Contralateral delay activity as a marker of visual working memory capacity: a multi-site registered replication

Visual working memory (VWM) is a temporary storage system capable of retaining information that can be accessed and manipulated by higher cognitive processes, thereby facilitating a wide range of cognitive functions. Electroencephalography (EEG) is used to understand the neural correlates of VWM with high temporal precision, and one commonly used EEG measure is an event-related potential called the contralateral delay activity (CDA). In a landmark study by Vogel and Machizawa (2004), the authors found that the CDA amplitude increases with the number of items stored in VWM and plateaus around three to four items, which is thought to represent the typical adult working memory capacity. Critically, this study also showed that the increase in CDA amplitude between two-item and four-item arrays correlated with individual subjects’ VWM performance. Although these results have been supported by subsequent studies, a recent study suggested that the number of subjects used in experiments investigating the CDA may not be sufficient to detect differences in set size and to provide a reliable account of the relationship between behaviorally measured VWM capacity and the CDA amplitude. To address this, the current study, as part of the #EEGManyLabs project, aims to conduct a multi-site replication of Vogel and Machizawa's (2004) seminal study on a large sample of participants, with a pre-registered analysis plan. Through this, our goal is to contribute to deepening our understanding of the neural correlates of visual working memory

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Traité élémentaire de la Chaleur.

Mode of access: Internet

Traité élémentaire de la chaleur.

Mode of access: Internet

Analytical modelling of milling for tool design and selection

Jun

Analytical simulation of milling : influence of tool geometry on cutting forces

June 17-2

A process monitoring module based on fuzzy logic and pattern recognition

AbstractThis article presents a plastic injection moulding monitoring module based on knowledge built on-line using feedback from production data. A fuzzy classifier was especially developed for this application. It is based on unsupervised and supervised classification methods. The role of the first one is to identify the functioning modes of the process whereas the role of the second one is to associate the state of the process to one of the identified functioning modes at the moment where a workpiece is injected. Furthermore this diagnosis module integrates an on-line learning method which allows to enrich and upgrade the initial knowledge during production. The results obtained show that the monitoring system is a solution for quality and productivity control having serious economical advantages. For example maintenance tasks can be anticipated and the size of the training set can be considerably reduced. The computing times show that the monitoring system can be used for the purpose of industrial applications without any decrease of production rate