Searching for serial refreshing in working memory:Using response times to track the content of the focus of attention over time

One popular idea is that, to support maintenance of a set of elements over brief periods of time, the focus of attention rotates among the different elements thereby serially refreshing the content of Working Memory (WM). In the research reported here, probe letters were presented between to-be-remembered letters. Response times to these probes were used to infer the status of the different items in WM. If the focus of attention cycles from one item to the next, its content should be different at different points in time and this should be reflected in a change in the response time patterns over time. Across a set of four experiments, we demonstrate a striking pattern of invariance in the response time patterns over time, suggesting that either the content of the focus of attention did not change over time or that response times cannot be used to infer the content of the focus of attention. We discuss how this pattern constrains models of WM, attention, and human information processing

Flexible attention allocation to visual and auditory working memory tasks: manipulating reward induces a trade-off

Prominent roles for general attention resources are posited in many models of working memory, but the manner in which these can be allocated differs between models or is not sufficiently specified. We varied the payoffs for correct responses in two temporally-overlapping recognition tasks, a visual array comparison task and a tone sequence comparison task. In the critical conditions, an increase in reward for one task corresponded to a decrease in reward for the concurrent task, but memory load remained constant. Our results show patterns of interference consistent with a trade-off between the tasks, suggesting that a shared resource can be flexibly divided, rather than only fully allotted to either of the tasks. Our findings support a role for a domain-general resource in models of working memory, and furthermore suggest that this resource is flexibly divisible

Resource-sharing in multiple component working memory

Working memory research often focuses on measuring the capacity of the system and how it relates to other cognitive abilities. However, research into the structure of working memory is less concerned with an overall capacity measure but rather with the intricacies of underlying components and their contribution to different tasks. A number of models of working memory structure have been proposed, each with different assumptions and predictions, but none of which adequately accounts for the full range of data in the working memory literature. We report 2 experiments that investigated the effects of load manipulations on dual-task verbal temporary memory and spatial processing. Crucially, we manipulated cognitive load around the measured memory span of each individual participant. We report a clear effect of increasing memory load on processing accuracy, but only when memory load is increased above each participant’s measured memory span. However, increasing processing load did not affect memory performance. We argue that immediate verbal memory may rely both on a temporary phonological store and on activated traces in long-term memory, with the latter deployed to support memory performance for supraspan lists and when a high memory load is coupled with a processing task. We propose that future research should tailor the load manipulations to the capacities of individual participants and suggest that contrasts between models of working memory may be more apparent than real

Multilab Direct Replication of Flavell, Beach, and Chinsky (1966): Spontaneous Verbal Rehearsal in a Memory Task as a Function of Age

Work by Flavell, Beach, and Chinsky indicated a change in the spontaneous production of overt verbalization behaviors when comparing young children (age 5) with older children (age 10). Despite the critical role that this evidence of a change in verbalization behaviors plays in modern theories of cognitive development and working memory, there has been only one other published near replication of this work. In this Registered Replication Report, we relied on researchers from 17 labs who contributed their results to a larger and more comprehensive sample of children. We assessed memory performance and the presence or absence of verbalization behaviors of young children at different ages and determined that the original pattern of findings was largely upheld: Older children were more likely to verbalize, and their memory spans improved. We confirmed that 5- and 6-year-old children who verbalized recalled more than children who did not verbalize. However, unlike Flavell et al., substantial proportions of our 5- and 6-year-old samples overtly verbalized at least sometimes during the picture memory task. In addition, continuous increase in overt verbalization from 7 to 10 years old was not consistently evident in our samples. These robust findings should be weighed when considering theories of cognitive development, particularly theories concerning when verbal rehearsal emerges and relations between speech and memory

The long-term consequences of retrieval demands during working memory

Although it is well known that distraction impairs immediate retrieval of items maintained in working memory (WM; e.g., during complex span tasks), some evidence suggests that these items are more likely to be recalled from episodic memory (EM) compared with items that were studied without any distraction (e.g., during simple span tasks). One account for this delayed advantage of complex span over simple span, or the McCabe effect (McCabe, Journal of Memory and Language, 58[2], 480–494, 2008), is that complex span affords covert retrieval opportunities that facilitate later retrieval from EM by cumulatively reactivating each successively presented item after distraction. This explanation focuses on the processing that occurs during presentation and maintenance of the items, but no work to date has explored whether the differential demands of immediate retrieval between simple and complex span may explain the effect. Accordingly, these experiments examined the impact of immediate retrieval demands on the McCabe effect by comparing typical immediate serial-recall instructions (i.e., recalling the words in their exact order of presentation) to immediate free-recall (Experiments 1–2) and no-recall (Experiments 2 and 3) instructions. The results suggested that the nature of retrieval may constrain the McCabe effect in some situations (Experiments 1–2), but its demands do not drive the McCabe effect given that it was observed in both serial-recall and no-recall conditions (Experiment 3). Instead, activities such as covert retrieval during the processing phase may underlie the McCabe effect, thus further evidencing the importance of processing in WM for the long-term retention of information

The Psychological Science Accelerator: Advancing Psychology Through a Distributed Collaborative Network

Source at https://doi.org/10.1177/2515245918797607.Concerns about the veracity of psychological research have been growing. Many findings in psychological science are based on studies with insufficient statistical power and nonrepresentative samples, or may otherwise be limited to specific, ungeneralizable settings or populations. Crowdsourced research, a type of large-scale collaboration in which one or more research projects are conducted across multiple lab sites, offers a pragmatic solution to these and other current methodological challenges. The Psychological Science Accelerator (PSA) is a distributed network of laboratories designed to enable and support crowdsourced research projects. These projects can focus on novel research questions or replicate prior research in large, diverse samples. The PSA’s mission is to accelerate the accumulation of reliable and generalizable evidence in psychological science. Here, we describe the background, structure, principles, procedures, benefits, and challenges of the PSA. In contrast to other crowdsourced research networks, the PSA is ongoing (as opposed to time limited), efficient (in that structures and principles are reused for different projects), decentralized, diverse (in both subjects and researchers), and inclusive (of proposals, contributions, and other relevant input from anyone inside or outside the network). The PSA and other approaches to crowdsourced psychological science will advance understanding of mental processes and behaviors by enabling rigorous research and systematic examination of its generalizability

Working Memory Underpins Cognitive Development, Learning, and Education

Working memory is the retention of a small amount of information in a readily accessible form. It facilitates planning, comprehension, reasoning, and problem-solving. I examine the historical roots and conceptual development of the concept and the theoretical and practical implications of current debates about working memory mechanisms. Then I explore the nature of cognitive developmental improvements in working memory, the role of working memory in learning, and some potential implications of working memory and its development for the education of children and adults. The use of working memory is quite ubiquitous in human thought, but the best way to improve education using what we know about working memory is still controversial. I hope to provide some directions for research and educational practice

Modeling working memory: An interference model of complex span

This article introduces a new computational model for the complex-span task, the most popular task for studying working memory. SOB-CS is a two-layer neural network that associates distributed item representations with distributed, overlapping position markers. Memory capacity limits are explained by interference from a superposition of associations. Concurrent processing interferes with memory through involuntary encoding of distractors. Free time in-between distractors is used to remove irrelevant representations, thereby reducing interference. The model accounts for benchmark findings in four areas: (1) effects of processing pace, processing difficulty, and number of processing steps; (2) effects of serial position and error patterns; (3) effects of different kinds of item-distractor similarity; and (4) correlations between span tasks. The model makes several new predictions in these areas, which were confirmed experimentally