Otto B. GarcÃ­a-Garibay

Victor de Lafuente

Frontiers in Computational Neuroscience

PubMed

Otto Braulio García-Garibay

Victor ede Lafuente

Directory of Open Access Journals

The M&#252;ller-Lyer illusion as seen by an artificial neural network

Frontiers - Publisher Connector

The MÃ¼ller-Lyer illusion as seen by an artificial neural network

GENERAL COMMENTARY
published: 19 February 2015
doi: 10.3389/fncom.2015.00021
The Müller-Lyer illusion as seen by an artificial neural
network
Otto B. García-Garibay and Victor de Lafuente*
Department of Developmental Neurobiology and Neurophysiology, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México
*Correspondence: lafuente@unam.mx
Edited by:
Hava T. Siegelmann, Rutgers University, USA
Reviewed by:
Maurizio Mattia, Istituto Superiore di Sanità, Italy
Keywords: Müller-Lyer, HMAX, visual illusion, visual system, perception
A commentary on
Complex cells decrease errors for
the Müller-Lyer illusion in a model of
the visual ventral stream
by Zeman, A., Obst, O., and Brooks, K.
R. (2014). Front. Comput. Neurosci. 8:112.
doi: 10.3389/fncom.2014.00112
Visual illusions are sensory percepts that
can’t be explained completely from the
observed image but that arise from the
internal workings of the visual system. In
them, we perceive something that is not
physically present in the image, and are
of interest to neuroscientists because they
reveal visual processing that we are not
normally aware of. For example, the simul-
taneous contrast illusion lets us appreciate
that we do not perceive luminance in abso-
lute values and that, instead, the visual
system calculates an object’s luminance in
relation to its surroundings (Figure 1A).
By dissociating our sensory percepts
from the physical characteristics of a
stimulus, visual illusions provide neuro-
scientists with a unique opportunity to
study the neuronal mechanisms underly-
ing our sensory experiences (Eagleman,
2001; Panagiotaropoulos et al., 2012). The
salient percepts that visual illusions create,
along with the fact that they arise from
internal processing, constantly stimulates
researchers to search for the mechanism
and the location within the brain where
illusions originate. However, illusions have
proven as difficult to explain as any other
perceptual phenomena.
The physiological origins of some illu-
sions have been investigated in animals,
some of which are known to perceive
them similarly to humans (Tudusciuc
and Nieder, 2010). This research shows
that perceptual phenomena such as visual
masking, flash suppression, filling-in,
motion-induced depth, and cyclopean
perception (random dot stereograms) are
present in early stages of the visual pro-
cessing in structures such as the thalamus,
and the primary and secondary visual cor-
tices (Carney et al., 1989; Macknik et al.,
2000; von der Heydt et al., 2000; Grinvald
and Hildesheim, 2004; Wilke et al., 2009).
TheMüller-Lyer illusion (MLI) is a sim-
ple and much studied geometrical illusion
that in its classical form consists of two
horizontal line segments that are perceived
to have different lengths depending on
whether they have arrowheads or arrow-
tails at their endpoints (Figures 1B–E).
In an effort to understand the neuronal
mechanisms behind the illusion previous
work by Zeman et al. (2013) demonstrated
that the MLI is present in the multi-
layered artificial network HMAX, which
is a model that incorporates many fea-
tures of the primate visual system (Serre
et al., 2005). The authors first trained
the network to categorize images of short
and long horizontal shafts, presented in
configurations that do not evoke the illu-
sion in humans. After this training they
asked the network to classify the shaft
lengths of images containing the classical
MLI.
The results show that the HMAX net-
work showed a bias in the classification
of the horizontal shafts, classifying the
ones with arrowheads as shorter than actu-
ally were. Interestingly, the magnitude of
the bias was similar to that measured in
humans, and this effect was also modu-
lated by the angle of the fins, with smaller
angles (closer to the horizontal shaft)
producing a larger bias. Importantly, the
authors demonstrated that the final classi-
fication layer, i.e., the layer that categorizes
the images as long or short, does not rely
only on units with high spatial frequen-
cies. This result fails to support the low-
level explanation of the illusion stating
the low-pass characteristics of the center-
surround and simple cells might be the
principal cause of the illusion (Figure 1F).
Furthermore, given that the network was
not trained with natural images, and
that it did not contain information rela-
tive to depth, the high-level “carpentered
world” explanation of the illusion was not
favored either (Figure 1G; Segall et al.,
1963; Ninio, 2014).
The new work of Zeman et al. (2014)
elaborates those previous results by
demonstrating that the magnitude of the
illusion increases after processing by lay-
ers of simple cells, and that it decreases
after processing by layers of complex
cells. The reduction of the illusion by
complex cells suggests that the property
of positional invariance (the ability to
respond to a stimulus despite its spa-
tial location) could make those neurons
less sensitive to the bias induced by the
illusion. These new results indicate that
the magnitude of the MLI might be
represented differently across different
neuronal populations, and that more
abstract representations of the images
might be less sensitive to the illusory
effects.
The mechanisms behind the illusion
are still elusive. As Zeman and col-
leagues show, the low-level explanation,
despite its attractive simplicity, might
not be the complete story. As has been
shown with random dots stereograms and
Frontiers in Computational Neuroscience www.frontiersin.org February 2015 | Volume 9 | Article 21 | 1
COMPUTATIONAL NEUROSCIENCE
García-Garibay and de Lafuente Modelling the Müller-Lyer illusion
FIGURE 1 | (A) In the simultaneous contrast illusion the uniformly gray
central bar appears more luminant at the right, when a dark background
surrounds it. (B) In the classical form of the Müller-Lyer illusion, the
horizontal line with arrowheads looks shorter than the horizontal line with
arrowtails. (C) The illusion is also present without the horizontal lines. (D)
Notice that the illusion is not present when the viewer analyzes local
features, for example, by determining whether the vertices are aligned
vertically. It can be appreciated that the vertices are aligned vertically (B),
even though this perception contradicts the illusory effect of the horizontal
lines having different lengths. (E) The contiguous version of the illusion
(depicted at the bottom) is hidden within a background of lines. The
Müller-Lyer figure appears above the background when both images are fused
by decreasing eye vergence, i.e., as if focusing an object behind the plane of
the image. (F) The low-level explanation states that the illusion arises from
the low-pass properties of center-surround (upper panel) and simple cells
(lower panel) at earlier stages of the visual processing. This hypothesis was
not favored by the results of Zeman and colleagues. (G) The “carpentered
world” explanation states that arrowheads and tails indicate that the lines are
corners at different depths and that the visual system calculates the size of
the lines taking this into account. The red lines have the same length.
other binocular versions of the illusion
(Figure 1E), the MLI can be generated
at a processing level beyond those of
simple center-surround receptive fields,
even in the absence of luminance contrast
(Julesz, 1971). Although the “carpentered
world” hypothesis is not necessary to
explain the illusion, the involvement
of the parietal and occipito-temporal
cortices suggest that it is likely that
higher cognitive processes are involved
(Weidner and Fink, 2007; Mancini et al.,
2011).
The MLI demonstrates that the intu-
itively simple instruction “compare the
length of the two horizontal lines” is not
carried by the visual system as straight-
forwardly as it subjectively feels. It is
clear that the visual system is compar-
ing something else across the drawings,
and it might be related to complete visual
objects, not to local information. When
asked about the size, our visual system
might be judging the size of the com-
plete objects. This can be demonstrated by
focusing our attention into a local feature
of the Müller-Lyer drawing, for example
by trying to determine whether the end-
points of the arrows are aligned vertically
(Figure 1D). It can be appreciated, even in
Figure 1B or Figure 1C, that vertices are
aligned vertically, a perception that indi-
cates the illusion is not present at a local
level.
The MLI illusion is a deceptively simple
perceptual experience that keeps attracting
the attention of neuroscientists. The work
of Zeman and colleagues suggests that two
often cited causes of the illusion, the low
pass filtering properties of visual neurons
and the “carpentered world” hypothesis,
are not needed to generate the illusion
within a primate-like visual system. Future
work will be needed to elucidate the mech-
anisms by which the brain estimates and
compares the size of visually identified
objects.
ACKNOWLEDGMENTS
We recognize the support from the
Dirección General de Asuntos del Personal
Académico de la Universidad Nacional
Autónoma de México and the Consejo
Nacional de Ciencia y Tecnología. We
thank Edgar Bolaños for technical
assistance.
REFERENCES
Carney, T., Paradiso, M. A., and Freeman, R. D.
(1989). A physiological correlate of the Pulfrich
effect in cortical neurons of the cat. Vision
Res. 29, 155–165. doi: 10.1016/0042-6989(89)
90121-1
Eagleman, D. M. (2001). Visual illusions and
neurobiology. Nat. Rev. Neurosci. 2, 920–926. doi:
10.1038/35104092
Grinvald, A., and Hildesheim, R. (2004). VSDI:
a new era in functional imaging of cortical
dynamics. Nat. Rev. Neurosci. 5, 874–885. doi:
10.1038/nrn1536
Julesz, B. (1971). Foundations of Cyclopean Perception.
Oxford: U. Chicago Press.
Macknik, S. L., Martinez-Conde, S., and Haglund,
M. M. (2000). The role of spatiotemporal
edges in visibility and visual masking. Proc.
Natl. Acad. Sci. U.S.A. 97, 7556–7560. doi:
10.1073/pnas.110142097
Mancini, F., Bolognini, N., Bricolo, E., and Vallar, G.
(2011). Cross-modal processing in the occipito-
temporal cortex: a TMS Study of the Müller-Lyer
illusion. J. Cogn. Neurosci. 23, 1987–1997. doi:
10.1162/jocn.2010.21561
Frontiers in Computational Neuroscience www.frontiersin.org February 2015 | Volume 9 | Article 21 | 2
García-Garibay and de Lafuente Modelling the Müller-Lyer illusion
Ninio, J. (2014). Geometrical illusions are not always
where you think they are: a review of some
classical and less classical illusions, and ways to
describe them. Front. Hum. Neurosci. 8:856. doi:
10.3389/fnhum.2014.00856
Panagiotaropoulos, T. I., Deco, G., Kapoor, V., and
Logothetis, N. K. (2012). Neuronal discharges and
gamma oscillations explicitly reflect visual con-
sciousness in the lateral prefrontal cortex. Neuron
74, 924–935. doi: 10.1016/j.neuron.2012.04.013
Segall, M. H., Campbell, D. T., and Herskovits, M.
J. (1963). Cultural differences in the perception
of geometric illusions. Science 139, 769–771. doi:
10.1126/science.139.3556.769
Serre, T., Wolf, L., and Poggio, T. (2005). “Object
recognition with features inspired by visual cor-
tex,” in IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 2005.
CVPR 2005, Vol. 2 (IEEE), 994–1000.
Tudusciuc, O., and Nieder, A. (2010). Comparison
of length judgments and the Müller-Lyer illusion
in monkeys and humans. Exp. Brain Res. 207,
221–231. doi: 10.1007/s00221-010-2452-7
von der Heydt, R., Zhou, H., and Friedman, H. S.
(2000). Representation of stereoscopic edges in
monkey visual cortex. Vision Res. 40, 1955–1967.
doi: 10.1016/S0042-6989(00)00044-4
Weidner, R., and Fink, G. R. (2007). The neural mech-
anisms underlying the Müller-Lyer illusion and
its interaction with visuospatial judgments. Cereb.
Cortex 17, 878–884. doi: 10.1093/cercor/bhk042
Wilke, M., Mueller, K. M., and Leopold, D. A. (2009).
Neural activity in the visual thalamus reflects
perceptual suppression. Proc. Natl. Acad. Sci.
U.S.A. 106, 9465–9470. doi: 10.1073/pnas.09007
14106
Zeman, A., Obst, O., and Brooks, K. R. (2014).
Complex cells decrease errors for the Müller-
Lyer illusion in a model of the visual ven-
tral stream. Front. Comput. Neurosci. 8:112. doi:
10.3389/fncom.2014.00112
Zeman, A., Obst, O., Brooks, K. R., and Rich, A.
N. (2013). The müller-lyer illusion in a compu-
tational model of biological object recognition.
PLoS ONE 8:e56126. doi: 10.1371/journal.pone.00
56126
Conflict of Interest Statement: The authors declare
that the research was conducted in the absence of any
commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 07 November 2014; paper pending published:
17 January 2015; accepted: 04 February 2015; published
online: 19 February 2015.
Citation: García-Garibay OB and de Lafuente V (2015)
The Müller-Lyer illusion as seen by an artificial neural
network. Front. Comput. Neurosci. 9:21. doi: 10.3389/
fncom.2015.00021
This article was submitted to the journal Frontiers in
Computational Neuroscience.
Copyright © 2015 García-Garibay and de Lafuente.
This is an open-access article distributed under the
terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other
forums is permitted, provided the original author(s) or
licensor are credited and that the original publication
in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these
terms.
Frontiers in Computational Neuroscience www.frontiersin.org February 2015 | Volume 9 | Article 21 | 3


http://dx.doi.org/10.3389/fncom.2015.00021

The MÃ¼ller-Lyer illusion as seen by an artificial neural network

Abstract

Similar works

Full text

Available Versions

Directory of Open Access Journals

Frontiers - Publisher Connector

Frontiers - Publisher Connector