory

Working memory representations in visual cortex mediate distraction effects

Although the contents of working memory can be decoded from visual cortex activity, these representations may play a limited role if they are not robust to distraction. We used model-based fMRI to estimate the impact of distracting visual tasks on working memory representations in several visual field maps in visual and frontoparietal association cortex. Here, we show distraction causes the fidelity of working memory representations to briefly dip when both the memorandum and distractor are jointly encoded by the population activities. Distraction induces small biases in memory errors which can be predicted by biases in neural decoding in early visual cortex, but not other regions. Although distraction briefly disrupts working memory representations, the widespread redundancy with which working memory information is encoded may protect against catastrophic loss. In early visual cortex, the neural representation of information in working memory and behavioral performance are intertwined, solidifying its importance in visual memory.

The relative roles of visual, parietal, and frontal cortex in working memory have been actively debated. Here, the authors show that distraction impacts visual working memory representations in primary visual areas, indicating that these regions play a key role in the maintenance of working memory.

Persistent Activity During Working Memory From Front to Back

Working memory (WM) extends the duration over which information is available for processing. Given its importance in supporting a wide-array of high level cognitive abilities, uncovering the neural mechanisms that underlie WM has been a primary goal of neuroscience research over the past century. Here, we critically review what we consider the two major "arcs" of inquiry, with a specific focus on findings that were theoretically transformative. For the first arc, we briefly review classic studies that led to the canonical WM theory that cast the prefrontal cortex (PFC) as a central player utilizing persistent activity of neurons as a mechanism for memory storage. We then consider recent challenges to the theory regarding the role of persistent neural activity. The second arc, which evolved over the last decade, stemmed from sophisticated computational neuroimaging approaches enabling researchers to decode the contents of WM from the patterns of neural activity in many parts of the brain including early visual cortex. We summarize key findings from these studies, their implications for WM theory, and finally the challenges these findings pose. Our goal in doing so is to identify barriers to developing a comprehensive theory of WM that will require a unification of these two "arcs" of research.

Stochastic sampling provides a unifying account of visual working memory limits

Research into human working memory limits has been shaped by the competition between different formal models, with a central point of contention being whether internal representations are continuous or discrete. Here we describe a sampling approach derived from principles of neural coding as a framework to understand working memory limits. Reconceptualizing existing models in these terms reveals strong commonalities between seemingly opposing accounts, but also allows us to identify specific points of difference. We show that the discrete versus continuous nature of sampling is not critical to model fits, but that, instead, random variability in sample counts is the key to reproducing human performance in both single- and whole-report tasks. A probabilistic limit on the number of items successfully retrieved is an emergent property of stochastic sampling, requiring no explicit mechanism to enforce it. These findings resolve discrepancies between previous accounts and establish a unified computational framework for working memory that is compatible with neural principles.

Temporal codes of visual working memory in the human cerebral cortex: Brain rhythms associated with high memory capacity

Visual working memory (vWM) is an important ability required for various cognitive tasks although its neural underpinnings remain unclear. While many studies have focused on theta (4-7 Hz) and gamma (> 30 Hz) rhythms as a substrate of vWM, here we show that temporal signals embedded in alpha (8-12 Hz) and beta (13-30 Hz) bands can be a good predictor of vWM capacity. Neural activity of healthy human participants was recorded with magnetoencephalography when they performed a classical vWM task (change detection). We analyzed changes in inter-peak intervals (IPIs) of oscillatory signals along with an increase in WM load (a number of to-be-memorized items, 1-6). Results showed a load-dependent reduction of IPIs in the parietal and frontal regions, indicating that alpha/beta rhythms became faster when multiple items were stored in vWM. Furthermore, this reduction in IPIs was positively correlated with individual vWM capacity, especially in the frontal cortex. Those results indicate that vWM is represented as a change in oscillation frequency in the human cerebral cortex.

Hierarchical recruitment of competition alleviates working memory overload in a frontoparietal model

The storage limitations of visual working memory have been the subject of intense research interest for several decades, but few studies have systematically investigated the dependence of these limitations on memory load that exceeds our retention abilities. Under this real-world scenario, performance typically declines beyond a critical load among low-performing subjects, a phenomenon known as working memory overload. We used a frontoparietal cortical model to test the hypothesis that high-performing subjects select a manageable number of items for storage, thereby avoiding overload. The model accounts for behavioral and electrophysiological data from high-performing subjects in a parameter regime where competitive encoding in its prefrontal network selects items for storage, interareal projections sustain their representations after stimulus offset, and weak dynamics in its parietal network limit their mutual interference. Violation of these principles accounts for these data among low-performing subjects, implying that poor visual working memory performance reflects poor control over frontoparietal circuitry, making testable predictions for experiments.