Cue predictability changes scaling in eye-movement fluctuations

Wallot, Sebastian; Coey, Charles A.; Richardson, Michael J.

doi:10.3758/s13414-015-0983-5

Cited by 6 publications

(7 citation statements)

References 31 publications

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“…3, is defined as the negative of the slope of the regression line. In this figure, it is evident that the human data have a larger α value than the AEs, which aligns with the results from Coey et al (2012) and Wallot et al (2015). The same analysis was carried out on all AE and human data.…”

Section: Psd Analysissupporting

confidence: 71%

“…The fractal structure is characterized by a scaling relation between the power of changes in the measured variable and the frequency with which changes of that size occur. In more recent research from Wallot, Coey, and Richardson (2015), eye movement spectral characteristics were generalized via power-law scaling, and the results indicated that this characteristic reflects a relative demand for voluntary control during visual tasks. To validate that these spectral characteristics are a pure effect of human eye movement, we need to verify that the eye-trackers used in such studies do not yield power-law scaling, which we can measure with AEs.…”

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confidence: 99%

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A study of artificial eyes for the measurement of precision in eye-trackers

et al. 2016

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The precision of an eye-tracker is critical to the correct identification of eye movements and their properties. To measure a system's precision, artificial eyes (AEs) are often used, to exclude eye movements influencing the measurements. A possible issue, however, is that it is virtually impossible to construct AEs with sufficient complexity to fully represent the human eye. To examine the consequences of this limitation, we tested currently used AEs from three manufacturers of eye-trackers and compared them to a more complex model, using 12 commercial eye-trackers. Because precision can be measured in various ways, we compared different metrics in the spatial domain and analyzed the power-spectral densities in the frequency domain. To assess how precision measurements compare in artificial and human eyes, we also measured precision using human recordings on the same eyetrackers. Our results show that the modified eye model presented can cope with all eye-trackers tested and acts as a promising candidate for further development of a set of AEs with varying pupil size and pupil-iris contrast. The spectral analysis of both the AE and human data revealed that human eye data have different frequencies that likely reflect the physiological characteristics of human eye movements. We also report the effects of sample selection methods for precision calculations. This study is part of the EMRA/COGAIN Eye Data Quality Standardization Project.Keywords Eye movements . Artificial eye . Precision . Data quality . Eye-tracker noise . Power-spectral density High-quality eye movement data are a prerequisite for the valid measurement of fixation durations, saccade amplitudes and velocities, and many other behavioral measures in eye movement research. Spatial accuracy and precision are two of the most important aspects of eye data quality. Accuracy is defined as the difference between the tracker-estimated gaze position and the actual gaze position, whereas precision is defined as the ability to reliably reproduce a measurement, given a fixating eye (ideally, a stable eye)-see Fig. 1. Accuracy and precision are two independent measurements of eye-tracking data quality-that is, they can be both good or poor, or one good and the other poor. The most commonly used measures of precision in eye-trackers are the sample-tosample root mean square angular displacement [RMS(S2S)] and the standard deviation (SD) of samples in a given time window. These values will change not only dependent on the actual precision level of the tracker but also on the calculation used, and the samples or time period included in that calculation. Longer periods increase the probability of fixational eye movements (tremor, microsaccades, and drift), which in turn will increase imprecision. Holmqvist, Nyström, and Mulvey (2012) showed that artificially increasing imprecision via the addition of Gaussian noise from 0.03°to 0.30°results in an increase of up to 200 ms in calculated fixation durations. If this range of noise, as measured by RMS(S2S) precis...

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Section: Psd Analysissupporting

confidence: 71%

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confidence: 99%

A study of artificial eyes for the measurement of precision in eye-trackers

et al. 2016

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“…5 and used for our time-series. We chose to use the amplitude of change in gaze position over time as our time-series in order to account for changes on both the X and Y axes, to avoid excessive computation and difficulties interpreting our outcome for fractal organization along just one axis (in alignment with 15 , 34 , 35 ). As DFA has not been validated on time-series with fewer than 1000 data points 19 , we excluded all time-series with fewer data points.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, collectively, fractal structure is thought to reflect the coordination of multiple interacting systems organized in an optimally flexible way (pink noise), without being too rigid (brown noise) or too random (white noise). Empirically, pink noise organization has been found for an array of cognitive tasks and modalities in adults—response times during shape discrimination 24 , mental rotation and translation 29 , bi-stable perception 18 , word naming 26 , and simple stimulus detection 30 ; tracing patterns during forced-trace problem-solving 31 and circle-drawing 32 , as well as eye movements during free viewing 33 , challenging visual search 34 , and text reading 35 .…”

Section: Introductionmentioning

confidence: 99%

Infants’ gaze exhibits a fractal structure that varies by age and stimulus salience

Stallworthy

Sifre

Berry

et al. 2020

Sci Rep

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The development of selective visual attention is critical for effectively engaging with an ever-changing world. Its optimal deployment depends upon interactions between neural, motor, and sensory systems across multiple timescales and neurocognitive loci. Previous work illustrates the spatio-temporal dynamics of these processes in adults, but less is known about this emergent phenomenon early in life. Using data (n = 190; 421 visits) collected between 3 and 35 months of age, we examined the spatio-temporal complexity of young children’s gaze patterns as they viewed stimuli varying in semantic salience. Specifically, we used detrended fluctuation analysis (DFA) to quantify the extent to which infants’ gaze patterns exhibited scale invariant patterns of nested variability, an organizational feature thought to reflect self-organized and optimally flexible system dynamics that are not overly rigid or random. Results indicated that gaze patterns of even the youngest infants exhibited fractal organization that increased with age. Further, fractal organization was greater when children (a) viewed social stimuli compared to stimuli with degraded social information and (b) when they spontaneously gazed at faces. These findings suggest that selective attention is well-organized in infancy, particularly toward social information, and indicate noteworthy growth in these processes across the first years of life.

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“…It was shown that the policy based on information maximization criteria [9] generates trajectories that share basic statistical properties with human eye movements. In this research, we set the goal of developing a control model of fixation selection that is capable of interpreting the scaling behaviour of human eye-movements [10,14,12,15] and provides a human level of performance to a computational agent.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Eye Movements During Visual Search

Vasilyev

2019

IEEE Trans. Cogn. Dev. Syst.

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We study the problem of an optimal oculomotor control during the execution of visual search tasks. We introduce a computational model of human eye movements, which takes into account various constraints of the human visual and oculomotor systems. In the model, the choice of the subsequent fixation location is posed as a problem of a stochastic optimal control, which relies on reinforcement learning methods. We show that if biological constraints are taken into account, the trajectories simulated under a learned policy share both basic statistical properties and a scaling behaviour with human eye movements. We validated our model simulations with human psychophysical eye-tracking experiments.

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Cue predictability changes scaling in eye-movement fluctuations

Cited by 6 publications

References 31 publications

A study of artificial eyes for the measurement of precision in eye-trackers

A study of artificial eyes for the measurement of precision in eye-trackers

Infants’ gaze exhibits a fractal structure that varies by age and stimulus salience

Optimal Control of Eye Movements During Visual Search

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