Parietal Contributions to Visual Working Memory Depend on Task Difficulty

The effects of structured input and working memory on the acquisition of English causative forms

Predicting distracted driving: The role of individual differences in working memory

<p class="a"><span lang="EN-US">In this research, authors examine how individual differences in working memory capacity and Working Memory updating influence differences in impulsivity in risky decision making and behaviors. This study makes attempt to address extension of related works on the relationship between working memory, impulsivity and risky decision making and behaviors in adolescents. A large sample (420) of boy secondary grade students involved in this research; this study indicated that working memory capacity and updating Working Memory moderate some of impulsivity and high risk behaviors and decision making. Individuals with low working memory capacity and high impulsivity compared to individuals with high working memory capacity and high impulsivity are more likely subjected to risky decision making. Further, high risk behaviors are more salient in individuals with low updating Working Memory and high impulsivity. </span></p>

Objectives: The purpose of the study was to identify the impact of age-related working memory capacity and cognitive load to word recognition using an eye tracker.Methods: A total of 48 participants (24 young, 24 elderly adults) who were divided according to working memory capacity (high, low) participated in the study. This study consisted of a digit load and word recognition task.Results: First, the elderly group had lower accuracy and slower response times than the young group. There was a significant difference between working memory capacity and cognitive load in response time. In the group with low working memory capacity, the response time increased under lower cognitive load, which was different from the group with a high working memory capacity. Next, the elderly group had a lower proportion of fixation than the young group by time interval. In addition, there was a significant interaction between group and cognitive load by time interval. In particular, the proportion of fixation decreased over time under a high cognitive load while the proportion of fixation increased under a low cognitive load in the elders, because the inhibition and switching abilities were weakened which made the elders unable to process information efficiently.Conclusion: The study results showed significant effects between working memory capacity and cognitive load in response time and proportion of fixation. This implies that complex factors influence language processing, which makes measuring accuracy insufficient. Thus, this research confirms the correlation among aging, working memory, and cognitive load through both online and offline measurement.

This study investigated the role of strategy instruction and working memory capacity (WMC) on problem solving solution accuracy in children with and without math disabilities (MD). Children in grade 3 (N = 204) with and without MD subdivided into high and low WMC were randomly assigned to 1 of 4 conditions: verbal strategies (e.g., underlining question sentence), visual strategies (e.g., correctly placing numbers in diagrams), verbal + visual strategies, and an untreated control. The dependent measures for training were problem solving accuracy and two working memory transfer measures (operation span and visual-spatial span). Three major findings emerged: (1) strategy instruction facilitated solution accuracy but the effects of strategy instruction were moderated by WMC, (2) some strategies yielded higher post-test scores than others, but these findings were qualified as to whether children were at risk for MD, and (3) strategy training on problem solving measures facilitated transfer to working memory measures. The main findings were that children with MD, but high WM spans, were more likely to benefit from strategy conditions on target and transfer measures than children with lower WMC. The results suggest that WMC moderates the influence of cognitive strategies on both the targeted and non-targeted measures.

Explaining the high working memory capacity of gifted children: Contributions of processing skills and executive control

Low working memory capacity in euthymic bipolar I disorder: No relation to reappraisal on emotion regulation

Does working memory capacity affect the ability to predict upcoming words in discourse?

The study of working memory capacity is of outmost importance in cognitive psychology as working memory is at the basis of general cognitive function. Although the working memory capacity limit has been thoroughly studied, its origin still remains a matter of strong debate. Only recently has the role of visual saliency in modulating working memory storage capacity been assessed experimentally and proved to provide valuable insights into working memory function. In the computational arena, attractor networks have successfully accounted for psychophysical and neurophysiological data in numerous working memory tasks given their ability to produce a sustained elevated firing rate during a delay period. Here we investigate the mechanisms underlying working memory capacity by means of a biophysically-realistic attractor network with spiking neurons while accounting for two recent experimental observations: 1) the presence of a visually salient item reduces the number of items that can be held in working memory, and 2) visually salient items are commonly kept in memory at the cost of not keeping as many non-salient items.Our model suggests that working memory capacity is determined by two fundamental processes: encoding of visual items into working memory and maintenance of the encoded items upon their removal from the visual display. While maintenance critically depends on the constraints that lateral inhibition imposes to the mnemonic activity, encoding is limited by the ability of the stimulated neural assemblies to reach a sufficiently high level of excitation, a process governed by the dynamics of competition and cooperation among neuronal pools. Encoding is therefore contingent upon the visual working memory task and has led us to introduce the concept of effective working memory capacity (eWMC) in contrast to the maximal upper capacity limit only reached under ideal conditions.

Training and plasticity of working memory

BackgroundWorking Memory Capacity (WMC) is thought to be related to executive control and focused memory search abilities. These two hypotheses make contrasting predictions regarding the effects of retrieval on forgetting. Executive control during memory retrieval is believed to lead to retrieval induced forgetting (RIFO) because inhibition of competing memory traces during retrieval renders them temporarily less accessible. According to this suggestion, superior executive control should increase RIFO. Alternatively, superior focused search abilities could diminish RIFO, because delimiting the search set reduces the amount of competition between traces and thus the need for inhibition. Some evidence suggests that high WMC is related to more RIFO, which is inconsistent with the focused search hypothesis.Methodology/Principal FindingsUsing the RIFO paradigm, we created distinct and overlapping categories to manipulate the amount of competition between them. This overlap increased competition between some categories while exclusive use of weak exemplars ensured negligible effects of output interference and integration.Low WMC individuals exhibited RIFO within and between overlapping categories, indicating the effect of resolving competition during retrieval. High WMC individuals only exhibited between-category RIFO, suggesting they experienced reduced competition resolution demands. Low WMC Individuals exhibited the strongest RIFO and no retrieval benefits when interference resolution demands were high.Conclusions/SignificanceOur findings qualify the inhibitory explanation for RIFO by incorporating the focused search hypothesis for materials that are likely to pose extraordinary challenges at retrieval. The results highlight the importance of considering individual differences in retrieval-induced effects and qualify existing models of these effects.

Is the n-back task a measure of unstructured working memory capacity? Towards understanding its connection to other working memory tasks

The Role of Individual Differences in Second Language Writing Corrective Feedback

Working memory (WM) permits maintenance of information over brief delays and is an essential executive function. Unfortunately, WM is subject to age-related decline. Some evidence supports the use of transcranial direct current stimulation (tDCS) to improve visual WM. A gap in knowledge is an understanding of the mechanism characterizing these tDCS linked effects. To address this gap, we compared the effects of two tDCS montages designed on visual working memory (VWM) performance. The bifrontal montage was designed to stimulate the heightened bilateral frontal activity observed in aging adults. The unilateral frontoparietal montage was designed to stimulate activation patterns observed in young adults. Participants completed three sessions (bilateral frontal, right frontoparietal, sham) of anodal tDCS (20 min, 2 mA). During stimulation, participants performed a visual long-term memory (LTM) control task and a visual WM task. There was no effect of tDCS on the LTM task. Participants receiving right unilateral tDCS showed a WM benefit. This pattern was most robust in older adults with low WM capacity. To address the concern that the key difference between the two tDCS montages could be tDCS over the posterior parietal cortex (PPC), we included new analyses from a previous study applying tDCS targeting the PPC paired with a recognition VWM task. No significant main effects were found. A subsequent experiment in young adults found no significant effect of either tDCS montage on either task. These data indicate that tDCS montage, age and WM capacity should be considered when designing tDCS protocols. We interpret these findings as suggestive that protocols designed to restore more youthful patterns of brain activity are superior to those that compensate for age-related changes.

Introduction Anatomy education is burdened by neurophobia, a student’s fear when learning neuroanatomy and clinical neurology. While neurophobia’s cause remains unknown, poor definition of structures in standard neurological specimens may affect students’ learning due to influences on cognitive load. In this study, we examined the effects of increased visual contrast between grey and white matter ‐ done via novel staining method ‐ with the goal of providing insight into how educators can adapt anatomical specimens to aid students during learning and application. Aim To determine if increasing contrast between grey and white matter on human brain slices aids students in learning neuroanatomy, and if the difference in contrast improves students’ performance during testing. Methods Undergraduate students at McMaster University with no prior neuroanatomical education (n = 102) were recruited for a 3‐day protocol. On Day 0, participants learned 12 neuroanatomical structures from a set of brain slices (transverse or coronal section, with low or high contrast). They were then asked to locate and recall these structures on unstained slices of the same section. The learn‐test phase was immediately repeated with a second set of slices counterbalancing section and contrast. Participants returned for a learning‐only session 24 hours later (Day 1) using the same specimens from Day 0, and were tested on unstained sets 48 hours after (Day 2). Participants then completed an Automated Operation Span Task (OSPAN) to assess working memory capacity (WMC), and a learning methods survey. Results Repeated Measures ANOVA tests were performed using Time (Day 0 v. Day 2) and Staining (stained v. unstained) as within‐subject variables. Though Time had an effect, with Day 0 performance being significantly better than that of Day 2 ( F (1, 101) = 26.93, p < .001, ηp 2 = 0.21), there was no effect of Staining. Participants performed relatively equally ( F (1, 101) = 0.234, p = .629, ηp 2 = .002). Participant data were sorted into quartiles based on WMC (OSPAN score). Independent sample t‐tests of the sorted data showed that participants with low WMC performed significantly worse than those with high WMC when learning from low‐contrast specimens, regardless of time ( t (47) = −2.164, p = .036, d = (5.58−4.04) ⁄ 2.49 = .618). However, performance between these groups was equalized after learning from high‐contrast slices ( t (47) = −0.53, p = .596, d = (5.38−4.92) ⁄ 2.98 = .154). Discussion/Conclusion Results show that increased contrast had little effect within individual results; however, it improved overall performance of low WMC participants, helping to match results of high WMC participants. This suggests that the use of high‐contrast brain slices may prove beneficial when teaching students with low WMC that struggle with neuroanatomy, potentially by reducing the cognitive load needed to locate structures so students can re‐allocate cognitive capacity towards learning. Further insight may be gained using eye‐tracking technology to observe student gaze patterns as they view the material to determine how structures are being viewed.