354,745 research outputs found

    University teachers’ focus on students: Examining the relationships between visual attention, conceptions of teaching and pedagogical training

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    Teachers’ focus on their students’ learning is considered central in high-quality university teaching. This frontline research introduces a novel way to study how focusing on students’ learning can be found on the level of teachers’ visual noticing combined with verbal interpretations, i.e. their professional vision, when they observe teaching situations. A central question is also, whether professional vision skills are connected to teachers’ pedagogical education. Two short videos depicting teaching during a lecture, including different types of trigger events, were presented to teachers (N = 49), who were asked to think aloud while watching, and numerically evaluate the success of the teaching, to reveal their interpretation of the teaching situation. The results showed that pedagogically trained teachers paid more visual attention on the students and less on the teacher. Visual noticing of critical incidents preceded the formulation of accurate verbal interpretations. Noticing that the students were not active was connected to learning facilitating conceptions, which were further connected with corresponding numerical evaluation of the successfulness of teaching. Teachers who visually notice the important incidents during teaching can also formulate a more accurate verbal interpretation of the situation. Contrary to studies at lower levels of education, our study did not found evidence on the connection between teaching experience and professional vision. At the university level, pedagogical education seems to be a stronger predictor of professional vision

    Frequency dependence of signal power and spatial reach of the local field potential

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    The first recording of electrical potential from brain activity was reported already in 1875, but still the interpretation of the signal is debated. To take full advantage of the new generation of microelectrodes with hundreds or even thousands of electrode contacts, an accurate quantitative link between what is measured and the underlying neural circuit activity is needed. Here we address the question of how the observed frequency dependence of recorded local field potentials (LFPs) should be interpreted. By use of a well-established biophysical modeling scheme, combined with detailed reconstructed neuronal morphologies, we find that correlations in the synaptic inputs onto a population of pyramidal cells may significantly boost the low-frequency components of the generated LFP. We further find that these low-frequency components may be less `local' than the high-frequency LFP components in the sense that (1) the size of signal-generation region of the LFP recorded at an electrode is larger and (2) that the LFP generated by a synaptically activated population spreads further outside the population edge due to volume conduction

    Cognitive visual tracking and camera control

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    Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

    User-centered visual analysis using a hybrid reasoning architecture for intensive care units

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    One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

    The Patient-Physician Relationship: Overcoming Language and Cultural Barriers

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    The patient-physician relationship governs the field of medicine, forming the basis for all relationships, interactions, and procedures in medicine. The degree to which a patient trusts his physician and thus is willing to be receptive to medical advice and adhere to assigned treatment is dependent on the quality of his relationship with his physician. The method of relationship chosen will dictate how the patient feels he is perceived and thus to what extend he will participate in his healthcare. A patient-centered approach to medicine will increase this confidence and lead to improved clinical results. Additionally, the rise of physician burnout has also had an effect on this foundational relationship, creating division between the patient and his physician primarily due to complaints against the excessive use of EHRs (electronic health records) and time constraints. Furthermore, in a country of immigrants, the differences in not only language but also between separate cultures and levels of health literacy divides physicians and large populations of their limited English proficiency (LEP) patients. This is a huge detriment to the patient-physician relationship. Lawmakers have created federal and state laws in an effort to install legal action to remedy this, but significant work is still needed to fully bridge the gap. Several solutions have been proposed to do this with the hopeful effect of finally providing equal and better care to all

    Analysing and modelling train driver performance

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    Arguments for the importance of contextual factors in understanding human performance have been made extremely persuasive in the context of the process control industries. This paper puts these arguments into the context of the train driving task, drawing on an extensive analysis of driver performance with the Automatic Warning System (AWS). The paper summarises a number of constructs from applied psychological research which are thought to be important in understanding train driver performance. A “Situational Model” is offered as a framework for investigating driver performance. The model emphasises the importance of understanding the state of driver cognition at a specific time (“Now”) in a specific situation and a specific context

    Situating emotional experience

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    Psychological construction approaches to emotion suggest that emotional experience is situated and dynamic. Fear, for example, is typically studied in a physical danger context (e.g., threatening snake), but in the real world, it often occurs in social contexts, especially those involving social evaluation (e.g., public speaking). Understanding situated emotional experience is critical because adaptive responding is guided by situational context (e.g., inferring the intention of another in a social evaluation situation vs. monitoring the environment in a physical danger situation). In an fMRI study, we assessed situated emotional experience using a newly developed paradigm in which participants vividly imagine different scenarios from a first-person perspective, in this case scenarios involving either social evaluation or physical danger. We hypothesized that distributed neural patterns would underlie immersion in social evaluation and physical danger situations, with shared activity patterns across both situations in multiple sensory modalities and in circuitry involved in integrating salient sensory information, and with unique activity patterns for each situation type in coordinated large-scale networks that reflect situated responding. More specifically, we predicted that networks underlying the social inference and mentalizing involved in responding to a social threat (in regions that make up the “default mode” network) would be reliably more active during social evaluation situations. In contrast, networks underlying the visuospatial attention and action planning involved in responding to a physical threat would be reliably more active during physical danger situations. The results supported these hypotheses. In line with emerging psychological construction approaches, the findings suggest that coordinated brain networks offer a systematic way to interpret the distributed patterns that underlie the diverse situational contexts characterizing emotional life
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