4,584 research outputs found

    Testing the limits of contextual constraint: interactions with word frequency and parafoveal preview during fluent reading

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    Contextual constraint is a key factor affecting a word's fixation duration and its likelihood of being fixated during reading. Previous research has generally demonstrated additive effects of predictability and frequency in fixation times. Studies examining the role of parafoveal preview have shown that greater preview benefit is obtained from more predictable and higher frequency words versus less predictable and lower frequency words. In two experiments, we investigated effects of target word predictability, frequency, and parafoveal preview. A 3 (Predictability: low, medium, high) × 2 (Frequency: low, high) design was used with Preview (valid, invalid) manipulated between experiments. With valid previews, we found main effects of Predictability and Frequency in both fixation time and probability measures, including an interaction in early fixation measures. With invalid preview, we again found main effects of Predictability and Frequency in fixation times, but no evidence of an interaction. Fixation probability showed a weak Predictability effect and Predictability-Frequency interaction. Predictability interacted with Preview in early fixation time and probability measures. Our findings suggest that high levels of contextual constraint exert an early influence during lexical processing in reading. Results are discussed in terms of models of language processing and eye movement control

    Early EEG correlates of word frequency and contextual predictability in reading

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    Previous research into written language comprehension has been equivocal as to whether word frequency and contextual predictability effects share an early time course of processing. Target word frequency (low, high) and its predictability from prior context (low, high) were manipulated across two-sentence passages. Context sentences were presented in full, followed by word-by-word presentation (300 ms SOA) of target sentences. ERPs were analysed across left-to-right and anterior-to-posterior regions of interest within intervals from 50 to 550 ms post-stimulus. The onset of significant predictability effects (50–80 ms) preceded that of frequency (P1, 80–120 ms), while both main effects were generally sustained through the N400 (350–550 ms). Critically, the frequency-predictability interaction became significant in the P1 and was sustained through the N400, although the specific configuration of effects differed across components. The pattern of findings supports an early, chronometric locus of contextual predictability in recognising words during reading

    A tool for subjective and interactive visual data exploration

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    We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets

    Flexible delivery of Er:YAG radiation at 2.94 µm with negative curvature silica glass fibers:a new solution for minimally invasive surgical procedures

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    We present the delivery of high energy microsecond pulses through a hollow-core negative-curvature fiber at 2.94 µm. The energy densities delivered far exceed those required for biological tissue manipulation and are of the order of 2300 J/cm(2). Tissue ablation was demonstrated on hard and soft tissue in dry and aqueous conditions with no detrimental effects to the fiber or catastrophic damage to the end facets. The energy is guided in a well confined single mode allowing for a small and controllable focused spot delivered flexibly to the point of operation. Hence, a mechanically and chemically robust alternative to the existing Er:YAG delivery systems is proposed which paves the way for new routes for minimally invasive surgical laser procedures

    Does segmentation always improve model performance in credit scoring?

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    Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache
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