2,026 research outputs found
Working Memory in Writing: Empirical Evidence From the Dual-Task Technique
The dual-task paradigm recently played a major role in understanding the role of working memory in writing. By reviewing recent findings in this field of research, this article highlights how the use of the dual-task technique allowed studying processing and short-term storage functions of working memory involved in writing. With respect to processing functions of working memory (namely, attentional and executive functions), studies investigated resources allocation, step-by-step management and parallel coordination of the writing processes. With respect to short-term storage in working memory, experiments mainly attempted to test Kellogg's (1996) proposals on the relationship between the writing processes and the slave systems of working memory. It is concluded that the dual-task technique revealed fruitful in understanding the relationship between writing and working memory
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201
A Human-Centric Metaverse Enabled by Brain-Computer Interface: A Survey
The growing interest in the Metaverse has generated momentum for members of
academia and industry to innovate toward realizing the Metaverse world. The
Metaverse is a unique, continuous, and shared virtual world where humans embody
a digital form within an online platform. Through a digital avatar, Metaverse
users should have a perceptual presence within the environment and can interact
and control the virtual world around them. Thus, a human-centric design is a
crucial element of the Metaverse. The human users are not only the central
entity but also the source of multi-sensory data that can be used to enrich the
Metaverse ecosystem. In this survey, we study the potential applications of
Brain-Computer Interface (BCI) technologies that can enhance the experience of
Metaverse users. By directly communicating with the human brain, the most
complex organ in the human body, BCI technologies hold the potential for the
most intuitive human-machine system operating at the speed of thought. BCI
technologies can enable various innovative applications for the Metaverse
through this neural pathway, such as user cognitive state monitoring, digital
avatar control, virtual interactions, and imagined speech communications. This
survey first outlines the fundamental background of the Metaverse and BCI
technologies. We then discuss the current challenges of the Metaverse that can
potentially be addressed by BCI, such as motion sickness when users experience
virtual environments or the negative emotional states of users in immersive
virtual applications. After that, we propose and discuss a new research
direction called Human Digital Twin, in which digital twins can create an
intelligent and interactable avatar from the user's brain signals. We also
present the challenges and potential solutions in synchronizing and
communicating between virtual and physical entities in the Metaverse
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Explanatory debugging: Supporting end-user debugging of machine-learned programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a âprogramâ that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on âexplanatory debuggingâ, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions
Multi-modal post-editing of machine translation
As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden QualitĂ€t maschineller Ăbersetzungssysteme (MĂ) post-editieren (PE) immer mehr Ăbersetzer die MĂ-Ausgabe, was im Vergleich zur herkömmlichen Ăbersetzung Zeit spart und Fehler reduziert. Anstatt primĂ€r Text zu generieren, mĂŒssen Ăbersetzer nun Fehler in ansonsten hilfreichen ĂbersetzungsvorschlĂ€gen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MĂ-Fehler mĂŒhsam und schwer zu erkennende Fehler fordern die Ăbersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere InteraktionsmodalitĂ€ten als Maus und Tastatur das PE unterstĂŒtzen können, indem wir die Ăbersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder ĂŒber Handgesten zu verĂ€ndern, Wörter per Drag & Drop neu anzuordnen oder all diese EingabemodalitĂ€ten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschĂ€tzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Ăbersetzungsumgebungen an die KL der Ăbersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen fĂŒr das automatische Post-Editieren (APE) vor und erörtern, wie diese die MĂ-Ausgabe an eine DomĂ€ne anpassen und dadurch die Korrektur von sich wiederholenden MĂ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP
From a simple EHR to the market lead: what technologies to add
Electronic health records (EHRs) can store, capture, and present patient data in an organized way that improves physiciansâ workflow and patient care. This makes EHRs key to addressing many of todayâs health care challenges. An interdisciplinary review and qualitative study of artificial intelligence, machine learning, natural language processing, and real-time location services in health care was conducted. The results show that in an industry where digitization is key, several recommendations can be made to leverage these technologies in ways that can improve current systems and help EHR vendors become the market lead
An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment
One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner\u27s experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning both employing subjective cognitive load assessment and natural language processing. It makes use of primary, empirical and deductive methods. In details, on one hand, cognitive load assessment is performed using well-known self-reporting instruments, borrowed from Human Factors, namely the Nasa Task Load Index and the Workload Profile. On the other hand, Natural Language Processing techniques, borrowed from Artificial Intelligence, are employed to calculate semantic similarity of textual information, provided by learners after attending a typical third-level class, and the content of the class itself. Subsequently, an investigation of the relationship of cognitive load assessment and textual similarity is performed to assess learning
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