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Temporal Structure of Human Gaze Dynamics Is Invariant During Free Viewing.

Marlow CA, Viskontas IV, Matlin A, Boydston C, Boxer A, Taylor RP - PLoS ONE (2015)

Bottom Line: We find H is robust regardless of the spatial complexity generated by the fractal images.The value we find for H of 0.57 shows that the gaze dynamics during free viewing of fractal images are consistent with a random walk process with persistent movements.Our research suggests the human visual system may have a common strategy that drives the dynamics of human gaze during exploration.

View Article: PubMed Central - PubMed

Affiliation: Keck Center for Integrative Neuroscience, University of California, San Francisco, California, 94143-044, United States of America; Physics Department, California Polytechnic State University, San Luis Obispo, California, 93407, United States of America.

ABSTRACT
We investigate the dynamic structure of human gaze and present an experimental study of the frequency components of the change in gaze position over time during free viewing of computer-generated fractal images. We show that changes in gaze position are scale-invariant in time with statistical properties that are characteristic of a random walk process. We quantify and track changes in the temporal structure using a well-defined scaling parameter called the Hurst exponent, H. We find H is robust regardless of the spatial complexity generated by the fractal images. In addition, we find the Hurst exponent is invariant across all participants, including those with distinct changes to higher order visual processes due to neural degeneration. The value we find for H of 0.57 shows that the gaze dynamics during free viewing of fractal images are consistent with a random walk process with persistent movements. Our research suggests the human visual system may have a common strategy that drives the dynamics of human gaze during exploration.

No MeSH data available.


Related in: MedlinePlus

Calculated change in gaze position, l, as a function of time along with resulting variation method plot of N(Δt) in inset for (A) participant Y1, (C) FTD5 viewing the computer-generated fractal image and (E) FTD5 viewing the natural landscape.Actual data is shown as black circles and surrogate data in red squares. Note time axis in insets have been cropped in order to see details of plot. The associated plot of–log N(Δt) vs. log Δt for (B) the actual and surrogate data for participant Y1, and the data for FTD5, (D) and (F). The linear behavior observed in the actual data demonstrates the scale invariance of l (t) within the measured range. The Hurst exponent H is listed on the plots and was obtained from the slope of the linear fit (slope = 2-H). Insets shows residuals of the linear fits.
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pone.0139379.g003: Calculated change in gaze position, l, as a function of time along with resulting variation method plot of N(Δt) in inset for (A) participant Y1, (C) FTD5 viewing the computer-generated fractal image and (E) FTD5 viewing the natural landscape.Actual data is shown as black circles and surrogate data in red squares. Note time axis in insets have been cropped in order to see details of plot. The associated plot of–log N(Δt) vs. log Δt for (B) the actual and surrogate data for participant Y1, and the data for FTD5, (D) and (F). The linear behavior observed in the actual data demonstrates the scale invariance of l (t) within the measured range. The Hurst exponent H is listed on the plots and was obtained from the slope of the linear fit (slope = 2-H). Insets shows residuals of the linear fits.

Mentions: Data from the healthy participant Y1 is used as a typical example of observations. Fig 2A and 2B show the participant’s vertical and horizontal gaze position along with the accompanying scan path. The magnitude of the change in gaze position with time, l(t), for this data is shown in Fig 3A. The variation method output N(Δt) is plotted as a function of Δt, and follows an inverse power law. This can be clearly observed in the linearity of the plot of–log N(Δt) with log Δt (black circles in Fig 3B). The data is plotted within the observational time limits of the experiment and the linear fit to the data is shown as a solid line. The scaling exponent for this particular gaze data is found to be 1.44 by a linear regressive fit of the scaling plot, giving a Hurst exponent value of H = 0.56. The plot is linear over the range marked by the solid arrows indicating l (t) is scale invariant across 1.5 orders of magnitude, consistent with observations of scale invariance in other physical systems in nature [18]. The observational time cut-offs are set by the length of time participants viewed the image (5 seconds) and the time resolution (8.3 ms) of the data collection. The deviations we see in the data from the linear, scale invariant behavior occur at the positions along the x-axis, marked by the arrows, where we would expect them to be based on the statistical criteria of our analysis. In order to reliably detect scale invariant structure we need at least a grid size of 1/5 of the maximum trace length (5 seconds). The coarse-scale observational cut-off in our study is thus 1 s, as marked by the arrow on Fig 3B. We also cannot detect structure below times which are 3 times the resolution (8.3 ms) of the data collected. This fine-scale cut-off is also marked on Fig 3B.


Temporal Structure of Human Gaze Dynamics Is Invariant During Free Viewing.

Marlow CA, Viskontas IV, Matlin A, Boydston C, Boxer A, Taylor RP - PLoS ONE (2015)

Calculated change in gaze position, l, as a function of time along with resulting variation method plot of N(Δt) in inset for (A) participant Y1, (C) FTD5 viewing the computer-generated fractal image and (E) FTD5 viewing the natural landscape.Actual data is shown as black circles and surrogate data in red squares. Note time axis in insets have been cropped in order to see details of plot. The associated plot of–log N(Δt) vs. log Δt for (B) the actual and surrogate data for participant Y1, and the data for FTD5, (D) and (F). The linear behavior observed in the actual data demonstrates the scale invariance of l (t) within the measured range. The Hurst exponent H is listed on the plots and was obtained from the slope of the linear fit (slope = 2-H). Insets shows residuals of the linear fits.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4589360&req=5

pone.0139379.g003: Calculated change in gaze position, l, as a function of time along with resulting variation method plot of N(Δt) in inset for (A) participant Y1, (C) FTD5 viewing the computer-generated fractal image and (E) FTD5 viewing the natural landscape.Actual data is shown as black circles and surrogate data in red squares. Note time axis in insets have been cropped in order to see details of plot. The associated plot of–log N(Δt) vs. log Δt for (B) the actual and surrogate data for participant Y1, and the data for FTD5, (D) and (F). The linear behavior observed in the actual data demonstrates the scale invariance of l (t) within the measured range. The Hurst exponent H is listed on the plots and was obtained from the slope of the linear fit (slope = 2-H). Insets shows residuals of the linear fits.
Mentions: Data from the healthy participant Y1 is used as a typical example of observations. Fig 2A and 2B show the participant’s vertical and horizontal gaze position along with the accompanying scan path. The magnitude of the change in gaze position with time, l(t), for this data is shown in Fig 3A. The variation method output N(Δt) is plotted as a function of Δt, and follows an inverse power law. This can be clearly observed in the linearity of the plot of–log N(Δt) with log Δt (black circles in Fig 3B). The data is plotted within the observational time limits of the experiment and the linear fit to the data is shown as a solid line. The scaling exponent for this particular gaze data is found to be 1.44 by a linear regressive fit of the scaling plot, giving a Hurst exponent value of H = 0.56. The plot is linear over the range marked by the solid arrows indicating l (t) is scale invariant across 1.5 orders of magnitude, consistent with observations of scale invariance in other physical systems in nature [18]. The observational time cut-offs are set by the length of time participants viewed the image (5 seconds) and the time resolution (8.3 ms) of the data collection. The deviations we see in the data from the linear, scale invariant behavior occur at the positions along the x-axis, marked by the arrows, where we would expect them to be based on the statistical criteria of our analysis. In order to reliably detect scale invariant structure we need at least a grid size of 1/5 of the maximum trace length (5 seconds). The coarse-scale observational cut-off in our study is thus 1 s, as marked by the arrow on Fig 3B. We also cannot detect structure below times which are 3 times the resolution (8.3 ms) of the data collected. This fine-scale cut-off is also marked on Fig 3B.

Bottom Line: We find H is robust regardless of the spatial complexity generated by the fractal images.The value we find for H of 0.57 shows that the gaze dynamics during free viewing of fractal images are consistent with a random walk process with persistent movements.Our research suggests the human visual system may have a common strategy that drives the dynamics of human gaze during exploration.

View Article: PubMed Central - PubMed

Affiliation: Keck Center for Integrative Neuroscience, University of California, San Francisco, California, 94143-044, United States of America; Physics Department, California Polytechnic State University, San Luis Obispo, California, 93407, United States of America.

ABSTRACT
We investigate the dynamic structure of human gaze and present an experimental study of the frequency components of the change in gaze position over time during free viewing of computer-generated fractal images. We show that changes in gaze position are scale-invariant in time with statistical properties that are characteristic of a random walk process. We quantify and track changes in the temporal structure using a well-defined scaling parameter called the Hurst exponent, H. We find H is robust regardless of the spatial complexity generated by the fractal images. In addition, we find the Hurst exponent is invariant across all participants, including those with distinct changes to higher order visual processes due to neural degeneration. The value we find for H of 0.57 shows that the gaze dynamics during free viewing of fractal images are consistent with a random walk process with persistent movements. Our research suggests the human visual system may have a common strategy that drives the dynamics of human gaze during exploration.

No MeSH data available.


Related in: MedlinePlus