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Perceptual learning retunes the perceptual template in foveal orientation identification
Abstract
What is learned during perceptual learning? We address this question by analyzing how perceptual inefficiencies improve over the course of perceptual learning (Dosher & Lu, 1998). Systematic measurements of human performance as a function of both the amount of external noise added to the signal stimulus and the length of training received by the observers enable us to track changes of the characteristics of the perceptual system (e.g., internal noise[s] and efficiency of the perceptual template) as perceptual learning progresses, and, therefore, identifies the mechanism(s) underlying the observed performance improvements. Two different observer models, the linear amplifier model (LAM) and the perceptual template model (PTM), however, have led to two very different theories of learning mechanisms. Here we demonstrate the failure of an LAM-based prediction – that the magnitude of learning-induced threshold reduction in high external noise must be less or equal to that in low external noise. In Experiment 1, perceptual learning of Gabor orientation identification in fovea showed substantial performance improvements only in high external noise but not in zero or low noise. The LAM-based model was “forced” to account for the data with a combination of improved calculation efficiency and (paradoxical) compensatory increases of the equivalent internal noise. Based on the PTM framework, we conclude that perceptual learning in this task involved learning how to better exclude external noise, reflecting retuning of the perceptual template. The data provide the first empirical demonstration of an isolable mechanism of perceptual learning. This learning completely transferred to a different visual scale in a second experiment.
History
Received June 5, 2003; published February 6, 2004
Citation
Lu, Z. & Dosher, B. A. (2004). Perceptual learning retunes the perceptual template in foveal orientation identification.
Journal of Vision, 4(1):5, 44-56,
Keywords
perceptual learning, linear amplifier model, perceptual template model, calculation efficiency, external noise exclusion, stimulus enhancement, internal noise reduction
Perceptual learning — improvements in performance
with training or practice — has been demonstrated in adult human observers
in a wide range of perceptual tasks (Ahissar & Hochstein, 1996; Ball & Sekuler, 1982; Beard, Levi, & Reich, 1995; DeValois, 1977; Dosher & Lu, 1998; Dosher & Lu, 1999; Fahle & Edelman, 1993; Fine & Jacobs, 2000; Fiorentini & Berardi, 1980; Fiorentini & Berardi, 1981; Furmanski & Engel, 2000; Karni & Sagi, 1991; Karni & Sagi, 1993; Mayer, 1983; McKee & Westheimer, 1978; Mollon & Danilova, 1996; Ramachandran & Braddick, 1973; Saarinen & Levi, 1995; Sagi & Tanne, 1994; Shiu & Pashler, 1992; Vogels & Orban, 1985). Most studies on perceptual learning have
investigated transfer or lack of transfer of perceptual learning to modified
forms of the same task or to different, related tasks (Ahissar & Hochstein,
1996; Ahissar & Hochstein, 1997; Ahissar, Laiwand, Kozminsky, &
Hochstein, 1998; Ball & Sekuler, 1987; Berardi & Fiorentini, 1987; Dorais & Sagi, 1997; Fiorentini & Berardi, 1980; Fiorentini & Berardi, 1981; Karni & Sagi, 1993; Liu & Vaina, 1998; Poggio, Fahle, & Edelman, 1992; Ramachandran & Braddick, 1973; Rubenstein & Sagi, 1993; Schoups, Vogels, & Orban, 1995; Shiu & Pashler, 1992). These studies do not directly assess
task-relevant changes to the perceptual system during learning itself; rather,
they assess the generalizability of perceptual learning at the end of training
or practice with important implications for the character and locus of learning.
But, how does the perceptual system change during
perceptual learning? What underlies the improved perceptual performance as a
result of practice or training? First investigated by Saarinen and Levi (1995) in perceptual learning of a Vernier
task, the mechanisms of perceptual learning have been the focus of a number of
recent studies (Chung & Tjan, & Levi, 2001; Dosher & Lu, 1998; Dosher & Lu, 1999; Gold, Bennett, & Sekuler, 1999; Li, Levi & Klein, 2003; Tjan, Chung, & Levi, 2002). Using the external noise approach (Dosher
& Lu, 1998; Lu & Dosher, 1998; Lu & Dosher, 1999), these studies directly evaluate the
mechanisms underlying performance improvements throughout perceptual learning by
analyzing the inefficiencies of the perceptual system over the course of
practice.
Originally developed by electrical engineers in
analyzing noisy amplifiers, the external noise method has become an important
tool widely used to characterize and analyze inefficiencies of the perceptual
system (Ahumada & Watson, 1985;
Burgess, Shaw, & Lubin, 1999; Burgess,
Wagner, Jennings, & Barlow, 1981; Lu
& Dosher, 1999; Nagaraja, 1964; Pelli, 1981; Pelli & Farell, 1999). In a typical application, the threshold
— signal stimulus energy required for an observer to achieve a given
performance level — is measured as a function of the contrast of external
noise (the “TVC” function). The method quantitatively assays
perceptual inefficiencies in terms of equivalent internal noise(s) and
efficiency of the perceptual template. By measuring TVC functions over the
course of perceptual learning, the external noise approach to perceptual
learning tracks changes of the characteristics of the perceptual system (e.g.,
internal noise[s] and efficiency of the perceptual template) as perceptual
learning progresses and therefore identifies the mechanism(s) underlying the
observed performance improvements (Dosher & Lu, 1998, 1999).
In a previous application of the external noise
approach, Dosher and Lu (1998, 1999)
found that perceptual learning improved performance (reduced contrast
thresholds) at all levels of external noise in an orientation identification
task in visual periphery. Detailed statistical analyses suggested that although
performance improvements in zero and high external noise co-occurred, the
magnitudes of these separate improvements were only partially, not perfectly,
coupled. Using a theoretical framework based on the perceptual template model
(PTM) of a human observer (Lu & Dosher, 1999), Dosher and Lu (1998, 1999)
identified a mixture of stimulus enhancement and external noise exclusion (see
below) as the mechanism of perceptual learning (Dosher & Lu, 1998; Dosher & Lu, 1999). The data pattern observed by Dosher and
Lu (1998, 1999), reduction of contrast threshold
throughout an entire range of external noise levels, was later replicated by
Gold et al. (1999) using the same external
noise approach in two different tasks: band-pass noise and novel face
identification. Although the data patterns were identical, Gold et al. (1999) concluded that perceptual learning enhances
processing efficiency only for the signal stimulus, a very different conclusion
from Dosher and Lu (1998, 1999).
The two drastically different theoretical
interpretations of the same data pattern stem from two different models of the
human observer, the linear amplifier model (LAM) in Gold et al. (1999) and the perceptual template model (PTM) in
Dosher and Lu (1998, 1999). Although it has been frequently shown
that LAM is an inadequate observer model for human performance (Burgess &
Colborne, 1988; Chung & Tjan, &
Levi, 2001; Eckstein, Ahumada, & Watson,
1997; Lu & Dosher, 2002a; Lu & Dosher, 1999; Pelli, 1985; Tjan et al., 2002), the LAM-based efficiency-improvement
account of perceptual learning nonetheless has been adopted by some researchers
because (1) it requires less systematic data to specify, and (2) it can provide
an adequate description of TVC functions at a single performance level. In
contrast, although the PTM requires slightly more data to specify, the PTM with
a single set of parameters has been shown to coherently account for human
performance over a wide range of performance levels or the full psychometric
functions (Lu & Dosher, 1999; Lu &
Dosher, 2001); the PTM-based accounts of
performance improvements in perceptual learning provide very strong constraints
on the magnitudes of perceptual learning at multiple performance levels.
The ability of the PTM to account for performance at different criterion performance levels with a single consistent set of parameters is by itself an important advantage. However the choice of model framework – LAM or PTM – also has significant substantial consequences in interpretation of the underlying mechanisms of perceptual learning. Attributing perceptual learning to improved-processing efficiency in a LAM leads to very strong predictive constraints on the relative magnitudes of perceptual learning in high and low external noise levels based on improved calculation efficiency. In contrast, the PTM accommodates independent mechanisms of expressions of perceptual learning in high and low external noise levels.
Detailed theoretical analyses of various external noise
methods, observer models including the LAM and the PTM, and theoretical accounts
of perceptual learning and attention based on these methods and models have been
presented in conferences (Lu & Dosher, 2002a; Lu & Dosher, 2002b) and are in preparation. In this study, we
investigate one theoretical constraint for the LAM-based efficiency account of
perceptual learning to be parsimonious: magnitude of threshold reduction in low
external noise cannot be less than that in high external noise.
We begin by reviewing the LAM, the PTM, and the
associated theoretical framework for interpreting the effects of perceptual
learning in external noise, as well as the empirical literature on the
relationship between learning magnitude and external noise
level.
The LAM (Figure 1a)
models the human observer in analogy to a noisy linear amplifier, consisting of
a noise-free linear amplification with perceptual or calculation efficiency
E, an equivalent
additive internal noise
Neq,
and a decision stage (Ahumada & Watson, 1985; Barlow, 1956; Burgess et al., 1981; Nagaraja, 1964; Pelli, 1981). The concept of perceptual or calculation
efficiency is not well understood; however, it is usually interpreted as a
reflection of the ability of the observer to utilize sensory information. The
equivalent additive noise determines the absolute threshold for the
observer.
For a signal stimulus embedded in Gaussian external
noise with SD
Next,
the LAM predicts that the threshold
c at a given
performance level τ (e.g., 70.7%
correct)
as
Note that the calculation efficiency
Eτ
in Equation 1 depends on the performance level
upon which threshold is defined. Whereas Equation
1 often provides excellent accounts of psychophysical data at a single
performance level in a wide range of perceptual tasks (for a review, see Burgess
et al., 1999), it in general fails to
account for human behavior at multiple performance levels, even with a
reasonable elaboration that relates
Eτ
to the corresponding performance levels (Lu & Dosher, 1999).
Figure 1. Linear
amplifier model (a) and performance signatures of the two mechanisms (b and c)
of perceptual learning and their mixture (d).
Because the LAM consists of two parameters, the
equivalent additive internal noise
(Neq)
and the calculation efficiency
(Eτ),
there are essentially two possible ways perceptual learning can improve the
performance (reducing thresholds) of the model: (1) increasing calculation
efficiency, which results in threshold reduction with equal magnitude (in log)
across the full range of external noise levels (Figure 1b), and/or (2) reducing equivalent internal
noise, which results in threshold reduction restricted in low external noise
conditions (Figure 1c). Therefore, a
“pure” efficiency account of perceptual learning (e.g., Gold et al.,
1999) predicts perceptual improvements with
equal magnitude across all the external noise levels, a prediction rejected by
Dosher and Lu (1999).
Here we focus on another theoretical constraint placed
by the LAM-based account of perceptual learning – the magnitude of
threshold reduction as a result of perceptual learning in high external noise
should be less or equal to that in low external noise (Figure 1d). This constraint follows directly from
the model prediction that efficiency improvements reduce thresholds with equal
magnitude across all the noise levels and internal noise reduction reduces
thresholds only in low external noise levels. Therefore, any mixture of the two
mechanisms should produce equal or larger threshold reduction in low external
noise. If threshold reduction with larger magnitude in high external noise were
observed, the LAM-based theory would be “forced” to generate an
apparently paradoxical account: perceptual learning improves calculation
efficiency yet increases (dis-improves) additive internal noise. Though
mathematically possible, such an account of perceptual learning would, however,
render the theory much less parsimonious, and additionally would require an
explanation of why practice increases the level of internal additive
noise.
The PTM (Lu & Dosher, 1999) attributes perceptual inefficiencies to three
limitations: internal additive noise sets the absolute thresholds for perceptual
tasks; perceptual templates, often not perfectly matched to the signal in the
stimulus, allow unnecessary influence of external noise or distractors on
performance; and internal multiplicative noise that increases with input
stimulus energy diminishes the benefit from increasing stimulus contrast and
therefore predicts Weber’s Law behavior. A PTM consists of five components
(Figure 2a): (1) a perceptual template, (2) a
nonlinear transducer function,
||•||γ,
(3) a multiplicative Gaussian internal noise whose SD is proportional (with a
factor of
Nmul)
to the total energy in the stimulus after the nonlinear transformation, (4) an
additive internal noise whose amplitude
(Nadd)
is independent of the stimulus strength, and (5) a decision process (see Lu
& Dosher, 1999,
for the formal development and quantitative tests for the form of the PTM
model). In the PTM, threshold signal contrast at a particular performance level
(i.e., d’) is
expressed as a function of external noise contrast
Next:
A full specification of the parameters of a PTM
requires measurements of TVC functions at a minimum of three threshold
performance levels. In contrast to LAM, the PTM has been shown to provide an
excellent account of threshold versus contrast functions at multiple performance
levels and full psychometric functions across a wide range of external noise
levels with a single set of parameters (Lu & Dosher, 1999).
Figure 2.
Perceptual template model (a) and performance signatures of the three mechanisms
of perceptual learning (b, c, and d).
Three mechanisms of perceptual learning can be
distinguished within the PTM: stimulus
enhancement reduces absolute thresholds by reducing internal additive
noise; perceptual template retuning
optimizes the perceptual template to exclude external noise or distractors; and
contrast-gain control reduction
decreases the impact of internal multiplicative noise. These three mechanisms
exhibit signature performance patterns (Figure
2) when we compare TVC functions at several points during perceptual
learning (Dosher & Lu, 1999).
Stimulus enhancement increases the
relative (vs. internal additive noise) gain of both the signal and the external
noise in the stimulus and is associated with performance improvements only in
low or zero external noise (Figure 2b).
Perceptual template retuning improves
the ability of the observer to exclude external noise and therefore is
associated with performance improvements only in high external noise (Figure 2c).
Contrast-gain control reduction
increases system response to stimulus contrast and is associated with
improvements throughout the full range of external noise (Figure 2d). In addition, we can distinguish various
mechanism mixtures by measuring TVC functions at multiple performance levels
(e.g., 70% and 80% correct).
The three mechanisms of perceptual learning in PTM
provide a complete mathematical basis to accommodate all possible systematic
patterns of performance improvements. An important theoretical question is
whether one can empirically isolate each of the three mechanisms of perceptual
learning within a task domain, and specify the circumstances under which these
mechanisms operate.
In the domain of visual attention, pure cases of
template retuning (Dosher & Lu, 2000a,
2000b; Lu & Dosher, 2000) and stimulus enhancement (Lu &
Dosher, 1998; Lu & Dosher, 2000; Lu, Liu, & Dosher, 2000)
have been documented separately and in different circumstances.
And, the results from the PTM approach have already proved useful in recasting
and reorganizing the existing attention literature (Dosher & Lu, 2000b).
In the PTM-based theoretical framework, very strong
constraints are placed on the relative magnitude of perceptual learning across
different performance levels for a given external noise condition (Dosher &
Lu, 1999). On the other hand, performance
improvements in the presence of high external noise are attributed to a
mechanism of perceptual template retuning, while improvements in the absence of
external noise are attributed to a separate stimulus enhancement mechanism. In
the LAM-based efficiency framework, performance improvements in the presence and
absence of external noise are completely coupled for improved efficiency;
additional improvements in the absence of external noise are accounted for by
internal noise reduction. An empirical demonstration of larger performance
improvements in high external noise than those in low external noise, a natural
prediction of the PTM-based framework, would pose an empirical challenge to the
LAM-based account of perceptual
learning.
The magnitude of perceptual learning may be highly
dependent on the eccentricity of the stimulus presentation, the complexity of
the task, and the presence or absence of mask/noise in the stimuli (Fine &
Jacobs, 2002). For simple low-level tasks presented in fovea, a number of studies have documented the absence of or only small amount of perceptual learning in a clear field (Dorais & Sagi, 1997; Fiorentini & Berardi, 1981; Furmanski & Engel, 2000; Johnson & Leibowitz, 1979; Matthews, Liu, Geesaman, & Qian, 1999; Ramachandran & Braddick, 1973). Other studies using hyper-acuity
(Bennett & Westheimer, 1991; McKee
& Westheimer, 1978) or unfamiliar task
situations (Matthews, Liu, & Qian, 2001; Vogels & Orban, 1985) did demonstrate perceptual learning in
noiseless foveal displays. And whether perceptual learning improves absolute
detection threshold in noiseless displays in fovea (Adini, Sagi, & Tsodyks,
2002; Mayer, 1983; Yu, Klein, & Levi, 2003) is still under debate. On the other hand,
substantially more learning in fovea has been observed over a wide range of
simple visual tasks using stimuli that contained external noise (Ball &
Sekuler, 1982; Dorais & Sagi, 1997; Fine & Jacobs, 2000; Furmanski & Engel, 2000; Gold et al., 1999; Saarinen & Levi, 1995; Schoups et al., 1995).
In this study, we exploited the external noise
dependency of the magnitude of perceptual improvements in fovea to test the
theoretical constraint set by the LAM-based theory of perceptual learning. The
aim of the study is to demonstrate that it is possible to observe a larger
magnitude of learning in the presence of high external noise than that in the
absence of external noise and therefore pose a challenge to the LAM-based
theoretical framework. Although learning may of course occur in some
circumstances in noiseless displays, the literature suggested that the magnitude
of learning in noiseless condition might be limited, whereas learning in high
noise circumstances might be more easily expressed.
In Experiment 1, we evaluated effects of perceptual
learning in a simple foveal orientation identification task over a full range of
systematically manipulated contrasts of external noise. We compared the LAM- and
PTM-based theoretical frameworks in their ability to account for the data. In
Experiment 2, we evaluated whether further perceptual improvements can be
obtained at a different viewing distance after the observers were trained at one
particular viewing
distance.
All stimuli were presented on a Nanao Technology
FlexScan-6600 monitor with a P4 phosphor and a 120 frames/s refresh rate. The
display was controlled by a 7500/100 Power Macintosh computer using a program
based on PsychToolbox (Brainard, 1997;
Pelli, 1997) in MATLAB (1998). A special circuit (Pelli & Zhang,
1991) combined two 8-bit output channels of
the video card and divided the full luminance range of the monitor (1 to 53
cd/m2) into 6144 distinct gray levels (12.6 bits). The display was
gamma corrected using a psychophysical procedure (Lu & Sperling, 1999). All displays were viewed
binocularly with natural pupil at a viewing distance of approximately 72 cm in
Experiment 1 and 36 cm in Experiment 2 in a dimly lighted
room.
The "signals" in the perceptual learning task were
Gabor patterns tilted ±8 deg
clockwise or counter-clockwise from 45
deg:
where background luminance
l0
= 27 cd/m2, Gabor center frequency
f
= 1.34 c/deg, and Gabor spatial window
σ
= 0.75 deg. The peak contrast
c was set by the
adaptive staircase procedures.
External noise images were generated using pixel
contrasts drawn independently from identical Gaussian distributions. To increase
the noise energy in the task-relevant spatial frequency channels, the external
noise images were filtered with a pass-band from one octave below to one octave
above the center frequency of the Gabors. The root mean square (RMS) contrast of
the filtered images was set at 0, 0.021, 0.041, 0.083, 0.124, 0.165, 0.248, and
0.33. Whereas the maximum possible contrast in the display is 1.0, we limited
the maximum SD of the external noise to 0.33 to conform to a Gaussian
distribution.
Both the Gabor patterns and the noise frames were
rendered on a 64
x
64 pixel grid (3.0
x
3.0 deg) and windowed by a 3.0-deg diameter disk to eliminate explicit
cues for 45 deg in the display (Figure 3a and
3b).
Figure 3. Stimuli
and procedures. a. A high-contrast Gabor pattern embedded in external noise with
increasing contrast levels. The Gabor is tilted 8 deg counter-clockwise from 45
deg. b. Same as a; the Gabor is tilted 8 deg clockwise from 45 deg. c. A display
sequence. d. A sample 3/1 adaptive staircase segment. e. A sample 2/1 adaptive
staircase segment.
The display sequence of a typical trial is shown in Figure 3c. Following a key press, a fixation-cross
appeared for 250 ms. A stimulus sequence was then presented in the center of the
display: a frame of random external noise, a Gabor patch tilted either +8 or
–8 deg from 45 deg, and another frame of random external noise, each
lasting 16.7 ms. Both noise frames in each trial were independent samples from
the same noise distribution. The noise is combined with the signal through
temporal integration. The subject identified the orientation of the Gabor patch
by pressing one of two keys. A brief beep followed each correct response.
Threshold contrasts at two performance criterion levels
were estimated for the orientation identification task at each of the eight
external noise levels using two interleaved staircase procedures (Levitt, 1971). One staircase procedure (Figure 3d) decreased signal contrast by 10% after
three successive correct responses and increased signal contrast by 10% after
every error (a three-down one-up or 3/1 staircase). It tracked a two-alternative
forced-choice threshold at 79.3% correct (d’ of 1.634) performance level.
The other staircase procedure (Figure 3e)
decreased signal contrast by 10% after two successive correct responses and
increased signal contrast by 10% after every error (a two-down one-up or 2/1
staircase). It tracked a two-alternative forced-choice threshold at 70.7%
correct (d’
of 1.089) performance level.
All the experimental conditions and staircases were
intermixed. There were 1,440 trials per session, consisting of 100 trials for
each 3/1 staircase and 80 trials for each 2/1 staircase at each external noise
level. Data were collected in 10 sessions on separate days. The staircases in
every new session started from the contrasts in the end of the previous session.
To get better estimates of the thresholds, we pooled
the data from the two staircases in each external noise condition and fitted
psychometric functions to them using a maximum likelihood procedure (Hays, 1981). For each observer, there were 360 trials
in each external noise condition in each training block. Five Weibull functions
(Wichmann & Hill, 2001)
with the same
max and
η, but independent
α ’s, were fit to the five
data sets in each external noise condition. Thresholds at
Pc = 70.7% and
Pc = 79.3% were
computed from the psychometric functions in order to quantify threshold versus
external noise contrast
functions.
Four graduate
students (aged 19 to 24 years), all with normal or corrected-to-normal vision
and naive to the purposes of the experiment, participated in Experiment 1 with
informed consent. Three of these four
observers participated in Experiment 2 immediately after they finished
Experiment 1.
In the LAM-based theoretical framework, there are two
mechanisms for perceptual improvements due to perceptual learning. The first
mechanism, perceptual learning-induced efficiency improvement, is modeled by
multiplying the perceptual efficiency
Eτ
in learning block t
by a learning parameter
AEτ(t).
This learning parameter may in general depend on the performance level on which
threshold is defined. If this dependency on criterion performance level occurs,
this represents a failure of parameter consistency of the model. The second
mechanism, perceptual learning-induced internal noise reduction, is modeled by
multiplying the equivalent internal noise by
Aeq(t).
From Equation 1 , we
have
The signature performance patterns of each of the two
mechanisms and their mixture are shown in Figure
1. Without losing generality, we set
Aeq
= 1.0 and
AE70.7%(1) = AE79.3%(1) = 1.This
simply scales all learning in relation to the initial performance level. A full
model of the data collected in Experiment 1, therefore, consists of
Neq,
E70.7%,
E79.3%,
Aeq(2,...,5),
AE70.7%(2,...,5),
and
AE79.3%(2,...,5),
a total of 15
parameters.
In the PTM-based theoretical framework, perceptual
learning impacts performance in one or a combination of three different ways:
(1) retuning the perceptual template differentially excludes external noise.
This is modeled by multiplying the amount of external noise in learning block
t by a learning parameter
Af(t);
(2) stimulus enhancement amplifies the stimulus (both the signal and the
external noise). This is mathematically equivalent to reducing internal additive
noise by
Aa(t)
(Lu & Dosher, 1998); (3) changes in
contrast-gain control properties result in a reduction of internal
multiplicative noise by
Am(t)
Equation
2 can be modified to incorporate the learning parameters as
follows:
The signature performance patterns of each of the three
mechanisms are shown in Figure 2. Again, without
losing generality, we set
Aa(1)
= 1,
Af(1)
= 1, and
Am(1)
= 1. A full model of the data collected in Experiment 1 therefore
consists of
Na,
Nm,,
β,
γ,
Aa(2,...,5),
Af(2,...,5),
and
Am(2,...,5),
a total of 16
parameters.
Six forms of the LAM-based models were considered: (1)
no perceptual learning (i.e., all learning parameters = 1.0), (2) changed
equivalent internal noise, (3) improved efficiencies with separate magnitudes at
different performance levels (independent
AE70.7%(t)
and
AE79.3%(t)),
(4) improved efficiencies with same magnitudes at different performance levels
(AE70.7%(t)
=
AE79.3%(t)),
(5) a combination of (2) and (3), and (6) a combination of (2) and (4). In
addition, eight forms of the PTM-based models were considered, ranging from no
change of any learning parameter with increased training to changes of all the
learning parameters with increased learning.
For each model form, the best-fitting model minimized
the least square difference between the log of the measured threshold contrasts
and the log of the model-predicted thresholds. The log is used to approximately
equate the SE of the measured thresholds. The goodness of fit is gauged by the
r2
statistic
where Σ and
mean()
were across all the practice and external noise conditions at both performance
levels. Of the six LAM-based and eight PTM-based models, some are reduced models
(proper subsets) of the others. F-tests
for nested models were used to compare these
models:
where
df1
=
kfull
–
kreduced
, and
df2
=
N
–
kfull.
The ks are the
number of parameters in each model, and
N is the number of predicted data
points. The minimal yet sufficient (i.e., statistically equivalent to the
maximum) model was selected as the best-fitting model for the data, separately
for the LAM-based and the PTM-based model
lattices. SDs were estimated for the parameters of
the best-fitting LAM-based and PTM-based models using a re-sampling method
(Maloney, 1990). Each of the 80 thresholds
was assumed to have resulted from a normal distribution with its mean and SD
equal to the estimated values from the experimental procedure. We
“re-generated” 1,000 copies of theoretical datasets by drawing one
sample from the 80 threshold distributions each time. We then fit the LAM-based
and PTM-based models to each copy of the re-sampled datasets and calculated the
SDs of the model parameters from the results of the
fits.
In 10 sessions of practice, observers identified the
orientation of a Gabor patch (a windowed sinusoidal grating) as tilted clockwise
or counter-clockwise (±8 deg) from 45 deg. The Gabor patches were tested in
fovea in eight levels of external noise. Thresholds at two criterion performance
levels (Pc = 70.7%
and Pc = 79.3%)
were estimated in each external noise condition using adaptive staircase
procedures (Figure 3d and 3e). This design
yielded a total of 20 [10 sessions
x 2 criterion levels] TVC
functions, each sampled at eight external noise levels. The average of these TVC
functions across all the observers are shown in Figure 4, pooled over every two sessions.
Figure 4. a.
Threshold versus external noise contrast (TVC) functions at two
performance-criterion levels (70.7% and 79.3% correct) over 10 training sessions
in Experiment 1, averaged across the four observers. The smooth curves represent
the best fit of the PTM model. The relative SEs of the thresholds are about 5%.
b.
Af
versus training session blocks for the four observers as well as
the “average” observer AVG. For the average observer AVG,
Af
reduced to 0.7289 after 10 sessions of practice.
Thresholds increased six-fold or more as external noise
increased, from about 0.086 to 0.56 averaged across the training sessions. As
expected, the less stringent performance criterion (70.7%) required lower
thresholds than the more stringent performance criterion (79.3%). The threshold
ratio between the two criterion levels is essentially constant across the eight
noise levels and training sessions (mean= 1.24; SE = 0.024). Ratio constancy
across external noise and practice levels indicates that practice did not alter
contrast-gain control properties of the perceptual system (Dosher & Lu, 1999; Lu & Dosher, 1999).
Table 1. Parameters of the best-fitting LAM-based model.
No significant threshold reduction was observed in the
noiseless condition on average over 10 days of practice. Contrast thresholds,
averaged across observers and criterion levels, were about 0.087 in sessions 1
and 2 and 0.084 in sessions 9 and 10. On the other hand, substantial threshold
reduction was observed in the high external noise conditions over 10 days of
practice. Contrast thresholds, averaged across observers and criterion levels,
reduced by about 33%, from 0.72 to 0.48 in the highest external noise condition.
The magnitude of the improvement is representative of individual observers. In
short, observers were specifically learning to exclude external noise.
Independent of any particular model, the data provided an empirical
demonstration of a pure, separable mechanism of perceptual learning that
operates only in the presence of large amounts of external
noise.
In the LAM-based theoretical framework, more learning
in high external noise than in low external noise requires a paradoxical
account: a mixture mechanism of improved efficiency and increased damage to
performance in internal noise. For the data shown in Figure 4a, the LAM-based model that assumes
improved efficiencies with the same magnitude at different performance levels
(AE70.7%(t)
=
AE79.3%(t))
and increased equivalent internal noise provided the best fit. With 11
parameters and r2= 0.9915, this model is statistically equivalent to
the most saturated model (F(4,65)=0.0031,
p > .95) and is superior to all the
models with fewer learning mechanisms: (1) F(4,69)=7.542,
p < 5 x 10–5,
for a comparison with the model that assumes modifications of calculation
efficiency but constant internal noise across training sessions; (2) F(4,
69)=22.72, p <
10–11, for a comparison with the model that assumes internal
noise changes but constant calculation efficiency across training sessions; and
(3) F(8,69)=12.12, p <
10–9,
for a comparison with the model that assumes no learning at all. The parameters
of the best-fitting model are shown in Table
1.
The predictions of the best-fitting LAM-based model are
plotted in Figure 5a, along with the
best-fitting
AE
values in Figure
5b,
and best-fitting
Aeq
values in Figure 5c.
Figure 5. a. Threshold
versus external noise contrast (TVC) functions at two performance-criterion
levels (70.7% and 79.3% correct) over 10 training sessions in Experiment 1,
averaged across the four observers. The smooth curves represent the best fit of
the LAM model.
AE
(b) and
Aeq
(c) as functions of training from the best-fitting LAM-based model.
According to the LAM-based model, perceptual learning
improved efficiency by a factor of 1.95. It also increased internal noise by a
factor of 1.4. Whereas learning-induced enhancement of efficiency results in
equivalent performance improvements (threshold reduction) across all the
external noise levels, an exactly compensatory increase of equivalent internal
noise is necessary to account for the lack of perceptual learning in the low
noise conditions. However, that perceptual learning increases equivalent
internal noise seems to be rather paradoxical, and the requirement that it does
so by exactly the amount required to cancel the efficiency improvement appears
to fail requirements of representativeness. Another paradoxical result from this
modeling exercise is that the estimated calculation efficiency depends on
performance criterion – in fact, lower efficiency for 79.3% correct than
70.7% correct. Both of these paradoxical results lead to questions about the
internal coherence of the efficiency model account of perceptual learning.
In the PTM theoretical framework, learning only in the
presence of relatively high external noise implies that perceptual learning
retuned the perceptual template to selectively exclude external noise. The
hypothesis was tested statistically. Perceptual template retuning was uniquely
identified as the mechanism of perceptual learning underlying the observed
performance improvements. With eight parameters and r2= 0.9915, this
model is statistically equivalent to the fullest model that assumes all three
perceptual learning mechanisms (F(8,64)=0.5582,
p > .75) and is superior to the
model that assumes no perceptual learning at all (F(4,72)= 12.63,
p < 10-7). The parameters
of the best-fitting model are shown in Table
2.
Table 2. Parameters of the best-fitting PTM-based model.
In summary, defining the impact of external noise to be
1.0 (100%) in (the average of) sessions 1 and 2, retuning of the perceptual
template during perceptual learning reduced the impact of external noise to
0.907, 0.783, 0.729, and 0.729 in sessions 3 and 4, 5 and 6, 7 and 8, and 9 and
10 (Figure 4b). In other words, by sessions 7 and 8 and sessions 9 and 10, the impact of the external noise was equivalent to 73% of the original impact of that same external noise in the beginning of the practice. The theoretical predictions of the best-fitting model are plotted in Figure 4a, along with the corresponding
Af
values for the training sessions in Figure 4b.
Experiment 2 was designed to evaluate the specificity
of learning to spatial scale. Most studies that evaluate specificity of
perceptual learning have used a three-stage design: initial evaluation of
performance levels in several conditions, training or practice in one particular
condition, and re-evaluation of performance levels in all the conditions. The
specificity of perceptual learning is then evaluated by comparing performance
levels before and after training.1 Another
design, frequently used in studies of cognitive learning but less frequently
used in studies of perceptual learning, involves two stages: training or
practice in one condition, and further training or practice in other conditions.
In this design, the specificity of learning is evaluated by measuring the amount
of further learning in the conditions not included in the initial training.
Depending on the learning rate and the number of trials involved in reliable
performance measures, the two designs have different pros and cons (Pennington
& Rehder, 1995). We chose the second
design in this study because measurements of TVC functions at eight external
noise and two performance levels involved relatively large numbers of trials
(1,440/session).
In pilot studies, we observed perceptual learning at
both 72- and 36-cm viewing distances. In the main experiment, three of the four
observers were tested with exactly the same procedure used in Experiment 1,
except at half the viewing distance. Over six training sessions, no further
performance improvement was found at any level of external noise (Figure 6). Statistical testing failed to identify
further learning (p > .25). This
suggests a complete transfer of perceptual learning of the orientation
identification task at fovea to a viewing distance at half of the original. If
transfer had not been complete, practice at the new scale would have produced
new learning, which was not observed. In other words, perceptual learning of
this task is scale invariant in the range tested (1 to 2). The best-fitting
model has four parameters
(Nadd,
Nmul,
β, γ ) with r2=
0.9959.
Figure 6. Threshold
versus external noise contrast (TVC) functions at two performance criterion
levels (70.7% and 79.3% correct) over six training sessions in Experiment 2,
averaged across the three observers. The smooth curves are the best fits of the
PTM model.
The observed lack of perceptual learning in the
noiseless condition and substantial learning in higher external noise conditions
in foveal orientation identification is consistent with the results of a number
of studies in the literature (Ball & Sekuler, 1982; Dorais & Sagi, 1997; Fine & Jacobs, 2000; Fiorentini & Berardi, 1981; Furmanski & Engel, 2000; Johnson & Leibowitz, 1979; Matthews et al., 1999; Ramachandran & Braddick, 1973; Saarinen & Levi, 1995; Schoups et al., 1995). On the other hand, several other
studies have demonstrated perceptual learning in fovea in noiseless displays
(Bennett & Westheimer, 1991; Matthews
et al., 2001; Mayer, 1983; McKee & Westheimer, 1978; Vogels & Orban, 1985; Yu et al., 2003). The exact nature of external noise
dependence of foveal perceptual learning in this and other tasks requires
further systematic investigation.
The observed pattern of perceptual learning – its
dependence on the amount of external noise added to the signal stimulus –
poses major challenges to the LAM-based accounts of perceptual learning (Gold et
al., 1999). The performance improvements in
high external noise conditions required improved calculation efficiency in the
LAM-based model, which predicts equivalent performance improvements (threshold
reduction) across all the external noise levels. However, because no learning or
less learning was observed in low external noise conditions, paradoxical
compensatory increases of the equivalent internal noise were necessary to
account for the lack of perceptual learning in the low noise conditions. This
plus the lack of a principled account of the calculation efficiency at different
performance-criterion levels render the LAM-based theoretical framework both
inconsistent and less
parsimonious.
In contrast, the PTM model provides a coherent account
of data in both attention and perceptual learning across multiple performance
levels and task situations (Lu & Dosher, 2002b; Tjan et al., 2002). We conclude, based on the PTM framework,
that perceptual learning in this task involved learning how to better exclude
external noise. The PTM framework specifies two separate mechanisms of improved
performance in noiseless and high noise conditions. The empirical result alone
demonstrates the possibility of observing one mechanism – external noise
exclusion – in the absence of the other.
The
nature of adult plasticity underlying these changes in performance with
perceptual learning in visual tasks is still under debate. Topographical
reorganization of cortical maps reflecting neuronal recruitment as a result of
perceptual learning has been documented in primary somatosensory cortex (Elbert,
Pantev, Wienbruch, Rockstroh, & Taub, 1995; Recanzone, Merzenich, & Schreiner, 1992) and primary auditory cortex (Bakin
& Weinberger, 1990; Durup & Fessard,
1935; Recanzone, Schreiner, & Merzenich,
1993; Weinberger, Ashe, Metherate,
McKenna, Diamond, & Bakin, 1990).
Cortical changes in primary visual cortex associated with perceptual learning
have shown a lack of topographical map reorganization (Crist, Li, & Gilbert,
2001; Ghose, Yang, & Maunsell, 2002; Schoups, Vogels, Qian, & Orban, 2001). While one study (Schoups et al., 2001) found some modest changes of orientation tuning in V1 that accounted for a fraction of the behavioral improvement, others (Crist et al., 2001; Ghose et al., 2002) failed to find any pronounced changes in
neural responsitivity associated with behavioral improvements with tasks suited
for early visual cortical areas. A recent computational model of perceptual
learning (Petrov, Dosher, & Lu, 2003)
accounted for a very complex behavioral data set in a non-stationary environment
through incremental channel re-weighting without altering early stages of visual
processing, lending an existence proof of re-weighting of early visual channels
as a plausible mechanism of perceptual learning (Dosher & Lu, 1998; Ghose et al., 2002; Mollon & Danilova, 1996). At the overall system level, a mechanism
of perceptual template retuning reflects channel re-weighting, which can have
larger consequences for external noise exclusion in high noise conditions.
In Experiment 2, we observed no further learning of the
foveal orientation identification task at a viewing distance half of the
original. The result suggests a complete transfer of perceptual learning to the
new viewing distance. Manipulating viewing distance while keeping the visual
display constant simultaneously changes the spatial frequency and the size of
the stimuli on the retina but preserves object frequency (Parish & Sperling,
1991). It corresponds to changes of
receptive field properties in V1 at different spatial frequency scales. Transfer
of perceptual learning from one viewing distance to another therefore implies
scale invariance in the learning mechanism and a form of learning that may
generalize within a hyper-column of visual system. It might also suggest that
equivalent computations at multiple resolution (or scales) of the visual pyramid
may share learning at one particular scale of resolution. Because the range of
viewing distance change was rather limited in this experiment (from 2 to 1), we
can’t draw any general conclusions about scale invariance of perceptual
learning in fovea. However, the results are highly suggestive and will certainly
deserve further investigation.
Based on perceptual learning of orientation identification in visual periphery, Dosher and Lu (1998,
1999) concluded that the mechanisms of perceptual learning consisted of a mixture of stimulus enhancement and template retuning. In this study, we found that a single template retuning accounted for performance improvements in foveal orientation identification. There are three primary differences between the two sets of experiments: (1) orientation identification was tested in fovea in the current study, but in the periphery in Dosher and Lu (1998,
1999), (2) there was a simultaneous
central letter identification task at fixation in Dosher and Lu (1998,
1999), and (3) observers identified
orientations at ±15 deg from vertical in Dosher and
Lu (1998,
1999) but ±8 deg from 45 deg in
the current study. Each of these factors probably partially contributes to the
different empirical results, although the relative importance of the
contributions remains to be specified. In general, they suggest that the
mechanism of perceptual learning for any particular task may depend on the exact
nature of the neural computation and/or visual pathway involved in performing
that task.
The current study provides the first empirical
demonstration of a pure, that is, isolated,
perceptual template retuning mechanism
of perceptual learning in a psychophysical study. The results are important for
theories of perceptual learning because they behaviorally demonstrate the
existence of an isolable mechanism. Much as the spectroscopic methods of atomic
physics enabled physicists to unravel the structure of atoms, applications of
the external noise method will enable us to discover the different mechanisms of
perceptual
learning.
This research was supported by National Science
Foundation Grants BCS-9911801 and BCS-9910678 and National Institute of Mental
Health Grant 1 R01 MH61834-01.
Commercial relationships: none.
Corresponding author: Zhong-Lin Lu.
Email:
zhonglin@usc.edu.
1In
certain designs, because of the inherent symmetry between the conditions, the
first stage can be
omitted.
Adini, Y., Sagi, D., & Tsodyks, M. (2002).
Context-enabled learning in the human visual system.
Nature, 415
(6873), 790-793. [PubMed]
Ahissar, M., & Hochstein,
S. (1996). Learning pop-out detection: Specificities to stimulus
characteristics. Vision Research, 36
(21), 3487-3500. [PubMed]
Ahissar, M., & Hochstein,
S. (1997). Task difficulty and the specificity of perceptual learning.
Nature, 387 (6631), 401-406. [PubMed]
Ahissar, M., Laiwand, R.,
Kozminsky, G., & Hochstein, S. (1998). Learning pop-out detection: Building
representations for conflicting target-distractor relationships.
Vision Research, 38 (20), 3095-3107. [PubMed]
Ahumada, A. J., & Watson,
A. B. (1985). Equivalent-noise model for contrast detection and discrimination.
Journal of the Optical Society of America A, 2
(7), 1133-1139. [PubMed]
Bakin, J. S., & Weinberger,
N. M. (1990). Classical-conditioning induces cs-specific receptive-field
plasticity in the auditory-cortex of the guinea-pig.
Brain Research, 536 (1-2), 271-286. [PubMed]
Ball, K., & Sekuler, R.
(1982). A specific and enduring improvement in visual motion discrimination.
Science, 218 (4573), 697-698. [PubMed]
Ball, K., & Sekuler, R.
(1987). Direction-specific improvement in motion discrimination.
Vision Research, 27 (6), 953-965. [PubMed]
Barlow, H. B. (1956). Retinal
noise and absolute threshold. Journal of the
Optical Society of America, 46, 634-639. [PubMed]
Beard, B. L., Levi, D. M., &
Reich, L. N. (1995). Perceptual learning in parafoveal vision.
Vision Research, 35 (12), 1679-1690. [PubMed]
Bennett, R. G., &
Westheimer, G. (1991). The effect of training on visual alignment discrimination
and grating resolution. Perception &
Psychophysics, 49 (6), 541-546. [PubMed]
Berardi, N., & Fiorentini,
A. (1987). Interhemispheric transfer of visual information in humans: Spatial
characteristics. Journal of Physiology,
384, 633-647. [PubMed]
Burgess, A. E., &
Colborne, B. (1988). Visual signal detection: IV. Observer inconsistency.
Journal of the Optical Society of America A,
2, 617-627. [PubMed]
Burgess, A. E., Shaw, R.,
& Lubin, J. (1999). Noise in imaging systems and human vision.
Journal of the Optical Society of America A,
16 (3), 618-618.
Burgess, A. E., Wagner, R. F.,
Jennings, R. J., & Barlow, H. B. (1981). Efficiency of human visual signal
discrimination. Science, 214 (4516),
93-94. [PubMed]
Chung S. T. L., Levi, D. M.,
& Tjan B. S. (2001). Perceptual learning in peripheral vision [Abstract].
Optics Express 9, 0-.
Crist, R. E., Li, W., &
Gilbert, C. D. (2001). Learning to see: Experience and attention in primary
visual cortex. Nature Neuroscience, 4
(5), 519-525. [PubMed]
De Valois, K. K. (1977).
Spatial frequency adaptation can enhance contrast sensitivity.
Vision Research, 17, 1057-1065. [PubMed]
Dorais, A., & Sagi, D.
(1997). Contrast masking effects change with practice.
Vision Research, 37 (13), 1725-1733. [PubMed]
Dosher, B. A., & Lu, Z.-L.
(1998). Perceptual learning reflects external noise filtering and internal noise
reduction through channel reweighting.
Proceedings of the National Academy of
Sciences of the U. S. A., 95 (23), 13988-13993. [PubMed]
Dosher, B. A., & Lu, Z.-L.
(1999). Mechanisms of perceptual learning.
Vision Research, 39 (19), 3197-3221. [PubMed]
Dosher, B. A., & Lu, Z.-L.
(2000a). Noise exclusion in spatial attention.
Psychological Science, 11 (2), 139-146.
[PubMed]
Dosher, B. A., & Lu, Z. L.
(2000b). Mechanisms of perceptual attention in precuing of location.
Vision Research, 40 (10-12), 1269-1292.
[PubMed]
Eckstein, M. P., Ahumada, A.
J., & Watson, A. B. (1997). Visual signal detection in structured
backgrounds. 2. Effects of contrast gain control, background variations, and
white noise. Journal of the Optical Society of
America A, 14 (9), 2406-2419. [PubMed]
Elbert, T., Pantev, C.,
Wienbruch, C., Rockstroh, B., & Taub, E. (1995). Increased cortical
representation of the fingers of the left hand in string players.
Science, 270 (5234), 305-307. [PubMed]
Fahle, M., & Edelman, S.
(1993). Long-term learning in vernier acuity: Effects of stimulus orientation,
range and of feedback. Vision Research, 33
(3), 397-412. [PubMed]
Fine, I., & Jacobs, R. A.
(2000). Perceptual learning for a pattern discrimination task.
Vision Research, 40 (23), 3209-3230. [PubMed]
Fine, I.,
& Jacobs, R. A. (2002). Comparing perceptual learning across tasks: A
review. Journal of Vision, 2 (2),
190-203. [PubMed]
Fiorentini, A., &
Berardi, N. (1980). Perceptual learning specific for orientation and spatial
frequency. Nature, 287 (5777), 43-44.
[PubMed]
Fiorentini, A., &
Berardi, N. (1981). Learning in grating waveform discrimination: Specificity for
orientation and spatial frequency. Vision
Research, 21 (7), 1149-1158. [PubMed]
Furmanski, C. S., &
Engel, S. A. (2000). Perceptual learning in object recognition: Object
specificity and size invariance. Vision
Research, 40 (5), 473-484. [PubMed]
Ghose, G. M., Yang, T., &
Maunsell, J. H. R. (2002). Physiological correlates of perceptual learning in
monkey V1 and V2. Journal of Neurophysiology,
87 (10), 1867-1888. [PubMed]
Gold, J., Bennett, P. J., &
Sekuler, A. B. (1999). Signal but not noise changes with perceptual learning.
Nature, 402 (6758), 176-178. [PubMed]
Johnson, C. A., &
Leibowitz, H. W. (1979). Practice effects for visual resolution in the
periphery. Perception and Psychophysics,
25, 439-442. [PubMed]
Karni, A., & Sagi, D.
(1991). Where practice makes perfect in texture-discrimination - evidence for
primary visual-cortex plasticity. Proceedings
of the National Academy of Sciences of the U.S.A., 88 (11), 4966-4970. [PubMed]
Karni, A., & Sagi, D.
(1993). The time course of learning a visual skill.
Nature, 365 (6443), 250-252. [PubMed]
Levitt, H. (1971). Transformed
up-down methods in psychoacoustics. Journal of
the Acoustical Society of America, 49, 467-477. [PubMed]
Li, R. W., Levi, D. M., &
Klein, S. A. (2003). Perceptual learning improves efficiency by retuning the
"template" for position discrimination. Manuscript submitted for
publication.
Liu, Z., & Vaina, L. M.
(1998). Simultaneous learning of motion discrimination in two directions.
Cognitive Brain Research, 6 (4),
347-349. [PubMed]
Lu, Z.-L., & Dosher, B.
(2002a). External noise methods and observer models [Abstract].
Investigative Ophthalmology and Visual Science
43: E-Abstract 2915.
Lu, Z.-L. & Dosher, B. A.
(2002b). Using external noise methods to isolate mechanisms of
attention/perceptual learning [Abstract]. Journal of Vision,
2(7), 67a, http://journalofvision.org/2/7/67/, doi:10.1167/2.7.67.
Lu, Z.-L., & Dosher, B. A.
(1998). External noise distinguishes attention mechanisms.
Vision Research, 38 (9), 1183-1198. [PubMed]
Lu, Z.-L., & Dosher, B. A.
(1999). Characterizing human perceptual inefficiencies with equivalent internal
noise. Journal of the Optical Society of
America A, 16 (3), 764-778. [PubMed]
Lu, Z.-L., & Dosher, B. A.
(2000). Spatial attention: Different mechanisms for central and peripheral
temporal precues? Journal of Experimental
Psychology: Human Perception and Performance, 26 (5), 1534-1548. [PubMed]
Lu, Z.-L., & Dosher, B. A.
(2001). Characterizing the spatial-frequency sensitivity of perceptual
templates. Journal of the Optical Society of
America A, 18 (9), 2041-2053. [PubMed]
Lu, Z.-L., Liu, C. Q.,
& Dosher, B. A. (2000). Attention mechanisms for multi-location first- and
second-order motion perception. Vision
Research, 40 (2), 173-186. [PubMed]
Lu, Z.-L., & Sperling,
G. (1999). Second-order reversed phi.
Perception & Psychophysics, 61 (6),
1075-1088. [PubMed]
Maloney, L. T. (1990).
Confidence intervals for the parameters of psychometric functions.
Perception & Psychophysics, 47 (2),
127-134. [PubMed]
Matthews, N., Liu, Z.,
Geesaman, B. J., & Qian, N. (1999). Perceptual learning on orientation and
direction discrimination. Vision Research, 39
(22), 3692-3701. [PubMed]
Matthews, N., Liu, Z., &
Qian, N. (2001). The effect of orientation learning on contrast sensitivity.
Vision Research, 41 (4), 463-471. [PubMed]
Mayer, M. (1983). Practice
improves adults' sensitivity to diagonals.
Vision Research, 23, 547-550. [PubMed]
McKee, S. P., & Westheimer,
G. (1978). Improvement in vernier acuity with practice.
Perception & Psychophysics, 24 (3),
258-262. [PubMed]
Mollon, J. D., & Danilova,
M.V. (1996). Three remarks on perceptual learning.
Spatial Vision, 10 (1), 51-58. [PubMed]
Nagaraja, N. S. (1964).
Effect of luminance noise on contrast thresholds.
Journal of the Optical Society of America, 54
(7), 950-955.
Parish, D. H., & Sperling,
G. (1991). Object spatial frequencies, retinal spatial frequencies, noise, and
the efficiency of letter discrimination.
Vision Research, 31 (7-8), 1399-1415.
[PubMed]
Pelli, D. G. (1981). Effects of
visual noise. Doctoral dissertation, University of Cambridge, Cambridge,
England.
Pelli, D. G. (1985). Uncertainty
explains many aspects of visual contrast detection and discrimination.
Journal of the Optical Society of America A,
2, 1508-1532. [PubMed]
Pelli, D. G. (1997). The
VideoToolbox software for visual psychophysics: Transforming numbers into
movies. Spatial Vision, 10 (4),
437-442. [PubMed]
Pelli, D. G., & Farell, B.
(1999). Why use noise? Journal of the Optical
Society of America A, 16 (3), 647-653. [PubMed]
Pelli, D. G., & Zhang, L.
(1991). Accurate control of contrast on microcomputer displays.
Vision Research, 31 (7-8), 1337-1350.
[PubMed]
Pennington, N., &
Rehder, B. (1995). Looking for transfer and interference. In D. L. Medin (Ed.),
The psychology of learning and motivation:
Advances in research and theory (pp. 223-289). San Diego, CA: Academic
Press.
Petrov, A. A., Dosher, B. A.,
& Lu, Z.-L. (2003). A computational model of perceptual learning through
incremental channel re-weighting predicts switch costs in non-stationary
contexts [Abstract].
Journal of Vision,
3(9), 670a,
http://journalofvision.org/3/9/670/, doi:10.1167/3.9.670.
Poggio, T., Fahle, M., &
Edelman, S. (1992). Fast perceptual learning in visual hyperacuity.
Science, 256 (5059), 1018-1021. [PubMed]
Ramachandran, V. S.,
& Braddick, O. (1973). Orientation-specific learning in stereopsis.
Perception, 2 (3), 371-376. [PubMed]
Recanzone, G. H., Merzenich,
M. M., & Schreiner, C. E. (1992). Changes in the distributed temporal
response properties of SI cortical neurons reflect improvements in performance
on a temporally based tactile discrimination task.
Journal of Neurophysiology, 67 (5),
1071-1091. [PubMed]
Recanzone, G. H., Schreiner,
C. E., & Merzenich, M. M. (1993). Plasticity in the frequency representation
of primary auditory cortex following discrimination training in adult owl
monkeys. Journal of Neuroscience, 13
(1), 87-103. [PubMed]
Rubenstein, B. S., &
Sagi, D. (1993). Effects of foreground scale in texture discrimination tasks:
Performance in size, shape, and content specific.
Spatial Vision, 7 (4), 293-310. [PubMed]
Saarinen, J., & Levi, D.
M. (1995). Perceptual learning in vernier acuity: What is learned?
Vision Research, 35 (4), 519-527. [PubMed]
Sagi, D., & Tanne, D. (1994).
Perceptual-learning - learning to see. Current
Opinion in Neurobiology, 4 (2), 195-199. [PubMed]
Schoups, A., Vogels, R., Qian,
N., & Orban, G. (2001). Practising orientation identification improves
orientation coding in V1 neurons. Nature, 412
(6846), 549-553. [PubMed]
Schoups, A. A., Vogels, R.,
& Orban, G. A. (1995). Human perceptual learning in identifying the oblique
orientation: Retinotopy, orientation specificity and monocularity.
Journal of Physiology, 483, 797-810. [PubMed]
Shiu, L.-p., & Pashler, H.
(1992). Improvement in line orientation discrimination is retinally local but
dependent on cognitive set. Perception &
Psychophysics, 52 (5), 582-588. [PubMed]
Tjan, B. S., Chung, S., &
Levi, D. (2002). Limitation of ideal-observer analysis in understanding
perceptual learning [Abstract].
Twenty-seventh annual interdisciplinary
conference, Jackson Hole, Wyoming.
Vogels, R., & Orban, G. A.
(1985). The effect of practice on the oblique effect in line orientation
judgments. Vision Research, 25 (11),
1679-1687. [PubMed]
Weinberger, N. M., Ashe, J.
H., Metherate, R., McKenna, T. M., Diamond, D. M., & Bakin, J. (1990).
Retuning auditory cortex by learning: A preliminary model of receptive field
plasticity. Concepts in Neuroscience,
1, 91-131.
Wichmann, F. A., & Hill,
N. J. (2001). The psychometric function I: Fitting, sampling and
goodness-of-fit. Perception and Psychophysics,
63, 1293-1313. [PubMed]
Yu, C., Klein, S. A., & Levi,
D. (2003). Perceptual learning of contrast discrimination [Abstract]. Journal of Vision,
3(9), 161a, http://journalofvision.org/3/9/161/, doi:10.1167/3.9.161.
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