scholarly journals Looking (for) patterns: Similarities and differences between infant and adult free scene-viewing patterns

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Daan Van Renswoude ◽  
Maartje EJ Raijmakers ◽  
Ingmar Visser
Keyword(s):  
Eye Movements ◽  
General Pattern ◽  
Viewing Time ◽  
Data Sets ◽  
Leftward Bias ◽  
Fixation Durations ◽  
Large Influence ◽  
Scene Viewing ◽  
Similarities And Differences ◽  
Insight Into

Systematic tendencies such as the center and horizontal bias are known to have a large influence on how and where we move our eyes during static onscreen free scene viewing. However, it is unknown whether these tendencies are learned viewing strategies or are more default tendencies in the way we move our eyes. To gain insight into the origin of these tendencies we explore the systematic tendencies of infants (3 - 20-month-olds, N = 157) and adults (N = 88) in three different scene viewing data sets. We replicated common findings, such as longer fixation durations and shorter saccade amplitudes in infants compared to adults. The leftward bias was never studied in infants, and our results indicate that it is not present, while we did replicate the leftward bias in adults. The general pattern of the results highlights the similarity between infant and adult eye movements. Similar to adults, infants’ fixation durations increase with viewing time and the dependencies between successive fixations and saccades show very similar patterns. A straightforward conclusion to draw from this set of studies is that infant and adult eye movements are mainly driven by similar underlying basic processes.

scholarly journals Eye movements during scene viewing: Evidence for mixed control of fixation durations

2008 ◽  
Vol 15 (3) ◽  
pp. 566-573 ◽  
Author(s):  
J. M. HENDERSON ◽  
G. L. PIERCE
Keyword(s):  
Eye Movements ◽  
Mixed Control ◽  
Fixation Durations ◽  
Scene Viewing

Experience with compound words influences their processing: An eye movement investigation with English compound words

2018 ◽  
Vol 71 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Barbara J Juhasz
Keyword(s):  
Eye Movements ◽  
Word Recognition ◽  
Semantic Processing ◽  
Time Course ◽  
Morphological Processing ◽  
Compound Words ◽  
Fixation Durations ◽  
Complex Words ◽  
The Impact ◽  
Insight Into

Recording eye movements provides information on the time-course of word recognition during reading. Juhasz and Rayner [Juhasz, B. J., & Rayner, K. (2003). Investigating the effects of a set of intercorrelated variables on eye fixation durations in reading. Journal of Experimental Psychology: Learning, Memory and Cognition, 29, 1312–1318] examined the impact of five word recognition variables, including familiarity and age-of-acquisition (AoA), on fixation durations. All variables impacted fixation durations, but the time-course differed. However, the study focused on relatively short, morphologically simple words. Eye movements are also informative for examining the processing of morphologically complex words such as compound words. The present study further examined the time-course of lexical and semantic variables during morphological processing. A total of 120 English compound words that varied in familiarity, AoA, semantic transparency, lexeme meaning dominance, sensory experience rating (SER), and imageability were selected. The impact of these variables on fixation durations was examined when length, word frequency, and lexeme frequencies were controlled in a regression model. The most robust effects were found for familiarity and AoA, indicating that a reader’s experience with compound words significantly impacts compound recognition. These results provide insight into semantic processing of morphologically complex words during reading.

scholarly journals Mondrian, Eye Movements, and the Oblique Effect

Perception ◽  
10.1068/p6160 ◽  
2009 ◽  
Vol 38 (5) ◽  
pp. 719-731 ◽  
Author(s):  
Jordan E Plumhoff ◽  
James A Schirillo
Keyword(s):  
Eye Movements ◽  
Fixation Duration ◽  
Oblique Effect ◽  
Abstract Painting ◽  
Saccade Length ◽  
Fixation Durations ◽  
Aesthetic Pleasure ◽  
The Aesthetic ◽  
Image Exploration ◽  
Insight Into

Observers prefer Mondrian's paintings in their original orientation compared to when rotated—“the oblique effect” (Latto et al, 2000 Perception29 981–987). We tested whether eye movements could provide any insight into this aesthetic bias. While recording fixation duration and saccade length, we presented eight Mondrian paintings dated 1921–1944 on a CRT in either their original or seven rotated positions to ten observers who used a Likert scale to report how (dis)pleasing they found each image. We report on eye-movement patterns from nine pairs of images that had a significant orientation effect. During the 20 s scans, fixation durations increased linearly, more so for pleasing images than for non-pleasing images. Moreover, saccade distances oscillated over the viewing interval, with larger saccade-distance oscillations for the pleasing images than the non-pleasing images. Both of these findings agree with earlier work by Nodine et al (1993 Leonardo26 219–227), and confirm that as an abstract painting becomes more aesthetically pleasing, it shows both a greater amount of diversive/specific types of image exploration and balance. Thus, any increase in visual fluency in localizing vertical and horizontal versus oblique lines can lead to an increase in the aesthetic pleasure of viewing Mondrian's work.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Distraction by deviant sounds during reading: An eye-movement study

2019 ◽  
Vol 72 (7) ◽  
pp. 1863-1875 ◽  
Author(s):  
Martin R Vasilev ◽  
Fabrice BR Parmentier ◽  
Bernhard Angele ◽  
Julie A Kirkby
Keyword(s):  
Eye Movements ◽  
Time Course ◽  
Lexical Processing ◽  
Repeated Sequence ◽  
Reading Performance ◽  
Sequence Capture ◽  
Fixation Durations ◽  
Movement Study ◽  
Task Irrelevant ◽  
Target Words

Oddball studies have shown that sounds unexpectedly deviating from an otherwise repeated sequence capture attention away from the task at hand. While such distraction is typically regarded as potentially important in everyday life, previous work has so far not examined how deviant sounds affect performance on more complex daily tasks. In this study, we developed a new method to examine whether deviant sounds can disrupt reading performance by recording participants’ eye movements. Participants read single sentences in silence and while listening to task-irrelevant sounds. In the latter condition, a 50-ms sound was played contingent on the fixation of five target words in the sentence. On most occasions, the same tone was presented (standard sound), whereas on rare and unexpected occasions it was replaced by white noise (deviant sound). The deviant sound resulted in significantly longer fixation durations on the target words relative to the standard sound. A time-course analysis showed that the deviant sound began to affect fixation durations around 180 ms after fixation onset. Furthermore, deviance distraction was not modulated by the lexical frequency of target words. In summary, fixation durations on the target words were longer immediately after the presentation of the deviant sound, but there was no evidence that it interfered with the lexical processing of these words. The present results are in line with the recent proposition that deviant sounds yield a temporary motor suppression and suggest that deviant sounds likely inhibit the programming of the next saccade.

scholarly journals High-Performance Computing of BEAST/BEAGLE in Bayesian Phylogenetics using SDumont Hybrid Resources

2020 ◽  
Author(s):  
Kary Ocaña ◽  
Micaella Coelho ◽  
Guilherme Freire ◽  
Carla Osthoff
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Current Knowledge ◽  
Data Sets ◽  
Length Estimation ◽  
Computationally Intensive ◽  
Mcmc Chain ◽  
Performance Computing ◽  
Insight Into

Bayesian phylogenetic algorithms are computationally intensive. BEAST 1.10 inferences made use of the BEAGLE 3 high-performance library for efficient likelihood computations. The strategy allows phylogenetic inference and dating in current knowledge for SARS-CoV-2 transmission. Follow-up simulations on hybrid resources of Santos Dumont supercomputer using four phylogenomic data sets, we characterize the scaling performance behavior of BEAST 1.10. Our results provide insight into the species tree and MCMC chain length estimation, identifying preferable requirements to improve the use of high-performance computing resources. Ongoing steps involve analyzes of SARS-CoV-2 using BEAST 1.8 in multi-GPUs.

scholarly journals WALD-EM: Wald Accumulation for Locations and Durations of Eye Movements

2020 ◽  
Author(s):  
Šimon Kucharský ◽  
Daan Roelof van Renswoude ◽  
Maartje Eusebia Josefa Raijmakers ◽  
Ingmar Visser
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Empirical Research ◽  
Process Models ◽  
Movement Behavior ◽  
Movement Data ◽  
Fixation Durations ◽  
Research Cycle ◽  
Fixation Locations ◽  
Basic Characteristics

Describing, analyzing and explaining patterns in eye movement behavior is crucial for understanding visual perception. Further, eye movements are increasingly used in informing cognitive process models. In this article, we start by reviewing basic characteristics and desiderata for models of eye movements. Specifically, we argue that there is a need for models combining spatial and temporal aspects of eye-tracking data (i.e., fixation durations and fixation locations), that formal models derived from concrete theoretical assumptions are needed to inform our empirical research, and custom statistical models are useful for detecting specific empirical phenomena that are to be explained by said theory. In this article, we develop a conceptual model of eye movements, or specifically, fixation durations and fixation locations, and from it derive a formal statistical model --- meeting our goal of crafting a model useful in both the theoretical and empirical research cycle. We demonstrate the use of the model on an example of infant natural scene viewing, to show that the model is able to explain different features of the eye movement data, and to showcase how to identify that the model needs to be adapted if it does not agree with the data. We conclude with discussion of potential future avenues for formal eye movement models.

scholarly journals Informative Frailty Indices from Binarized Biomarkers

2020 ◽  
Author(s):  
Garrett Stubbings ◽  
Spencer Farrell ◽  
Arnold Mitnitski ◽  
Kenneth Rockwood ◽  
Andrew Rutenberg
Keyword(s):  
Medical Knowledge ◽  
Data Sets ◽  
Canadian Study ◽  
Adverse Health Outcomes ◽  
Health And Nutrition ◽  
Health Measure ◽  
Upper Limits ◽  
Insight Into ◽  
Better Than ◽  
Biomarker Data

AbstractFrailty indices (FI) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FI from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey (NHANES) and the Canadian Study of Health and Aging (CSHA) data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, the upper limits of the FI observed in various data sets, and highlights general difficulties in obtaining absolute scales for comparing FI between studies.

scholarly journals Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

scholarly journals ASSESSING LIDAR TRAINING DATA QUANTITIES FOR CLASSIFICATION MODELS

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 101-106
Author(s):  
O. Majgaonkar ◽  
K. Panchal ◽  
D. Laefer ◽  
M. Stanley ◽  
Y. Zaki
Keyword(s):  
Point Cloud ◽  
Classification Performance ◽  
Training Data ◽  
Lidar Data ◽  
Data Sets ◽  
Classification Models ◽  
Fundamental Properties ◽  
Point Cloud Classification ◽  
The Impact ◽  
Insight Into

Abstract. Classifying objects within aerial Light Detection and Ranging (LiDAR) data is an essential task to which machine learning (ML) is applied increasingly. ML has been shown to be more effective on LiDAR than imagery for classification, but most efforts have focused on imagery because of the challenges presented by LiDAR data. LiDAR datasets are of higher dimensionality, discontinuous, heterogenous, spatially incomplete, and often scarce. As such, there has been little examination into the fundamental properties of the training data required for acceptable performance of classification models tailored for LiDAR data. The quantity of training data is one such crucial property, because training on different sizes of data provides insight into a model’s performance with differing data sets. This paper assesses the impact of training data size on the accuracy of PointNet, a widely used ML approach for point cloud classification. Subsets of ModelNet ranging from 40 to 9,843 objects were validated on a test set of 400 objects. Accuracy improved logarithmically; decelerating from 45 objects onwards, it slowed significantly at a training size of 2,000 objects, corresponding to 20,000,000 points. This work contributes to the theoretical foundation for development of LiDAR-focused models by establishing a learning curve, suggesting the minimum quantity of manually labelled data necessary for satisfactory classification performance and providing a path for further analysis of the effects of modifying training data characteristics.

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