Literature DB >> 12536135

A gain-control model relating nulling results to the duration of dynamic motion aftereffects.

W A van de Grind1, M J M Lankheet, R Tao.   

Abstract

Strength of the motion aftereffect (MAE) is most often quantified by its duration, a high-variance and rather 'subjective' measure. With the help of an automatic gain-control model we quantitatively relate nulling-thresholds, adaptation strength, direction discrimination threshold, and duration of the dynamic MAE (dMAE). This shows how the nulling threshold, a more objective two-alternative forced-choice measure, relates to the same system property as MAE-durations. Two psychophysical experiments to test the model use moving random-pixel-arrays with an adjustable luminance signal-to-noise ratio. We measure MAE-duration as a function of adaptation strength and compare the results to the model prediction. We then do the same for nulling-thresholds. Model predictions are strongly supported by the psychophysical findings. In a third experiment we test formulae coupling nulling threshold, MAE-duration, and direction-discrimination thresholds, by measuring these quantities as a function of speed. For the medium-to-high speed range of these experiments we found that nulling thresholds increase and dMAE-durations decrease about linearly, whereas direction discrimination thresholds increase exponentially with speed. The model description then suggests that the motion-gain decreases, while the noise-gain and model's threshold increase with speed.

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Year:  2003        PMID: 12536135     DOI: 10.1016/s0042-6989(02)00495-9

Source DB:  PubMed          Journal:  Vision Res        ISSN: 0042-6989            Impact factor:   1.886


  8 in total

1.  Motion adaptation: net duration matters, not continuousness.

Authors:  Sven P Heinrich; Anja M Schilling; Michael Bach
Journal:  Exp Brain Res       Date:  2005-11-18       Impact factor: 1.972

2.  Modelling fast forms of visual neural plasticity using a modified second-order motion energy model.

Authors:  Andrea Pavan; Adriano Contillo; George Mather
Journal:  J Comput Neurosci       Date:  2014-07-31       Impact factor: 1.621

3.  Perceptual learning reconfigures the effects of visual adaptation.

Authors:  David P McGovern; Neil W Roach; Ben S Webb
Journal:  J Neurosci       Date:  2012-09-26       Impact factor: 6.167

4.  The Role of Bottom-Up and Top-Down Cortical Interactions in Adaptation to Natural Scene Statistics.

Authors:  Selam W Habtegiorgis; Christian Jarvers; Katharina Rifai; Heiko Neumann; Siegfried Wahl
Journal:  Front Neural Circuits       Date:  2019-02-13       Impact factor: 3.492

5.  Congruent audio-visual stimulation during adaptation modulates the subsequently experienced visual motion aftereffect.

Authors:  Minsun Park; Randolph Blake; Yeseul Kim; Chai-Youn Kim
Journal:  Sci Rep       Date:  2019-12-18       Impact factor: 4.379

6.  Dynamics of spatial distortions reveal multiple time scales of motion adaptation.

Authors:  Neil W Roach; Paul V McGraw
Journal:  J Neurophysiol       Date:  2009-10-07       Impact factor: 2.714

7.  Modelling adaptation to directional motion using the Adelson-Bergen energy sensor.

Authors:  Andrea Pavan; Adriano Contillo; George Mather
Journal:  PLoS One       Date:  2013-03-15       Impact factor: 3.240

8.  Habituation of visual adaptation.

Authors:  Xue Dong; Yi Gao; Lili Lv; Min Bao
Journal:  Sci Rep       Date:  2016-01-07       Impact factor: 4.379

  8 in total

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