Using similar stimuli, Nishida & Sato (1992) found that motion affect-effect (MAE) and motion discrimination show quite different dependencies on displacement. At displacements around 0.5 cycles of the lowest frequency, while motion discrimination was still almost perfect, normal MAE vanished and MAE in the same direction as that of the adapting stimuli was observed, which they named "positive MAE”. Similar results were obtained for both 1D and 2D filtered patterns.

References:

   Nishida & Sato (1992) Positive motion after-effect induced by bandpass-filtered random-dot kinematograms. Vision Research 32(9):1635–1646

Here is the math behind this stimulus:

  adaptmod = rectanglewave(time,2*period,0,0.5)
  continuous = jump<40
  dtime = period-mod(time,period)
  sampling = continuous*dtime+(1-continuous)*round(dtime*jump)/jump
  tmod = speed*sampling*adaptmod
  top = pnoise(x/g+tmod,(dim-1)*(y+1000)/g)
  bottom = pnoise(x/g-tmod,(dim-1)*(y+1000)/g)
  filterednoise = cnt*(float(y>sep/2)*top+float(y<-sep/2)*bottom)/100
  fixation = (abs(x)<1 | abs(y)<1) & r<10
  z = filterednoise*(1-fixation)-fixation

The whole stimulus is generated in real-time using a GLSL shader that runs right inside your WebGL-compatible browser. The plain Math behind the stimulus was converted to this optimized GLSL shader using the new Psykinematix Pro Edition. Translation to Matlab and Python code is also possible !

This whole widget was also automatically generated using Psykinematix Pro Edition. The parameters that control the stimulus properties through the sliders are the same as the ones you would define as dependent or independent variables when using the stimulus in an actual psychophysical experiment. The widget creation is otherwise fully customizable with your own logo, copyright, links, etc.

To learn more about the widget creation, click on the "Made With" button above !