scholarly journals Cognitive strategies revealed by clustering eye movement transitions

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Šimon Kucharský ◽  
Ingmar Visser ◽  
Gabriela-Olivia Truțescu ◽  
Paulo Guirro Laurence ◽  
Martina Zaharieva ◽  
...  
Keyword(s):  
Eye Movement ◽  
Empirical Data ◽  
Cognitive Strategies ◽  
Movement Patterns ◽  
Cognitive Tasks ◽  
Response Behavior ◽  
Unsupervised Method ◽  
Solution Processes ◽  
Movement Data ◽  
Different Response

In cognitive tasks, solvers can adopt different strategies to process information which may lead to different response behavior. These strategies might elicit different eye movement patterns which can thus provide substantial information about the strategy a person uses. However, these strategies are usually hidden and need to be inferred from the data. After an overview of existing techniques which use eye movement data for the identification of latent cognitive strategies, we present a relatively easy to apply unsupervised method to cluster eye movement recordings to detect groups of different solution processes that are applied in solving the task. We test the method's performance using simulations and demonstrate its use on two examples of empirical data. Our analyses are in line with presence of different solving strategies in a Mastermind game, and suggest new insights to strategic patterns in solving Progressive matrices tasks.

Throwing the baby out with the bathwater: Problems in modeling aggregated eye-movement data

2003 ◽  
Vol 26 (4) ◽  
pp. 482-483 ◽  
Author(s):  
Gary Feng
Keyword(s):  
Eye Movement ◽  
Empirical Data ◽  
Data Set ◽  
Severe Problem ◽  
Movement Data ◽  
Parameter Values

Parameters in E-Z Reader models are estimated on the basis of a simple data set consisting of 30 means. Because of heavy aggregation, the data have a severe problem of multicolinearity and are unable to adequately constrain parameter values. This could give the model more power than the empirical data warrant. Future models should exploit the richness of eye movement data and avoid excessive aggregation.

Age Differences in Eye Movements During Reading: Degenerative Problems or Compensatory Strategy?

2019 ◽  
Vol 24 (4) ◽  
pp. 297-311
Author(s):  
José David Moreno ◽  
José A. León ◽  
Lorena A. M. Arnal ◽  
Juan Botella
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Meta Analysis ◽  
Age Groups ◽  
Behavioral Changes ◽  
Aging Effects ◽  
Movement Data ◽  
Sentence Reading ◽  
The Difference ◽  
Eye Movements During Reading

Abstract. We report the results of a meta-analysis of 22 experiments comparing the eye movement data obtained from young ( Mage = 21 years) and old ( Mage = 73 years) readers. The data included six eye movement measures (mean gaze duration, mean fixation duration, total sentence reading time, mean number of fixations, mean number of regressions, and mean length of progressive saccade eye movements). Estimates were obtained of the typified mean difference, d, between the age groups in all six measures. The results showed positive combined effect size estimates in favor of the young adult group (between 0.54 and 3.66 in all measures), although the difference for the mean number of fixations was not significant. Young adults make in a systematic way, shorter gazes, fewer regressions, and shorter saccadic movements during reading than older adults, and they also read faster. The meta-analysis results confirm statistically the most common patterns observed in previous research; therefore, eye movements seem to be a useful tool to measure behavioral changes due to the aging process. Moreover, these results do not allow us to discard either of the two main hypotheses assessed for explaining the observed aging effects, namely neural degenerative problems and the adoption of compensatory strategies.

Do Successor Effects in Reading Reflect Parafoveal Processing? Evidence From Corpus-Based and Experimental Eye Movement Data

2014 ◽  
Author(s):  
Bernhard Angele ◽  
Elizabeth R. Schotter ◽  
Timothy Slattery ◽  
Tara L. Chaloukian ◽  
Klinton Bicknell ◽  
...  
Keyword(s):  
Eye Movement ◽  
Parafoveal Processing ◽  
Movement Data

Challenges in Interpretability of Neural Networks for Eye Movement Data

2020 ◽  
Author(s):  
Ayush Kumar ◽  
Prantik Howlader ◽  
Rafael Garcia ◽  
Daniel Weiskopf ◽  
Klaus Mueller
Keyword(s):  
Neural Networks ◽  
Eye Movement ◽  
Movement Data

scholarly journals Saliency-Based Gaze Visualization for Eye Movement Analysis

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5178
Author(s):  
Sangbong Yoo ◽  
Seongmin Jeong ◽  
Seokyeon Kim ◽  
Yun Jang
Keyword(s):  
Eye Movements ◽  
Visual Attention ◽  
Eye Movement ◽  
Visual Stimuli ◽  
Saliency Map ◽  
Gaze Behavior ◽  
The Gaze ◽  
Movement Data ◽  
Visual Clues ◽  
Human Visual Attention

Gaze movement and visual stimuli have been utilized to analyze human visual attention intuitively. Gaze behavior studies mainly show statistical analyses of eye movements and human visual attention. During these analyses, eye movement data and the saliency map are presented to the analysts as separate views or merged views. However, the analysts become frustrated when they need to memorize all of the separate views or when the eye movements obscure the saliency map in the merged views. Therefore, it is not easy to analyze how visual stimuli affect gaze movements since existing techniques focus excessively on the eye movement data. In this paper, we propose a novel visualization technique for analyzing gaze behavior using saliency features as visual clues to express the visual attention of an observer. The visual clues that represent visual attention are analyzed to reveal which saliency features are prominent for the visual stimulus analysis. We visualize the gaze data with the saliency features to interpret the visual attention. We analyze the gaze behavior with the proposed visualization to evaluate that our approach to embedding saliency features within the visualization supports us to understand the visual attention of an observer.

The “Orienting Reflex” as an Input Indicator in “Subliminal” Perception

1972 ◽  
Vol 35 (1) ◽  
pp. 103-110
Author(s):  
Phillip Kleespies ◽  
Morton Wiener
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Exposure Duration ◽  
Visual Input ◽  
Orienting Reflex ◽  
Subliminal Perception ◽  
Thematic Content ◽  
Movement Data ◽  
Report Data ◽  
Input Indicator

This study explored (1) for evidence of visual input at so-called “subliminal” exposure durations, and (2) whether the response, if any, was a function of the thematic content of the stimulus. Thematic content (threatening versus non-threatening) and stimulus structure (angular versus curved) were varied independently under “subliminal,” “part-cue,” and “identification” exposure conditions. With Ss' reports and the frequency and latency of first eye movements (“orienting reflex”) as input indicators, there was no evidence of input differences which are a function of thematic content at any exposure duration, and the “report” data were consistent with the eye-movement data.

scholarly journals Detection of developmental dyslexia with machine learning using eye movement data

Array ◽  
2021 ◽  
pp. 100087
Author(s):  
Peter Raatikainen ◽  
Jarkko Hautala ◽  
Otto Loberg ◽  
Tommi Kärkkäinen ◽  
Paavo Leppänen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Movement ◽  
Developmental Dyslexia ◽  
Movement Data

Eye Movement Patterns Can Distinguish Schizophrenia from the Major Affective Disorders and Healthy Control Subjects

2021 ◽  
Author(s):  
David St Clair ◽  
Graeme MacLennan ◽  
Sara A. Beedie ◽  
Eva Nouzová ◽  
Helen Lemmon ◽  
...  
Keyword(s):  
Eye Movement ◽  
Affective Disorders ◽  
Movement Patterns ◽  
Control Subjects ◽  
Healthy Control

scholarly journals A State-of-the-Art Review on Empirical Data Collection for External Governed Pedestrians Complex Movement

2018 ◽  
Vol 2018 ◽  
pp. 1-42 ◽  
Author(s):  
Xiaomeng Shi ◽  
Zhirui Ye ◽  
Nirajan Shiwakoti ◽  
Offer Grembek
Keyword(s):  
Data Collection ◽  
Empirical Data ◽  
Empirical Studies ◽  
Movement Patterns ◽  
Simulation Models ◽  
Straight Line ◽  
Challenges And Opportunities ◽  
Pedestrian Traffic ◽  
Random Flows ◽  
Complex Movement

Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians’ complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements.

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