Previous Article |
Table of Contents
| Next Article 
Vol. 96, Issue 20, 11681-11686, September 28, 1999
Departments of * Cognitive Sciences, and Contributed by George Sperling, August 6, 1999
An ambiguous motion paradigm, in which the direction of apparent
motion is determined by salience (i.e., the extent to which an area is
perceived as figure versus ground), is used to assay the amplification
of color by attention to color. In the red-green colored gratings used
in these experiments, without attention instructions, salience depends
on the chromaticity difference between colored stripes embedded in the
motion sequence and the yellow background. Selective attention to red
(or to green) alters the perceived direction of motion and is found to
be equivalent to increasing the physical redness (or greenness) by
25-117%, depending on the observer and color. Whereas attention to a
color drastically alters the salience of that color, it leaves color appearance unchanged. A computational model, which embodies separate, parallel pathways for object perception and for salience, accounts for
99% of the variance of the experimental data.
"Visual attention" refers to a class of mechanisms that
select incoming information for subsequent processing from a particular set of locations in space and an interval in time. When the attended locations are determined by previous instructions to attend to a
location, we say the mechanism is "top-down" attention, because the attentional selection depends on the interpretation, memory, and
execution of the instructions; all high-level cognitive processes. Locations in space and time can also be selected "preattentively" by "bottom-up" mechanisms. For example, attending to red items in
a many-colored scene would be an example of a top-down process. On the
other hand, a single red item in a field of gray items would be
selected on the basis of its uniqueness by preattentive, bottom-up processes.
An abstract representation of visual space, which we term a
"salience map," records, at each location of the visual field, the total attentional strength resulting from bottom-up plus top-down selection. The salience map is assumed to record only the attentional strength at a location; it does not record information about particular attributes (shape, color, texture, etc.) that produced this strength. A
representation of total attentional strength is an essential component
of theories for a variety of attentional tasks and phenomena, such as
modeling the sequence of locations searched in visual search tasks (1),
accounting for the ability to perceive motion in alternating-feature
stimuli (2) and in isoluminant color gratings (3), gaining access to
short-term visual memory (4, 5), figure-ground segmentation (2), and
shape processing/object recognition (6).
The term "salience map" was popularized by Koch and Ullman (6),
who used the concept to describe a winner-take-all network that
determined a spatial location from which information from various
topographic feature maps is combined and directed to a central
processor. Related concepts have emerged independently as an
"attention map" (7), a "priority map" (8), a "selective tuning mechanism" (9), a "hierarchical pruning mechanism"
(10), and under other names, with different authors giving somewhat different interpretations to these concepts. In Discussion,
we consider in what respects the salience map and computations proposed here differ from these others.
The present study poses two questions: First, how can the impact of
top-down, attentional selection be measured? In particular, when an
observer is instructed to attend to a feature of the visual scene,
e.g., red, can the resulting increase in visual salience be expressed
as an amplification of the feature, as if the experimenter had actually
increased the redness of the stimuli? Second, what is the spatial
resolution of attention, i.e., of the salience map? If regions of high
salience occur adjacent to regions of low salience, at what point is
the resolution of the salience map exceeded? These questions are
addressed within an ambiguous motion paradigm that lays the groundwork
for a general computational model of visual attention similar to the
kinds of theories found in low-level vision [e.g., visual optics (11),
contrast sensitivity (12-15), and motion perception (16)].
We chose the red-green color axis as the dimension along which
attention exerts its influence. An ambiguous motion stimulus (2) is
used to assess the effectiveness of attention to color. Without
attention instructions, the probability of perceiving motion in a
particular direction depends on the chromaticity difference (redness or
greenness) between a colored stimulus embedded in the motion sequence
and the background (3). In the stimuli in these experiments, selective
attention to red (or to green) is found to be equivalent to increasing
the physical redness (or greenness) by 25-117%, depending on the
observer and color. The data are used to construct a computational
salience-map model that very accurately accounts for the experimental data.
Overall Plan.
The paradigm for measuring attention utilizes a
sensitive assay method, the "alternating-feature" motion paradigm
(2). The paradigm uses motion stimuli that are completely neutral to the first-order (luminance-based) and second-order (texture-based) motion systems, but nonetheless give rise to clear and consistent apparent motion (see ref. 17 for a review). The remarkable fact is that
the direction and strength of apparent motion in these "third-order" motion stimuli can be strongly influenced by
attention (2, 17). Therefore, we can calibrate attention (a top-down factor) against bottom-up factors, such as color saturation and spatial
frequency, to determine attentional color amplification prior to the
third-order motion computation.
Motion Systems.
In the ideal case, a motion system receives a
time-varying image as input and produces a motion flow field as output.
The flow field is a map of two-dimensional visual space in which each local neighborhood is represented by a vector that indicates the direction and velocity of movement in that location. The output of a
motion system does not directly indicate anything about the identity of
the objects or events that were responsible for the flow field. Such
information must be extracted by other subsystems that deal with
texture, color, shape, and other image properties. Ultimately,
information from many different subsystems is combined to generate perception.
Psychology
Measuring the amplification of attention
,
,§, and Neurobiology and
Behavior, and Institute of Mathematical Behavioral Sciences,
University of California, Irvine, CA 92697; and ¶ Laboratory of
Brain Processes, Department of Psychology, University of Southern
California, Los Angeles, CA 90089
ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
Controlling Salience. There are two different ways to increase the salience of a region: an adjustment of a physical property or an adjustment of attention. For example, increasing the redness of a slightly reddish patch embedded in a large background of homogeneous yellow will make the reddish area more salient. The effectiveness of this manipulation depends primarily on bottom-up, preattentive processing. Deliberately attending to red (versus attending to green) in a stimulus that contains both red and green patches will also make the red regions more salient. This depends on a two-stage top-down process; that is, the high-level cognitive process of attending alters the low-level processing of visual inputs. [We know that this is not a high-level process operating directly on salience because salience is altered even in stimuli that are too brief for the observer to consciously discriminate the to-be-attended features (2).]
The hallmark of the study of spatial resolution in low-level vision is the application of techniques from linear systems analysis. For a linear system, the response to a sum of input components is the same as the sum of the system's response to each component individually. Because Fourier analysis can decompose an arbitrary input into its sinusoidal components, knowledge of a linear system's response to a basis set of sinusoids is sufficient to predict the system's response to any input whatsoever. Our approach, therefore, is to measure the extent to which the salience map can resolve sinusoidal stimuli spanning a wide range of spatial frequencies. For any requested distribution of visual salience, these data will, in principle, enable the prediction of the observer's achievable distribution of salience.Stimuli. In the present study, ambiguous motion stimuli are used to assess the relative effectiveness of bottom-up and top-down factors. The motion stimuli consist of a temporal sequence of five spatially coincident frames, each of which contains a vertical sinusoidal grating. The gratings appear inside a rectangular aperture 10.7 cm wide and 6.6 cm tall. Temporal frequency was fixed at 2.5 Hz, stimulus width was 4 cycles, and spatial frequency was 0.50 cycles per degree at a viewing distance of 0.75 m. The stimuli used two types of gratings, an isoluminant red-green grating and a contrast-modulated noise grating. A motion sequence was constructed by alternating between red-green and contrast-modulated texture frames, with each successive frame displaced 90° consistently to the right or left, relative to its predecessor. In such a stimulus, the high-contrast texture patches are perceived as figure. Alternating with the texture stimuli are isoluminant color stimuli (20) containing side-by-side red and green regions (Fig. 1). In a red-green stimulus, the color of the area perceived as figure (i.e., having greater salience) by the motion system depends on which color differs more from the yellow background, which is itself simply a 50/50 mixture of red and green. In such a color stimulus, the amount by which a reddish or greenish area differs from the background is here called "chromaticity difference" and is designated as |R| or |G|. |R| and |G| vary from 0.0 (yellow, the background color) to +1.0 (purest available green or purest red) (Fig. 1).
|
Procedure. Three kinds of experimental sessions were conducted in sequence. First, in neutral "baseline" sessions, no attentional instructions were given, and observers simply made left/right direction judgments. An individual trial consisted of a 500-msec blank frame containing a fixation point, followed by a 5-frame motion stimulus (5 frames @ 100 msec per frame), followed by another fixation frame. Following the motion sequence, observers were required to enter a direction of motion judgment. On each trial, a chromaticity difference of red or green was chosen randomly from one of three chromaticity differences: 0.32 (pale red or green, barely discriminable from yellow), 0.60 (intermediate red or green), to 1.0 (highly saturated red or green). The chromaticity difference of the other color was chosen as 1.0, so that stimuli varied from having a large green advantage (|R| = 0.32, |G| = 1.0) to having a large red advantage (|R| = 1.0, |G| = 0.32). The starting frame type (texture or color) was chosen randomly. In subsequent sessions, observers viewed similar sequences of stimuli at different distances to yield four spatial-frequency conditions (number of spatial cycles per degree of visual angle).
After the baseline sessions, observers were instructed to attend to a particular color; again, sessions were conducted at four distances. Finally, the entire procedure was repeated with instructions to attend to the opposite color. Two observers completed the entire series in about 64 sessions; 17,600 observations (including practice); a third observer completed only the neutral and the "attend to red" conditions. Stimulus sequences were designed so that there were always two potential interpretations on every trial. When red areas are rendered with a greater chromaticity difference from the background than green areas, |R| > |G|, we say that the stimulus has a "red advantage." Red-advantage stimuli appeared to move in one direction, henceforth called the red direction. When green regions had a greater chromaticity difference, |R| < |G|, there was a green stimulus advantage, and apparent movement was in the opposite direction. Attending to red produced a similar effect to increasing the chromaticity difference of red. In other words, increasing the salience of an area either by increasing its redness or greenness, or by selectively attending to red or to green, increased the probability of perceiving motion in the direction consistent with that color.| |
RESULTS |
|---|
|
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
|
|---|
Observers' responses are plotted as psychometric functions: the
percent of red-consistent apparent motion judgments versus the red
stimulus advantage |R| 
|G|, (green advantage is indicated as
a negative value of red advantage). Obviously, the greater the red
stimulus advantage, the more likely observers are to perceive motion in
the red-consistent direction. In Fig.
2, the only difference between
the psychometric functions within a panel is the attentional state
the
stimuli are identical for all three curves. The size of the lateral
shift of a curve indicates the size of the attentional effect. The
slope of the psychometric functions indicates sensitivity, the steeper
the slope, the greater the sensitivity. Analysis of the data in Fig. 2
indicates that sensitivity (slope) decreases as spatial frequency
increases (panels from top to bottom). The size of the attentional
effect (lateral shift) is independent of spatial frequency. As spatial
frequency increases from top to bottom in Fig. 2, the horizontal shift
of the curves is the same, although it appears to be smaller when the
psychometric functions are shallower at high spatial frequencies.
|
| |
THE MODEL |
|---|
|
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
|
|---|
Components. For each subject, the continuous curves in Fig. 2 account for 99% of the variance of the data. They are derived from a model that includes both a bottom-up and a top-down attentional process (Fig. 3). In the model, there are two types of inputs: visual stimuli and attentional instructions. Visual stimuli are analyzed along various dimensions. Shown are channels that carry depth, orientation, texture, and color signals. In the present experiments, only the color and texture channels are critical for the motion computation.
|
Top-Down and Bottom-Up Control of Salience. In the model, instructions to attend to a color, say red, are assumed to increase the gain of the attended color signal to a value greater than 1.0, so that the attended color input is amplified. For example, if top-down attention to red were to amplify the red-filter output by 1.3, this would indicate an attentional amplification of 30%. Top-down attentional amplification is independent of spatial frequency and of the color (or texture) composition of the input stimuli.
Figure/Ground.
The bottom-up process described above is
assumed to be automatic figure/ground segmentation: Small areas,
areas that contain high-contrast texture, and areas that differ
markedly from their surroundings are assigned higher salience values
relative to large, homogeneous areas, and tend to become segmented as
figure rather than as ground. The present theory does not offer
specific algorithms to determine figure/ground marking in general,
but such rules would be similar to those that have been proposed in the
first half of the century by the Gestaltists and that have been
proposed, for example, by attention theorists (e.g., refs. 1 and
6-10). For the restricted stimuli used in the present experiment, the only computation needed is the amount by which the local texture (in
the texture stimuli) or the local color (in the color stimuli) differs
from the homogeneous backgrounda much simpler computation.
Computational Efficiency.
Although the model is cast within a
physiologically plausible framework, it is a computational model that
describes the microprocesses of attention. As a computational model, it
accounts for 60 data points for each observer (5 stimuli, 3 attention
states, 4 viewing distances) with minimally only four parameters
estimated from an observer: the amplification factors
G, R, for
selective attention to green or to red; the amount of noise N, which
determines the slope of the psychometric functions; and the spatial
"corner" frequency fc, which
describes the spatial frequency at which sensitivity has been reduced
by 1/2. (Spatial filtering in texture and color is assumed to be
identical.) These parameters would have been sufficient to generate the
predictions similar to Fig. 2. However, our primary interest is not in
obtaining the absolutely most parsimonious computational account, but
in demonstrating that a simple model that is consonant with reasonable
physiological processes can provide a quite accurate account.
Attentional Amplification Is Constant, Only the Manifestation
Varies with Spatial Frequency and with the Red/Green Composition
of the Stimuli.
We wish to test the hypothesis that a single
parameter suffices to describe attentional amplification of an attended
color (G and
R) under all conditions. To test
this hypothesis, it is necessary to make all other parts of the model
as accurate as possible so that an attention parameter is not used by
the fitting algorithm to correct deficiencies elsewhere in the model. In order that small errors in describing the shape of the spatial filter function with a single corner parameter would not affect the
above conclusion, we used three parameters to completely describe the
spatial filter function for each observer (Fig.
4b). These spatial
"tuning functions" represent the sensitivity of the channels as a
function of spatial frequency. The corner frequency is between 2 and
4.5 cycles per degree for the three observers. All the changes in slope
of the psychometric functions with spatial frequency are a consequence
of the limited spatial resolution of this single filter. Additionally,
a bias parameter was used for observer EB to account for the fact that
the stimuli were not perfectly equal for him in the neutral attention
conditionred was somewhat more salient. Given accurate specification
of the nonattentional aspects of performance, a single attention
parameter for "attention to red" and another parameter for
"attention to green" was sufficient to predict for each of the
observers, 99% of the variation in performance with attention for the
5 stimuli in each of 4 viewing distances.
|
| |
DISCUSSION |
|---|
|
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
|
|---|
Attention Modifies Only Salience, Not Appearance. It is critical to note that, in the model, the color input to only the salience map is amplified: color inputs to object perception processes are unchanged. This corresponds to the empirical fact that attention to color does not change the appearance of color (26), although it may make judgments of appearance slightly more reliable from trial to trial. It is perfectly obvious in observing our stimuli that they do not change their appearance when attention is selectively directed toward red or green. A change of a few percent in redness or greenness would be easily visible and does not occur when deliberately attending to red or green in stationary stimuli. Certainly, the equivalent changes in salience, 25% and more, are orders of magnitude larger than any possible change in appearance because of attention. This makes perfect sense from functional and physiological points of view: Attention to color should direct processing to colored locations so that whatever is present there (including color) is processed more accurately, but attention should not alter the appearance of the attended color. The distinction between the large effect of selective attention in altering the importance of an object or area of the visual field (its salience), and the small effect of attention in altering the representation of the object itself is absolutely critical, especially in gaining understanding of the neurophysiological correlates of attention.
The salience map, which provides the input to the third-order motion computation, also provides the input to other processes. Because most scenes contain enormously more information than can be remembered (27), there is a selection of to-be-remembered locations; we assume that this selection is determined by the salience map. The salience map is also assumed to control input to object-recognition processes. It directs object-discrimination processes to compute the shape of those parts of the field that have been categorized as figure, and not to compute the shape of those parts that have been categorized as ground.Parallel Attentional Processes. The selection of some parts of the visual input for object processing and, ultimately, for storage in memory, obviously involves attenuation of the to-be-forgotten items (or equivalently, amplification of the to-be-remembered items). At the point where the to-be-forgotten items are excluded from memory, they will have been attenuated almost to 0, relative to the to-be-remembered items. Thus, according to the computational model set forth here, there are two conceptually different representations of the stimulus: First, there is the complex representation of the stimulus itself, in which the local relations between features are unaltered by selective attention. Second, there is the relatively simple salience map representation, in which internal relations between features are dramatically altered by attention (bottom-up or top-down). Corresponding to these two representations are two different kinds of attentional amplification: first, attentional amplification that determines salience; second, attentional attenuation that determines selection for subsequent processing.
Previous computational theories (e.g., refs. 1 and 6-10) have dealt primarily with selection, and have not been concerned with the possibility of two qualitatively different representations, one for salience, another for selection. Typically, these models have a "winner-take-all" architecture, in which one item or object at a time is selected for further processing according to "preattentive" (bottom-up) and attentive (top-down) mechanisms. Can physiological data discriminate between the inner workings of all these proposed attentional mechanisms? There has been explosive growth of research that deals with attention effects measured in brain-imaging studies of human subjects, and in single cell records of behaving primates, including quantitative modeling of single cell responses in an attentional system (30); nevertheless, the physiological basis of the salience map and of the attentional processes proposed here remains to be discovered.How the Salience Model Applies to Common Paradigms In third-order motion, output of the salience map is the input to a field of motion detectors that compute a motion flow field (Fig. 3). In other tasks, other recipients of the salience map output become critical. For example, in a partial report (iconic memory) experiment, an observer views a stimulus with, say, three rows of letters, and a cue directs him/her to report a particular row. Here, we assume that the salience map controls the input to short-term memory. That is, the attentional cue produces top-down activation of the appropriate region of the salience map; this is the mechanism by which access to short-term memory is controlled. In experiments where only one row is displayed, top-down activation is unnecessary; bottom-up activation of the salience map determines that it is the row with letters, not a blank row, that is recorded in memory. Insofar as third-order motion reveals dynamic properties of the salience map rather than of the third-order motion-detection process, it is enormously more efficient to derive the properties of the salience map from motion experiments than from partial-report experiments.
In pattern recognition, the salience map is assumed to determine what part of the field is sent to object recognition mechanisms. Pattern/shape recognition processes are assumed to operate only on the parts of the field that are designated as figure, i.e., those parts that have high values of salience. Normally, figure/ground segregation is a bottom-up process, determined by the texture, shape, and other properties of the visual input. Sometimes, figure/ground segregation is ambiguous, as in Fig. 5a; more often, it is not. For example, in a forest, we normally see the trees as "figure" and the spaces between the trees as "ground." However, when we wish to know whether the space between two trees will enable usand not whatever is chasing usto fit through, the space
has to become figure. In terms of the salience map computation,
attending to the space between trees produces a sufficient increase in
the salience value of that area, so that the open space, not the trees, is sent forward to pattern-recognition processes. That observers can
compute the shape of the space between trees is nicely illustrated in
Fig. 5b.
|
Guided Search. Perhaps visual search is the paradigm that has been used the most to study visual pattern recognition. Typically, an observer views an array of items that includes a target and nontargets (distracters). The search process consists of examining the elements, either in serial or in parallel, or in some combination, to discover the target. Theories of visual search involve the strategic allocation of processing resources (1, 28, 29), according to priorities that are determined by a priori probabilities of finding particular kinds of targets in particular locations and by the properties of the stimulus being viewed. Because it combines both bottom-up and top-down influences, the salience map provides an ideal mechanism to implement precisely this kind of "guided search."
Summary and Conclusions. The full panoply of predictions that can be derived from the computational model still remains to be tested. However, the experimental paradigm that has been put forward here has enabled us to measure the functional amplification produced by visual attention to color in a variety of conditions. The model draws an important distinction between the very large attentional amplification of the salience of an attended color, while the appearance of the color itself is not significantly changed. The attentional amplification of salience is considerable: The maximum amplification values for each of the three observers were 46%, 26%, and 117%. The computational model put forward here is consistent with our overall understanding of the attentional components of the brain processes involved in attentionally determined apparent motion, in object recognition, and in short-term memory, and it provides a highly accurate account of a considerable data set.
| |
ACKNOWLEDGEMENTS |
|---|
This work was supported by the U.S. Air Force Office of Scientific Research, Life Sciences, Visual Information Processing Program.
| |
FOOTNOTES |
|---|
§ To whom reprint requests should be addressed. E-mail: sperling{at}uci.edu.
| |
REFERENCES |
|---|
|
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
THE MODEL
DISCUSSION
REFERENCES
|
|---|
| 1. | Cave, K. R. & Wolfe, J. M. (1990) Cognit. Psychol. 22, 225-271 [CrossRef][ISI][Medline] . |
| 2. | Lu, Z.-L. & Sperling, G. (1995) Nature (London) 377, 237-239 [CrossRef][Medline] . |
| 3. |
Lu, Z.-L., Lesmes, L. A. & Sperling, G.
(1999)
Proc. Natl. Acad. Sci. USA
96,
8289-8294
|
| 4. | Rumelhart, D. E. (1970) J. Math. Psychol. 7, 191-218 . |
| 5. | Sperling, G. & Weichselgartner, E. (1995) Psychol. Rev. 102, 503-532 [CrossRef]. |
| 6. | Koch, C. & Ullman, S. (1985) Hum. Neurobiol. 4, 219-227 [ISI][Medline] . |
| 7. | Mozer, M. (1991) The Perception of Multiple Objects: A Connectionist Approach (MIT Press, Cambridge, MA). |
| 8. | Ahmad, S. & Omohundro, S. (1991) International Computer Science Institute Technical Report tr-91-040 (Univ. California, Berkeley). |
| 9. | Tsotsos, J. K., Culhane, S. M., Wai, W. Y. K., Lai, Y., Davis, N. & Nuflo, F. (1995) Artificial Intelligence 78, 507-545 [CrossRef][ISI]. |
| 10. | Burt, P. (1988) 9th International Conference on Pattern Recognition, Rome, Italy (IEEE Computer Society Press, Washington, DC), pp. 977-987. |
| 11. |
Campbell, F. W. & Gubisch, R. W.
(1966)
J. Physiol.
186,
558-578
|
| 12. | Lowry, E. M. & DePalma, J. J. (1961) J. Opt. Soc. Am. 51, 740-746 [Medline] . |
| 13. | Lowry, E. M. & DePalma, J. J. (1962) J. Opt. Soc. Am. 52, 328-335 . |
| 14. |
Davidson, T. M.
(1966)
Science
152,
797-799
|
| 15. | Campbell, F. W., Nachmias, J. & Jukes, J. (1970) J. Opt. Soc. Am. 60, 555-559 [Medline] . |
| 16. | Anderson, S. J. & Burr, D. C. (1985) Vision Res. 25, 1147-1154 [CrossRef][ISI][Medline] . |
| 17. | Lu, Z.-L. & Sperling, G. (1995) Vision Res. 35, 2697-2722 [CrossRef][ISI][Medline] . |
| 18. | Lu, Z.-L. & Sperling, G. (1996) Curr. Dir. Psychol. Sci. 5, 44-53 . |
| 19. | Sperling, G. & Lu, Z.-L. (1998) in High-Level Motion Processing, ed. Watanabe, T. (MIT Press, Cambridge, MA), pp. 153-183. |
| 20. | Anstis, S. & Cavanagh, P. (1983) in Color Vision, eds. Mollon, J. D. & Sharpe, E. T. (Academic, New York), pp. 155-166. |
| 21. | Chubb, C. & Sperling, G. (1988) J. Opt. Soc. Am. A 5, 1986-2006 [ISI][Medline] . |
| 22. | Chubb, C. & Sperling, G. (1989) Proceedings: Workshop on Visual Motion (IEEE Computer Society Press, Washington, DC), pp. 126-138. |
| 23. | Werkhoven, P., Sperling, G. & Chubb, C. (1993) Vision Res. 33, 463-485 [Medline] . |
| 24. | van Santen, J. P. H. & Sperling, G. (1984) J. Opt. Soc. Am. A 1, 451-473 [ISI][Medline] . |
| 25. | Adelson, E. H. & Bergen, J. R. (1985) J. Opt. Soc. Am. A 2, 284-299 [ISI][Medline] . |
| 26. | Prinzmetal, W., Amiri, H., Allen, K. & Edwards, T. (1998) J. Exp. Psych. Hum. Percept. Perform. 24, 261-282 . |
| 27. | Sperling, G. (1960) Psychol. Monogr. 74, 1-29 . |
| 28. | Koopman, B. O. (1957) Oper. Res. 5, 613-626 [CrossRef]. |
| 29. | Sperling, G. & Dosher, B. (1986) in Handbook of Perception and Performance, eds. Boff, K., Kaufman, L. & Thomas, J. (Wiley, New York), Vol. 1, pp. 1-65. |
| 30. |
Reynolds, J. H., Chelazzi, L. & Desimone, R.
(1999)
J. Neurosci.
19,
1736-1753
|
| 31. | Rubin, E. (1915) Synsoplevede Figurer: Studien i psykologisk analyse (Gyldendalske, Copenhagen). German trans (1921): Visuell wahrgenommene Figuren: Studien in psychologischer Analyse (Gyldendalske, Copenhagen). |
Copyright © 1999 by The National Academy of Sciences 0027-8424/99/9611681-6$2.00/0
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg What's this?
This article has been cited by other articles in HighWire Press-hosted journals:
|
|
 |
|
  X. Li, Z.-L. Lu, B. S. Tjan, B. A. Dosher, and W. Chu Blood oxygenation level-dependent contrast response functions identify mechanisms of covert attention in early visual areas PNAS, April 22, 2008; 105(16): 6202 - 6207. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. M. Muller, S. Andersen, N. J. Trujillo, P. Valdes-Sosa, P. Malinowski, and S. A. Hillyard Feature-selective attention enhances color signals in early visual areas of the human brain PNAS, September 19, 2006; 103(38): 14250 - 14254. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 P. De Weerd, E. Smith, and P. Greenberg Effects of Selective Attention on Perceptual Filling-in. J. Cogn. Neurosci., March 1, 2006; 18(3): 335 - 347. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z.-L. Lu, L. A. Lesmes, and G. Sperling Perceptual motion standstill in rapidly moving chromatic displays PNAS, December 21, 1999; 96(26): 15374 - 15379. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
