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

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.

Storage for free: a surprising property of a simple gain-control model of motion aftereffects

“…During testing the balance of activity in the total population of motion sensors (together covering all tuning directions) is then shifted towards the opponent direction, causing an opponently directed MAE (Mather, 1980). Here we use a straightforward implementation of this general idea (see also van de Grind, Lankheet, & Tao, 2003;Grunewald, 1996;Grunewald & Lankheet, 1996) and explore the recovery properties of such a model in quantitative detail.…”

“…Leaving out the influence of neighbouring (non-optimally activated) channels has a quantitative influence that can be compensated by a small increase of the projection weighting factors in the reduced twochannel model. Qualitatively nothing is gained by using the complete multi-channel model to explain results obtained with a single adaptation direction (see also van de Grind et al, 2003). We have carried out simulation studies with these networks in Matlab/Simulink to check the validity of the reduction from 12 (or more) channels to a two-channel model.…”

“…Van de Grind et al (2003) proposed an explicit circuit to implement this gain control stage, and showed how such a model could quantitatively explain the relation between nulling-thresholds and MAE-durations for the dMAE. Fig.…”

“…In the present paper we will show that this model can also explain storage phenomena. Interestingly, even though gain-controls based on multiplicative or subtractive principles can be used to explain the occurrence of MAEs (van de Grind et al, 2003), they do not give 'storage' phenomena for free, as the divisive gain control of Fig. 2 does.…”

“…Because model parameters have been estimated previously for the dMAE ( van de Grind et al, 2003) it is also possible to test storage predictions for the dMAE. Section 4 therefore also presents results on the dMAE, and then shows that our model works equally well for dMAEs and sMAEs, but requires different parameter values for the responsible networks.…”