June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Modelling cue weighting for naturalistic vergence and accommodation responses
Author Affiliations & Notes
  • Patricia Riddell
    School of Psychology and Clinical Language Sciences, University of Reading, Raading, United Kingdom
  • Anna M Horwood
    School of Psychology and Clinical Language Sciences, University of Reading, Raading, United Kingdom
  • Peter Scarfe
    School of Psychology and Clinical Language Sciences, University of Reading, Raading, United Kingdom
  • Footnotes
    Commercial Relationships   Patricia Riddell, None; Anna Horwood, None; Peter Scarfe, None
  • Footnotes
    Support  Department of Health Research Capacity Development Fellowship award PDA 01/05/031 to A.M.H.
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 5411. doi:
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      Patricia Riddell, Anna M Horwood, Peter Scarfe; Modelling cue weighting for naturalistic vergence and accommodation responses. Invest. Ophthalmol. Vis. Sci. 2017;58(8):5411.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Over the past 15 years, we have created a large data set of adult and child accommodation and vergence responses to different combinations of distance cue (blur, proximity and disparity: Horwood & Riddell, 2008). Here we examined whether these physiological responses could be modelled within a weighted averaging cue combination framework, which has been primarily used to model perceptual estimates (Landy et al. 1995).

Methods : Since it is not possible to determine perceptual psychometric functions for sensitivity to each cue from physiological responses, cue weightings for disparity, blur, proximity, and residual cues, were determined relative to a single prior derived from the intercept of the stimulus response function. The weights generated for the four single-cue conditions were then used to predict responses to two- and three-cue conditions.

Results : Results demonstrated that a standard cue weighted average model provided a good fit to the gain of vergence responses in two and three cue conditions. Figure 1 provides an example of model fits to the data. Red symbols show the average gain for the vergence response in different cue conditions. Responses to single cues (B - blur; D - disparity, P - proximal and O - residual) were used to provide cue weights for the model which then predicted responses to combined cue conditions. Blue symbols show the predicted gain from a weighted average cue combination model. These closely match the response gains in each condition.

Conclusions : Differences in the reliability of the cues is sufficient to determine their relative contribution to driving physiological responses. We further examine how the weighted averaging model can be applied to accommodative responses and to measure how cue weightings alter across the lifespan.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Gain of the vergence response in single cue conditions (B - blur; D - dispartiy, P - proximal and O - residual cues), and in combined cue conditions. Red symbols indicate means for our adult sample and blue symbols are the model fits to combined cue combinations modelled from the responses to single cue conditions

Gain of the vergence response in single cue conditions (B - blur; D - dispartiy, P - proximal and O - residual cues), and in combined cue conditions. Red symbols indicate means for our adult sample and blue symbols are the model fits to combined cue combinations modelled from the responses to single cue conditions

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