Slower Processing Speed Research Articles

Game-supported cognitive strategy training for slowed information processing speed after acquired brain injury: study protocol for a randomised controlled trial

IntroductionMany individuals with acquired brain injury tend to experience problems with slowed information processing speed (IPS). A potentially beneficial and cost-effective supplement for cognitive rehabilitation of impaired IPS may be...

Spectrum-image dual-modality fusion empowered accurate and efficient classification system for traditional Chinese medicine

Traditional Chinese medicine (TCM) influences the Chinese and global medical systems, with its quality essential to its effectiveness. The origin of TCM material impacts the quality of the same TCM materials. However, the existing origin classification methods of the same TCM materials from different places mainly have two disadvantages: slow processing speed and extensive experience. To address these issues, a fast and real-time technology, laser-induced breakdown spectroscopy (LIBS), is introduced into our solution. We propose a TCM classification system that combines one-dimensional LIBS spectra with two-dimensional images. This dual-modality fusion approach represents a significant advancement in multi-view data analysis for TCM classification. As a case study, we focus on wolfberry and construct a new dataset comprising 10,800 pairs of LIBS spectrum and image data to fill the gap. To achieve superior multiple feature fusion, a two-stage fusion network (TFNet) in a coarse-to-fine way is proposed. In the first coarse fusion, the Depth Attention Fusion (DAF) module is applied to extract the key features of stacked spectrum and image. In the second fine fusion, the Line to Area (LTA) module entirely focuses on and highlights the critical spectral line features. Experimental accuracy is over 0.99 with less computation and parameters, indicating the high efficiency and accuracy of the proposed TFNet. Therefore, the classification system achieves exceptional accuracy and efficiency due to its simple sample preparation, real-time data collection and the high-accuracy lightweight network.

Effects of white matter hyperintensities on isolated executive function assessed by the Trail Making Test.

White matter hyperintensities (WMHs) on MRI are associated with cognitive dysfunction, particularly slow processing speed and executive dysfunction. However, it is not clear whether WMHs burden affects isolated executive function independent of aging when WMHs are assessed separately in periventricular hyperintensity (PVH) and deep and subcortical white matter hyperintensity (DSWMH). To assess the relationship between the degree of WMHs and the performance on the Trail Making Test (TMT), which can evaluate isolated ability of set-shifting and working memory. 74 participants who visited our memory clinic and underwent the TMT subtests (TMT-A and TMT-B) and the Mini-Mental State Examination (MMSE). All subjects performed the TMT within the time limits and their MMSE scores were 24 or higher, and they were diagnosed as having normal cognition or mild cognitive impairment. The extent of PVH and DSWMH was graded from 0 to 3 using the Fazekas scale. We obtained testing time to complete the TMT-A and TMT-B, and calculated TMT-B minus TMT-A. We performed correlation analyses between the degree of WMHs and the time measures of the TMT subtests with adjustment of age. Average scores of the MMSE were not different among the groups either by PVH grade or by DSWMH grade. In contrast, average time required for the TMT-A, TMT-B, and TMT-B minus TMT-A increased along with exacerbation of PVH and DSWMH grade. After the adjustment of age we found significant association between only DSWMH grade and the time difference of TMT-B minus TMT-A. Exacerbation of PVH and DSWMH differentially affected isolated executive functions assessed by the TMT subtests independent of age and general cognitive function.

Fluent contextual reading is associated with greater synchronization of the visual and auditory networks, fluent reading and better speed of processing in children with dyslexia

The asynchrony theory of dyslexia postulates weaker visual (orthographical processing) and auditory (phonological processing) network synchrony in dyslexic readers. The weaker visual-auditory network synchronization is suggested to contribute to slow processing speed, which supports cognitive control, contributing to single-word reading difficulty and lower reading fluency. The current study aims to determine the neurobiological signature for this theory and to examine if prompting enhanced reading speed through deleted text is associated with a greater synchronization of functional connectivity of the visual and auditory networks in children with dyslexia and typical readers (TRs). We further aimed to determine if the change in visual-auditory connectivity prompted by deleted text is associated with reading fluency and processing speed abilities. Nineteen children with dyslexia and 21 typical readers ages 8–12 years old participated in a fMRI under two types of reading conditions: a still text condition and deleted text condition, in which letters was sequentially deleted from the screen. Effects of diagnostic group and condition on functional connectivity (FC) of visual and auditory networks were examined. Results revealed a significant overall effect of condition with a marginally significant Group × Condition interaction, such that as compared with TRs, children with dyslexia showed a significantly greater increase in visual-auditory FC between the still and deleted text conditions. Additionally, for children with dyslexia, this FC increase was significantly correlated with better reading fluency and verbal/nonverbal processing speed.These results support a relationship between the synchronization of the visual and auditory networks, fluent reading and increased speed of processing abilities in children with dyslexia, which can help guide fluency-based intervention strategies.

A novel and failsafe blockchain framework for secure OTA updates in connected autonomous vehicles

Connected Autonomous Vehicles (CAVs) are becoming data centers on wheels, amassing petabytes of data. They require a combination of software and hardware systems to operate reliably and simply in real-time. However, due to the transient nature of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) networks and CAVs' dependency on a connected software system, we need a new paradigm for secure and immutable data exchange, which emulates smart contracts. For this reason, CAV manufacturers need to maintain the latest version of software to protect CAVs from system failure and cyber threats in real-time, which is difficult with traditional centralized management. The key challenges that we must overcome are the authentication and secure sharing of the Over-The-Air (OTA) software updates and the scalability of distribution at the level of CAVs.This paper proposes a secure and scalable software updates framework in a distributed manner for the CAVs, leveraging Blockchain (BC) with smart contract technology. The framework is able to overcome slow processing speed, which is one of the significant limitations of BC in terms of lower latency and less packet overhead with a high level of security against possible cyber-attacks. We use a salting-based hashing scheme over the traditional Elliptic Curve Cryptography (ECC) Key. This process ensures multi-factor authenticated protection from malicious transactions while downloading and installing any new feature update in CAVs. Moreover, by using Hyperledger BC, our framework provides cost-free transactions while successfully upgrading and deploying OTA software patches in any system of CAVs. This paper extends our previous work [51] by analysing our proposed BC framework's immutability and load management capability. Our simulation results show that our proposed framework is robust, secure, and failsafe for CAV manufacturers and end-users.

Image Splicing Compression Algorithm Based on the Extended Kalman Filter for Unmanned Aerial Vehicles Communication

The technology of unmanned aerial vehicles (UAVs) is extensively utilized across various domains due to its exceptional mobility, cost-effectiveness, rapid deployment, and flexible configuration. However, its limited storage space and the contradiction between the large amount of image data returned and the limited bandwidth in emergency scenarios necessitate the development of a suitable UAVs image compression technology. In this research, we propose an image splicing compression algorithm based on the extended Kalman filter for UAV communication (SEKF-UC). Firstly, we introduce the concept of image splicing to enhance the compression ratio while ensuring image quality, leveraging the characteristics of deep learning-based compression, which are relatively unaffected by the size of the compressed data. Secondly, to address the issue of slow processing speed of deep neural networks (DNN) caused by the substantial volume of data involved in image splicing, we incorporate an extended Kalman filter module to expedite the process. Simulation experiments show that our proposed algorithm outperforms existing methods in multiple ways, achieving a significant compression ratio improvement, from 2:1 to 25:1, with a marginal reduction of 6.5% in structural similarity (SSIM) compared to the non-spliced approach. Moreover, for deep neural networks (DNN), our method incorporating the extended Kalman filter module achieves the same error level with only 30 iterations—a significant reduction compared to the traditional BP algorithm’s requirement of over 4000 iterations—while improving the average network operation speed by an impressive 89.76%. Additionally, our algorithm excels in image quality, with peak signal-to-noise ratio (PSNR) improving by 92.7% and SSIM by 42.1%, at most, compared to other algorithms. These results establish our algorithm as a highly efficient and effective solution, suitable for various image processing and data compression applications.

Reduced alpha2 power is associated with slowed information processing speed in multiple sclerosis.

Cognitive impairment is common in multiple sclerosis (MS), significantly impacts daily functioning, is time-consuming to assess, and is prone to practice effects. We examined whether the alpha band power measured with magnetoencephalography (MEG) is associated with the different cognitive domains affected by MS. Sixty-eight MS patients and 47 healthy controls underwent MEG, T1- and FLAIR-weighted magnetic resonance imaging (MRI), and neuropsychological testing. Alpha power in the occipital cortex was quantified in the alpha1 (8-10 Hz) and alpha2 (10-12 Hz) bands. Next, we performed best subset regression to assess the added value of neurophysiological measures to commonly available MRI measures. Alpha2 power significantly correlated with information processing speed (p < 0.001) and was always retained in all multilinear models, whereas thalamic volume was retained in 80% of all models. Alpha1 power was correlated with visual memory (p < 0.001) but only retained in 38% of all models. Alpha2 (10-12 Hz) power in rest is associated with IPS, independent of standard MRI parameters. This study stresses that a multimodal assessment, including structural and functional biomarkers, is likely required to characterize cognitive impairment in MS. Resting-state neurophysiology is thus a promising tool to understand and follow up changes in IPS.

Cerebral microhaemorrhage count is related to processing speed, but not level of symptom reporting, independently of age, psychological status and premorbid functioning, after first-ever mild traumatic brain injury

Cerebral microhaemorrhage is a commonly identified neuropathological consequence of mild traumatic brain injury (mTBI) and can be identified in vivo using susceptibility weighted imaging (SWI). This study aimed to determine whether SWI-detected microhaemorrhages are more common in individuals after a single, first-ever, mTBI event relative to trauma controls (TC) and to investigate whether a linear relationship exists between microhaemorrhage numbers and cognition or symptom reporting in the post-acute period after injury, independently of age, psychological status and premorbid level of functioning. Microhaemorrhagic lesions were identified by expert clinical examination of SWI for 78 premorbidly healthy adult participants who were admitted to hospital after a traumatic injury and had suffered a first-ever mTBI (n = 47) or no head strike (n = 31). Participants underwent objective cognitive examination of processing speed, attention, memory, and executive function as well as self-reported post-concussion symptomatology. Bootstrapping analyses were used as data were not normally distributed. Analyses revealed that the mTBI group had significantly more microhaemorrhages than the TC group (Cohen’s d = 0.559). These lesions were only evident in 28% of individuals. The mTBI participants demonstrated a significant linear association between number of microhaemorrhages and processing speed, independently of age, psychological status, or premorbid level of functioning. This study shows that a single mTBI causes cerebral microhaemorrhages to occur in a minority of premorbidly healthy individuals. Greater microhaemorrhage count is independently associated with slower processing speed, but not symptom reporting, during the post-acute injury period.

Association of Bilateral Vestibulopathy With and Without Hearing Loss With Cognitive-Motor Interference

The past years, evidence suggested that the primary symptoms traditionally associated with bilateral vestibulopathy (BV) do not represent the full picture of this patient population. Recent literature also demonstrated cognitive impairment. However, although multitasking and dual-tasking are widely present in everyday activities, most of these studies assessed cognitive function only in single-task conditions. To uncover the association of BV with and without hearing loss with cognitive and motor performance and cognitive-motor interference. This prospective case-control study assessed persons with an isolated BV and persons with BV and a concomitant hearing loss compared with a healthy control group. Data were analyzed in December 2022. The study was conducted at Ghent University (Ghent, Belgium). Data collection took place between March 26, 2021, and November 29, 2022. All participants completed the 2BALANCE dual-task protocol, comprising a static and a dynamic motor task that was combined with 5 visual cognitive tasks. These cognitive tasks assessed mental rotation, visuospatial memory, working memory, response inhibition (executive function), and processing speed. All cognitive tasks were performed in a single-task condition (while seated) and in a dual-task condition (combined with a static and a dynamic motor task). The static task comprised balancing on a force platform with foam pad, and the dynamic task comprised walking at a self-selected speed on the GAITRite Walkway. Both motor tasks were performed in the single-task and dual-task condition. Nineteen persons with BV and hearing loss (mean [SD] age, 56.70 [10.12] years; 10 women [52.6%]), 22 persons with an isolated BV (mean [SD] age, 53.66 [13.35] years; 7 women [31.8%]), and 28 healthy control participants were included (mean [SD] age, 53.73 [12.77] years; 12 women [42.9%]). Both patient groups had mental rotation and working memory impairment in a single-task condition and slower processing speed when walking (ie, during the dynamic dual-task condition). Additionally, the patient group with hearing loss had impaired visuospatial memory and executive function deficits in single-task and dual-task conditions, while this could only be elicited when performing a motor task in persons with isolated BV (ie, when dual-tasking). The findings of this case-control study suggest an association between vestibular function and cognitive and motor performance, even greater in persons with a concomitant hearing loss than in persons with an isolated BV.

Neurophenotypes of COVID-19: Risk factors and recovery outcomes

Coronavirus disease 2019 (COVID-19) infection is associated with risk of persistent neurocognitive and neuropsychiatric complications. It is unclear whether the neuropsychological manifestations of COVID-19 present as a uniform syndrome or as distinct neurophenotypes with differing risk factors and recovery outcomes. We examined post-acute neuropsychological profiles following SARS-CoV-2 infection in 205 patients recruited from inpatient and outpatient populations, using an unsupervised machine learning cluster analysis, with objective and subjective measures as input features. This resulted in three distinct post-COVID clusters. In the largest cluster (69%), cognitive functions were within normal limits, although mild subjective attention and memory complaints were reported. Vaccination was associated with membership in this “normal cognition” phenotype. Cognitive impairment was present in the remaining 31% of the sample but clustered into two differentially impaired groups. In 16% of participants, memory deficits, slowed processing speed, and fatigue were predominant. Risk factors for membership in the “memory-speed impaired” neurophenotype included anosmia and more severe COVID-19 infection. In the remaining 15% of participants, executive dysfunction was predominant. Risk factors for membership in this milder “dysexecutive” neurophenotype included disease-nonspecific factors such as neighborhood deprivation and obesity. Recovery outcomes at 6-month follow-up differed across neurophenotypes, with the normal cognition group showing improvement in verbal memory and psychomotor speed, the dysexecutive group showing improvement in cognitive flexibility, and the memory-speed impaired group showing no objective improvement and relatively worse functional outcomes compared to the other two clusters. These results indicate that there are multiple post-acute neurophenotypes of COVID-19, with different etiological pathways and recovery outcomes. This information may inform phenotype-specific approaches to treatment.

Cognitive, Disability, and Treatment Outcome Implications of Symptom-Based Phenotyping in Late-Life Depression

Late-life depression is associated with substantial heterogeneity in clinical presentation, disability, and response to antidepressant treatment. We examined whether self-report of severity of common symptoms, including anhedonia, apathy, rumination, worry, insomnia, and fatigue were associated with differences in presentation and response to treatment. We also examined whether these symptoms improved during treatment with escitalopram. Eighty-nine older adults completed baseline assessments, neuropsychological testing and providing self-reported symptom and disability scales. They then entered an 8-week, placebo-controlled randomized trial of escitalopram, and self-report scales were repeated at the trial's end. Raw symptom scale scores were combined into three standardized symptom phenotypes and models examined how symptom phenotype severity was associated with baseline measures and depression improvement over the trial. While rumination/worry appeared independent, severity of apathy/anhedonia and fatigue/insomnia were associated with one another and with greater self-reported disability. Greater fatigue/insomnia was also associated with slower processing speed, while rumination/worry was associated with poorer episodic memory. No symptom phenotype severity score predicted a poorer overall response to escitalopram. In secondary analyses, escitalopram did not improve most phenotypic symptoms more than placebo, aside for greater reductions in worry and total rumination severity. Deeper symptom phenotype characterization may highlight differences in the clinical presentation of late-life depression. However, when compared to placebo, escitalopram did not improve many of the symptoms assessed. Further work is needed to determine whether symptom phenotypes inform longer-term course of illness, and which treatments may best benefit specific symptoms.

Cognitive Information Processing Speed

Abstract: Using visual learning and meditation based techniques to develop brain training apps that can significantly improve cognitive performance. This paper seeks to understand what causes slow cognitive processing speed and designs an application to diagnose it in early stages and provide support to people having slow processing speed. Moreover, significant primary data is collected to validate the hypothesis that visual learning techniques presented through mobile applications can improve cognitive performance.

Slow information processing speed elucidates impairment in executive function tests in people with Multiple Sclerosis (P13-3.008)

<h3>Objective:</h3> Analyze the association between performance on speeded and nonspeeded tasks of executive functions (EFs) and slow information processing speed (IPS) in people with Multiple Sclerosis (PwMS). <h3>Background:</h3> IPS and EFs are impaired domains in PwMS. However, it is unclear if executive deficits can be explained by slow IPS. <h3>Design/Methods:</h3> 74 PwMS were included (RR=86.2%, PP=4.3%, SP=9.6%; 74% female; age: 46.12±12.48 years; Physical disability (EDSS): 3.60±2.57; disease duration: 14.85±10.69 years). Measures: IPS: Symbol Digit Modalities Test; EFs: Speeded task of cognitive flexibility (Verbal Fluency), nonspeeded task of cognitive flexibility (Brixton Spatial Anticipation Test), nonspeeded task of working memory (Verbal Backward Digit Span); Clinical variables: EDSS, Fatigue Severity Scale and Beck Depression Inventory-II. A probit regression was performed, the results are shown in predicted probabilities format. <h3>Results:</h3> 47% of PwMS have slow IPS. IPS was associated with EDSS and all EFs measures (<i>p</i>=0.00) and did not correlated with fatigue and depression (<i>p</i>&gt;0.05). For the regression model, IPS, EDSS and EFs tasks were analyzed. Brixton and backward span tests lost significance when physical disability was controlled. Finally, the regression model had verbal fluency (Coefficient:-.49;<i>p</i>=0.001) and physical disability (Coefficient:.21; <i>p</i>=0.02) as independent variables. R²28%. Prob.chi2=0.00. For the predicted probabilities, verbal fluency was divided into 4 groups, controlling physical disability, and it was found that the probability of having poor verbal fluency with poor IPS is very high in the 1st and 2nd groups (.89 and .68, lowest confidence interval 0.53) and the probability of having high verbal fluency and slow IPS is very low in the 3th and 4th groups (.38 and .14, highest confidence interval 0.50), with no overlapping confidence intervals. Correctly classified 72%. <h3>Conclusions:</h3> Impairments on speeded task of EFs can be associated with slow IPS. It is important to include speeded and unspeeded tasks in the assessment of EFs to capture the effective deficit. <b>Disclosure:</b> Maria Sol Román has nothing to disclose. Miss Bardoneschi has nothing to disclose. Ms. Gatti has nothing to disclose. Mrs. De Los Santos has nothing to disclose. Mr. Iglesias Passada has nothing to disclose. Miss Daniele has nothing to disclose. Maria Laura Saladino has nothing to disclose. Fernando Caceres has nothing to disclose. Sandra Vanotti has received research support from Merck S.A.,Buenos Aires, Argentina, an affiliate Merck S.A.,Buenos Aires, Argentina, an affiliate of Merck KGaA, Darmstadt, Germany Merck KGaA, Darmstadt, Germany.

Neural effects of fatigue on cognition in relapsing multiple sclerosis detected by magnetoencephalography: Towards an imaging biomarker (P11-3.007)

<h3>Objective:</h3> Determining functional differences in MS patients during a task, compared to controls. <h3>Background:</h3> Fatigue affects 80% of patients with MS. Fatigue can also have adverse effects on cognitive abilities. MEG is a noninvasive functional brain imaging technique that measures the magnetic brain waves arising from neuronal activity during rest or during a cognitive task. MEG may help detect functional changes that happen in MS patients with fatigue and cognitive difficulties. <h3>Design/Methods:</h3> This study examined the effects of fatigue on cognition in patients with MS using the modified fatigue impact scale(MFIS) and symbol digits modalities test(SDMT) before and during MEG study respectively. MEG data from 6 patients with MS were compared to 3 control subjects. MEG data were filtered into 4 different frequency bands: theta, alpha, beta, and gamma. Network brain activity for each frequency band was mapped for the strength of connectivity across 27 regions in each hemisphere, resulting in 1431 pairings in the brain. The false discovery rate was used to adjust for multiple testing. Only the most statistically significant connections were based on a p-value of less than 0.05. <h3>Results:</h3> MS patients had most coherent beta and gamma connectivity in the fusiform and frontal regions respectively. The control subjects had more coherent connections in the frontal-parietal, frontal-temporal, and frontal-occipital regions. Individual MFIS scores in MS-patients were higher than the controls and correlated with lower SDMT scores in the MS patients. <h3>Conclusions:</h3> MEG was able to detect clear differences in active networks during SDMT tasks in patients with MS compared to controls. Slow processing speed has been associated with reduced frontal lobe activation in younger adults. Our study cohort supported the lack of coherent beta activity in the frontal lobe. The limitation of our study is a small sample size. The complete data analysis of 10 patients will be presented at AAN-2023. <b>Disclosure:</b> Dr. Memon has nothing to disclose. Mr. Hammami has nothing to disclose. Mr. Memon has nothing to disclose. Dr. Bowyer has nothing to disclose.

Processing Speed is Related to the General Psychopathology Factor in Youth

The relationship between the p factor and cognition in youth has largely focused on general cognition (IQ) and executive functions (EF). Another cognitive construct, processing speed (PS), is dissociable from IQ and EF, but has received less research attention despite being related to many different mental health symptoms. The present sample included 795 youth, ages 11–16 from the Colorado Learning Disabilities Research Center (CLDRC) sample. Confirmatory factor analyses tested multiple p factor models, with the primary model being a second-order, multi-reporter p factor. We then tested the correlation between the p factor and a latent PS factor. There was a significant, negative correlation between the p factor and PS (r(87) = -0.42, p < .001), indicating that slower processing speed is associated with higher general mental health symptoms. This association is stronger than previously reported associations with IQ or EF. This finding was robust across models that used different raters (youth and caregiver) and modeling approaches (second-order vs. bifactor). Our findings indicate that PS is related to general psychopathology symptoms. This research points to processing speed as an important transdiagnostic construct that warrants further exploration across development.

Rice grains and grain impurity segmentation method based on a deep learning algorithm-NAM-EfficientNetv2

An appropriate image segmentation algorithm is required for discriminating between full grains and grain impurities. In this study, a lightweight fully convolutional segmentation algorithm based on NAM EfficientNetV2 was proposed to solve the problem of slow processing speed in mobile terminal equipment owing to limited computing resources and a large number of model parameters and improve the detection accuracy. First, a standardized NAM attention mechanism was introduced to replace the SE attention mechanism used in EfficientNetV2 and the improved NAM-EfficientNetV2 network was used as a feature extraction structure. Then, in the up-sampling process, the multi-scale features output by the shallow network are fused to effectively use low-level semantics to encode spatial details and fully convolutional pixel segmentation technology is used to achieve rice grain and impurity segmentation. Finally, compared with the baseline model, the detection accuracy of the model was further improved on a self-made dataset. The comprehensive evaluation index F1 of rice grain and its impurities were 95.26% and 93.27%, respectively, and the model parameter amount was 20.6 M. Combined with post-processing, detecting an image on the GPU device took an average of 0.103 s and 0.301 s on the CPU device. The experimental results showed that the improved algorithm is more lightweight, which provides a reference for the model to be deployed in mobile terminal equipment to realize the function of real-time detection of grain impurity in the combine harvester.

Neuroprotective influence of macular xanthophylls and retinal integrity on cognitive function among persons with multiple sclerosis

BackgroundNo studies to date have examined if macular xanthophyll accumulation and retinal integrity are independently associated with cognitive function in individuals with multiple sclerosis (MS). This study explored whether macular xanthophyll accumulation and structural morphometry in the retina were associated with behavioral performance and neuroelectric function during a computerized cognitive task among persons with MS and healthy controls (HCs). Methods42 HCs and 42 individuals with MS aged 18–64 years were enrolled. Macular pigment optical density (MPOD) was measured using heterochromatic flicker photometry. Optic disc retinal nerve fiber layer (odRNFL), macular retinal nerve fiber layer, and total macular volume were assessed via optical coherence tomography. Attentional inhibition was assessed using an Eriksen flanker task while underlying neuroelectric function was recorded using event-related potentials. ResultsPersons with MS had a slower reaction time, lower accuracy, and delayed P3 peak latency time during both congruent and incongruent trials compared with HCs. Within the MS group, MPOD explained variance in incongruent P3 peak latency, and odRNFL explained variance in congruent reaction time and congruent P3 peak latency. ConclusionsPersons with MS exhibited poorer attentional inhibition and slower processing speed, yet higher MPOD and odRNFL levels were independently associated with greater attentional inhibition and faster processing speed among persons with MS. Future interventions are necessary to determine if improvements in these metrics may promote cognitive function among persons with MS.

Investigating the impact of self-pacing on the L2 listening performance of young learner candidates with differing L1 literacy skills

In online environments, listening involves being able to pause or replay the recording as needed. Previous research indicates that control over the listening input could improve the measurement accuracy of listening assessment. Self-pacing also supports the second language (L2) comprehension processes of test-takers with specific learning difficulties (SpLDs) or, more specifically, of learners with reading-related learning difficulties who might have slower processing speed and limited working memory capacity. Our study examined how L1 literacy skills influence L2 listening performance in the standard single-listening and self-paced administration mode of the listening section of the Test of English as a Foreign Language (TOEFL) Junior Standard test. In a counterbalanced design, 139 Austrian learners of English completed 15 items in a standard single-listening condition and another 15 in a self-paced condition. L1 literacy skills were assessed via a standard reading, non-word reading, word-naming, and non-word repetition test. Generalized Linear Mixed-Effects Modelling revealed that self-pacing had no statistically significant effect on listening scores nor did it boost the performance of test-takers with lower L1 literacy scores indicative of reading-related SpLDs. The results indicate that young test-takers might require training in self-pacing or that self-paced conditions may need to be carefully implemented when they are offered to candidates with SpLDs.

Adult age-related changes in the specificity of episodic memory representations: A review and theoretical framework.

We provide a comprehensive review describing research on the qualitative representational nature of older adults' episodic memories. Our review considers several broad theoretical frameworks and decades of research converging on a universal principle of adult aging: Episodic memory in older adulthood is characterized as being less specific in nature than in younger adulthood. Going beyond earlier specific reviews on related topics in the false memory, neuroscience, and reading comprehension literatures, our review synthesizes findings from these fields with more recent research from the precision literature, along with several new studies on age differences in the specificity of associative aspects of episodic memory, where age deficits have long been reported. We also sketch a new theoretical framework as inspiration for future research that can better elucidate the mechanisms underpinning age differences in the specificity of memory representations, including reduced attentional resources and slower speed of processing. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Co-Occurring Autism Spectrum and Borderline Personality Disorder: An Emerging Clinical Challenge Seeking Informed Interventions.

Co-Occurring Autism Spectrum and Borderline Personality Disorder: An Emerging Clinical Challenge Seeking Informed Interventions.