Search

One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin. [ABSTRACT FROM AUTHOR]

Abstract: Generalized social anxiety disorder (GSAD) is characterized by excessive fears of scrutiny and negative evaluation, but neural circuitry related to scrutiny in GSAD has been little studied. In this study, 16 unmedicated adults with GSAD and 16 matched healthy comparison (HC) participants underwent functional magnetic resonance imaging to assess neural response to viewed images of faces simulating movement into eye contact versus away from eye contact. GSAD patients were then treated for 8 weeks with paroxetine, and 15 patients were re-imaged. At baseline, GSAD patients had elevated neural response to eye contact in parahippocampal cortex, inferior parietal lobule, supramarginal gyrus, posterior cingulate and middle occipital cortex. During paroxetine treatment, symptomatic improvement was associated with decreased neural response to eye contact in regions including inferior and middle frontal gyri, anterior cingulate, posterior cingulate, precuneus and inferior parietal lobule. Both the magnitude of GSAD symptom reduction with paroxetine treatment and the baseline comparison of GSAD vs. HCs were associated with neural processing of eye contact in distributed networks that included regions involved in self-referential processing. These findings demonstrate that eye contact in GSAD engages neurocircuitry consistent with the heightened self-conscious emotional states known to characterize GSAD patients during scrutiny. [Copyright &y& Elsevier]

Can people react to objects in their visual field that they do not consciously perceive? We investigated how visual perception and motor action respond to moving objects whose visibility is reduced, and we found a dissociation between motion processing for perception and for action. We compared motion perception and eye movements evoked by two orthogonally drifting gratings, each presented separately to a different eye. The strength of each monocular grating was manipulated by inducing adaptation to one grating prior to the presentation of both gratings. Reflexive eye movements tracked the vector average of both gratings (pattern motion) even though perceptual responses followed one motion direction exclusively (component motion). Observers almost never perceived pattern motion. This dissociation implies the existence of visual-motion signals that guide eye movements in the absence of a corresponding conscious percept. [ABSTRACT FROM PUBLISHER]

The praxis system comprises a network of brain regions dedicated to complex skilled movements. Following suggestions of a female advantage on learned movements (Chipman and Hampson in Neuropsychologia 44(12):2315–2329, ), we investigated how males and females acquire skilled movements. Subjects viewed and imitated sequences of hand movements, which were repeated ten times. Subjects’ imitations were captured by a data glove, and the temporal and spatial characteristics of the imitations were compared to the model sequence. We propose an account of the computations required for imitating hand movement sequences and define the errors that may arise from failures at these computations. Our results demonstrate a female advantage in the acquisition of hand movement sequences and show that this sex difference is accounted for by a female advantage in planning, rather than an advantage in execution or online control. Further, the female advantage is specific to the production of items within a sequence and does not affect the proper ordering of items. Our findings have important implications for sex-sensitive instructional strategies, as well as for understanding the human praxis system. [ABSTRACT FROM AUTHOR]

Latent semantic analysis (LSA) and transitional probability (TP), two computational methods used to reflect lexical semantic representation from large text corpora, were employed to examine the effects of word predictability on Chinese reading. Participants' eye movements were monitored, and the influence of word complexity (number of strokes), word frequency, and word predictability on different eye movement measures (first-fixation duration, gaze duration, and total time) were examined. We found influences of TP on first-fixation duration and gaze duration and of LSA on total time. The results suggest that TP reflects an early stage of lexical processing while LSA reflects a later stage. [ABSTRACT FROM AUTHOR]

Imitation is a common and effective way for humans to learn new behaviors. Until now, the study of imitation has been hampered by the challenge of measuring how well an attempted imitation corresponds to its stimulus model. We describe a new method for quantifying the fidelity with which observers imitate complex series of gestures. Wearing a data glove that transduced movements of their digits, subjects viewed and then reproduced a sequence of gestures from memory. The velocity profile of each digit’s flexion or extension was used to segment movements made during an imitation into gestures that can be compared against corresponding gestures in the stimulus model. The outcome is a multivariate description of each imitation, including its temporal characteristics, as well as spatial errors (in individual gestures and in the ordering of those gestures). As a demonstration, we applied this method to data from an imitation learning experiment with gesture sequences. With repetition, overall fidelity of imitation improved, with various aspects of the imitation improving at different rates. Confirming the approach’s usefulness, when we varied the complexity associated with imitation, that variation was robustly reflected in our measures of imitation quality. Finally, we describe a simple way to extend our methods to make them useful not only in assessing imitation and imitation learning, but also in various settings in which the detection and characterization of subtle abnormalities in movement production is paramount. [ABSTRACT FROM AUTHOR]

Abstract: Inspection time (IT) is the most popular simple psychometric measure that is used to account for a large part of the variance in human mental ability, with the estimated corrected correlation between IT and IQ being −0.50. In this study, we investigate the relationship between IT and the performance and oculomotor variables measured during three simple visual tasks. Participants’ ITs were first measured using a slight variation of the standard IT task, which was followed by the three simple visual tasks that were designed to test participants’ visual–attentional control and visual working memory under varying degrees of difficulty; they included a visual search task, a comparative visual search task, and a visual memorization task. Significant correlations were found between IT and performance variables for each of the visual tasks. The implications of the correlation between IT and performance-related variables are discussed. Oculomotor variables on the other hand only correlated significantly with IT during the retrieval phase of the visual memorization task, which is likely a product of differences in participants’ ability to memorize objects during the loading phase of the experiment. This leads us to the conclusion that the oculomotor variables we measured do not correlate with IT in general, but may in the case where a systematic benefit would be realized. [Copyright &y& Elsevier]

When asked to indicate the starting position of a fast moving stimulus, observers do not indicate the actual starting position but a later position on the motion trajectory. This perceptual illusion is known as the “Fröhlich effect”. We present a neural model aimed at simulating this phenomenon based on feedforward and feedback connections. The basic simulation mechanisms seem to be compatible with the attentional and the motion extrapolation account. A comparison between simulated and empirical results showed that the model is capable of generating the same main effects as those found in the empirical data. [ABSTRACT FROM AUTHOR]

Abstract: Visual search is a fundamental and routine task of everyday life. Studying visual search promises to shed light on the basic attentional mechanisms that facilitate visual processing. To investigate visual attention during search processes, numerous studies measured the selectivity of observers’ saccadic eye movements for local display features. These experiments almost entirely relied on simple, artificial displays with discrete search items and features. The present study employed complex search displays and targets to examine task-driven (top-down) visual guidance by low-level features under more natural conditions. Significant guidance by local intensity, contrast, spatial frequency, and orientation was found, and its properties such as magnitude and resolution were analyzed across dimensions. Moreover, feature-ratio effects were detected, which correspond to distractor-ratio effects in simple search displays. These results point out the limitations of current purely stimulus-driven (bottom-up) models of attention during scene perception. [Copyright &y& Elsevier]

Using eye tracking for the investigation of visual attention has become increasingly popular during the last few decades. Nevertheless, only a small number of eye tracking studies have employed 3D displays, although such displays would closely resemble our natural visual environment. Besides higher cost and effort for the experimental setup, the main reason for the avoidance of 3D displays is the problem of computing a subject's current 3D gaze position based on the measured binocular gaze angles. The geometrical approaches to this problem that have been studied so far involved substantial error in the measurement of 3D gaze trajectories. In order to tackle this problem, we developed an anaglyph-based 3D calibration procedure and used a well-suited type of artificial neural network—a parametrized self-organizing map (PSOM)—to estimate the 3D gaze point from a subject's binocular eye-position data. We report an experiment in which the accuracy of the PSOM gaze-point estimation is compared to a geometrical solution. The results show that the neural network approach produces more accurate results than the geometrical method, especially for the depth axis and for distant stimuli. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an affine parameter estimation algorithm from block motion vectors for extracting accurate motion information with the assumption that the undergoing motion can be characterized by an affine model. The motion may be caused either by a moving camera or a moving object. The proposed method first extracts motion vectors from a sequence of images by using size-variable block matching and then processes them by adaptive robust estimation to estimate affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a continuous weight function based on a Sigmoid function. During the estimation process, we tune the Sigmoid function gradually to its hard-limit as the errors between the model and input data are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. Experimental results show that the suggested approach is very effective in estimating affine parameters reliably. [Copyright &y& Elsevier]

The Enhanced Restricted Focus Viewer (ERFV) is a unique software tool for tracking the visual attention of users in hyperlinked environments, such as Web sites. The software collects data, such as mouse clicks along with the path of users' visual attention, as they browse a site. Unlike traditional eye-tracking procedures, the ERFV requires no hardware to operate other than a personal computer. In addition to time and cost savings, the ERFV allows the administration of usability testing to groups of participants simultaneously. A laboratory test comparing the ERFV to a hardware-based eye-tracking system showed that the two methods compared favorably in terms of how well they track a user's visual attention. The value of the ERFV as a usability testing tool was demonstrated through an experiment that evaluated two Web sites that were equivalent in content but that differed in terms of design. Although several open issues concerning the ERFV still remain, some of these issues are being addressed through ongoing research efforts. [ABSTRACT FROM AUTHOR]

The Area Activation Model (Pomplun, Reingold, Shen, & Williams, 2000) is a computational model predicting the statistical distribution of saccadic endpoints in visual search tasks. Its basic assumption is that saccades in visual search tend to foveate display areas that provide a maximum amount of task-relevant information for processing during the subsequent fixation. In the present study, a counterintuitive prediction by the model is empirically tested, namely that saccadic selectivity towards stimulus features depends on the spatial arrangement of search items. We find good correspondence between simulated and empirically observed selectivity patterns, providing strong support for the Area Activation Model. [Copyright &y& Elsevier]

Discusses the comparative visual search as an experimental paradigm for investigating the underlying complex processes of perception and cognition. Modes of visual processing which correspond to consecutive stages; Role of perceptual grouping in visual search; Components of visual perception involved in a standard visual search task.

Alzheimer’s disease is a degenerative brain disease that impairs memory, thinking skills, and the ability to perform even the most basic tasks. The primary challenge in this domain is accurate early stage disease detection. When the disease is detected at an early stage, medical professionals can prescribe medications to reduce brain shrinkage. Although the disease may not be curable, these interventions can extend the patient’s life by slowing down the rate of shrinkage. The four cognitive states of the human brain are cognitive normal (CN), mild cognitive impairment convertible (MCIc), mild cognitive impairment non-convertible (MCInc), and Alzheimer’s disease (AD). Mild cognitive impairment convertible (MCIc) is the early stage of Alzheimer’s disease. Individuals with MCIc will develop Alzheimer’s disease for a few years. However, it is difficult to detect this state through medical investigations. The mild cognitive impairment non-convertible state (MCInc) is the state immediately before MCIc. MCInc is a common condition in people of all ages, where minor memory issues arise as a result of normal aging. Early detection of AD can be claimed if and only if the transition from MCInc to MCIc is complete. Deep learning algorithms can be promising techniques for identifying the progression stage of a disease using magnetic resonance imaging. In this study, a novel deep learning algorithm was proposed to improve the classification accuracy of MCIc vs. MCInc. This study utilized the advantages of local binary patterns along with squeeze and excitation networks (SENet). Without the squeeze and excitation network, the classification accuracy of MCIc versus MCInc was 82%. The classification accuracy improved by 86% with the use of SENet. The experimental results show that the proposed model achieves better performance for MCInc vs. MCIc classification in terms of accuracy, precision, recall, F1 score, and ROC. [ABSTRACT FROM AUTHOR]

In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient's MRI without requiring long-term iEEG inspection. [ABSTRACT FROM AUTHOR]

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study. [ABSTRACT FROM AUTHOR]

The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study. [ABSTRACT FROM AUTHOR]

An observer's pupil dilates and constricts in response to variables such as ambient and focal luminance, cognitive effort, the emotional stimulus content, and working memory load. The pupil's memory load response is of particular interest, as it might be used for estimating observers' memory load while they are performing a complex task, without adding an interruptive and confounding memory test to the protocol. One important task in which working memory's involvement is still being debated is visual search, and indeed a previous experiment by Porter, Troscianko, and Gilchrist ( Quarterly Journal of Experimental Psychology, 60, 211-229, 2007) analyzed observers' pupil sizes during search to study this issue. These authors found that pupil size increased over the course of the search, and they attributed this finding to accumulating working memory load. However, since the pupil response is slow and does not depend on memory load alone, this conclusion is rather speculative. In the present study, we estimated working memory load in visual search during the presentation of intermittent fixation screens, thought to induce a low, stable level of arousal and cognitive effort. Using standard visual search and control tasks, we showed that this paradigm reduces the influence of non-memory-related factors on pupil size. Furthermore, we found an early increase in working memory load to be associated with more efficient search, indicating a significant role of working memory in the search process. [ABSTRACT FROM AUTHOR]

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%. [ABSTRACT FROM AUTHOR]

One of the major challenges for protein docking methods is to accurately discriminate native-like structures from false positives. Docking methods are often inaccurate and the results have to be refined and re-ranked to obtain native-like complexes and remove outliers. In a previous work, we introduced AccuRefiner, a machine learning based tool for refining protein-protein complexes. Given a docked complex, the refinement tool produces a small set of refined versions of the input complex, with lower root-mean-square-deviation (RMSD) of atomic positions with respect to the native structure. The method employs a unique ranking tool that accurately predicts the RMSD of docked complexes with respect to the native structure. In this work, we use a deep learning network with a similar set of features and five layers. We show that a properly trained deep learning network can accurately predict the RMSD of a docked complex with 1.40 Å error margin on average, by approximating the complex relationship between a wide set of scoring function terms and the RMSD of a docked structure. The network was trained on 35000 unbound docking complexes generated by RosettaDock. We tested our method on 25 different putative docked complexes produced also by RosettaDock for five proteins that were not included in the training data. The results demonstrate that the high accuracy of the ranking tool enables AccuRefiner to consistently choose the refinement candidates with lower RMSD values compared to the coarsely docked input structures. [ABSTRACT FROM AUTHOR]

Cervical cancer can be cured if it is initially screened and giving timely treatment to the patients. This paper proposes an optimization technique for exposing and segmenting the cancer portion in cervical images using transform and windowing technique. The image processing steps are preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation involved in the proposed work. Initially, Gabor transform is enforced on the cervical test image to modify the pixels associated with the spatial domain into multi-resolution domain. Subsequently, the parameters of the multi-level features are extracted from the Gabor transformed cervical image. Then, the extracted features are optimized using the Genetic Algorithm (GA), and the optimistic prominent part is classified by the Convolutional Neural Networks (CNN). Finally, the Finite Segmentation Algorithm (FSA) is used to detect and segment the cancer region in cervical images. The proposed GA based CNN classification method describes the effectual detection and classification of cervical cancer by the parameters such as sensitivity, specificity and accuracy. The experimental results are shown 99.37% of average sensitivity, 98.9% of average specificity and 99.21% of average accuracy, 97.8% of PPV, 91.8% of NPV, 96.8% of FPR and 90.4% of FNR. [ABSTRACT FROM AUTHOR]

We address the question of inferring the search target from fixation behavior in visual search. Such inference is possible since during search, our attention and gaze are guided toward visual features similar to those in the search target. We strive to answer two fundamental questions: what are the most powerful algorithmic principles for this task, and how does their performance depend on the amount of available eye movement data and the complexity of the target objects? In the first two experiments, we choose a random-dot search paradigm to eliminate contextual influences on search. We present an algorithm that correctly infers the target pattern up to 50 times as often as a previously employed method and promises sufficient power and robustness for interface control. Moreover, the current data suggest a principal limitation of target inference that is crucial for interface design: if the target pattern exceeds a certain spatial complexity level, only a subpattern tends to guide the observers׳ eye movements, which drastically impairs target inference. In the third experiment, we show that it is possible to predict search targets in natural scenes using pattern classifiers and classic computer vision features significantly above chance. The availability of compelling inferential algorithms could initiate a new generation of smart, gaze-controlled interfaces and wearable visual technologies that deduce from their users׳ eye movements the visual information for which they are looking. In a broader perspective, our study shows directions for efficient intent decoding from eye movements. [ABSTRACT FROM AUTHOR]

A previous study (Vision Research 51 (2011) 1192–1205) found evidence for semantic guidance of visual attention during the inspection of real-world scenes, i.e., an influence of semantic relationships among scene objects on overt shifts of attention. In particular, the results revealed an observer bias toward gaze transitions between semantically similar objects. However, this effect is not necessarily indicative of semantic processing of individual objects but may be mediated by knowledge of the scene gist, which does not require object recognition, or by known spatial dependency among objects. To examine the mechanisms underlying semantic guidance, in the present study, participants were asked to view a series of displays with the scene gist excluded and spatial dependency varied. Our results show that spatial dependency among objects seems to be sufficient to induce semantic guidance. Scene gist, on the other hand, does not seem to affect how observers use semantic information to guide attention while viewing natural scenes. Extracting semantic information mainly based on spatial dependency may be an efficient strategy of the visual system that only adds little cognitive load to the viewing task. [ABSTRACT FROM AUTHOR]

Highlights: [•] Focus of peri-saccadic mislocalization follows salient stimuli, not necessarily the saccade goal. [•] Attracting effect of salient stimuli is not diminished by visible background. [•] Absence of salient stimulus including saccade target results in less compression. [•] A strong visual reference can influence perceived location of peri-saccadic flashes. [ABSTRACT FROM AUTHOR]

Abstract: The perception of objects in our visual world is influenced by not only their low-level visual features such as shape and color, but also their high-level features such as meaning and semantic relations among them. While it has been shown that low-level features in real-world scenes guide eye movements during scene inspection and search, the influence of semantic similarity among scene objects on eye movements in such situations has not been investigated. Here we study guidance of eye movements by semantic similarity among objects during real-world scene inspection and search. By selecting scenes from the LabelMe object-annotated image database and applying latent semantic analysis (LSA) to the object labels, we generated semantic saliency maps of real-world scenes based on the semantic similarity of scene objects to the currently fixated object or the search target. An ROC analysis of these maps as predictors of subjects’ gaze transitions between objects during scene inspection revealed a preference for transitions to objects that were semantically similar to the currently inspected one. Furthermore, during the course of a scene search, subjects’ eye movements were progressively guided toward objects that were semantically similar to the search target. These findings demonstrate substantial semantic guidance of eye movements in real-world scenes and show its importance for understanding real-world attentional control. [Copyright &y& Elsevier]

Inhibition of return (IOR) represents a well-known mechanism of human perception that biases attentional orienting to novel locations in the environment. Behaviorally, IOR reflects slower reaction time (RT) to stimuli presented in previously cued locations. In this study, we examined within patients with schizophrenia this inhibitory aftereffect using two different cue types—eye gaze and standard peripheral cues. Results indicated that patients showed evidence of IOR, as reflected in a 3.2% slowing in RT to previously peripherally cued locations. However, for eye gaze, patients failed to show evidence of IOR and instead had 1.7% faster RT to targets presented following delay in locations that had been previously cued. This inhibitory failure correlated strongly with reduced neuropsychological performance and global symptoms ratings of attention and bizarre behavior. Reduced inhibitory aftereffect in RT for eye-gaze cues may reflect disease-related abnormalities in social attention. [ABSTRACT FROM AUTHOR]

Abstract: Saccadic eye movements are used to quickly and accurately orient our fovea within our visual field to obtain detailed information from various locations. The accuracy of these eye movements is maintained throughout life despite constant pressure on oculomotor muscles and neuronal structures by growth and aging; this maintenance appears to be a product of an adaptive mechanism that continuously accounts for consistent post-saccadic visual error, and is referred to as saccadic adaptation. In this paper, we present a new paradigm to test saccadic adaptation under circumstances that more closely resemble natural visual error in everyday vision, whereas previous saccadic adaptation paradigms study adaptation in a largely restricted form. The paradigm achieves this by positioning a stimulus panel atop an identically colored background relative to the gaze position of the participant. We demonstrate the paradigm by successfully decreasing participants’ saccadic amplitudes during a common visual search task by shifting the stimulus panel in the opposite direction of the saccade by 50% of the saccadic amplitude. Participants’ adaptation reached approximately 60% of the 50% back-shift during the adaptation phase, and was uniformly distributed across saccadic direction. The adaptation time-course found using the new paradigm is consistent with that achieved using previous paradigms. Task-performance results and the manner in which eye movements changed during adaptation were also analyzed. [Copyright &y& Elsevier]

The distractor-ratio effect refers to the finding that search performance in a conjunctive visual search task depends on the relative frequency of two types or subsets of distractors when the total number of items in a display is fixed. Previously, Shen, Reingold, and Pomplun (2000) examined participants' patterns of eye movements in a distractor-ratio paradigm and demonstrated that on any given trial saccadic endpoints were biased towards the smaller subset of distractors and participants flexibly switched between different subsets across trials. The current study explored the boundary conditions of this tendency to flexibly search through a smaller subset of distractors by examining the influence of several manipulations known to modulate search efficiency, including stimulus discriminability (Experiment 1), within-dimension versus crossdimension conjunction search and distractor heterogeneity (Experiment 2). The results indicated that the flexibility of visual guidance and saccadic bias exemplified by the distractor-ratio effect is a robust phenomenon that mediates search efficiency by adapting to changes in the relative informativeness of stimulus dimensions and features. [ABSTRACT FROM AUTHOR]

Examines the eye movements of chess players in attempts to choose the best move. Evaluation on the fixation trials and amplitude cascades; Ability of expert player to encode chess configurations to guide peripheral movements of the eye; Production of patterns in saccadic selectivity by piece saliency.

This study explores a proposition that individuals with dyslexia develop enhanced peripheral vision to process visual-spatial information holistically. Participants included 18 individuals diagnosed with dyslexia and 18 who were not. The experiment used a comparative visual search design consisting of two blocks of 72 trials. Each trial presented two halves of the display each comprising three kinds of shapes in three colours to be compared side-by-side. Participants performed a conjunctive search to ascertain whether the two halves were identical. In the first block, participants were provided no instruction regarding the visual-spatial processing strategy they were to employ. In the second block, participants were instructed to use a holistic processing strategy-to defocus their attention and perform the comparison by examining the whole screen at once. The results did not support the hypothesis associating dyslexia with talents for holistic visual processing. Using holistic processing strategy, both groups scored lower in accuracy and reacted faster, compared to the first block. Impaired readers consistently reacted more slowly and did not exhibit enhanced accuracy. Given the extant evidence of strengths for holistic visual processing in impaired readers, these findings are important because they suggest such strengths may be task dependent. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The morphological constituents of English compounds (e.g., 'butter' and 'fly' for 'butterfly') and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make judgments difficult, and a computational alternative based on a general model might be a way to average across subjective differences. In the present study, we propose two approaches based on latent semantic analysis (Landauer & Dumais in Psychological Review 104:211-240, ): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent from a clustering analysis of morphological family members (e.g., 'butterfingers' or 'buttermilk' for 'butter'). The proposed models successfully predicted participants' transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, on the basis of differences in raters' morphological processing in the different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed. [ABSTRACT FROM AUTHOR]

E-readers are fast rivaling print as a dominant method for reading. Because they offer accessibility options that are impossible in print, they are potentially beneficial for those with impairments, such as dyslexia. Yet, little is known about how the use of these devices influences reading in those who struggle. Here, we observe reading comprehension and speed in 103 high school students with dyslexia. Reading on paper was compared with reading on a small handheld e-reader device, formatted to display few words per line. We found that use of the device significantly improved speed and comprehension, when compared with traditional presentations on paper for specific subsets of these individuals: Those who struggled most with phoneme decoding or efficient sight word reading read more rapidly using the device, and those with limited VA Spans gained in comprehension. Prior eye tracking studies demonstrated that short lines facilitate reading in dyslexia, suggesting that it is the use of short lines (and not the device per se) that leads to the observed benefits. We propose that these findings may be understood as a consequence of visual attention deficits, in some with dyslexia, that make it difficult to allocate attention to uncrowded text near fixation, as the gaze advances during reading. Short lines ameliorate this by guiding attention to the uncrowded span. [ABSTRACT FROM AUTHOR]

People with dyslexia, who ordinarily struggle to read, sometimes remark that reading is easier when e-readers are used. Here, we used eye tracking to observe high school students with dyslexia as they read using these devices. Among the factors investigated, we found that reading using a small device resulted in substantial benefits, improving reading speeds by 27%, reducing the number of fixations by 11%, and importantly, reducing the number of regressive saccades by more than a factor of 2, with no cost to comprehension. Given that an expected trade-off between horizontal and vertical regression was not observed when line lengths were altered, we speculate that these effects occur because sluggish attention spreads perception to the left as the gaze shifts during reading. Short lines eliminate crowded text to the left, reducing regression. The effects of attention modulation by the hand, and of increased letter spacing to reduce crowding, were also found to modulate the oculomotor dynamics in reading, but whether these factors resulted in benefits or costs depended on characteristics, such as visual attention span, that varied within our sample. [ABSTRACT FROM AUTHOR]

Previous research indicates that removing initial strokes from Chinese characters makes them harder to read than removing final or internal ones. In the present study, we examined the contribution of important components to character configuration via singular value decomposition. The results indicated that when the least important segments, which did not seriously alter the configuration (contour) of the character, were deleted, subjects read as fast as when no segments were deleted. When the most important segments, which are located in the left side of a character and written first, were deleted, reading speed was greatly slowed. These results suggest that singular value decomposition, which has no information about stroke writing order, can identify the most important strokes for Chinese character identification. Furthermore, they also suggest that contour may be correlated with stroke writing order. [ABSTRACT FROM AUTHOR]

People with dyslexia, who face lifelong struggles with reading, exhibit numerous associated low-level sensory deficits including deficits in focal attention. Countering this, studies have shown that struggling readers outperform typical readers in some visual tasks that integrate distributed information across an expanse. Though such abilities would be expected to facilitate scene memory, prior investigations using the contextual cueing paradigm failed to find corresponding advantages in dyslexia. We suggest that these studies were confounded by task-dependent effects exaggerating known focal attention deficits in dyslexia, and that, if natural scenes were used as the context, advantages would emerge. Here, we investigate this hypothesis by comparing college students with histories of severe lifelong reading difficulties (SR) and typical readers (TR) in contexts that vary attention load. We find no differences in contextual-cueing when spatial contexts are letter-like objects, or when contexts are natural scenes. However, the SR group significantly outperforms the TR group when contexts are lowpass filtered natural scenes [F(3, 39) = 3.15, p<.05]. These findings suggest that perception or memory for low spatial frequency components in scenes is enhanced in dyslexia. These findings are important because they suggest strengths for spatial learning in a population otherwise impaired, carrying implications for the education and support of students who face challenges in school. [ABSTRACT FROM AUTHOR]

Color coding is used to guide attention in computer displays for such critical tasks as baggage screening or air traffic control. It has been shown that a display object attracts more attention if its color is more similar to the color for which one is searching. However, what does similar precisely mean? Can we predict the amount of attention that a display color will receive during a search for a given target color? To tackle this question, two color-search experiments measuring the selectivity of saccadic eye movements and mapping out its underlying color space were conducted. A variety of mathematical models, predicting saccadic selectivity for given target and display colors, were devised and evaluated. The results suggest that applying a Gaussian function to a weighted Euclidean distance in a slightly modified HSI color space is the best predictor of saccadic selectivity in the chosen paradigm. Hue and intensity information by itself provides a basis for useful predictors, spanning a possibly spherical color space of saccadic selectivity. Although the current models cannot predict saccadic selectivity values for a wide variety of visual search tasks, they reveal some characteristics of color search that are of both theoretical and applied interest, such as for the design of human–computer interfaces. [ABSTRACT FROM AUTHOR]

Perceptual integration of audio—visual stimuli is fundamental to our everyday conscious experience. Eye-movement analysis may be a suitable tool for studying such integration, since eye movements respond to auditory as well as visual input. Previous studies have shown that additional auditory cues in visual-search tasks can guide eye movements more efficiently and reduce their latency. However, these auditory cues were task-relevant since they indicated the target position and onset time. Therefore, the observed effects may have been due to subjects using the cues as additional information to maximize their performance, without perceptually integrating them with the visual displays. Here, we combine a visual-tracking task with a continuous, task-irrelevant sound from a stationary source to demonstrate that audio—visual perceptual integration affects low-level oculomotor mechanisms. Auditory stimuli of constant, increasing, or decreasing pitch were presented. All sound categories induced more smooth-pursuit eye movement than silence, with the greatest effect occurring with stimuli of increasing pitch. A possible explanation is that integration of the visual scene with continuous sound creates the perception of continuous visual motion. Increasing pitch may amplify this effect through its common association with accelerating motion. [ABSTRACT FROM AUTHOR]

The reported research extends classic findings that after briefly viewing structured, but not random, chess positions, chess masters reproduce these positions much more accurately than less-skilled players. Using a combination of the gaze-contingent window paradigm and the change blindness flicker paradigm, we documented dramatically larger visual spans for experts while processing structured, but not random, chess positions. In addition, in a check-detection task, a minimized 3 × 3 chessboard containing a King and potentially checking pieces was displayed. In this task, experts made fewer fixations per trial than less-skilled players, and had a greater proportion of fixations between individual pieces, rather than on pieces. Our results provide strong evidence for a perceptual encoding advantage for experts attributable to chess experience, rather than to a general perceptual or memory superiority. [ABSTRACT FROM AUTHOR]