Measures and Limits of Models of Fixation Selection

Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure) and the KL-divergence (a distance measure of probability distributions) combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection . We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced

Measurement of Epstein-Barr virus DNA load using a novel quantification standard containing two EBV DNA targets and SYBR Green I dye

<p>Abstract</p> <p>Background</p> <p>Reactivation of Epstein-Barr virus (EBV) infection may cause serious, life-threatening complications in immunocompromised individuals. EBV DNA is often detected in EBV-associated disease states, with viral load believed to be a reflection of virus activity. Two separate real-time quantitative polymerase chain reaction (QPCR) assays using SYBR Green I dye and a single quantification standard containing two EBV genes, Epstein-Barr nuclear antigen-1 (EBNA-1) and BamHI fragment H rightward open reading frame-1 (BHRF-1), were developed to detect and measure absolute EBV DNA load in patients with various EBV-associated diseases. EBV DNA loads and viral capsid antigen (VCA) IgG antibody titres were also quantified on a population sample.</p> <p>Results</p> <p>EBV DNA was measurable in ethylenediaminetetraacetic acid (EDTA) whole blood, peripheral blood mononuclear cells (PBMCs), plasma and cerebrospinal fluid (CSF) samples. EBV DNA loads were detectable from 8.0 × 10²to 1.3 × 10⁸copies/ml in post-transplant lymphoproliferative disease (n = 5), 1.5 × 10³to 2.0 × 10⁵copies/ml in infectious mononucleosis (n = 7), 7.5 × 10⁴to 1.1 × 10⁵copies/ml in EBV-associated haemophagocytic syndrome (n = 1), 2.0 × 10²to 5.6 × 10³copies/ml in HIV-infected patients (n = 12), and 2.0 × 10²to 9.1 × 10⁴copies/ml in the population sample (n = 218). EBNA-1 and BHRF-1 DNA were detected in 11.0% and 21.6% of the population sample respectively. There was a modest correlation between VCA IgG antibody titre and BHRF-1 DNA load (rho = 0.13, p = 0.05) but not EBNA-1 DNA load (rho = 0.11, p = 0.11).</p> <p>Conclusion</p> <p>Two sensitive and specific real-time PCR assays using SYBR Green I dye and a single quantification standard containing two EBV DNA targets, were developed for the detection and measurement of EBV DNA load in a variety of clinical samples. These assays have application in the investigation of EBV-related illnesses in immunocompromised individuals.</p

Integrating Mechanisms of Visual Guidance in Naturalistic Language Production

Situated language production requires the integration of visual attention and lin-guistic processing. Previous work has not conclusively disentangled the role of perceptual scene information and structural sentence information in guiding visual attention. In this paper, we present an eye-tracking study that demonstrates that three types of guidance, perceptual, conceptual, and structural, interact to control visual attention. In a cued language production experiment, we manipulate percep-tual (scene clutter) and conceptual guidance (cue animacy), and measure structural guidance (syntactic complexity of the utterance). Analysis of the time course of lan-guage production, before and during speech, reveals that all three forms of guidance affect the complexity of visual responses, quantified in terms of the entropy of atten-tional landscapes and the turbulence of scan patterns, especially during speech. We find that perceptual and conceptual guidance mediate the distribution of attention in the scene, whereas structural guidance closely relates to scan-pattern complexity. Furthermore, the eye-voice span of the cued object and its perceptual competitor are similar; its latency mediated by both perceptual and structural guidance. These results rule out a strict interpretation of structural guidance as the single dominant form of visual guidance in situated language production. Rather, the phase of the task and the associated demands of cross-modal cognitive processing determine the mechanisms that guide attention

Scenes, saliency maps and scanpaths

The aim of this chapter is to review some of the key research investigating how people look at pictures. In particular, my goal is to provide theoretical background for those that are new to the field, while also explaining some of the relevant methods and analyses. I begin by introducing eye movements in the context of natural scene perception. As in other complex tasks, eye movements provide a measure of attention and information processing over time, and they tell us about how the foveated visual system determines what to prioritise. I then describe some of the many measures which have been derived to summarize where people look in complex images. These include global measures, analyses based on regions of interest and comparisons based on heat maps. A particularly popular approach for trying to explain fixation locations is the saliency map approach, and the first half of the chapter is mostly devoted to this topic. A large number of papers and models are built on this approach, but it is also worth spending time on this topic because the methods involved have been used across a wide range of applications. The saliency map approach is based on the fact that the visual system has topographic maps of visual features, that contrast within these features seems to be represented and prioritized, and that a central representation can be used to control attention and eye movements. This approach, and the underlying principles, has led to an increase in the number of researchers using complex natural scenes as stimuli. It is therefore important that those new to the field are familiar with saliency maps, their usage, and their pitfalls. I describe the original implementation of this approach (Itti & Koch, 2000), which uses spatial filtering at different levels of coarseness and combines them in an attempt to identify the regions which stand out from their background. Evaluating this model requires comparing fixation locations to model predictions. Several different experimental and comparison methods have been used, but most recent research shows that bottom-up guidance is rather limited in terms of predicting real eye movements. The second part of the chapter is largely concerned with measuring eye movement scanpaths. Scanpaths are the sequential patterns of fixations and saccades made when looking at something for a period of time. They show regularities which may reflect top-down attention, and some have attempted to link these to memory and an individual’s mental model of what they are looking at. While not all researchers will be testing hypotheses about scanpaths, an understanding of the underlying methods and theory will be of benefit to all. I describe the theories behind analyzing eye movements in this way, and various methods which have been used to represent and compare them. These methods allow one to quantify the similarity between two viewing patterns, and this similarity is linked to both the image and the observer. The last part of the chapter describes some applications of eye movements in image viewing. The methods discussed can be applied to complex images, and therefore these experiments can tell us about perception in art and marketing, as well as about machine vision