Human optokinetic nystagmus: A stochastic analysis

Waddington, Jonathan; Harris, Christopher M.

doi:10.1167/12.12.5

Cited by 15 publications

(11 citation statements)

References 48 publications

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“…The optokinetic response is an involuntary sawtooth movement of the eye that occurs in response to moving stimuli such as a rotating drum, or drifting bars on a computer screen [2] . The response consists of an alternating sequence of slow phases (SPs) during which the eyes track a feature of the moving stimulus, and quick phases (QPs) where the eyes move rapidly in the opposite direction to the moving stimulus [2] , [3] (see Fig. 1 ).…”

Section: Introductionmentioning

confidence: 99%

An Optokinetic Nystagmus Detection Method for Use With Young Children

Sangi

Thompson

Turuwhenua

2015

IEEE J. Transl. Eng. Health Med.

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The detection of vision problems in early childhood can prevent neurodevelopmental disorders such as amblyopia. However, accurate clinical assessment of visual function in young children is challenging. optokinetic nystagmus (OKN) is a reflexive sawtooth motion of the eye that occurs in response to drifting stimuli, that may allow for objective measurement of visual function in young children if appropriate child-friendly eye tracking techniques are available. In this paper, we present offline tools to detect the presence and direction of the optokinetic reflex in children using consumer grade video equipment. Our methods are tested on video footage of children (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$N = 5$ \end{document} children and 20 trials) taken as they freely observed visual stimuli that induced horizontal OKN. Using results from an experienced observer as a baseline, we found the sensitivity and specificity of our OKN detection method to be 89.13% and 98.54%, respectively, across all trials. Our OKN detection results also compared well (85%) with results obtained from a clinically trained assessor. In conclusion, our results suggest that OKN presence and direction can be measured objectively in children using consumer grade equipment, and readily implementable algorithms.

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Section: Introductionmentioning

confidence: 99%

An Optokinetic Nystagmus Detection Method for Use With Young Children

Sangi

Thompson

Turuwhenua

2015

IEEE J. Transl. Eng. Health Med.

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“…The method of moments is infeasible due to poor convergence (the reciprocal Normal has no finite moments; Harris and Waddington, 2012). Maximum likelihood estimation of parameters requires vast amounts of data to distinguish between models (Waddington and Harris, 2012). There is also the problem of under-sampling at extreme values (Harris and Waddington, 2012) which is further exacerbated by the tendency of many investigators to discard “outliers.” It is easier in the rate-domain, although large data sets are still needed.…”

Section: Introductionmentioning

confidence: 99%

Manual choice reaction times in the rate-domain

Harris

Waddington

Biscione

et al. 2014

Front. Hum. Neurosci.

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Over the last 150 years, human manual reaction times (RTs) have been recorded countless times. Yet, our understanding of them remains remarkably poor. RTs are highly variable with positively skewed frequency distributions, often modeled as an inverse Gaussian distribution reflecting a stochastic rise to threshold (diffusion process). However, latency distributions of saccades are very close to the reciprocal Normal, suggesting that “rate” (reciprocal RT) may be the more fundamental variable. We explored whether this phenomenon extends to choice manual RTs. We recorded two-alternative choice RTs from 24 subjects, each with 4 blocks of 200 trials with two task difficulties (easy vs. difficult discrimination) and two instruction sets (urgent vs. accurate). We found that rate distributions were, indeed, very close to Normal, shifting to lower rates with increasing difficulty and accuracy, and for some blocks they appeared to become left-truncated, but still close to Normal. Using autoregressive techniques, we found temporal sequential dependencies for lags of at least 3. We identified a transient and steady-state component in each block. Because rates were Normal, we were able to estimate autoregressive weights using the Box-Jenkins technique, and convert to a moving average model using z-transforms to show explicit dependence on stimulus input. We also found a spatial sequential dependence for the previous 3 lags depending on whether the laterality of previous trials was repeated or alternated. This was partially dissociated from temporal dependency as it only occurred in the easy tasks. We conclude that 2-alternative choice manual RT distributions are close to reciprocal Normal and not the inverse Gaussian. This is not consistent with stochastic rise to threshold models, and we propose a simple optimality model in which reward is maximized to yield to an optimal rate, and hence an optimal time to respond. We discuss how it might be implemented.

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“…Consistent with this, in the contralateral bias model with strabismus, we found the L2-norm of the weights from each cortical area to the contralateral NOT to be 1.3 times greater than the L2-norm of the ipsilateral weights. Note that we cannot make a quantitative comparison between the weights estimated by Kiorpes et al and Waddington and Harris (2012) showed that the eye velocity during the slow phase increases as stimulus speed increases, but saturates at high stimulus speeds. For normal human subjects, when the stimulus speed is 10 deg/s, the slow phase eye velocity is around 8 deg/s.…”

Section: Discussionmentioning

confidence: 77%

“…The actual timing, amplitude and direction of the fast phase are variable (Waddington & Harris, 2012), rather than fixed. The fast phase of OKN also appears to take on object-targeting properties like saccades (Harrison, Freeman, & Sumner, 2014.…”

Section: Developmental Model Of the Optokinetic Reflexmentioning

confidence: 99%

An active-efficient-coding model of optokinetic nystagmus

2016

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Optokinetic nystagmus (OKN) is an involuntary eye movement responsible for stabilizing retinal images in the presence of relative motion between an observer and the environment. Fully understanding the development of OKN requires a neurally plausible computational model that accounts for the neural development and the behavior. To date, work in this area has been limited. We propose a neurally plausible framework for the joint development of disparity and motion tuning in the visual cortex and of optokinetic and vergence eye-movement behavior. To our knowledge, this framework is the first developmental model to describe the emergence of OKN in a behaving organism. Unlike past models, which were based on scalar models of overall activity in different neural areas, our framework models the development of the detailed connectivity both from the retinal input to the visual cortex and from the visual cortex to the motor neurons. This framework accounts for the importance of the development of normal vergence control and binocular vision in achieving normal monocular OKN behaviors. Because the model includes behavior, we can simulate the same perturbations as past experiments, such as artificially induced strabismus. The proposed model agrees both qualitatively and quantitatively with a number of findings from the literature on both binocular vision and the optokinetic reflex. Finally, our model makes quantitative predictions about OKN behavior using the same methods used to characterize OKN in the experimental literature.

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Human optokinetic nystagmus: A stochastic analysis

Cited by 15 publications

References 48 publications

An Optokinetic Nystagmus Detection Method for Use With Young Children

An Optokinetic Nystagmus Detection Method for Use With Young Children

Manual choice reaction times in the rate-domain

An active-efficient-coding model of optokinetic nystagmus

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