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Masking by fast gratings
Abstract
Perception of an oriented pattern is impaired in the presence of a superimposed orthogonal mask. This masking effect most likely arises in visual cortex, where neuronal responses are suppressed by masks having a broad range of orientations. Response suppression is commonly ascribed to lateral inhibition between cortical neurons. Recent physiological results, however, have cast doubt on this view: powerful suppression has been observed with masks drifting too rapidly to elicit much of a response in cortex. We show here that the same is true for perceptual masking. From contrast discrimination thresholds, we estimated the cortical response to drifting patterns of various frequencies, and found it greatly reduced above 15-20 Hz. In the same subjects, we measured the strength of masking by the same patterns and found it equally strong for masks drifting slowly (2.7 Hz) as for masks drifting rapidly (27-38 Hz). Fast gratings thus cause strong masking while eliciting weak cortical responses. Our results might be explained by inhibition from cortical neurons that respond to unusually high frequencies, and yet do not make their signals fully available for perceptual judgments. A more parsimonious explanation, however, is that masking does not involve lateral inhibition from cortex. Masking might operate in retina or thalamus, which respond to much higher frequencies than cortex. Masking might also be due to thalamic signals to cortex, perhaps through depression at thalamocortical synapses.
History
Received February 7, 2002; published July 12, 2002
Citation
Meier, L. & Carandini, M. (2002). Masking by fast gratings.
Journal of Vision, 2(4):2, 293-301,
Keywords
orientation, contrast, suppression, gain control, inhibition, thalamus, cortex
Contrast discrimination of oriented patterns relies on
neurons in visual cortex. The response of these neurons can be estimated from
perceptual discrimination thresholds by ascribing these thresholds to fixed
increments in neural response. This method originates with the studies of
Fechner (1860), and has been recently
shown to estimate correctly the overall responses of neurons in the visual
cortex during a contrast discrimination task
(Boynton, Demb, Glover, & Heeger, 1999).
Indeed, responses in visual cortex correlate with perceptual decisions on a
trial-by-trial basis
(Ress, Heeger, & Nadell, 2001).
Superimposing an orthogonal mask on an oriented test
pattern causes both physiological and perceptual effects. Physiologically, the
mask suppresses neuronal responses to the test in the visual cortex of
anesthetized cats
(Morrone, Burr, & Maffei, 1982;
Bonds, 1989) and monkeys
(Carandini, Heeger, & Movshon, 1997).
This suppression can also be seen in EEG signals evoked in awake humans
(Burr & Morrone, 1987;
Ross & Speed, 1991;
Candy, Skoczenski, & Norcia, 2001).
Perceptually, the mask impairs the detection of the test, and worsens contrast
discrimination for a wide range of test contrasts
(Legge & Foley, 1980;
Ross, Speed, & Morgan, 1993;
Foley, 1994). Perceptual masking can be
easily understood in terms of physiological suppression. Suppression shifts the
curve relating neural response to contrast rightward, so that higher contrasts
are needed to achieve a given response
(Bonds, 1989;
Heeger, 1992;
Carandini et al., 1997). This effect
increases the contrast needed for detection, and shifts to higher contrasts the
region where the neurons are most sensitive to changes in contrast
(Legge & Foley, 1980;
Foley, 1994).
How does the mask reduce the effective contrast of the
test? The common interpretation is that the cortical neurons involved receive
lateral inhibition from other cortical neurons selective for the orthogonal
orientation. From the initial proposals (e.g.,
Blakemore, Carpenter, & Georgeson, 1970)
to current computational models (e.g.,
Heeger, 1992;
Foley, 1994;
Adini, Sagi, & Tsodyks, 1997;
Watson & Solomon, 1997;
Itti, Koch, & Braun, 1999), lateral
inhibition between cortical neurons has become the dominant theory.
Recent findings on suppression, however, have cast
doubts on the lateral inhibition explanation. If suppression originated from
cortical neurons, it would share the properties of those neurons. At least in
the cat, however, this does not seem to be the case. First, most neurons in cat
visual cortex respond to stimuli from both eyes
(Hubel & Wiesel, 1962), whereas
suppression is largely monocular
(DeAngelis, Robson, Ohzawa, & Freeman, 1992).
Dichoptic effects have been observed but appear to operate much more slowly
(Sengpiel, Baddeley, Freeman, Harrad, & Blakemore, 1998).
Second, visual adaptation strongly reduces the responses of cat cortical neurons
(Albrecht, Farrar, & Hamilton, 1984),
whereas it
Figure
1. Examples of stimuli in the two experiments. In real
experiments, the patches were more distant. Left. A stimulus in the first
experiment. Right. A stimulus in the second experiment. In both examples, the
increment contrast is on the left side.
does not affect the strength of suppression
(Freeman, Durand, Kiper, & Carandini, in press).
Third, neurons in cat visual cortex barely respond to stimuli drifting more
rapidly than 10-15 Hz
(Movshon, Thompson, & Tolhurst, 1978;
Saul & Humphrey, 1992), whereas
suppression is strong even with mask gratings drifting at rates in excess of 20
Hz (Freeman et al., in press).
Given that lateral inhibition might not explain
physiological suppression, we wondered whether it explains perceptual masking.
We followed the above argument based on drift rate, and asked whether there are
patterns that drift too rapidly to elicit much of a response in cortex but do
cause strong masking. From contrast discrimination thresholds, we estimated the
cortical response to stimuli drifting at various drift rates. We then examined
the masking caused by those same stimuli. We knew that stimuli drifting rapidly
would elicit small responses when presented alone. Would they cause masking?
We measured thresholds for contrast discrimination of a
vertical test in the absence and in the presence of a horizontal mask. Test and
mask were the product of a drifting sinusoidal grating and a Gaussian window.
Subjects performed a 2-alternative spatial forced-choice between stimuli
appearing 8 deg to the left and to the right of a fixation mark. Stimuli were
observed monocularly from a chin rest placed 80 cm away. The standard deviation
of the Gaussian window was 0.5 deg, spatial frequency was 1.5 cycles/deg, and
duration was 375 ms.
The first experiment
(Figure 1, left) involved simple
contrast discrimination of vertical patterns. The subject was presented two test
stimuli, one on the left and one on the right, each with the same pedestal
contrast (0, 1, 2, 4, 8, 16, or 32%). On one side an increment contrast was
added, and the subject reported its location by pressing one of two keys. This
measurement was repeated at the following test drift rates: 2.7, 13, 27, 38, and
54 Hz.
The second experiment
(Figure 1, right) measured the strength
of masking by horizontal masks drifting at various rates. Test stimuli were
superimposed to a mask of 30% contrast. This measurement was repeated at the
following mask drift rates: 2.7, 13, 27, 38, and 54 Hz. Test drift rate was
fixed at the lowest value explored in the previous experiment, 2.7 Hz. As a
control, this experiment included the condition in which the mask was absent.
The two experiments thus shared the control measurement of contrast
discrimination thresholds for a vertical 2.7-Hz test stimulus in the absence of
a mask.
Increment contrast was determined by a staircase
procedure (QUEST,
Watson & Pelli, 1983), which aimed
for the contrast yielding 75% correct performance, and was given 30 trials to
converge. We then fitted the percentage of correct answers with a Weibull
psychometric function using the maximum likelihood method
(Watson, 1979), and thus estimated the
threshold contrast corresponding to 75% correct.
Experiments took 25-35 min and were each repeated 6-8
times after learning had stabilized. To reduce the effects of involuntary
saccades, test stimuli moved in opposite directions, which were randomized from
trial to trial. To minimize the effects of adaptation, we also randomized speed,
direction, and pedestal contrast. Two subjects participated, one of the authors
(L.M.) and a paid naïve observer (S.G.).
Stimuli were generated by the Psychophysics Toolbox
(Brainard, 1997; Pelli, 1997) and
presented on a calibrated 21” CRT (Sony Multiscan G500, mean luminance 37
cd/m2) driven by a graphics board with a refresh rate of 159 Hz
(MacPicasso 850; VillageTronic, Sarstedt, Germany). Except for the 38- and 54-Hz
stimuli, we doubled the resolution of our gray scale from 256 (8 bit) to 512 by
interleaving frames. When interleaving, test stimuli with pedestal and increment
contrast alternated, whereas the mask was in every frame. With this method, the
smallest increment contrast possible was about 0.5%.
Stimuli of 54 Hz were mostly invisible, except for
brief flashes. These flashes were most likely artefactual, due to involuntary
microsaccades matching the direction of one of the stimuli
(Riggs, Armington, & Ratliff, 1954).
Microsaccades would improve a subject’s chances to guess the right
response, leading to an overestimated neural response. While subject L.M.
reported sporadic flashes, subject S.G. reported seeing the flashes in all 54-Hz
test gratings. We thus did not attempt to measure response to 54-Hz stimuli in
this subject, and simply imposed them to be zero.
To estimate the neural response from discrimination
thresholds, we followed the methods of
Boynton et al. (1999). We took the
cumulative response of those neurons that respond to the test to depend on test
contrast c as
follows:
This function has
parameters m,
n, σ, and
K, and is illustrated in
Figure 2B. We made the classical
assumption (Fechner, 1860;
Legge & Foley, 1980;
Boynton et al., 1999;
Gorea & Sagi, 2001) that subjects can
discriminate between contrasts c and
c+T only if these contrasts elicit
responses that differ by at least
ΔR:
 Figure
2. Estimation of neural response and effects of masking.
A. Contrast discrimination thresholds for a vertical test pattern. Error bars
represent ± 1 standard error (N =
18). Curve is fit of the model; dotted lines indicate one standard error of the
fits. B. Neural response estimated from discrimination thresholds. Horizontal
and vertical lines illustrate the estimation method: at 2.5% pedestal contrast,
the contrast discrimination threshold T
that corresponds to ΔR = 1 in
neural response is 0.75%. This value is reported on the ordinate of A. C.
Effects of masking by a superimposed horizontal mask (N = 6). Red lines are
copied from A to facilitate comparison. D. Estimated neural responses. Red lines
are copied from B to facilitate comparison. The arrow indicates the effect of
masking on the estimated contrast responses. Subject L.M., 2.7-Hz stimuli.
This expression implicitly defines a function
T(c), which we fitted to our threshold
measurements. The result of one of such fits is illustrated in
Figure 2A. We searched for parameters
K,
σ,
m, and
n to specify the function
R(c), and numerically solved Equation 2
to find the best least square fits of
T(c) to the data. Because the units of
response are arbitrary, we chose ΔR = 1. To reduce the number of free
parameters, we also fixed m to a single
value for each subject (m = 3.55 for
L.M., m = 3.00 for S.G., obtained from
fits of data with 2.7-Hz test alone). Error bars represent
± 1 standard error (commonly based on
6-8 measurements). Confidence intervals around fitted curves are
± 1 standard error of the fits, which
were repeated independently for each set of measurements.
To estimate the strength of masking, we quantify the
reduction in effective test contrast caused by the mask. We compute the degree
to which the mask shifts the estimated neural responses to the right in the
logarithmic contrast axis (Figure 2D).
We find the lateral shift s by
minimizing the area between
R30(sc)
and
R0(c),
where
R0
and
R30 are the
responses in the absence of a mask and in the presence of a 30% contrast mask. A
value of s = 2, for example, means that
masking has doubled the test contrast needed to obtain a given response. A value
of s = 1, instead, means that
masking had no effect.
Our entire data set is illustrated in supplementary
figures. Data and fits of T(c) for
measurements of threshold contrast (as in
Figure 2A and 2C) are available both
for subject L.M. and
for subject S.G. The corresponding
response versus contrast curves (as in
Figure 2B and 2D) are available as
well, both for subject L.M. and
for subject S.G. The latter figures also
illustrate the lateral shift estimated in each masking condition. The extent of
this lateral shift is represented by the length of the horizontal bars shown in
each panel of the right
column.
Our first experiment measured contrast discrimination
thresholds for vertical drifting stimuli. As expected, the relation between
threshold increment contrast and pedestal contrast
(Figure 2A)
shows the familiar “dipper” shape, with increment contrast
thresholds being lowest when the pedestal contrast is around 2%, the contrast
needed for detection
(Nachmias & Sansbury, 1974). Also
as expected, these data are well fitted by the predictions of a simple model
based on a detector with saturating neural response
(Legge & Foley, 1980). This neural
response (Figure 2B) is estimated from
contrast discrimination thresholds by assuming that the subject is at threshold
when the neural response increases by a given amount. Increment contrast
thresholds are thus lowest where the neural response function is steepest.
Consider now the estimated neural response to test
stimuli drifting at different rates
(Figure 3A). In line with previous
studies (e.g., Robson, 1966;
Kelly, 1979;
Watson, 1986;
Georgeson, 1987), in both subjects the
estimated neural response is strong for drift rates of 2.7 Hz and 13 Hz,
substantially weaker for drift rates of 27 Hz and 38 Hz, and negligible for a
drift rate of 54
Hz. Figure
3. Estimated neural response to test patterns alone and
in the presence of an orthogonal mask. A. Responses to test alone, drifting at
five different rates. B. Responses to the 2.7 test alone (black) and in the
presence of masks drifting at five rates. Top and bottom panels correspond to
subjects L.M. and S.G. Confidence intervals are omitted to avoid clutter. An
example of their size is in
Figure 2.
Our second experiment measured the degree of masking
caused by a horizontal mask. Masking had powerful effects
(Figure 2C): except for a limited range
of pedestal test contrasts, the mask substantially increased the increment test
contrast required for discrimination. The mask impaired detection at the lowest
test contrasts and impaired discrimination for a broad range of test contrasts
(Legge & Foley, 1980;
Ross et al., 1993;
Foley, 1994).
The effect of the mask on the estimated neural response
to the test stimulus (Figure 2D) is
largely a rightward shift: the mask increased the test contrast needed to obtain
a given neural response. Because the scale in the abscissa is logarithmic, a
rightward shift indicates a divisive effect. Just as with physiological
suppression, masking divides the effective contrast seen by the neural mechanism
(Heeger, 1992;
Foley, 1994;
Watson & Solomon, 1997).
We now ask our main question: Do masks drifting rapidly
cause the same masking as masks drifting slowly? The answer is affirmative for
masks drifting as fast as 27-38 Hz. This effect can be seen by comparing the
response to the test without a mask with those measured in the presence of masks
drifting at different rates
(Figure 3B). In both subjects, the
response suppression caused by masks drifting at 13 and 27 Hz was strong,
similar to that caused by stimuli at 2.7 Hz. Masks drifting at 54 Hz, instead,
caused essentially no masking.
This behavior can be quantified by plotting suppression
strength as a function of mask drift rate
(Figure 4B). We measure suppression
strength by the reduction in effective test contrast (see
“Methods”). The latter is the degree to which the mask shifts the
estimated neural responses to the right in a logarithmic contrast axis
(Figure 2D). A value of 2 means that
the mask has divided by 2 the test contrast seen by the neural mechanism.
Equivalently, it means that the mask has doubled the test contrast needed to
obtain a given neural response. Plotting reduction in effective test contrast
versus mask drift rate (Figure 4B)
confirms the qualitative impression that whether its drift rate is 2.7, 13, 27,
or 38 Hz, a drifting mask causes a substantial amount of suppression.
By comparison, we have seen that the neural responses
elicited by the mask are reduced above 13 Hz. This dependence can be observed by
plotting the estimated neural responses at 30% contrast (the mask contrast) as a
function of drift rate (Figure 4A). The
slower stimuli (2.7 Hz and 13 Hz) generate an approximately equally strong
response, whereas the 27-Hz and 38-Hz stimuli elicit a response that is about
half as strong.  Figure
4. Dependence on stimulus drift rate of estimated neural
responses and of suppression strength. Rows are for two subjects, L.M. (top) and
S.G. (bottom). A. Estimated neural response to test at 30% contrast, as a
function of test drift rate. Data are taken from
Figure 3A. Curves are fits by a
descriptive function. B. Strength of suppression caused by masks of different
drift rates. Suppression is measured as an increase in test contrast needed to
obtain a given response (see “Methods”), estimated from
Figure 3B. Continuous curves are fits
by a descriptive function. Dashed curves are taken from A and rescaled to fit
suppression caused by a 2.7-Hz mask. C. Reduction in response to the 2.7-Hz test
caused by the mask, as a function of mask drift rate. Data are taken from
Figure 3B.
The tuning curves for responses and for suppression are
rather different. If one rescales the tuning curve of the responses to account
for suppression with the slowest stimuli
(Figure 4B, dashed lines), this curve
underestimates suppression caused by a 27-Hz mask. If one instead rescales to
fit suppression caused by a 27-Hz mask, one overestimates suppression caused by
slower masks (not shown). The curve fitted to the responses cannot fit the
suppression data because neural responses to a pattern drifting at 27 Hz are
half as strong as those elicited by slower patterns
(Figure 4A), whereas suppression caused
by such a fast pattern is as strong as (or stronger than) that elicited by
slower patterns (Figure 4B). Finally,
the fitted curves allow a rough estimate of the high frequency cutoff drift
rates, where the curves reach half of their maximal value. This cutoff drift
rate lies around 32 Hz for the neural response (33 Hz for L.M. and 32 Hz for
S.G.), and is higher, around 43 Hz, for suppression (41 Hz for L.M., 46 Hz for
S.G.).
A similar conclusion can be drawn if one measures
masking by the decrease in estimated neural response to the test. In addition to
shifting rightward the curves relating response to test contrast, masking
slightly alters the shape of these curves
(Figure 3B). Therefore, one might want
to measure suppression in additional ways, for example, by measuring the
vertical shift rather than the horizontal shift. The results of such a
measurement are illustrated in
Figure 4C, where we plot the estimated
neural response to a 30% contrast, 2.7 Hz test in the presence of a mask, as a
function of mask drift rate. Except for the 54 Hz mask, all masks reduced the
responses. As for the reduction in effective contrast, the curve relating
response and frequency clearly underestimates the reduction in response caused
by fast masks.
To summarize, there is a substantial difference between
the neural responses elicited by drifting stimuli and the masking caused by
these stimuli. Fast gratings cause strong masking while eliciting weak cortical
responses.
We have found patterns that do not elicit much of a
response in cortex but do cause strong masking. We estimated neural responses
using a classic method, based on discrimination performance
(Fechner, 1860;
Boynton et al., 1999;
Gorea & Sagi, 2001). We found that
27-38-Hz stimuli elicit substantially smaller neural responses than slower
stimuli. In the same subjects, we found that 27-Hz or even 38-Hz stimuli can be
as strong in causing masking as slower stimuli.
These results agree well with physiological
measurements of suppression in visual cortex. In visual cortex of anesthetized
cats, responses vanish at frequencies above 10-15 Hz
(Movshon et al., 1978;
Saul & Humphrey, 1992), and yet
suppression is strong even with masks drifting at rates in excess of 20 Hz
(Freeman et al., in press). Similarly,
in EEG signals from human visual cortex, response suppression has been observed
with masks having frequencies as high as 30 Hz
(Burr & Morrone, 1987).
Our results are also consistent with previous
psychophysical studies. Studies of perceptual responses to stimuli of different
frequency have established that sensitivity declines rapidly above 10-20 Hz
(e.g., Robson, 1966;
Kelly, 1979;
Watson, 1986;
Georgeson, 1987). Studies of masking
between stimuli with different temporal characteristics have revealed masking at
higher mask frequencies, between 16 and 30 Hz
(Anderson & Burr, 1985;
Lehky, 1985;
Burr, Ross, & Morrone, 1986;
Hess & Snowden, 1992;
Boynton & Foley, 1999).
Earlier studies of masking differ from ours in a number
of ways. First, rather than concentrating on the measurement of thresholds and
other psychophysical quantities, we estimate the underlying neural responses.
Second, rather than relying on explicit models, we have measured suppression
simply by its effect on the estimated neural responses to the test. In
particular, we do not assume that test and mask are seen by the same channels
(as in Legge & Foley, 1980;
Anderson & Burr, 1985;
Lehky, 1985;
Burr et al., 1986;
Hess & Snowden, 1992) or that masking
operates through lateral inhibition (as in
Foley, 1994;
Watson & Solomon, 1997;
Boynton & Foley, 1999).
Rather, we measure suppression from the shift in contrast responses, as we do in
our physiological work
(Freeman et al., 2002). Third, we have
estimated neural responses to each drifting grating in the presence and in the
absence of the mask in the same subjects, so we can compare the two
directly.
Still, our study resembles earlier ones, and in
particular that of
Boynton and Foley (1999). These
authors obtained threshold data such as those in
Figure 2C for different combinations of
test and mask frequency. They fitted these data with a model involving an
excitatory mechanism and a divisive inhibitory mechanism
(Foley, 1994). The frequency tuning of the
inhibitory mechanism was found to be much broader than that of the excitatory
mechanism, and to extend beyond 20 Hz. This result is in agreement with our
findings, which were obtained using slightly different stimuli. Indeed, in our
study (1) test and mask were orthogonal; (2) stimuli were drifting; and (3)
except for orientation and (in general) frequency, mask and test had same visual
attributes. Our analysis differs from that of the earlier study as well: (1) for
each subject we estimated neural responses to the mask alone, to the test alone,
and to the test plus mask; (2) we observed the effects of masking directly on
the estimated neural responses; and (3) we compared the frequency tuning of
masking to that observed for the observer as a whole.
One limitation of our study is the use of a single test
drift rate (2.7 Hz). There might be something arbitrary about comparing the
visual system's sensitivity to gratings of various frequencies to how various
frequencies affect the response to a single frequency. We chose the low value of
2.7 Hz to be conservative, as one might suspect that the responses to faster
tests might be reduced by even faster masks than those we found. Indeed, there
are indications that if we had repeated our measurements with a test of
different drift rate, we still would have found masking by fast gratings.
Burr and colleagues (1986) measured the
elevation in detection threshold caused by masks of various frequencies; The
cut-offs at high frequency appear similar (20-30 Hz) whether the test drifted at
0.3 Hz or at 8 Hz (their Figure 4, c, and d). Likewise,
Boynton and Foley (1999) reported
similar frequency tuning of their divisive inhibition whether tests flickered at
1 Hz or at 10 Hz (their Figure 9). While these studies employed masks that were
parallel to the test, there is little reason to believe that changes in
estimated neural response to the test would depend on mask orientation.
One possible interpretation of our results is that
suppression originates from lateral inhibition in cortex, and is preferentially
caused by neurons selective for high drift rates. The pool of inhibitory neurons
would respond well to high frequencies, while for some reason not being as
available for contrast perception as neurons responding to lower frequencies.
This interpretation, however, does not explain our physiological observations in
cat visual cortex
(Freeman et al., in press); Here, no
neurons have their preferred frequency above 20 Hz, and only a tiny minority of
neurons respond at all to gratings drifting so rapidly. Yet, gratings drifting
faster than 20 Hz are powerful masks, and often cause the same amount of
suppression as masks drifting 10 times slower.
A simpler explanation is that masking is not due to
lateral signals from the cortex, but rather to feedforward signals from the
lateral geniculate nucleus (LGN). Indeed, both in cat (e.g.,
Saul & Humphrey, 1990;
1992;
Freeman et al., in press) and in monkey
(Hawken, Shapley, & Grosof, 1996),
LGN responds to stimuli drifting too rapidly to elicit responses in cortex.
Moreover, there is a strong similarity between the tuning for frequency of LGN
responses and of the strength of suppression
(Freeman et al., in press).
But how could a suppressive signal from LGN reach
cortex? After all, direct thalamocortical inhibition is not believed to exist.
One possibility is that suppression operates already at the level of LGN
neurons, or even in the retina. Indeed, responses of LGN neurons are not at all
immune to suppression
(Freeman et al., in press). Suppression
might then be simply inherited by cortex from its afferents. Another possibility
is that signals responsible for suppression are relayed from LGN to cortex, for
example, by the well known mechanism of synaptic depression
(Carandini, Heeger, & Senn, 2002;
Freeman et al., in press). To see how,
consider the simplified case of a V1 cell that receives all its inputs from LGN
afferents, and endow these inputs with synaptic depression. The V1 cell responds
to an optimal grating but not to an orthogonal grating. The individual LGN
neurons, however, are not selective for orientation, so depression at their
synapses is insensitive to stimulus orientation. Both gratings, then, cause an
equally strong synaptic depression. Depression is even stronger in response to
the plaid obtained by summing the two gratings, so the resulting responses in
the V1 neuron are smaller than those to the test alone.
A feedforward model of suppression makes a number of
predictions, which we are setting out to test. First, masking due to thalamic
signals would be reduced when the mask surrounds the test, without overlapping
it. The masking phenomena that do occur in these circumstances
(Polat & Sagi, 1993;
Kapadia, Ito, Gilbert, & Westheimer, 1995;
Zenger & Sagi, 1996;
Adini et al., 1997;
Zenger, Braun, & Koch, 2000) could be
due to intracortical inhibition, and should be reduced when mask drift rate
becomes too high for cortex. Second, masking due to thalamic signals should be
immune to pattern adaptation. This prediction was found correct for
physiological suppression in anesthetized cats
(Freeman et al., in press), and there is
evidence that it might be correct also for perceptual suppression
(Foley & Chen, 1997). Third, masking
due to thalamic signals would be monocular. Masking phenomena that occur
dichoptically (with the test in one eye and the mask in the other) would have to
be due to intracortical mechanisms. Indeed, dichoptic masking might fall into
the category of binocular rivalry, whose effects take quite a long time to
develop
(Blake, 2001).
We have found that fast drifting patterns do not elicit
much of a response in the cortex but do cause strong masking. A similar result
was found recently when measuring suppression in the responses of neurons in
primary visual cortex. One possible interpretation of our results is that
masking originates from lateral inhibition. The inhibitory neurons involved in
it would have to be responsive to very high drift rates, and yet their response
would be somewhat unavailable for contrast
perception. An alternative explanation,
more parsimonious, is that masking is retinal or thalamic. In alternative or in
addition, it might be due to feedforward signals from the thalamus, perhaps
through depression at the very first synapse into the
cortex.
We thank Barbara Zenger-Landolt, Concetta Morrone, and
Journal of Vision reviewers for helpful
comments. This work was supported by the Swiss National Science Foundation.
Commercial Relationships: None.
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