2D Visual Noise are useful random stimuli for a variety of tasks, from investigating and comparing first-order (luminance modulation) and second-order (contrast modulation) mechanisms to masking spatially or temporally a target stimulus for example. Visual noise stimuli are not simply random: noise can be binary (2 luminance levels only) or with intermediate levels, can be derived from a Gaussian or uniform distribution, can be white (i.e. with equal energy at across the spatial frequency spectrum), pink or 1/f (with an energy inversely proportional to the spatial frequency) or even spatially or temporally filtered.

References:

  Kersten (1984) Spatial summation in visual noise. Vision Research 24(12):1977–1990

  Rovamo & Kukkonen (1996) The effect of noise check size and shape on grating detectability. Vision Research 36(2):271–279

  Blackwell (1998) The effect of white and filtered noise on contrast detection thresholds. Vision Research 38(2): 267–280

  Schofield & Georgeson (1999) Sensitivity to modulations of luminance and contrast in visual white noise: separate mechanisms with similar behaviour. Vision Research 39(16):2697–2716

  Gorea et al (2000) Visual sensitivity to temporal modulations of temporal noise. Vision Research 40(28):3817–3822

  Manahilov et al (2003) Temporal properties of the visual responses to luminance and contrast modulated noise. Vision Research 43(17):1855–1867

  Ledgeway & Hutchinson (2005) The influence of spatial and temporal noise on the detection of first-order and second-order orientation and motion direction. Vision Research 45(16):2081–2094

  Calvert et al (2005) Human cortical responses to contrast modulations of visual noise. Vision Research 45(17):2218–2230

  Hansen & Hess (2012) On the effectiveness of noise masks: Naturalistic vs. un-naturalistic image statistics. Vision Research 60: 101–113

Here is the math behind this stimulus:

 uniformnoise = 0.5*(unoise(x,round(time*tf),g)+1)  # Rescale to [0,1] range
 levelnoise = round((levels-1)*uniformnoise)/(levels-1) # Re-sample
 z = 2*levelnoise-1                   # Scale back to [-1,1]

v1.1 (Aug 3rd 2020)

• Addition of “Number of levels” as parameter (suggested by Kenzo Sakurai, Tohoku Gakuin University, Sendai, Japan),
• Use 100% contrast instead of 50%.

v1.0 (July 31th 2020)

• Initial release.

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 fully 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 run in Psykinematix. The widget creation is otherwise fully customizable with your own logo, copyright, links, etc.

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