Perceptual Learning: Cortical
Changes When Cats Learn
a New Trick
A new study has found that the tuning properties of neurons in the primary
visual cortex of cats change as they learn an orientation-discrimination task,
casting new light on the neuronal basis of perceptual learning.
Yuka Sasaki1,2, Joshua Gold3,
and Takeo Watanabe4
Performance on perceptual tasks
improves with training. Sustained
improvements that reflect better
processing of incoming sensory
information are called perceptual
learning. Because perceptual learning
is a robust phenomenon even in adults
who have long passed the well-known
early ‘critical period’ of cortical
plasticity, understanding its neural
basis should lead to a better
understanding of neural plasticity
in the adult brain . In a recent
report in Current Biology, Hua and
colleagues  provide new insights
into a possible role for plasticity at
the level of the primary visual cortex
in cats for a form of visual perceptual
Visual information is processed in
multiple stages, from the retina through
subcortical and cortical visual areas
to higher-order cortical areas that use
the visual information to guide
behavior. The issue of which brain
area(s) undergoes the changes
responsible for perceptual learning
has been controversial. In one view,
which we call the ‘V1 hypothesis’ ,
perceptual learning is associated
with changes in the primary visual
cortex (V1), the earliest cortical area
ontowhich visual signalsare projected.
Psychophysical studies have shown
that some forms of perceptual learning
are highly specific for features of
the training stimuli, such as their
orientation, motion direction, contrast,
location and even the eye-of-origin.
Such high degrees of specificity are
consistent with some of the properties
of neurons in V1, so the V1 hypothesis
assumes that perceptual learning
is associated with changes in the
sensory representation in V1.
There are, however, other
hypotheses that posit that perceptual
learning involves changes further along
the visual processing pathway. For
example, according to the read-out
hypothesis [4,5], changes in V1 either
do not occur or are not sufficient
to yield better performance in
a perceptual task. Instead, perceptual
learning is associated with changes
in connectivity between the sensory
representation of the visual stimulus
(possibly limited to the features
experienced during training) and
a higher decision-making unit.
Which hypothesis do
neurophysiological studies support?
The results of many functional
magnetic resonance imaging (fMRI)
studies with human subjects are in
accord with the V1 hypothesis. In
particular, fMRI signals in the region
of V1 representing the trained location
are changed in association with
perceptual learning [6,7]. However,
single unit-recording studies in
monkeys have provided inconsistent
results. For example, Schoups et al. 
found that orientation tuning
curves of monkey V1 neurons
changed as a result of training on an
orientation identification task. Using
a similar — but not identical — method,
Ghose et al.  found no evidence for
found neural activity changes in area
V4 of monkeys after training on an
orientation-discrimination task ,
or in the lateral intraparietal area (LIP)
after training on a motion direction-
discrimination task , but neither
study reported changes in V1.
In contrast to these previous studies
in monkeys, Hua et al.  found
substantial changes in V1 of cats
in association with perceptual
learning. The cats were trained
on an orientation-discrimination
task. Their ability to discriminate
low-contrast stimuli improved
with training, with the biggest
improvements centered on the
ranges of spatial frequencies used
during training. These improvements
in performance were accompanied
by a refinement of the tuning
properties of V1 neurons responding
specifically to or around the spatial
frequency of the trained orientation
stimulus. The results provide
compelling evidence for changes
in V1 neurons in association with
perceptual learning. What can and
what cannot be inferred from the
results? A number of questions and
answers come to mind.
Might the differences between the
various studies, mentioned above, be
due to differences in what is measured
by fMRI versus single-neuron
recordings? BOLD signal measured
by fMRI is thought to correlate more
highly with field potentials in neuronal
activity, which can be driven strongly
by synaptic potentials, than with
the action potentials measured by
single-neuron recording .
Therefore, it is possible that fMRI
activation in V1 primarily reflects
not the spiking output of V1, but
rather its input, which arises from
both the ascending visual pathway
and direct and indirect projections
from higher cortical areas. Accordingly,
fMRI activation in V1 [6,7,12] in
association with perceptual learning
might arise from changes in the
higher-order structures that provide
it with input. However, the results
of Hua et al.  suggest that this
explanation is at best incomplete,
because they found changes in the
output activity of V1 neurons.
Do different species have different
mechanisms of perceptual learning?
We do not yet know the answer
to this question, but a survey of
prior results suggests that this is a
possibility. Evidence for learning-
related changes in V1 come from
studies of cats and humans, whereas
most of the evidence against this
hypothesis comes from studies with
monkeys. Further studies are needed
to clarify this issue.
Do different procedures lead to
different types of neuronal changes?
The answer is highly likely to be ‘yes’.
To study perceptual learning, different
groups often use experimental
procedures that differ in at least one
of the following four ways. First, there
are three different types of single-task
procedures used in perceptual learning
studies: detection of (the presence of)
a feature ; discrimination between
features ; and mere exposure to
a feature that is irrelevant to a given
task . Furthermore, some recent
studies have involved training on two
to imagine that exactly the same neural
circuit is recruited for these different
Second, perceptual learning has
been studied for tasks involving
different visual features, including
orientation, motion direction, color,
contrast, spatial and temporal
frequency and vernier acuity.
These different features are processed
in different ways and, at least in part,
at different sites in the brain.
feature are used, the procedures can
vary. For example, the procedures
for an orientation-identification task
used by Hua et al. , Schoups et al. 
and Ghose et al.  are substantially
different. In the study by Hua et al. ,
a cat was presented with two large
visual patches, each of which
contained a different orientation
(Figure 1). The task was to
indicate which patch contained
a pre-determined orientation.
After a correct response, the contrast
of the patches decreased for the next
trial. In the study by Schoups et al. ,
a monkey was presented with a single
patch that was much smaller than
that used by Hua et al. . The task
was to judge whether the orientation
of the patch was either clockwise or
counterclockwise compared to
a predetermined reference orientation.
The key experimental variable was
the angular difference between the
presented and reference orientations,
instead of stimulus contrast as in Hua
et al. . In the study by Ghose et al. ,
the monkeys were required to judge
whether the orientations of two serially
presented patches were identical.
Fourth, task difficulty varies across
that different degrees of task difficulty,
and possibly the associated
differences in attentional demands,
lead to changes in different cortical
Thus, given the many different
conditions that have been used in
studies of perceptual learning, it is
perhaps not so surprising that such
different results have been obtained.
Indeed, it seems likely that the neural
locus of perceptual learning is not
confined to a single site in the brain.
Rather, it is important to continue
to have studies like the work of
Hua et al.  that carefully characterize
the conditions that give rise to
a particular form of plasticity, in V1
1. Sasaki, Y., Nanez, J.E., and Watanabe, T.
(2010). Advances in visual perceptual
learning and plasticity. Nat. Rev. Neurosci.
2. Hua, T., Bao, P., Huang, C., Wang, Z., Xu, J.,
and Lu, Z.L. (2010). Perceptual learning
improves contrast sensitivity of V1 neurons in
cats. Curr. Biol. 20, 887–894.
3. Karni, A., and Sagi, D. (1991). Where practice
makes perfect in texture discrimination:
evidence for primary visual cortex plasticity.
Proc. Natl. Acad. Sci. USA 88, 4966–4970.
4. Dosher, B.A., and Lu, Z.L. (1998). Perceptual
learning reflects external noise filtering and
internal noise reduction through channel
reweighting. Proc. Natl. Acad. Sci. USA
5. Law, C.T., and Gold, J.I. (2008). Neural
correlates of perceptual learning in
a sensory-motor, but not a sensory,
cortical area. Nat. Neurosci. 11, 505–513.
6. Furmanski, C.S., Schluppeck, D., and
Engel, S.A. (2004). Learning strengthens
the response of primary visual cortex to
simple patterns. Curr. Biol. 14, 573–578.
7. Yotsumoto, Y., Sasaki, Y., Chan, P.,
Vasios, C.E., Bonmassar, G., Ito, N.,
Nanez, J.E., Sr., Shimojo, S., and Watanabe, T.
(2009). Location-specific cortical activation
changes during sleep after training for
perceptual learning. Curr. Biol. 19,
8. Schoups, A., Vogels, R., Qian, N., and Orban, G.
(2001). Practising orientation identification
improves orientation coding in V1 neurons.
Nature 412, 549–553.
9. Ghose, G.M., Yang, T., and Maunsell, J.H.
(2002). Physiological correlates of perceptual
learning in monkey V1 and V2. J. Neurophysiol.
10. Yang, T., and Maunsell, J.H. (2004). The effect
of perceptual learning on neuronal responses
in monkey visual area V4. J. Neurosci. 24,
11. Logothetis, N.K., Pauls, J., Augath, M.,
Trinath, T., and Oeltermann, A. (2001).
Neurophysiological investigation of the basis
of the fMRI signal. Nature 412, 150–157.
12. Schwartz, S., Maquet, P., and Frith, C. (2002).
Neural correlates of perceptual learning:
a functional MRI study of visual texture
discrimination. Proc. Natl. Acad. Sci. USA 99,
13. Huang, X., Lu, H., Tjan, B.S., Zhou, Y., and
Liu, Z. (2007). Motion perceptual learning:
when only task-relevant information is learned.
J. Vis. 7, 1–10.
14. Watanabe, T., Nanez, J.E., and Sasaki, Y.
(2001). Perceptual learning without perception.
Nature 413, 844–848.
15. Xiao, L.Q., Zhang, J.Y., Wang, R., Klein, S.A.,
Levi, D.M., and Yu, C. (2008). Complete transfer
of perceptual learning across retinal locations
enabled by double training. Curr. Biol. 18,
16. Kourtzi, Z., Betts, L.R., Sarkheil, P., and
Welchman, A.E. (2005). Distributed neural
plasticity for shape learning in the human
visual cortex. PLoS Biol. 3, e204.
17. Ahissar, M., and Hochstein, S. (1997).
Task difficulty and the specificity of
perceptual learning. Nature 387, 401–406.
1Athinoula A. Martinos Center for Biomedical
Imaging, Department of Radiology,
Massachusetts General Hospital,
Charlestown, MA, USA.2Department of
Radiology, Harvard Medical School, Boston,
MA, USA.3Department of Neuroscience,
University of Pennsylvania, Philadelphia,
PA, USA.4Department of Psychology,
Boston University, Boston, MA, USA.
Figure 1. The perceptual learning experiment of Hua et al. .
In training, a cat had to choose the one stimulus that contained the same orientation as the
pre-determined orientation. Upon a ‘correct’ response, the luminance contrast of the pre-
sented stimuli decreased.
Current Biology Vol 20 No 13