September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Characterizing the Signal Gain and Internal Noise Profile of Spatial Vision with qCSF
Author Affiliations & Notes
  • Fang Hou
    Psychology, The Ohio State University, Columbus, Ohio, United States
  • Luis A Lesmes
    Adaptive Sensory Technology, LLC., Boston, Massachusetts, United States
  • Chang-Bing Huang
    Institute of Psychology, Chinese Academy of Sciences, Beijing, China
  • Zhong-Lin Lu
    Psychology, The Ohio State University, Columbus, Ohio, United States
  • Footnotes
    Commercial Relationships   Fang Hou, None; Luis Lesmes, Adaptive Sensory Technology, LLC (I), Adaptive Sensory Technology, LLC (E), Adaptive Sensory Technology, LLC (P); Chang-Bing Huang, None; Zhong-Lin Lu, Adaptive Sensory Technology, LLC (I), Adaptive Sensory Technology, LLC (P)
  • Footnotes
    Support  This research was supported the National Eye Institute (EY021553)
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 203. doi:
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    • Get Citation

      Fang Hou, Luis A Lesmes, Chang-Bing Huang, Zhong-Lin Lu; Characterizing the Signal Gain and Internal Noise Profile of Spatial Vision with qCSF. Invest. Ophthalmol. Vis. Sci. 2016;57(12):203.

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

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Abstract

Purpose : Previously, we derived the signal gain and internal noise profiles in spatial vision as functions of spatial frequency through modeling the contrast sensitivity functions (CSF) measured in a range of external noise levels with the method of constant stimuli using a multi-channel perceptual template model (mPTM) (Chen et al., 2014; Hou, Lu, & Huang, 2014). Here, we extended and validated the qCSF method (Lesmes, et al., 2010) originally developed in zero external noise to measure CSF in multiple external noise conditions. The mPTM was used to extract the signal gain and internal noise profiles of spatial vision.

Methods : The CSFs of five normal observers in zero and high external noise conditions were measured in a 4AFC sinewave grating orientation identification task with the qCSF procedure and Ψ method (Kontsevich & Tyler, 1999). External noise was generated by filtering high contrast Gaussian white noise with a one-octave-wide raised cosine filter centered at the test grating spatial frequency. The mPTM, with both signal gain and internal noise profiles described by log parabola functions, was fit to the data.

Results : We found that (1) Consistent with previous studies, the CSFs in the high external noise condition were virtually flat; (2) The CSFs obtained with the qCSF and Ψ methods were highly correlated in both external noise conditions (r = 0.95 ± 0.03); (3) The standard deviation of the CSFs obtained with 100 qCSF trials was 0.06 ± 0.01 and 0.07 ± 0.003 decimal log unit in the zero and high external noise conditions, respectively, with no significant difference between the two (p > 0.55). (3) The bias of the CSFs obtained with 100 qCSF trials was 0 ± 0.017 and 0.006 ± 0.041 decimal log unit in the two external noise conditions, with no significant difference (p > 0.75). (4) The mPTM accounted for 96.3 ± 2.0% of the variance in the CSF data. The estimated signal gain profile was relatively flat. The magnitude of internal noise elevated with increasing spatial frequency.

Conclusions : The qCSF method can be extended to provide efficient, precise, and accurate measures of CSF in different external noise conditions. The CSFs obtained with 200 qCSF trials in zero and high external noise conditions can be used to reliably estimate the signal gain and internal noise profiles of spatial vision.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

The CSFs in zero and high external noise conditions measured by the qCSF and Ψ methods.

The CSFs in zero and high external noise conditions measured by the qCSF and Ψ methods.

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