Posts about motion-detection

More is not always better


MotionClouds are random dynamic stimuli optimized to study motion perception.

Notably, this method was used in the following paper:

  • Claudio Simoncini, Laurent U. Perrinet, Anna Montagnini, Pascal Mamassian, Guillaume S. Masson. More is not always better: dissociation between perception and action explained by adaptive gain control. Nature Neuroscience, 2012 URL

In this notebook, we describe the scripts used to generate such stimuli.

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Colored Motion Clouds

Exploring colored Motion Clouds

By construction, Motion Clouds are grayscale:

In [1]:
import os
import numpy as np
import MotionClouds as mc
fx, fy, ft = mc.get_grids(mc.N_X, mc.N_Y, mc.N_frame)
name = 'color'
In [2]:
env = mc.envelope_gabor(fx, fy, ft, V_X=0., V_Y=0.)
mc.figures(env, name + '_gray', do_figs=False)
mc.in_show_video(name + '_gray')

However it is not hard to imagine extending them to the color space. A first option is to create a diferent motion cloud for each channel:

In [3]:
colored_MC = np.zeros((env.shape[0], env.shape[1], 3, env.shape[2]))

for i in range(3):
    colored_MC[:, :, i, :] = mc.rectif(mc.random_cloud(mc.envelope_gabor(fx, fy, ft, V_X=0., V_Y=0.)))

mc.anim_save(colored_MC, os.path.join(mc.figpath, name + '_color'))
mc.in_show_video(name + '_color')

Note that the average luminance is also a random cloud:

In [4]:
mc.anim_save(mc.rectif(colored_MC.sum(axis=2)), os.path.join(mc.figpath, name + '_gray2'))
mc.in_show_video(name + '_gray2')

We may create a strictly isoluminant cloud:

In [5]:
luminance = colored_MC.sum(axis=2)[:, :, np.newaxis, :]
mc.anim_save(colored_MC/luminance, os.path.join(mc.figpath, name + '_isocolor'))
mc.in_show_video(name + '_isocolor')

There are now many more possibilities, such as

  • weighting the different R, G and B channels to obtain a better tuning to psychophysics,
  • let only the predominant channels be activated (like the one corresponding to the red and green channels which correspond to the maximal responses of cones).