Poorer Task Performance Research Articles

Sparse projection infinite selection ensemble for imbalanced classification

Imbalanced datasets pose frequent and challenging problems to many real-world applications. Classification models are often biased towards the majority class when learning from class-imbalanced data. Typical imbalanced learning (IL) approaches, e.g., SMOTE, AdaCost, and Cascade, often suffer from poor performance in complex tasks where class overlapping or a high imbalance ratio occurs. In this paper, we systematically investigate the IL problem and propose a novel framework named sparse projection infinite selection ensemble (SPISE). SPISE iteratively resamples balanced subsets and combines the classifiers trained on these subsets for imbalanced classification. The diversity of classifier ensembles and the similarity between the subsets and the whole dataset are considered in this process. Specifically, we present a graph-based approach named infinite subset selection to adaptively sample diverse and similar subsets. Additionally, a random sparse projection is combined with feature selection at the beginning of each iteration to augment the training features and enhance the diversity of the generated subsets. SPISE can be easily adapted to most existing classifiers (e.g., support vector machine and random forest) to boost their performance for IL. Quantitative experiments on 26 imbalanced benchmark datasets substantiate the effectiveness and superiority of the proposed model compared with other popular approaches.

Is there a causal relationship between executive function and liability to mental health and substance use? A Mendelian randomization approach.

Poorer performance in tasks testing executive function (EF) is associated with a range of psychopathologies such as schizophrenia, major depressive disorder (MDD) and anxiety, as well as smoking and alcohol consumption. We used two-sample bidirectional Mendelian randomization to examine whether these may reflect causal relationships and the direction of causation. We used genome-wide association study summary data (N = 17 310 to 848 460) for a common EF factor score (cEF), schizophrenia, MDD, anxiety, smoking initiation, alcohol consumption, alcohol dependence and cannabis use disorder (CUD). We found evidence of increased cEF on reduced schizophrenia liability (OR = 0.10; CI: 0.05 to 0.19; p-value = 3.43 × 10-12), MDD liability (OR = 0.52; CI: 0.38 to 0.72; p-value = 5.23 × 10-05), drinks per week (β = -0.06; CI: -0.10 to -0.02; p-value = 0.003) and CUD liability (OR = 0.27; CI: 0.12 to 0.61; p-value = 1.58 × 10-03). We also found evidence of increased schizophrenia liability (β = -0.04; CI: -0.04 to -0.03; p-value = 3.25 × 10-27) and smoking initiation on decreased cEF (β = -0.06; CI: -0.09 to -0.03; p-value = 6.11 × 10-05). Our results indicate potential causal relationships between cEF and mental health and substance use. Further studies are required to improve our understanding of the underlying mechanisms of these effects, but our results suggest that EF may be a promising intervention target for mental health and substance use.

Assessing Quality of Control in Tactile Cyber–Physical Systems

We evolve a methodology and define a metric to evaluate Tactile Cyber-Physical Systems (TCPS). Towards this goal, we use the step response analysis, a well-known control-theoretic method. The adoption includes replacing the human operator (or master) with a controller with known characteristics and analyzing its response to slave side step disturbances. The resulting step response curves demonstrate that the Quality of Control (QoC) metric is sensitive to control loop instabilities and serves as a good indicator of potential factors that contribute to operator-side cybersickness. Through experiments, we demonstrate how QoC accounts for network overheads such as the link latency and jitter and non-networking overheads such as the testbed settings and robot performances in a TCPS. We show that there is a one-to-one correlation between QoC and end-to-end latency, jitter, and packet drops of a TCPS implementation. We show through experiments how QoC can be used to estimate positional errors in tactile-visual control applications. Since higher positional errors can result in poor task performance, estimating them is useful in developing a better-performing TCPS. We also evaluate a TCPS using Fitts’ test and compare its results with QoC. We show that QoC is useful in distinguishing TCPS with differences in their specifications that are not detectable using Fitts’ test.

Investigating sentence processing and working memory in patients with mild Alzheimer and elderly people.

Linguistic disorders are one of the common problems in Alzheimer's disease, which in recent years has been considered as one of the key parameters in the diagnosis of Alzheimer (AD). Given that changes in sentence processing and working memory and the relationship between these two activities may be a diagnostic parameter in the early and preclinical stages of AD, the present study examines the comprehension and production of sentences and working memory in AD patients and healthy aged people. Twenty-five people with mild Alzheimer's and 25 healthy elderly people participated in the study. In this study, we used the digit span to evaluate working memory. Syntactic priming and sentence completion tasks in canonical and non-canonical conditions were used for evaluating sentence production. We administered sentence picture matching and cross-modal naming tasks to assess sentence comprehension. The results of the present study revealed that healthy elderly people and patients with mild Alzheimer's disease have a significant difference in comprehension of relative clause sentences (P <0.05). There was no significant difference between the two groups in comprehension of simple active, simple active with noun phrase and passive sentences (P> 0.05). They had a significant difference in auditory and visual reaction time (P <0.05). Also there was a significant difference between the two groups in syntactic priming and sentence completion tasks. However, in non-canonical condition of sentence completion, the difference between the two groups was not significant (P> 0.05). The results of the present study showed that the mean scores related to comprehension, production and working memory in people with mild Alzheimer's were lower than healthy aged people, which indicate sentence processing problems at this level of the disease. People with Alzheimer have difficulty comprehending and producing complex syntactic structures and have poorer performance in tasks that required more memory demands. It seems that the processing problems of these people are due to both working memory and language problems, which are not separate from each other and both are involved in.

Class-wise confidence-aware active learning for laparoscopic images segmentation.

Segmentation tasks are important for computer-assisted surgery systems as they provide the shapes of organs and the locations of instruments. What prevents the most powerful segmentation approaches from becoming practical applications is the requirement for annotated data. Active learning provides strategies to dynamically select the most informative samples to reduce the annotation workload. However, most previous active learning literature has failed to select the frames that containing low-appearing frequency classes, even though the existence of these classes is common in laparoscopic videos, resulting in poor performance in segmentation tasks. Furthermore, few previous works have explored the unselected data to improve active learning. Therefore, in this work, we focus on these classes to improve the segmentation performance. We propose a class-wise confidence bank that stores and updates the confidence scores for each class and a new acquisition function based on a confidence bank. We apply confidence scores to explore an unlabeled dataset by combining it with a class-wise data mixture method to exploit unlabeled datasets without any annotation. We validated our proposal on two open-source datasets, CholecSeg8k and RobSeg2017, and observed that its performance surpassed previous active learning studies with about [Formula: see text] improvement on CholecSeg8k, especially for classes with a low-appearing frequency. For robSeg2017, we conducted experiments with a small and large annotation budgets to validate situation that shows the effectiveness of our proposal. We presented a class-wise confidence score to improve the acquisition function for active learning and explored unlabeled data with our proposed class-wise confidence score, which results in a large improvement over the compared methods. The experiments also showed that our proposal improved the segmentation performance for classes with a low-appearing frequency.

A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting

Recent studies have demonstrated the great success of graph convolutional networks in short-term traffic forecasting (e.g., 15–30 min ahead) tasks by capturing dependencies in road network structure. Based on these models, long-term forecasting can be achieved by two approaches: (1) recursively generating a one-step-ahead prediction and (2) adapting the models to sequence-to-sequence (seq2seq) learning. However, in practice, these two approaches often show poor performance in long-term forecasting tasks. The recursive approach suffers from the error accumulation problem, as the model is trained based on one-step-ahead loss. On the other hand, seq2seq shows convergence issues that limit its application. To address the issues for long-term forecasting, in this paper, we propose a universal framework that directly transforms any existing state-of-the-art models for one-step-ahead prediction to achieve more accurate long-term forecasting. The proposed framework consists of two components—a base model and a bias block. The base model is assumed to be a well-trained state-of-the-art one-step-ahead forecasting model, and the bias block is constructed by a spatiotemporal graph neural network composed of gated temporal convolution layers and graph convolution layers. The base model and the bias block are residually-connected so that we can substantially reduce the training complexity. Extensive experiments are conducted on existing benchmark datasets. We experiment with several state-of-the-art models in the literature as base models, and our results demonstrate the ability of the proposed universal framework to greatly improve the long-term prediction accuracy for all models.

Intracranial Self-Stimulation and Memory in Rats: A Sistematic Review.

Intracranial self-stimulation (ICSS) is a technique by which rats press a lever to stimulate their brains through an electrode chronically implanted in brain reward areas. Currently only two laboratories in the world, one in India and one in Spain, are intensively studying the effect of this kind of deep brain stimulation on learning and memory. This paper will present the main findings. Different groups of young and old healthy and brain-damaged rats with electrodes implanted in the medial forebrain bundle received a treatment of ICSS after being trained in several paradigms of implicit and explicit learning. Memory was tested over short and long-term periods. Structural and molecular post-mortem analyses of their brains were examined in relation to memory results. ICSS enhances implicit and explicit memory, especially in animals showing poor performance in the learning tasks, such as brain-damaged subjects. At the structural and molecular level, ICSS enhances size and dendritic arborization and promotes neurogenesis in specific hippocampal areas. ICSS also regulates the expression of genes related to learning and memory. Through activating reward and neural plasticity mechanisms, ICSS in the medial forebrain bundle is a promising technique for memory-enhancing treatments.

Talent management: the way out of poor task performance

PurposeThe paper investigates how talent management influences employee performance in the banking industry in Nigeria. Despite various economic policies of the Central Bank of Nigeria aimed at reshaping the banking industry, talent management and development for the digital age is the concern of the most bank. Rapid digital transformation has been affecting the banking industry, which requires the banking industry to rethink a strategic way to achieve inclusive, resilient, and sustainable growth.Design/methodology/approachThe questionnaires were used to obtain information from 302 full-time employees of the top five banks in the Nigerian banking industry. The collected data were analyzed using the Partial Least Square-Structural Equation Model (PLS-SEM).FindingsThis study shows that talent attraction and development significantly and positively influence task performance. In comparison, talent retention was found to have no significant effect on task performance. This study found that work engagement positively mediates talent attraction, development and task performance. Work engagement did not mediate the relationship between talent retention and task performance.Originality/valueFor the industry to motivate high-performing employees in this digital economy, talent management will need to be carefully designed to create the most enduring competitive advantage. In conclusion, this study will benefit the Nigerian banking industry by apprehending the predictors of task performance so that the prevalence of poor task performance among the employees is well managed.

The mediating role of gaze patterns in the association of child sleep disturbances and core symptoms of autism spectrum disorder.

Children with autism spectrum disorder (ASD) are at high risk for sleep disturbances, but the mechanism underlying the association between sleep disturbances and ASD core symptoms is largely unknown. This study examined the relationship between sleep disturbances and ASD core symptoms, and the mediating role of gaze patterns during the facial emotion recognition (FER) task. The study included 57 children with ASD and 59 age- and intelligence-matched typically developing (TD) controls aged 3-7 years. Parents reported their children's sleep disturbances and ASD core symptoms using the Children's Sleep Habits Questionnaire (CSHQ) and Social Communication Questionnaire (SCQ). Children's gaze patterns during the FER task were recorded by an eye tracking method. We found (1) ASD children had more severe sleep disturbances than TD children; (2) ASD children had atypical gaze patterns and poor FER task performance as determined by lower accuracy and longer reaction time; (3) sleep disturbances were significantly associated with ASD core symptoms of social interaction, communication, and restricted, repetitive and stereotyped patterns of behavior; and (4) atypical gaze patterns partially mediated the association between sleep disturbances and ASD core symptoms. These findings suggest the need for more comprehensive clinical interventions and more effective sleep interventions to improve ASD core symptoms. LAY SUMMARY: Sleep disturbances are very common in children with autism spectrum disorder (ASD). The current study found that sleep disturbances were significantly associated with ASD core symptoms, and gaze patterns during facial emotion recognition task could partially mediate this relationship.

Simulation Study on Natural Lighting Projection Through Façade Optimisation for A Proposed Disaster and Relief Center

Natural and artificial lighting is essential for humans to carry out and support daily routines from simple to complicated tasks. However, insufficient lighting in buildings leads to poor task performance and visual discomfort and may cause Sick Building Syndrome (SBS) such as itchy, watering eyes, and eye irritation. On the other hand, excessive light, particularly natural lighting, may contribute to heat gain in buildings and thermal discomfort and glare among occupants if a building is not well designed. Previous scholars found that a comfortable indoor environment with proper lighting improves occupants’ performance and satisfaction. Therefore, as a passive design strategy that promotes sustainability, natural lighting projection through façade optimisation is proposed and analysed implemented/studied for a proposed design of the Pontian Disaster and Relief Center. Revit Architecture software is used to develop 3D modelling of the design and simulate the interior solar pattern. Consequently, the application of shading devices is investigated along with on-site data collection to determine the climate pattern and other measurements to create more accurate data. Moreover, a case study on the Diamond Building was referred to assist the researcher for an appropriate pattern or form to maximise the solar radiation image, ultimately creating a new construction system. Finally, this study found that an application of natural lighting system for semi-open concept is considered sufficient towards maintaining occupants’ visual comfort, but solely based on simulation results.

Reduced Glx and GABA Inductions in the Anterior Cingulate Cortex and Caudate Nucleus Are Related to Impaired Control of Attention in Attention-Deficit/Hyperactivity Disorder.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that impairs the control of attention and behavioral inhibition in affected individuals. Recent genome-wide association findings have revealed an association between glutamate and GABA gene sets and ADHD symptoms. Consistently, people with ADHD show altered glutamate and GABA content in the brain circuitry that is important for attention control function. Yet, it remains unknown how glutamate and GABA content in the attention control circuitry change when people are controlling their attention, and whether these changes can predict impaired attention control in people with ADHD. To study these questions, we recruited 18 adults with ADHD (31–51 years) and 16 adults without ADHD (28–54 years). We studied glutamate + glutamine (Glx) and GABA content in the fronto-striatal circuitry while participants performed attention control tasks. We found that Glx and GABA concentrations at rest did not differ between participants with ADHD or without ADHD. However, while participants were performing the attention control tasks, participants with ADHD showed smaller Glx and GABA increases than participants without ADHD. Notably, smaller GABA increases in participants with ADHD significantly predicted their poor task performance. Together, these findings provide the first demonstration showing that attention control deficits in people with ADHD may be related to insufficient responses of the GABAergic system in the fronto-striatal circuitry.

Spinal cord microstructural changes are connected with the aberrant sensorimotor cortical oscillatory activity in adults with cerebral palsy

Previous animal models have illustrated that reduced cortical activity in the developing brain has cascading activity-dependent effects on the microstructural organization of the spinal cord. A limited number of studies have attempted to translate these findings to humans with cerebral palsy (CP). Essentially, the aberrations in sensorimotor cortical activity in those with CP could have an adverse effect on the spinal cord microstructure. To investigate this knowledge gap, we utilized magnetoencephalographic (MEG) brain imaging to quantify motor-related oscillatory activity in fourteen adults with CP and sixteen neurotypical (NT) controls. A subset of these participants also underwent cervical-thoracic spinal cord MRI. Our results showed that the strength of the peri-movement beta desynchronization and the post-movement beta rebound were each weaker in the adults with CP relative to the controls, and these weakened responses were associated with poorer task performance. Additionally, our results showed that the strength of the peri-movement beta response was associated with the total cross-sectional area of the spinal cord and the white matter cross-sectional area. Altogether these results suggest that the altered sensorimotor cortical activity seen in CP may result in activity-dependent plastic changes within the spinal cord microstructure, which could ultimately contribute to the sensorimotor deficits seen in this population.

Sex differences in the cognitive performance in adults: role of impaired sleep.

ObjectivesPoor sleep quality negatively affects cognitive performance. However, there are limited data on sex differences in functional outcomes of impaired sleep on cognition. Therefore, the aim of this study was to evaluate the association between sleep quality and performance of men and women in cognitive tests.Material and MethodsAfter screening, 97 individuals with and without insomnia complaints participated of this study. Behavioral scales were evaluated using a number of instruments and the sleep pattern was recorded by actigraph. Subsequently, the participants were submitted to visuospatial/verbal working memory (WM), visual attention, and psychomotor vigilance tests (PVT).ResultsThe actigraphic recordings indicated that men sleep later (d=-0.56, p<0.05), fall asleep faster (d=0.42, p<0.05), showed shorter sleep duration (d=0.53, p<0.05), and more sleep fragmentation than women (d=-0.41, p<0.05). The performance in the cognitive tasks also showed sex differences: the men showed better performance in the visuospatial short-term memory (d=-0.78, p<0.05); verbal (d=-0.61, p<0.05), and visuospatial-WM tests (d=-0.84, p<0.05); they also responded faster in the PVT (d=0.69, p<0.05), although made more mistakes (d=-0.85, p<0.01). Longer sleep latency was associated with poor performance in visual attention (r=0.52, p<0.05) and verbal memory tasks (r=-0.30, p<0.05) in men.ConclusionOur results suggest that difficulty in falling asleep was associated with cognitive impairment, especially in men. Sex differences in sleep quality and cognitive skills should be taken into account in future research in this field.

Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A &lt;em&gt;task-specific&lt;/em&gt; IQA has recently been proposed to learn an image-amenability-predicting controller simultaneously with a target task predictor. This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e.g. segmentation and classification neural networks in modern clinical applications. In this work, we propose an extension to this task-specific IQA approach, by adding a &lt;em&gt;task-agnostic&lt;/em&gt; IQA based on auto-encoding as the target task. Analysing the intersection between low-quality images, deemed by both the task-specific and task-agnostic IQA, may help to differentiate the underpinning factors that caused the poor target task performance. For example, common imaging artefacts may not adversely affect the target task, which would lead to a low task-agnostic quality and a high task-specific quality, whilst individual cases considered clinically challenging, which can not be improved by better imaging equipment or protocols, is likely to result in a high task-agnostic quality but a low task-specific quality. We first describe a flexible reward shaping strategy which allows for the adjustment of weighting between task-agnostic and task-specific quality scoring. Furthermore, we evaluate the proposed reinforcement learning algorithm, using a clinically challenging target task of prostate tumour segmentation on multiparametric magnetic resonance (mpMR) images. Based on experimental results using mpMR images from 850 patients, it was found that &lt;em&gt;a&lt;/em&gt;) The task-agnostic IQA may identify artefacts, but with limited impact on the accuracy of cancer segmentation networks. A Dice score of 0.367±0.017 was obtained after rejecting 10% of low quality images, compared to 0.354±0.016 from a non-selective baseline; &lt;em&gt;b&lt;/em&gt;} The task-specific IQA alone improved the performance to 0.415±0.020, at the same rejection ratio. However, this system indeed rejected both images that impact task performance due to imaging defects and due to being clinically challenging; and &lt;em&gt;c&lt;/em&gt;) The proposed reward shaping strategy, when the task-agnostic and task-specific IQA are weighted appropriately, successfully identified samples that need re-acquisition due to defected imaging process, as opposed to clinically challenging cases due to low contrast in pathological tissues or other equivocacy in radiological presentation.&lt;br&gt;Our code is available at https://github.com/s-sd/task-amenability/tree/v1

Effects of working memory load on frontal connectivity in children with autism spectrum disorder: a fNIRS study

Individuals with autism spectrum disorder (ASD) perform poorly in working memory (WM) tasks, with some literature suggesting that their impaired performance is modulated by WM load. While some neuroimaging and neurophysiological studies have reported altered functional connectivity during WM processing in individuals with autism, it remains largely unclear whether such alterations are moderated by WM load. The present study aimed to examine the effect of WM load on functional connectivity within the prefrontal cortex (PFC) in ASD using functional near-infrared spectroscopy (fNIRS). Twenty-two children with high-functioning ASD aged 8–12 years and 24 age-, intelligent quotient (IQ)-, sex- and handedness-matched typically developing (TD) children performed a number n-back task with three WM loads (0-back, 1-back, and 2-back). Hemodynamic changes in the bilateral lateral and medial PFC during task performance were monitored using a multichannel NIRS device. Children with ASD demonstrated slower reaction times, specifically during the “low load” condition, than TD children. In addition, the ASD and TD groups exhibited differential load-dependent functional connectivity changes in the lateral and medial PFC of the right but not the left hemisphere. These findings indicate that WM impairment in high-functioning ASD is paralleled by load-dependent alterations in right, but not left, intrahemispheric connectivity during WM processing in children with ASD. A disruption of functional neural connections that support different cognitive processes may underlie poor performance in WM tasks in ASD.

Phonological Short-Term Memory: When Bilingualism Matters

ABSTRACT Non-word repetition tasks are widely used to assess phonological short-term memory (PSTM). Results of previous research on the performance of monolingual and bilingual children in PSTM tasks are inconclusive. Although in some studies bilinguals did outperform monolinguals in the repetition of non-words, most studies reported comparatively weak results of bilingual children, especially when they were tested in their L2. In this study, four-year-old monolingual and bilingual children acquiring German (N = 1,441) were tested with both German-based (GBNW) and quasi-universal non-words (QUNW). It was hypothesized that bilinguals would outperform monolinguals both in (a) GBNW under the condition of comparable German language skills and (b) QUNW without any preconditions because QUNW do not presuppose a good command of German. Bilinguals yielded significantly lower results in GBNW, but not in QUNW. After the exclusion of children with limited German language skills from both groups, bilinguals outperformed monolinguals in both tasks, especially in QUNW. It can be assumed that bilingualism puts higher demands on PSTM than monolingualism and thus contributes to its faster development. Unnecessary medical examinations and therapies that are sometimes prescribed to bilinguals due to a poor performance in German-based PSTM tasks can be avoided if QUNW are used instead of language-specific items.

Graph Representation Learning Beyond Node and Homophily

Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing graph representation learning approaches are designed mainly under the node homophily assumption: connected nodes tend to have similar labels and optimize performance on node-centric downstream tasks. Their design is apparently against the task-agnostic principle and generally suffers poor performance in tasks, e.g., edge classification, that demands feature signals beyond the node-view and homophily assumption. To condense different feature signals into the embeddings, this paper proposes PairE, a novel unsupervised graph embedding method using two paired nodes as the basic unit of embedding to retain the high-frequency signals between nodes to support node-related and edge-related tasks. Accordingly, a multi-self-supervised autoencoder is designed to fulfill two pretext tasks: one retains the high-frequency signal better, and another enhances the representation of commonality. Our extensive experiments on a diversity of benchmark datasets clearly show that PairE outperforms the unsupervised state-of-the-art baselines, with up to 101.1\% relative improvement on the edge classification tasks that rely on both the high and low-frequency signals in the pair and up to 82.5\% relative performance gain on the node classification tasks.

BioBERT and Similar Approaches for Relation Extraction.

In biomedicine, facts about relations between entities (disease, gene, drug, etc.) are hidden in the large trove of 30million scientific publications. The curated information is proven to play an important role in various applications such as drug repurposing and precision medicine. Recently, due to the advancement in deep learning a transformer architecture named BERT (Bidirectional Encoder Representations from Transformers) has been proposed. This pretrained language model trained using the Books Corpus with 800M words and English Wikipedia with 2500M words reported state of the art results in various NLP (Natural Language Processing) tasks including relation extraction. It is a widely accepted notion that due to the word distribution shift, general domain models exhibit poor performance in information extraction tasks of the biomedical domain. Due to this, an architecture is later adapted to the biomedical domain by training the language models using 28million scientific literatures from PubMed and PubMed central. This chapter presents a protocol for relation extraction using BERT by discussing state-of-the-art for BERT versions in the biomedical domain such as BioBERT. The protocol emphasis on general BERT architecture, pretraining and fine tuning, leveraging biomedical information, and finally a knowledge graph infusion to the BERT model layer.

Face Processing in Developmental Prosopagnosia: Altered Neural Representations in the Fusiform Face Area.

Rationale: Face expertise is a pivotal social skill. Developmental prosopagnosia (DP), i.e., the inability to recognize faces without a history of brain damage, affects about 2% of the general population, and is a renowned model system of the face-processing network. Within this network, the right Fusiform Face Area (FFA), is particularly involved in face identity processing and may therefore be a key element in DP. Neural representations within the FFA have been examined with Representational Similarity Analysis (RSA), a data-analytical framework in which multi-unit measures of brain activity are assessed with correlation analysis.Objectives: Our study intended to scrutinize modifications of FFA-activation during face encoding and maintenance based on RSA.Methods: Thirteen participants with DP (23–70 years) and 12 healthy control subjects (19–62 years) participated in a functional MRI study, including morphological MRI, a functional FFA-localizer and a modified Sternberg paradigm probing face memory encoding and maintenance. Memory maintenance of one, two, or four faces represented low, medium, and high memory load. We examined conventional activation differences in response to working memory load and applied RSA to compute individual correlation-matrices on the voxel level. Group correlation-matrices were compared via Donsker’s random walk analysis.Results: On the functional level, increased memory load entailed both a higher absolute FFA-activation level and a higher degree of correlation between activated voxels. Both aspects were deficient in DP. Interestingly, control participants showed a homogeneous degree of correlation for successful trials during the experiment. In DP-participants, correlation levels between FFA-voxels were significantly lower and were less sustained during the experiment. In behavioral terms, DP-participants performed poorer and had longer reaction times in relation to DP-severity. Furthermore, correlation levels were negatively correlated with reaction times for the most demanding high load condition.Conclusion: We suggest that participants with DP fail to generate robust and maintained neural representations in the FFA during face encoding and maintenance, in line with poorer task performance and prolonged reaction times. In DP, alterations of neural coding in the FFA might therefore explain curtailing in working memory and contribute to impaired long-term memory and mental imagery.

Crossed effects of audio-visual environment on indoor soundscape perception for pleasant open-plan office environments

An open-plan office (OPO) layout promotes communication and teamwork among workers and provides flexibility in working; however, it also adversely affects work satisfaction, health, and worker privacy, leading to dissatisfaction and poor task performance. In this study, the subjective responses in an audio-visual OPO environment were analyzed to investigate the effects of the environment on indoor soundscape perception for designing pleasant OPOs. The outdoor environments of four OPOs with four different visual layouts were divided based on two conditions with different window views (good and bad views) to model eight visual stimuli. The auditory aspect was evaluated by computer simulation using three sound sources. For subjective assessment, virtual reality technology was employed with 24 audio-visual stimuli, and thirty-four subjects were asked to rate the perceived affective quality and overall quality. Task performance was quantified using auditory digit span tasks. The spatial preference decreased with the Loud, Variable, and Reverberant attributes and increased with the Bright, Orderly, and Wide attributes. For the same OPO soundscape, the window area ratio and floor area showed positive correlations with visual privacy, visual satisfaction, speech privacy, and sound satisfaction. However, work satisfaction and task performance exhibited contradictory relationships with the visual parameters. Furthermore, wide, open workspaces yielded high work satisfaction, whereas narrow, confined work spaces yielded high task performance. These results indicate that the relationship between work satisfaction and task performance must be considered for a balanced OPO design, providing practical guidance for designing pleasant OPO environments.