pyllusion 1.3

Description:

pyllusion 1.3

A Parametric Framework to Generate Visual Illusions using Python
Overview
Visual illusions are fascinating phenomena that have been used and
studied by artists and scientists for centuries, leading to important
discoveries about how conscious perception is generated by the brain.
Instead of crafting them by hand, Pyllusion offers a framework to
manipulate and generate illusions in a systematic way.
The parametric approach implemented in this software proposes to
describe illusions using a set of parameters, such as for instance
the difference and illusion strength. These two parameters can be
modulated to independently impact either the objective difference of the
core components of the illusion (e.g., the difference between the two
segments in the Müller-Lyer illusion)
or the intensity of the illusion effect (e.g., the angle of the
“distractors” arrows).
Describing illusions using a set of parameters aims at fostering
reproducible science, allowing neuroscientists to easily report,
generate and manipulate similar stimuli regardless of the display format
and software.
Installation
pip install https://github.com/RealityBending/Pyllusion/zipball/master

You can also find the installation instructions for Python 3
here.
Contribution
You have some ideas? Want to improve things, add new illusions, and help
us shake people’s brain? Let us know, we would be very happy to have you
on board :relaxed:.
Share Your Data
If you have used Pyllusion in your experiments, and have made your
scripts and data open access, feel free to also reference the link to
your data by opening an
issue with the
Experiment Report template.
Citation
You can cite the package as follows:

Makowski, D., Lau, Z. J., Pham, T., Paul Boyce, W., & Annabel Chen, S. H. (2021). A Parametric Framework to Generate Visual Illusions Using Python. Perception, 50(11), 950-965.

Features
Delboeuf Illusion
The Delboeuf
illusion is an
optical illusion of relative size perception, where circles of identical
size appear as different because of their surrounding context. The
illusion was named for the Belgian philosopher, mathematician,
experimental psychologist, hypnotist, and psychophysicist Joseph Remi
Leopold Delboeuf (1831–1896), who created it in 1865.
import pyllusion

delboeuf = pyllusion.Delboeuf(illusion_strength=3)
delboeuf.to_image()


Ebbinghaus Illusion
The Ebbinghaus
illusion is an
optical illusion of relative size perception, where circles of identical
size appear as different because of their surrounding context (the right
red circle appears as smaller because its surrounding circle is larger).
The illusion was named after its creator the German psychologist Hermann
Ebbinghaus (1850–1909), though it got popularized by Edward B. Titchener
in a 1901 textbook of experimental psychology.
The Ebbinghaus illusion is considered a high-level integration illusion
(King et al., 2017) which has been considered relatively unaffected
amongst schizophrenics (as compared to healthy controls), who have
problems in contextual processing of visual stimuli. Specifically,
greater disorganized schizophrenia symptoms are related to a higher
resistance towards the Ebbinghaus illusion (Uhlhaas et al., 2006).
Reduced sensitivity of schizophrenics to this illusion has been used to
reflect how prior knowledge influences perceptual organization to a
lesser extent, i.e., reduced top-down influence, in schizophrenia
(Silverstein & Keane, 2011).
ebbinghaus = pyllusion.Ebbinghaus(illusion_strength=2)
ebbinghaus.to_image()


Müller-Lyer Illusion
The Müller-Lyer
illusion is
an optical illusion causing the participant to perceive two segments as
being of different length depending on the shape of the arrows. The
illusion was named after its creator the erman sociologist Franz Carl
Müller-Lyer (1857–1916) in 1889.
The Müller-Lyer illusion is a high-level integration illusion requiring
contextual processing by the brain (King et al., 2017). The effect of
this illusion in schizophrenics appears to be more mixed, with some
studies finding greater resistance to it (e.g., Parnas et al., 2001) and
others showing increased susceptibility (e.g., Kantrowitz et al., 2009).
There is some evidence that susceptibility to the Müller-Lyer illusion
is negatively correlated with autistic traits in the typical population
(but not with the Ebbinghaus nor the Ponzo illusion) (Chouinard et al.,
2013).
mullerlyer = pyllusion.MullerLyer(illusion_strength=30)
mullerlyer.to_image()


Ponzo Illusion
The Ponzo illusion
is an optical illusion of relative size perception, where horizontal
lines of identical size appear as different because of their surrounding
context (the top line appear as longer, as it is interepreted as being
in the distance). The illusion was named after its creator the Italian
psychologist Mario Ponzo (1882–1960) in 1911, who suggested that the
human mind judges an object’s size based on its background.
Ponzo illusion is considered a high-level integration illusion as it is
cognitively demanding in the sense that depth cues have to be correctly
interpreted to signal changes in visual distance (King et al., 2017),
requiring higher-level cortical processes (Song et al., 2011). Similar
to the Ebbinghaus illusion, it is also shown to have less effect in
biasing perception in schizophrenic subjects (Kantrowitz et al., 2009).
ponzo = pyllusion.Ponzo(illusion_strength=20)
ponzo.to_image()


Vertical–horizontal Illusion
The vertical–horizontal
illusion
illustrates the tendency for observers to overestimate the length of a
vertical line relative to a horizontal line of the same length (Shipley
et al., 1949).
verticalhorizontal = pyllusion.VerticalHorizontal(illusion_strength=-90)
verticalhorizontal.to_image()


Zöllner Illusion
The Zöllner
illusion is an
optical illusion, where horizontal lines are perceived as not parallel
because of their background. It is named after its discoverer, the
German astrophysicist Johann Karl Friedrich Zöllner in 1860.
zollner = pyllusion.Zollner(illusion_strength=75)
zollner.to_image()


Rod and Frame Illusion
The Rod and frame
illusion
is an optical illusion causing the participant to perceive the rod to be
oriented congruent with the orientation of the frame.
rodframe = pyllusion.RodFrame(illusion_strength=-11)
rodframe.to_image()


Poggendorff Illusion
The Poggendorff
illusion is an
optical illusion that involves the misperception of the position of one
segment of a transverse line that has been interrupted by the contour of
an intervening structure. It is named after Johann Christian Poggendorff
who discovered in Zöllner’s illusion after 1860. The magnitude of the
illusion depends on the properties of the obscuring pattern and the
nature of its borders.
poggendorff = pyllusion.Poggendorff(illusion_strength=-50)
poggendorff.to_image()


Simultaneous Contrast illusion
A neutral gray target will appear lighter or darker than it does in
isolation when compared to, respectively, a dark gray or light gray
target. Simultaneous
contrast, identified
by Michel Eugène Chevreul, refers to the manner in which the colors of
two different objects affect each other.
In the image here, the two inner rectangles are exactly the same shade
of grey, but the upper one appears to be a lighter grey than the lower
one due to the background provided by the outer rectangles.
contrast = pyllusion.Contrast(illusion_strength=-50)
contrast.to_image()


White Illusion
White’s illusion
is a brightness illusion in which rectangles of the same grey color are
perceived of different luminance depending on their background.
white = pyllusion.White(illusion_strength=100)
white.to_image()






Kanizsa Square
The Kanizsa Square is an illusory
contour illusion. See
Keane et
al., 2019.
Some studies have tested the effect of the Kanizsa Square in individuals
with schizophrenia, but the finding of greater resistance to the
illusion is not robust (King et al., 2017).


TO DO (consider helping!)

Autostereograms
Autostereograms are
images made of a pattern that is horizontally repeated (with slight
variations) which, when watched with the appropriate focus, will
generate an illusion of depth.
For instance, in the image below, the autostereogram automatically
adds a guide (you can disable it by setting guide=False), the two red
dots. Look at them and relax your eyes until you see a new red dot
between them two. Then, try focusing on this new red dot until it gets
very sharp and until your eyes stabilize. You should then be able to
perceive the letters 3D as carved in the figure
It can take a bit of time to “get there”, but once you are used to it,
it’s a mind-blowing experience 🤯
autostereograms = pyllusion.Autostereogram(stimulus="3D", width=1600, height=900)
autostereograms.draw()






The function is highly customisable, and we can use a black and white
image as a depth mask (in this case, the picture of a
skull
that you will see as emerging from the background), and customise the
pattern used by providing another function (here, the image_circles()
function to which we can provide additional arguments like blackwhite,
the number of circles n, their size range and their transparency with
alpha).

autostereograms = pyllusion.Autostereogram(stimulus="docs/img/depthmask.png",
pattern=pyllusion.image_circles,
color="blackwhite",
alpha=0.75,
size_min=0.005,
size_max=0.03,
n=1000)
autostereograms.draw()












Pareidolia
Pareidolia is the tendency to incorrectly perceive of a stimulus as an
object pattern or meaning known to the observer. Liu et
al. (2014),
in their study “Seeing Jesus in toast”, famously (the study got
awarded an Ignobel prize) investigated the correlates of face pareidolia
by blending images of faces with noise-like images.
Blending of images can be achieved: as followed
pareidolia = pyllusion.Pareidolia(pattern="docs/img/snake.png",
n=[20, 300, 4000],
sd=[4, 2, 1],
weight=[3, 2, 1],
alpha=80,
blur=0.5)
pareidolia.draw()


Transparency From Motion (TFM)
In visual perception, the kinetic depth
effect refers to
the phenomenon whereby the three-dimensional structural form of an
object can be perceived when the object is moving (Wallach & O’Connell,
1953; Mamassian &
Wallace, 2010).
One of its derivative is the Transparency-From-Motion illusion,
consisting in the superposition of two dot clouds moving in different
directions that results in the perception of two transparent layers (See
;
Schütz, 2014;
Wexler et
al., 2015;
Schütz &
Mamassian, 2016
and http://lab-perception.org/demo/p/tfm for a demo).
parameters = pyllusion.motiontransparency_parameters(angle=45)
images = pyllusion.motiontransparency_images(parameters)

pyllusion.images_to_gif(images, path="Transparency_From_Motion.gif", fps=parameters["FPS"])




Pinna illusion
See also Zeljic et
al., 2021.

TO DO (consider helping!)


Monnier-Shevell illusion
See also David Novick’s tweets
here,
and
here.

TO DO (consider helping!)






PsychoPy Integration
Pyllusion can be easily integrated into
PsychoPy for running experiments as well!
# Load packages
import pyllusion
from psychopy import visual, event

# Create parameters
delboeuf = pyllusion.Delboeuf(illusion_strength=1, difference=2)

# Initiate Window
window = visual.Window(size=[1920, 1080], winType='pygame',
color='white', fullscr=False)

# Display illusion
delboeuf.to_psychopy(window)

# Refresh and close window
window.flip()
event.waitKeys() # Press any key to close
window.close()


References
Bertamini, M. (2017). Programming visual illusions for
everyone.
Springer.
Chouinard, P. A., Noulty, W. A., Sperandio, I., & Landry, O. (2013).
Global processing during the Müller-Lyer illusion is distinctively
affected by the degree of autistic traits in the typical population.
Experimental Brain Research, 230(2), 219–231.
Kantrowitz, J. T., Butler, P. D., Schecter, I., Silipo, G., & Javitt, D.
C. (2009). Seeing the world dimly: The impact of early visual deficits
on visual experience in schizophrenia. Schizophrenia Bulletin, 35(6),
1085–1094. doi:10.1093/schbul/sbp100
King, D. J., Hodgekins, J., Chouinard, P. A., Chouinard, V. A., &
Sperandio, I. (2017). A review of abnormalities in the perception of
visual illusions in schizophrenia. Psychonomic bulletin & review, 24(3),
734-751.
Parnas, J., Vianin, P., Saebye, D., Jansson, L., Volmer-Larsen, a, &
Bovet, P. (2001). Visual binding abilities in the initial and advanced
stages of schizophrenia. Acta Psychiatrica Scandinavica, 103(3),
171–180. doi:10.1034/j.1600-0447.2001.00160.x
Silverstein, S. M., & Keane, B. P. (2011). Perceptual organization
impairment in schizophrenia and associated brain mechanisms: Review of
research from 2005 to 2010. Schizophrenia Bulletin, 37(4), 690–699.
doi:10.1093/schbul/sbr052
Song, C., Schwarzkopf, D. S., & Rees, G. (2011). Interocular induction
of illusory size perception. BMC Neuroscience 27, 12(1).
Uhlhaas, P. J., Phillips, W. A., Schenkel, L. S., & Silverstein, S. M.
(2006b). Theory of mind and perceptual context‐processing in
schizophrenia. Cognitive Neuropsychiatry, 11(4), 416–436.