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Covert attention enhances letter identification without affecting channel tuning
Abstract
Directing covert attention to the target location enhances sensitivity, but it is not clear how this enhancement comes about. Knowing that a single spatial frequency channel mediates letter identification, we use the critical-band-masking paradigm to investigate whether covert attention affects the spatial frequency tuning of that channel. We find that directing attention to the target location halves threshold energy without affecting the channel’s spatial frequency tuning.
History
Received October 9, 2002; published February 2, 2004
Citation
Talgar, C. P., Pelli, D. G., & Carrasco, M. (2004). Covert attention enhances letter identification without affecting channel tuning.
Journal of Vision, 4(1):3, 22-31,
Keywords
covert attention, letter identification, spatial vision, critical-band masking, spatial frequency channel, off-frequency looking, channel switching
Covert attention is enhanced processing of visual
information at a cued location without eye movements. It affects psychophysical
performance of many early visual processing tasks, some considered to be
“preattentive,” including contrast detection (Carrasco,
Penpeci–Talgar, & Eckstein, 2000; Foley & Schwarz, 1998; Lee, Koch, & Braun, 1997; Lu &
Dosher, 1998), orientation discrimination, detection, and
localization (Baldassi & Burr, 2000;
Carrasco et al., 2000; Lee, Itti,
Koch, & Braun, 1999; Morgan, Ward, &
Castet, 1998), texture segmentation
(Yeshurun & Carrasco, 1998, 2000), spatial acuity (Morgan et al., 1998; Yeshurun & Carrasco, 1999), visual search (Carrasco &
McElree, 2001; Nakayama &
MacKeben, 1989), and letter
identification (MacKeben, 1999). These
studies, in conjunction with neurophysiological studies using single–unit
recordings and neuroimaging techniques, indicate that covertly attending to a
location modulates low–level visual processes (Brefczynski & DeYoe, 1999; Ito & Gilbert, 1999; Martinez et al., 1999; McAdams & Maunsell, 1999a, 1999b; Motter, 1993; Reynolds & Desimone, 1999; Ress, Backus, & Heeger, 2000; Roelfsema, Lamme, & Spekreijse, 1998; Treue & Martinez Trujillo, 1999).
In this study, we assess
whether covert attention can change the tuning of a spatial frequency channel
(DeValois & DeValois, 1988; Graham,
1989). Specifically, we investigate two
hypotheses:
(a) Covert attention
shifts the peak frequency of the channel. This possibility is raised by
the “resolution hypothesis”: attention increases spatial resolution
at the attended location. Attention improves performance in both acuity and
hyperacuity tasks in the absence of distracters (Yeshurun & Carrasco, 1999), with or without a local mask
(Carrasco, Williams, & Yeshurun, 2002). Furthermore, in a texture
segmentation task, attention enhances or impairs performance depending on
whether spatial resolution is too low or too high for the scale of the texture.
The authors concluded that attending to a location is similar to reducing the
size of the visual filter used by the observer (Talgar & Carrasco, 2003; Yeshurun & Carrasco, 1998, 2000). This resolution hypothesis is
consistent with findings showing that the receptive field constricts around the
attended stimulus, causing the unattended stimulus to be left outside of the
neuron’s receptive field (Moran & Desimone, 1985; Reynolds & Desimone, 1999; Treue & Maunsell, 1996). Such a compression of the receptive
field might increase the peak frequency of the channel.
(b) Covert attention
alters the channel bandwidth, making it better matched to the signal.
There is no consensus as to whether attention increases the selectivity of the
neuronal response. Some have reported that attention narrows the tuning (for
orientation and color) of neurons in V4 (Reynolds & Desimone, 1999; Reynolds, Pasternak, & Desimone,
2000; Spitzer, Desimone, & Moran, 1988), whereas others have found an
increased gain but unchanged tuning (for orientation) in V4 (McAdams &
Maunsell, 1999a) and (for direction of
motion) in MT/MST areas (Treue & Martinez Trujillo, 1999). Increased contrast sensitivity for a
grating could arise through a narrowing of the channel tuning, but increased
sensitivity for a broadband stimulus such as a letter would arise from a
widening of the bandwidth. In general, sensitivity would be increased by better
matching the channel to the noise-normalized signal.
To investigate these two hypotheses, we chose a task
that isolates the spatial frequency channel that mediates the identification of
broadband stimuli, letters. A broadband stimulus could be seen through channels
with various tunings, allowing us to test for shifts of peak frequency of the
channel as a result of directing covert attention. Given that observers have
multiple independent channels with various peak frequencies, one would expect a
broadband stimulus such as a letter to activate many channels. However, using a
critical–band–masking paradigm with unfiltered letters, the same
filter tuning is found for detection of narrowband gratings and identification
of broadband letters (Solomon & Pelli, 1994). Critical–band masking of
letters allows us to measure the effects of covert attention on the tuning of a
spatial frequency channel.
Covert attention can be directed to a certain part of
the visual scene either in a sustained (observers voluntarily direct their
attention) or transient (exogenously controlled by the sudden onset of a
peripheral cue) fashion (Cheal & Lyon, 1991; Jonides & Irwin, 1981; Nakayama & Mackeben, 1989). We directed transient covert
attention to the target location by using a peripheral cue (e.g., Carrasco et
al., 2000, 2001, 2002; Talgar & Carrasco, 2003; Yeshurun & Carrasco, 1998, 1999, 2000). To quantify the attentional benefit,
we used two control conditions. The “central–neutral” cue
appeared at the center of the display. To test for the possibility that this cue
reduces the extent of the attentional spread by attracting attention to its
location, away from the peripheral target locations (Pashler, 1998), we also employed a
“distributed–neutral” peripheral cue presented at all possible
target locations. By simultaneously stimulating the detectors at all candidate
locations, the distributed–neutral cue should reduce uncertainty as well
as any differences in the onset time of activation in response to the
central–neutral and the peripheral cues.
Three individuals (two graduate students and one
research assistant) from New York University were observers in this experiment.
All had normal or corrected-to-normal vision. Two were naive to the purposes of
this study. The third was an author.
Stimuli were displayed on a gamma–corrected
computer monitor in a dark room. A video attenuator drove the green gun of the
IBM 21" Multiscan color monitor (Pelli & Zhang, 1991), whose frame rate was 75 Hz. The
background luminance of the monitor was 16 cd/m2, corresponding to
the middle of the monitor’s range.
The stimuli were created by an Apple Macintosh G4
computer using MATLAB 5.2.1 and the Psychophysics Toolbox (Brainard, 1997; Pelli, 1997; http://psychtoolbox.org). A letter was added
to a background of visual noise. The noise covered an area twice as high and
twice as wide as the letter, centered on the letter. The noise square was
outlined in black to clearly demarcate the location. The letter subtended
1.2° and the noise
2.4°.
The contrast of the letters is specified by the Weber
contrast
where
L is luminance of
the letter and  is the mean luminance of the background. The contrast
energy of the letter is the product of the squared contrast and ink area. More
generally, energy is defined as the contrast power of the signal, integrated
over space  where
c(x,y)
is the contrast
function
and
L(x,y)
is the luminance at location
x,y. Threshold
contrast was measured over 60 trials of the modified Quest staircase procedure
(King–Smith, Grigsby, Vingrys, Benes, & Supowit, 1994; Watson & Pelli, 1983) using an 82% correct criterion and a
β of 3.5.
For noise, the root mean square
(RMS) contrast is defined as
where the angle brackets denote expected value
over all x,y. The
RMS contrast of the noise was 0.35. The noise was static and made up of square
checks: 2 × 2 pixels. The 2 × 2 check size is advantageous in
quadrupling the power spectral density of the noise, and in minimizing the
potential for luminance artifacts due to the finite slew rate of the CRT's video
amplifier (Brainard, Pelli, & Robson, 2002; Pelli & Farell, 1999). Each check was an increment or
decrement sampled from a zero mean Gaussian distribution truncated at two SDs
(Pelli & Farell, 1999). The power
spectral density of a random checkerboard (with stochastically independent check
luminances) equals the product of contrast power
 and the area of a noise
check. The white noise was high– or low–pass filtered at one of 26
cut–off frequencies: 0, 0.125, 0.25, ..., 2.875, 3, ∞ c/deg. MATLAB
functions were used to fast–fourier transform the noise matrix, zero all
the unwanted frequencies, and invert the fast–fourier transform. The
signal, like the noise, was uniform within each check. This was achieved by
computing the stimulus image at 1/2 of the final size and then expanding to the
final size by pixel replication.On each trial, the target
(N,
Z, or
X) appeared with
equal probability at 1 of 8 random locations at 9° eccentricity.
Distracters (Vs) occupied the remaining 7 locations. All letters had equal
contrast, and were presented at non-cardinal locations, for which contrast
sensitivity is similar (30°, 60°,
120°, 150°, 210°,
240°,
300°, and
330°; Cameron, Tai, &
Carrasco, 2002; Carrasco et al., 2001).
There were three cue conditions: (1)
Central–neutral cue condition: On
one third of the blocks, the cue was a black dot, subtending 0.8° of visual
angle, that appeared at fixation. (2)
Distributed–neutral cue
condition: On another third of the blocks, 8 dots appeared 12.5°
away from fixation, one dot adjacent to each possible target location. (3)
Peripheral cue condition: In the
remaining blocks, a single dot appeared, 12.5° away from fixation, adjacent
to the actual target location. All 3 cues were informative regarding the time of
display onset. Each observer completed 10 blocks (5 in low–pass and 5 in
high–pass noise) at each cut–off frequency for each cueing
condition. The order was counterbalanced. Each observer contributed 104 practice
and 780 experimental data points.
Observers viewed the display binocularly at a distance
of 57 cm. A small fixation cross (0.2° × 0.2°) was present at the
center of the screen throughout the block, except when obscured by the
central-neutral cue dot (Figure 1). Observers
were instructed to fixate on this cross throughout the trial and to report the
target identity by pressing the corresponding key (N, Z, or X) on the computer
keyboard. Feedback for an incorrect response was given by a low–frequency
tone. On each trial, the cue (40 ms) was followed by an interstimulus interval
(60 ms) (i.e., a stimulus onset asynchrony of 100 ms), after which the target
and distracters appeared for 40 ms.. Given that about 250 ms are needed for
saccades to occur (Mayfrank, Mobashery, Kimmig, & Fischer, 1986), eye movements in response to the cue
could not occur before stimulus offset, because it was only 140 ms after cue
onset.
Figure 1. A
schematic representation of a trial sequence. In one third of the blocks, the
target was preceded by a central–neutral cue (a dot in the center of the
display), in another third by a distributed–neutral cue (a dot adjacent to
each of the eight possible target locations), and in the remaining blocks by a
peripheral cue (a single dot adjacent to the actual target location). Note that
the eight noise patches were outlined in black to demarcate the locations.
Using the critical–band–masking procedure,
we measured the tuning of the channel mediating letter identification. The
sigmoidal functions in Figure 2A are the energy
threshold for identifying a letter in low– or high–pass noise for
one observer in the three cueing conditions. We assume that the energy threshold
E is linearly
related to the total power passed by the channel filter
G(f)
mediating letter identification (Majaj, Pelli, Kurshan, &
Palomares, 2002; Solomon & Pelli,
1994):
where
E0 is the energy threshold
measured in the absence of noise (zero cut–off frequency for
low–pass noise and infinity for high–pass noise), and
f is the radial frequency. The filter
gain is proportional to the derivative of the measured
threshold
The filter is assumed to have a parametric
form; log power gain is a parabolic function of log
frequency
and its parameters are estimated by a maximum
likelihood fit (Majaj et al., 2002). Figure 2. A.
Energy thresholds in low–pass (x; solid line) and high–pass
(o; dashed line) noise for one observer (AMG) for the three cueing conditions.
Each data point represents the energy threshold of the observer when low–
or high–pass noise with a given cut–off frequency was superimposed
on the letter. In this sigmoidal function, the steepest rise or fall in the
threshold energy occurs at the noise frequency that interferes most. Because
energy is proportional to squared contrast, an energy ratio of 1/2 implies a
contrast ratio of
 . B. Graph
of the channel derived from the results plotted in A. The solid and dashed
curves in each graph represent the channel used by this observer to identify
letters in the low- and high-pass noise conditions, respectively.Patterson and Nimmo-Smith (1980) reported that in a listening task,
subjects could switch channels to avoid noise and perform with a lower
threshold, a process that they termed
off-frequency listening.
Correspondingly, observers might look off-frequency in order to use the
noise-free part of the spectrum to reduce their thresholds (Pelli, 1981). When low-pass noise is superimposed on
a broadband stimulus (e.g., a letter), an ideal observer would use the
noise-free high-frequency region of the signal spectrum to perform perfectly.
Correspondingly, when high-pass noise is superimposed on this stimulus, the
ideal observer would utilize the noise-free low-frequency end of the signal
spectrum to maintain perfect performance. This is called off-frequency looking
or channel switching (Majaj et al., 2002;
Pelli, 1981).
To assert that the observers utilized a single channel,
we must make sure that there was no channel switching (i.e., the low- and
high-pass noises are additive). Two
noises are said to be additive if their sum leads to a threshold energy
elevation that is equivalent to the sum of the threshold energy elevations
yielded by each noise alone. If observers switch channels to utilize the
noise-free part of the signal spectrum to perform the task, noise additivity
would be violated. To detect any such violations, Majaj et al. (2002) used the “noise additivity
ratio”
where
 is the threshold
elevation produced by the display noise, and the subscript indicates the type of
noise. For any given cut-off frequency, the high- and low-pass noises are
complementary in that their sum is white noise. Therefore, a noise-additivity
ratio of 1 indicates that the observer uses the same channel to perform the
task, whereas a ratio considerably below one would be indicative of channel
switching (Majaj et al., 2002). The noise
additivity ratio for people detecting narrowband stimuli such as sine-wave
gratings is approximately 0.7 (Pelli, 1981;
Solomon & Pelli, 1994). With
broadband signals, whose spectra extend beyond the cut-off frequencies of the
low- and high-pass noise, an observer free to choose any channel should use the
noise-free part of the signal spectrum to identify the signal, attaining a very
low threshold in either high- or low-pass noise. However, this strategy cannot
avoid white noise, so the noise additivity ratio would be practically zero.
Contrary to this prediction, a noise additivity ratio of 0.7 is found for human
observers, which is too near 1 to be taken as evidence for channel switching
(Majaj et al., 2002). Figure 3A plots the
ratio of the energy threshold with a peripheral cue to that with a
central– (orange) or distributed– (yellow) neutral cue. A ratio of 1
would indicate that the peripheral cue had no effect on threshold. The results
show that the thresholds attained in the peripheral cue condition were 0.5 to
0.63 times those with central– and distributed–neutral cue
conditions. Thus, the peripheral cue halved the observers’ energy
thresholds.
Figure 3B plots the average energy
threshold for each observer under the three different cueing conditions.
Observer thresholds in the peripheral cue condition were significantly lower
than those in the central- and distributed-cue conditions
(p
< .05).
 Figure 3. A. The benefit of informative
cueing. The ratio of the threshold energy with a peripheral cue to that with a
central– (orange) or distributed– (yellow) neutral cue. A ratio of 1
would indicate no effect of a peripheral cue to the location of the target. The
error bars represent ±1 SD across all noise cut-off frequencies. B. Average
threshold energy in each cue condition for each observer.
Figure 2A plots the
energy thresholds for one observer in the three different cueing conditions.
Each data point represents the energy threshold of the observer for a letter in
low– or high–pass noise with a given cut–off frequency. The
steepest part of this sigmoidal function occurs at the noise frequency that
interferes most with the task. The derived channel mediating this task is
plotted in Figure 2B. The two curves in each
graph represent the filter gain of the channel used by this observer in the
low– and high–pass noise conditions. The low– and
high–pass estimates of the channel are independent, so their agreement
demonstrates reliability of the measurement.
Figure 4 shows that
peak frequency was unaffected by cue condition for all three observers. The
average ratio of the channel peak frequency with a peripheral cue to that with a
central– or distributed–neutral cue is not significantly different
from 1
(p
> .1), indicating that the observers used the same channel to perform
the task under the three different cueing conditions.
Figure 4. Peak
channel frequency in each condition for each observer.
We report
bandwidth in octaves as log base 2 of
the high–frequency limit divided by the low–frequency limit. These
two frequencies correspond to those at which the power gain of the channel is
half maximum (Majaj et al., 2002). Figure 2B shows the channel mediating the letter
identification task. Figure 5 shows that none
of the observers show an effect of cue condition on channel bandwidth. Moreover,
the average ratio of the channel bandwidth (in octaves) with a peripheral cue to
that with a central– or distributed–neutral cue did not differ
significantly from 1
(p
> .1), indicating that the channel’s bandwidth is independent of
the cueing condition. Figure 5. Channel bandwidth in each condition for
each observer.
There are no systematic differences between the
high– and low–pass conditions with regard to the bandwidth of the
spatial frequency channel mediating the task. The average bandwidth of 1.6
octaves ± 0.3 (mean ± SD) for letters is consistent with previous
estimates for letters and gratings (Majaj et al., 2002; Solomon & Pelli, 1994).
Figure 6 depicts the
peak channel frequencies for each of the three observers in the cueing
conditions for high- and low-pass noise conditions. As in prior work (Majaj et
al., 2002), for two of the three observers,
the estimated channel frequency was slightly higher in the presence of low- than
high-pass noise, but, as in that work, the small change in frequency was
insufficient to improve threshold much, and thus we do not take it as evidence
of channel switching. The third observer, AMG, showed no difference in the
channel frequency revealed by low- and high-pass noise.
Figure 6. Peak
channel frequency in each condition for both low- and high-pass noise for each
observer.
Figure 7 illustrates
the average noise additivity ratios for the three observers in the three
conditions. None of the ratios differed significantly from 1, confirming that
there was no channel
switching. Figure 7. Noise additivity ratios for each cue
condition for each observer. The error bars represent ±1 SD across all
noise cut-off frequencies.
Our experiments were designed to investigate whether
the peripheral cue increases sensitivity by changing the channel tuning.
Changing channel tuning (e.g., by switching channel) to avoid the noise would
improve sensitivity, but Figures 6 and 7 show no effect of cueing condition on channel
tuning. There is no channel switching under any condition.
Using a critical–band–masking paradigm, we
examined whether covert attention affects the spatial frequency channel
mediating letter identification. Directing covert attention to the
target’s location halves the observer’s energy threshold (
contrast). Contrary to both of our hypotheses, there is no change in the tuning
of the channel that mediates the task: Neither center frequency nor bandwidth is
affected.1 Increased sensitivity for letter identification is
consistent with previous psychophysical studies showing enhanced sensitivity for
sinusoidal gratings (Cameron, Tai, & Carrasco,
2002; Carrasco et al., 2000; Carrasco, Talgar, &
Cameron, 2001; Lu & Dosher, 1998) and with neurophysiological findings
indicating that attention increases the effective contrast of the attended
stimulus by a factor of 1.5 (Reynolds et al., 2000; Treue & Maunsell, 1996). We find factors of
and  in contrast for the neutral– and
distributed–neutral cue, respectively. This finding of no effect of
attention at the first stage of filtering is consistent with a texture
segmentation study in which covert attention was shown to operate at the second
stage of filtering (Yeshurun & Carrasco, 2000) and with a motion study in which
attention was shown to affect the third stage of filtering (Sperling & Lu,
1998).There are four ways (a–d) that one might attempt
to explain the attentional enhancement of contrast sensitivity by the peripheral
cue, but only the last two (c and d) are consistent with our results:
(a) A head start in
processing at the target location in the peripheral cue condition. It is
likely that any visual stimulus presented close to the target location will
start activating detectors in that region in advance of the actual target
presentation. Were this the source of the peripheral cue advantage over the
central cue, then the distributed cue — in which a cue dot appeared next
to each possible target location — should have a threshold much closer to
that of the peripheral cue than that of the central cue. Instead, the
distributed-cue threshold is much closer to that of the central cue, rejecting
this explanation.
(b) A narrowing of
the spread of attention in the central-neutral cue condition. The benefit
of the peripheral cue relative to the central cue may be simply due to the
central cue drawing attention to the center of the display, away from the target
(Pashler, 1998). However, because the
distributed cue condition presents no cue in the center, this explanation cannot
account for the similar effect of both neutral cues. A similar result has been
found for acuity (Carrasco et al., 2002).
(c) External noise
reduction. This hypothesis maintains that attention attenuates stimuli
outside of the focus of attention (e.g., Dosher & Lu, 2000; Eckstein, 1998; Morgan et al., 1998; Palmer, 1994; Prinzmetal,
Amiri, Allen, & Edwards, 1998;
Shaw, 1980; Shiu & Pashler, 1994). Noise-limited models consider the
effects of external noise arising from distracters and masks that may be
confused with the signal because of spatial and temporal uncertainty (intrinsic
and extrinsic) of target and distracter location. These models predict that
performance will decrease with increasing set size (i.e., uncertainty) (e.g.,
Eckstein, 1998; Foley & Schwarz, 1998; Palmer, 1994; Pelli, 1985; Shaw, 1980).
Each of the three cueing conditions had its own degree
of uncertainty. For the peripheral cue condition, there was one relevant
location. For the distributed cue condition there were eight. For the central
cue condition, there is a question regarding the degree of uncertainty. The
eight patches of noise were always highly visible. Assuming vast uncertainty
would be inconsistent with the high visibility of the noise patches and the
small difference in the effectiveness of the two cues. It seems more reasonable
to assume that attention is restricted to just the eight patches, which are
saliently outlined in black. This predicts little or no difference between the
two different neutral cues, as we found.
(d) Signal
enhancement. Other studies support the finding that the attentional
effect goes beyond the reduction of uncertainty and maintain that attention
improves the quality of the stimulus representation (Cameron et al., 2002; Carrasco et al., 2000, 2002; Lu & Dosher, 1998; Muller et al., 1998; Posner, 1980; Yeshurun & Carrasco, 1999). The attentional increase in contrast
sensitivity is independent of spatial uncertainty (Cameron et al., 2002), even when localization performance
indicates that there is no uncertainty regarding target location (Carrasco et
al., 2000). Likewise, with brief
displays, cueing the target location improves performance more than predicted by
a signal-detection model of spatial uncertainty (Carrasco et al., 2002; Morgan et al., 1998).
Eckstein, Shimozaki, and Abbey (2002) used the classification image
paradigm to compare the template that observers use when their attention is
directed to a possible target’s location to that when attention is
directed elsewhere. The target was presented on a background of random visual
noise. The average noise pattern present when the observer made false alarms is
an estimate of the template used to detect the target. Finding that observers
use the same templates under valid and invalid cueing conditions, the authors
concluded that cueing the target location does not alter early visual processing
at that location, which is consistent with our finding of unchanged tuning.
Alas, it is hard to extrapolate to more complex tasks from their classification
image result because that paradigm seems to be limited to extremely simple
visual tasks, typically binary classification. Fortunately, critical-band
masking can be applied to any task, allowing us to show here that attention does
not affect frequency tuning in letter identification.
Covert attention reduces the observer’s contrast
threshold by a factor of √2 without altering the spatial frequency tuning
of the channel mediating the letter identification task.
This work was supported by NSF Grant
BCS–9910734/HCP to MC, NIH grant EY04432 to DGP, and a Beatrice and Samuel
A. Seaver Foundation grant to New York University for “Neuroimaging
studies of emotional and attentional influences on cognition and
perception.” We thank Marialuisa Martelli for her insightful comments,
AnnaMarie Giordano and Sam Ling for their careful observations and
contributions, and Miguel Eckstein for his helpful comments on the manuscript.
Corresponding author: Cigdem P. Talgar
Email: Cigdem.Talgar@med.nyu.edu.
Commercial relationships: none.
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