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

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.

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Cognitive strategies revealed by clustering eye movement transitions

Journal of Eye Movement Research ◽

10.16910/jemr.13.1.1 ◽

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.

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

European Psychologist ◽

10.1027/1016-9040/a000344 ◽

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.

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

PsycEXTRA Dataset ◽

10.1037/e528942014-650 ◽

2014 ◽

Author(s):

Bernhard Angele ◽

Elizabeth R. Schotter ◽

Timothy Slattery ◽

Tara L. Chaloukian ◽

Klinton Bicknell ◽

...

Keyword(s):

Eye Movement ◽

Parafoveal Processing ◽

Movement Data

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Challenges in Interpretability of Neural Networks for Eye Movement Data

ACM Symposium on Eye Tracking Research and Applications ◽

10.1145/3379156.3391361 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ayush Kumar ◽

Prantik Howlader ◽

Rafael Garcia ◽

Daniel Weiskopf ◽

Klaus Mueller

Keyword(s):

Neural Networks ◽

Eye Movement ◽

Movement Data

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Saliency-Based Gaze Visualization for Eye Movement Analysis

Sensors ◽

10.3390/s21155178 ◽

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.

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Selecting single cell clustering parameter values using subsampling-based robustness metrics

BMC Bioinformatics ◽

10.1186/s12859-021-03957-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ryan B. Patterson-Cross ◽

Ariel J. Levine ◽

Vilas Menon

Keyword(s):

Single Cell ◽

Optimal Parameter ◽

Clustering Algorithms ◽

Cell Types ◽

Parameter Selection ◽

Data Set ◽

Biologically Relevant ◽

Cell Clustering ◽

Parameter Values ◽

Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

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

Perceptual and Motor Skills ◽

10.2466/pms.1972.35.1.103 ◽

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.

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