| Volume 1, Number 2, Article 7, Pages 137-144 |
doi:10.1167/1.2.7 |
http://journalofvision.org/1/2/7/ |
ISSN 1534-7362 |
The symmetry magnification function varies with detection task
Christopher W. Tyler |
Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA |
|
Abstract
Detection of the presence of bilateral symmetry was investigated at various retinal eccentricities for static and dynamic noise reflected around a vertical axis . At a low detection criterion (60% correct), peak duration sensitivities were high and varied little (<0.2 log units) from 0º eccentricity to 10º eccentricity for either static or dynamic targets. Duration thresholds for symmetry in dynamic noise fields were significantly higher (about 100 ms) than those for static symmetry detection (about 40 ms), despite the fact that the information was refreshed many times during the threshold presentation period. The spatial summation width for symmetry processing was evaluated with randomization around the axis of symmetry. The estimated summation width for static symmetry detection was approximately constant with eccentricity for short duration stimuli. For long duration stimuli, the summation width was substantially greater in central vision but decreased with eccentricity, the first known visual function to exhibit such reverse magnification behavior (Tyler, 1999). These findings suggest that static and dynamic symmetry detection are supported by different neural mechanisms and that these mechanisms are relatively invariant across the retina, unlike known mechanisms of spatial processing.
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History
Received May 8, 2001; published December 5, 2001
Citation
Tyler, C. W. (2001). The symmetry magnification function varies with detection task.
Journal of Vision, 1(2):7, 137-144,
http://journalofvision.org/1/2/7/,
doi:10.1167/1.2.7.
Keywords
symmetry, detection, eccentricity, peripheral, cortical magnification
for related articles by these authors
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Cortical magnification is the area of cortex that
represents a unit area of retina; the variation of magnification with
eccentricity is expressed by the magnification function, but each visual
representation area could have a different magnification function. The
parameters of the magnification function for a particular task are therefore
potentially diagnostic of the cortical area whose properties define the limits
of the performance. This concept provides a bridge between anatomy and
psychophysical performance. If two tasks exhibit different magnification
properties (other than simple scaling), the implication is that their limits are
set by different cortical projection geometries (after control of optical
factors). Perhaps other explanations are possible, but the presence of
magnification properties would generate a differential hypothesis to motivate
corresponding anatomical or physiological studies.
The defining property of the magnification function for
a particular task is the change in minimum angle of resolution for a particular
task with distance from the fovea. To a first approximation, many magnification
functions can be fitted with a linear function of resolution angle
A versus
eccentricity
E
of the
form
in degrees, where
m is the slope of the line (which is
always positive) and
E2
is the intercept, which is always negative for real resolution and positive
m.
Levi, Klein, and Aitsebaomo (1985) pointed
out that the characteristic value for such linear magnification is the
constant
E2 because
m just plays the
role of a scaling parameter. For our purposes, note that
E2
is so called because it has the property that angle
A doubles when the
stimulus reaches that eccentricity, i.e., when
E =
E2.
However, magnification functions often deviate from
linearity, and can be well-fitted by an exponential function of the
form  ,
which may be seen to be of similar form to the linear
approximation by taking the logarithm of both
sides:  .
In these functions,
μ is the logarithmic slope and
Elog
2 is the eccentricity at which log
A increases by
0.3.
Reflection symmetry is a visual property of relevance
to humans because other humans and most animals have pronounced bilateral
symmetry, while inanimate objects in the natural environment typically do not
have obvious symmetry ( Julesz, 1971;
Barlow & Reeves, 1979;
Tyler, 1996a). There has been extensive
interest in the mechanisms of human symmetry processing
( Wagemans, 1995;
Tyler, 1996b). In previous studies, we
have investigated the detectability of static and dynamic symmetry around a
central axis across varying distances of separation of the symmetric regions by
a noise mask. The function of separation represents the summation range over
which symmetry can be perceived, and its width provides a measure of the
symmetry summation width.
Tyler, Hardage, and Miller (1995) found
that detection of static targets was possible for widths up to 6 arcmin for
unscaled targets and with up to 64º of separation by a blank mask for
eccentricity-scaled targets
( Tyler & Hardage, 1996). Symmetry
detection in dynamic noise showed a much narrower summation width with a noise
mask ( Tyler et al., 1995). In a further
exploration of unscaled noise, it was found that symmetry detection did not show
the expected decline with eccentricity, either in sensitivity or summation width
( Tyler, 1999). In the current study, we
provide more detailed analysis of the eccentricity properties and compare the
eccentricity functions for static and dynamic reflection symmetry, again using
unscaled noise.
Previous studies have reported reduced detectability
for static symmetry when the axis is placed in eccentric vision
( Julesz, 1971;
Corballis & Roldan, 1974;
Saarinen, 1988;
Herbert & Humphrey, 1993;
Saarinen, Rovamo, & Virsu, 1989;
Barrett, Whitaker, McGraw, & Herbert, 1999).
Most of these studies found, by various methods, that sensitivity declined with
eccentricity by various methods, but the declines reported were surprisingly
gradual if symmetry detection is considered as a position-based task.
Corballis and Roldan (1974), for
example, found little difference between symmetry detectability at 0º and
at about 6º eccentric. We therefore wished to determine the peripheral
scaling properties for symmetry detection in terms of both peak sensitivity and
spatial tuning range. Rather than presupposing an eccentricity scaling for the
noise elements, as did previous authors, the noise here consisted of unscaled
binary random elements of fixed size, allowing us to determine the scaling
empirically.
The stimuli were generated on a Macintosh IIfx with a
Motorola 68040 CPU and were presented on a noninterlaced monochrome display with
a frame rate of 67 Hz, subtending 23.5° wide by 17.6° high at a
viewing distance of 57 cm. Patterns consisted of 307,200 black and white pixels
at a dot density of 100%, with a random placement bilaterally reflected about a
vertical axis. The dots subtended 2.2 arcmin in diameter. In the symmetric
test patterns ( Figure 1a and 1b), all points
greater than a specified distance about the designated axis were
mirror-reflected, providing for a gap of random dots in the central strip
separating the halves of the symmetry pattern (no gap in
Figure 1a; 40 pixels in
Figure 1b). In the asymmetric null patterns
( Figure 1c), every pixel was colored either
black or white at random throughout the image. Lighting was ambient
fluorescent, with the screen hooded to reduce
glare. Figure 1.
Examples of test stimuli with a red fixation mark at the most peripheral
placement (10° eccentric at the 57 cm viewing distance). a. Fully
symmetric pattern. b. A new symmetric pattern but with the central 40 pixels
replaced with random dots to obliterate the central axis cue. c. Random null
pattern.
As in the precursor study
( Tyler, 1999), the independent variables
were width of the random gap and distance of the axis of symmetry from fixation.
The vertical axis of symmetry was located in the center of the monitor, and a
red fixation point was placed at 2º, 5º or 10º to the right of
fixation. Measurements for a range of durations, from 30 ms to 1800 ms, were
taken by the method of constant stimuli with duration increments of about 0.25
octaves to the nearest 15 ms chosen to span a range of performance levels from
chance (50% correct) to 100% correct. Each trial consisted of a brief (<300
ms) period of dynamic random fields with frame durations of 15 ms, followed by
the symmetric or asymmetric test period, followed by another similar period of
random fields. The noise in the test period was either invariant for the test
duration (static) or continually refreshed (dynamic) at the same rate as the
pre- and posttest periods. The pre- and posttest noise presentations were
designed to eliminate masking by luminance onset and offset effects, but to mask
any luminance afterimages following the presentation of static patterns. Tests
consisted of blocks of 50 trials for each duration and eccentricity condition,
and observers were instructed to identify any symmetry in the pattern with a
“yes” response and asymmetry (random appearance) with a
“no” response.
Tyler et al., (1995) showed that criterion
bias effects were minimal in this paradigm.
Four observers participated in the experiments.
Observer L.H. was experienced in psychophysical testing; A.I. and C.A. were
naive observers. L.H. was functionally monocular (with essentially no vision in
the right eye); the other two observers used uncorrected (normal) binocular
vision. All were given practice to become familiar with the detection task and
the position and orientation of the symmetry axis to be detected in each block
of
trials.
Duration psychometric functions of percent correct as a
function of test duration were measured for a range of different widths of
occluding noise separating the symmetric halves of the patterns, constraining
the symmetry to separations varying from 0 to 3° away from its reflection
axis. This procedure was followed at eccentricities of 2, 5 and
10 º of the symmetry axis
relative to fixation. Sample psychometric functions are provided in
Figure 2 to illustrate the importance of the
criterion percent correct level. As described by
Tyler et al. (1995), duration psychometric
functions for symmetry detection do not have a uniform slope but often exhibit a
steep portion for low detection levels, followed by a sharp corner into an
extended plateau region, where increasing duration affords no improvement in
detection probability. Beyond this plateau region, performance again rises
rapidly (although this rise is not strongly evident in the data of
Figure 2). This figure is provided to
illustrate the justification for the separate analyses at high and low criterion
levels for the peripheral data in the present
study. Figure 2.
Examples of duration psychometric functions for static symmetry detection as a
function of eccentricity (different line colors) and noise gap (different
symbols; gap increasing to right) on a log duration abscissa for observer L.H.
Foveal - turquoise, 2º - azure, 5º - yellow, 10º - magenta.
These examples are shown to illustrate the qualitative properties of such
functions. Note that the functions all have a steep slope up through the 60%
level. Beyond this level, functions with zero or small gaps (leftmost curves)
continue steeply upwards, while those for large gaps tend to level out at values
around 70% to 90% for up to a log unit increase in duration before sensitivity
improves again.
A quantitative evaluation of the properties of such
psychometric functions was provided in
Tyler et al. (1995). It should be
emphasized that this plateau behavior does not reflect simply the presence of
fast and slow mechanisms of symmetry processing. If the stimuli were
homogeneous, the fast process would always be able to carry performance to 100%
before the slow process took effect. Instead, the behavior must reflect an
inhomogeneity in the population of random symmetry patterns, such that some
(about one half of the population of samples where the plateau is at 75%) are
inaccessible to the fast process and must wait for the slow process to operate
before being detectable at all. At all eccentricities and noise gap widths,
some of the patterns had symmetry detectable in less than 100 ms. For noise gap
widths greater than a few pixels, most of the remaining patterns required
durations of 2 sec or more for the symmetry to be detectable. The jump from ~50
ms to ~2 sec is puzzling in relation to known pattern processing mechanisms,
because one might expect that the bilateral symmetry patterns could be processed
with two attentional fixations. The typical attentional processing rate is
similar to the saccadic refixation rate of 3 per sec
( Loftus, Duncan, & Gehrig, 1992;
Duncan, Ward, & Shapiro, 1994),
implying that one should be able to detect the symmetry in a presentation time
of about 800 ms. The need for a further second or two to reach 100% performance
remains hard to explain.
Duration psychometric functions such as those in
Figure 2 were analyzed at the criterion
level of 60% correct in Tyler (1999) to
reveal a reverse eccentricity scaling property for the width of the early
detection region. This result is seen in the replot of
Figure 3a, where the foveal noise-masking
function shows a broad skirt at the lowest sensitivity levels, while the
peripheral functions do not have this feature.
Figure 3. Log duration
sensitivities plotted for static symmetry at the eccentricity of the edges of
the noise strips as a function of noise width and axis eccentricity. The same
sensitivities apply at both edges of the noise strips, as indicated by the line
reflection of each data set about each symmetry axis. a. Data at the 60%
criterion on the psychometric function, replotted from
Tyler (1999) on a linear sensitivity
ordinate. b. Width tunings for the 80% (filled symbols) and 90% (open symbols)
criteria, plotted in the same format as panel “a.” Median standard
error was 0.1 log units. Note the gradual decline in peak sensitivity with
eccentricity for all three functions and the widening of the width-tuning
functions with eccentricity (except in the long-duration skirts).
The present paper provides a comparative analysis of
the scaling properties of the upper regions of the static symmetry detection
functions with those for symmetry in dynamic noise
( Jenkins, 1983).
Figure 3b shows the performance of the same
observer who was shown in Figure 3a at
criterion levels of 80% and 90% correct. Each datum point represents the
outcome of 100 to 150 trials, which translates into a mean variation of about
0.1 of any given sensitivity level. The focus of the present paper is the
variation of peak duration sensitivity for symmetry with eccentricity. It can
be seen in Figure 3a that the peak
sensitivity declines gradually with eccentricity, but that the decline is only
about a factor of 2 by 10º, in contrast to the factor of about 14 expected
for other position-discrimination tasks such as Vernier acuity
( Levi et al., 1985). This behavior is
considered further in “Quantitative Analysis.”
As expected from
( Tyler, 1999), sensitivities were highest
at each eccentricity when symmetry pattern halves were not separated by a random
strip. At the high criterion levels of
Figure 3b, the measured tunings widths for
symmetry detection are narrower than they are at the low criterion level of
Figure 3a. With increasing noise
separations, duration sensitivities decreased rapidly toward the measurement
limit of 0.056 (1.8 sec). The summation
widths at half height of these functions
( Figure 4) were also relatively stable for
all observers across eccentricity. The mean summation width for the three
observers at the eccentricities where all three were tested did not vary
significantly (p > 0.1). Thus, the implication is that the symmetry structure
in the most detectable patterns often extends a substantial distance from the
symmetry axis. At the more demanding 90% level, the defining property is
restricted to elements within about ±0.5º of the symmetry
axis. Figure 4.
Sensitivity summation widths at half height (±0.3 log units down from
peak), although different among observers, remain approximately constant for
detection of symmetry in static noise out to 10° eccentricity.
Figure 5. Example of
sensitivity summation behavior for detection of symmetry in dynamic fields at
the 60% detection level out to 10° eccentricity. Median standard error was
0.1 log units. Note minimal fall off in peak sensitivity.
For symmetry detection in dynamic fields
( Figure 5), the width tunings across
eccentricity at a threshold criterion of 60% resemble those for static symmetry
at this high percent correct ( Figure 3)
except that they tend to increase in width. Duration sensitivities show little
variation in peak sensitivity for all three observers from 0º to 10º
in the periphery. Half-height summation widths tended to broaden with
increasing eccentricity ( Figure 6), doubling
from the very tight value of 0.15º with central fixation to about 0.3º
at 10º eccentricity (increase significant at
p < 0.05). There was no evidence
for a secondary skirt to the function similar to that observed for static
symmetry detection at 0º eccentricity and >1.5º noise gap (as found
at the 60% level in Tyler et al., 1995).
Figure 6.
Sensitivity summation widths for detection of symmetry in dynamic noise, plotted
in the same format as in Figure 5, are
narrow in central view but increase substantially by 10° eccentricity.
Symbols indicate three different observers
The main point of interest in this study was to
evaluate the change in sensitivity for the two types of symmetry detection task
as a function of eccentricity. For conventional linear plots of duration
thresholds against eccentricity, the data fell on curved trajectories, but
switching to log duration versus linear eccentricity captured the curvature in
the data within experimental error. These plots for sensitivity as a function
of eccentricity derived from data such as those in
Figures 3 and
5 are shown in
Figure 7 (upper panels).
Figure 7.
Eccentricity functions for dynamic and static symmetry detection at the 60% and
90% detection levels for three observers. The peak duration thresholds are
plotted on a logarithmic ordinate on which the functions show no significant
curvature. Note that the 60% functions are almost flat, while both sets of 90%
functions increase substantially with eccentricity.
A second set of analyses was conducted for the
criterion of 90% correct on the duration psychometric functions
( Figure 7, lower panels). It may be seen
that they fall on straight lines similar to those of the upper panels in
Figure 7 but with higher duration thresholds
and steeper log slopes in the eccentricity functions. The equation defining
this behavior
is
The data imply that duration sensitivities bear a
logarithmic relationship to effective contrast sensitivity (as would be expected
if they were governed by an exponential decay process) and perform the
eccentricity analysis in these log-linear coordinates. In symbolic form, such a
process would be defined
by
and
 .
Although this approach to the eccentricity functions
captures the eccentricity variation in a simple manner, it complicates the issue
of the doubling eccentricity for these tasks, because the power functions are
accelerating with eccentricity. Nevertheless, we can readily derive the
eccentricity for which the power fits would generate a doubling of sensitivity
(Elog2),
just as for the linear fits. The negative intercepts are very shallow in
comparison to those for grating
acuity. Table
1 . Mean Magnification Function Parameters for the Four
Test Conditions
|
Mean Y Intercept (ms)
|
Mean Slope
(dl/deg)
|
Mean
r2
|
Doubling Eccentricity
(Elog2)
|
|
Static 60%
|
39 ± 2
|
0.11 (± 0.012)
|
0.80 (± 0.07)
|
41.8º (-21.6º)
|
|
Dynamic 60%
|
77 ± 6
|
0.18 (± 0.009)
|
0.97 (± 0.03)
|
30º (-10.1º)
|
|
Static 90%
|
62 ± 6
|
0.65 (± 0.035)
|
0.95 (± 0.04)
|
8.1º (-2.7º)
|
|
Dynamic 90%
|
129 ± 16
|
0.30 (± 0.004)
|
0.97 (± 0.02)
|
21.3º (-2.9º)
|
Note that the error terms on the
Elog2
values are given only in the decreasing direction because in the increasing
direction the error could exceed infinity for the shallowest slopes. The
decreasing direction is the one needed to distinguish the values from those for
acuity.
Summary statistics for the mean behavior of the three
observers are provided in Table 1and plotted
in Figure 7. These fits reveal that there
is a consistent decrease of about a factor of two in the detection threshold
(that is, at 0º noise gap) for static symmetry detection relative to
dynamic symmetry. Summation widths for static symmetry detection are larger
than those for dynamic symmetry detection for each observer out to 5º, but
by 10º, the respective summation widths converge. The slopes for the 60%
cases are not significantly different from zero, while those for 90% case are
significantly steeper ( p <
0.05).
The
Elog2
values of eccentricity at which duration sensitivity is doubled range from a
high of 42º for the static symmetry at low sensitivity (60% correct) to a
low of 8º for static symmetry at high sensitivity (90% correct), with the
dynamic symmetry values falling between these extremes.
Expected
E2s
for contrast detection tasks are in the range of 3º to 5º
( Levi et al., 1985), so those for the
symmetry sensitivities are all significantly flatter than predicted (taking the
criterion of 2 σ, p < 0.05) if
this task was mediated by the local ganglion receptive field mechanisms
implicated in contrast detection (Virsu & Rovamo,
1979).
Previous studies reviewed in the
“Introduction” may be divided into two classes: those that assumed
some eccentricity magnification function for their stimulus variation and found
that symmetry detection was approximately equated by this manipulation, and
those that assumed no magnification and found relatively minor changes in
performance with eccentricity. Those that assumed a magnification do not provide
strong support for this assumed scaling if symmetry processing is invariant with
spatial frequency, because any assumed scaling would provide equal sensitivity.
Taken together, these results suggest that symmetry processing is surprisingly
robust to eccentric presentation, which provides the motivation for the
finer-grained analysis of the present work. Given the apparent dissociation
between processes mediating the low and high performance levels on the duration
psychometric function
( Tyler et al., 1995), eccentricity
functions are evaluated for four diverse aspects of symmetry processing: 60%
dynamic, 90% dynamic, 60% static, and 90% static performance.
The results show that observers were able to perform
low-performance static and dynamic symmetry detection with approximately equal
sensitivity from fixation to 10º in the periphery (a range that spans about
half the extent of the cortical projection to area V1;
Horton & Hoyt, 1991). This is a
remarkable result considering that symmetry detection is essentially a
position-mediated task in which the positions of the dots on either side of the
symmetry axis must be compared for successful performance. Such position tasks
typically have a very steep eccentricity scaling function
( Levi et al., 1985) characterized by a
negative intercept of about −0.6º. Similarly,
Tyler and Hardage (1996) found that the
duration sensitivity for symmetry detection in band-limited noise fell according
to the -0.6º intercept. Other investigators using contrast or noise
correlation thresholds, such as
Saarinen (1988),
Herbert and Humphrey (1993), and
Barrett et al. (1999), have found the
fall off with eccentricity to match the magnification function for luminance
detection.
It may seem that the exponential fits of
Figure 6 (straight lines on log-linear
coordinates) would be liable to exaggerate the size of the negative intercepts
due to the concave curvature of the analytic functions through the negative
eccentricity range. The evident straightening of the curvature in the explicit
curvature range is the chief argument for the exponential construct, but it has
plausibility for the present temporal sensitivity analysis on the assumption
that the signal effectiveness decays exponentially with increasing duration.
However, even if a linear extrapolation were assumed,
the negative intercepts are still well outside the range of traditional acuity
tasks. Linear extrapolations are generated by extrapolating the tangential
slope at zero eccentricity to the zero sensitivity level (= infinite stimulus
duration), the tangential slope being the lowest point at which the function is
empirically defined. These linear-extrapolation negative intercepts range from
−7.1º to −38.8º, evenly split between being larger and
being smaller than the exponential extrapolations. So it is clear that the
exponential assumption has not generated an overestimation of the negative
intercepts. Even when ignoring the curved portion of the functions and focusing
on the lowest levels by linear extroplation, the negative intercepts are truly
much greater than those for grating acuity (and, a fortiori, for positional
acuity tasks), regardless of the form of analysis.
It is therefore incontestable that different symmetry
detection tasks evince different eccentricity scaling functions, ranging from
almost flat to extremely steep. This diversity may be taken as evidence that the
neural processing limiting symmetry perception in the various situations is
operating at different levels of the cortical processing hierarchy. Tasks
matching the luminance-detection scaling slope may be limited by the
signal/noise ratio in the primary projection cortex. Tasks showing flat
functions (as in the present study at the 60% correct level) or negative scaling
(as in the width functions of Tyler, 1999)
may be limited by the operation of a specialized symmetry detection mechanism
that is constrained by factors other than the noise level of the input from
primary cortex.
The present unscaled stimuli were made broadband in
both energy spectrum and retinal location in order to allow the visual system to
use any available mechanisms for symmetry detection. More colloquially, this
study provides insight into the detectability of preferred symmetric designs
such as Persian rugs. The data imply that duration sensitivities bear a
logarithmic relationship to effective contrast sensitivity (as would be expected
if they were governed by an exponential decay process) and validate the
eccentricity analysis in these log-linear
coordinates.
Barlow, H. B., & Reeves,
B. C. (1979). The versatility and absolute efficiency of detecting mirror
symmetry in random dot displays. Vision
Research, 19, 783-793.
[PubMed]
Barrett, B. T., Whitaker,
D., McGraw, P. V., & Herbert, A. M. (1999). Discriminating mirror symmetry
in foveal and extra-foveal vision. Vision
Research, 39, 3737-3744.
[PubMed]
Corballis, M. C., &
Roldan, C. E. (1974). On the perception of symmetrical and repeated patterns.
Perception
&
Psychophysics,
16, 136-142.
Duncan, J., Ward, R., &
Shapiro, K. (1994). Direct measurement of attentional dwell time in human
vision. Nature, 369, 313-315.
[PubMed]
Gurnsey, R., Herbert, A.
M., & Kenemy, J. (1998). Bilateral symmetry embedded in noise is detected
accurately only at fixation. Vision Research,
38, 3795-3803.
[PubMed]
Herbert, A. M., &
Humphrey, G. (1993). Bilateral symmetry detection: Testing a 'callosal'
hypothesis. Perception, 25,
463-480.
Horton, J. C., & Hoyt,
W. F. (1991). The representation of the visual field in human striate cortex: A
revision of the classic Holmes map. Archives
of Ophthalmology, 109, 816-824.
[PubMed]
Jenkins, B. (1983). Spatial
limits to the detection of transpositional symmetry in dynamic dot textures.
Journal
of
Experimental Psychology. Human
Perception and Performance, 9, 258-269.
[PubMed]
Julesz, B. (1971).
Foundations of Cyclopean Perception.
Chicago: University of Chicago Press.
Levi, D. M., Klein, S. A.,
& Aitsebaomo, A. P. (1985). Vernier acuity, crowding and cortical
magnification.
Vision
Research, 25, 963-978.
[PubMed]
Loftus, G. R., Duncan, J.,
& Gehrig, P. (1992). On the time course of perceptual information that
results from a brief visual presentation.
Journal of Experimental Psychology. Human
Perception and Performance, 18,
530-549.
[PubMed]
Saarinen, J. (1988).
Detection of mirror symmetry in random dot patterns at different eccentricities.
Vision Research, 28, 755-759.
[PubMed]
Saarinen, J, Rovamo, J.,
& Virsu, V. (1989). Analysis of spatial structure in eccentric vision.
Investigative
Ophthalmology and Vision Science, 30,
293-296.
Tyler,
C. W. (1996a). Human symmetry perception. In C. W. Tyler (Ed.),
Human Symmetry Perception and
its
Computational Analysis (pp.
157-172). Zeist, The Netherlands: VSP BV.
Tyler, C. W. (1996b). Ed.,
Human Symmetry Perception and its
Computational Analysis (pp. 157-172). Zeist, The Netherlands: VSP
BV.
Tyler, C. W. (1999). Human
symmetry detection exhibits reverse eccentricity scaling.
Visual Neuroscience, 16, 919-922.
[PubMed]
Tyler, C. W., & Hardage, L. K. (1996). Mirror
symmetry detection: Predominance of second-order pattern processing throughout
the visual field. In C. W. Tyler (Ed.), Human
Symmetry Perception and its Computational Analysis (pp. 157-172). Zeist,
The Netherlands: VSP BV.
Tyler, C. W., Hardage, L.,
& Miller, R. (1995). Multiple mechanisms in the detection of mirror
symmetry. Spatial
Vision,
9, 79-100.
[PubMed]
Virsu, V., & Rovamo, J. (1979). Visual resolution,
contrast sensitivity, and the cortical magnification factor.
Exp Brain Res, 37, 475-494.
Wagemans, J. (1995).
Detection of visual symmetries. Spatial
Vision, 9, 9-32.
[PubMed]
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