 |
 Previous Article | Next Article 
The Journal of Neuroscience, March 1, 1999, 19(5):1736-1753
Competitive Mechanisms Subserve Attention in Macaque Areas V2
and V4
John H.
Reynolds1,
Leonardo
Chelazzi2, and
Robert
Desimone1
1 Laboratory of Neuropsychology, National Institute of
Mental Health, National Institutes of Health, Bethesda, Maryland
20892-4415, and 2 Dipartimento di Scienze Neurologiche e
della Visione, Sezione di Fisiologia, University of Verona, Verona,
Italy
 |
ABSTRACT |
It is well established that attention modulates visual processing
in extrastriate cortex. However, the underlying neural mechanisms are
unknown. A consistent observation is that attention has its greatest
impact on neuronal responses when multiple stimuli appear together
within a cell's receptive field. One way to explain this is to assume
that multiple stimuli activate competing populations of neurons and
that attention biases this competition in favor of the attended
stimulus. In the absence of competing stimuli, there is no competition
to be resolved. Accordingly, attention has a more limited effect on the
neuronal response to a single stimulus. To test this interpretation, we
measured the responses of neurons in macaque areas V2 and V4 using a
behavioral paradigm that allowed us to isolate automatic sensory
processing mechanisms from attentional effects. First, we measured each
cell's response to a single stimulus presented alone inside the
receptive field or paired with a second receptive field stimulus, while
the monkey attended to a location outside the receptive field. Adding
the second stimulus typically caused the neuron's response to move toward the response that was elicited by the second stimulus alone. Then, we directed the monkey's attention to one element of the pair.
This drove the neuron's response toward the response elicited when the
attended stimulus appeared alone. These findings are consistent with
the idea that attention biases competitive interactions among neurons,
causing them to respond primarily to the attended stimulus. A
quantitative neural model of attention is proposed to account for these results.
Key words:
spatial attention; monkey; extrastriate cortex; V2; V4; model
| |
INTRODUCTION |
Experiments on attention in
extrastriate visual cortex can be divided into two types. Those that
have used a single receptive field stimulus have found that attention
can increase the magnitude of neuronal responses (Bushnell et
al., 1981 ; Mountcastle et al., 1987; Spitzer et al.,
1988; Treue and Maunsell, 1996; Gottlieb et al., 1998). In contrast,
studies using multiple receptive field stimuli have found that the
effect of attention depends on the neuron's stimulus selectivity. If
two stimuli appear together within a neuron's receptive field, the
response is smaller when attention is directed to the poorer stimulus
relative to when attention is directed to the preferred stimulus (Moran
and Desimone, 1985; Treue and Maunsell, 1996; Luck et al., 1997).
The purpose of the present experiments was to test a model that can
unify these two streams of research by explaining both types of results
as arising from a common neural mechanism. This "biased-competition
model" (Desimone and Duncan, 1995) depends on two assumptions.
(1) When multiple stimuli appear together, they activate
populations of neurons that automatically compete with one another. (2)
Attending to a stimulus biases this competition in favor of neurons
that respond to the attended stimulus. We tested these hypotheses by
recording neuronal responses in areas V2 and V4, where attention has
been studied previously using both single and multiple receptive field stimuli.
We tested the first hypothesis of the model in Experiment 1. We
measured neuronal responses to two stimuli, both preferred and
nonpreferred, within the receptive field when the monkey was not
required to attend to either stimulus. The stimuli were presented one
at a time and also together as a pair. If the first hypothesis of the
model is true, then the response to a preferred stimulus should be
suppressed by the nonpreferred stimulus, because of the action of the
competing neuronal population activated by that stimulus.
We tested the second hypothesis of the model in Experiment 2. As in
Experiment 1, we measured neuronal responses to two receptive field
stimuli, presented individually and as a pair. We then measured the
response to the pair while the monkey attended to each individual stimulus. If the second hypothesis of the model is true, then this
should cause the pair response to move toward the response that was
elicited when the attended stimulus appeared alone.
A simple three-parameter implementation of the biased-competition model
demonstrates that it can satisfy the two linear constraints that are
imposed by the results of these experiments. Using the parameters
derived to fit these data, the model also fits previously published
data on response modulation when attention is directed to a single
receptive field stimulus. We conclude by describing two easily tested
predictions of the model.
| |
MATERIALS AND METHODS |
Surgery
Many of the details of the recording techniques have been
described previously (Miller et al., 1993a). Briefly, three
adult male rhesus monkeys (Macaca mulatta) were surgically
implanted with a head post, a scleral eye coil, and recording chambers. Surgery was conducted under aseptic conditions with isofluorane anesthesia, and antibiotics and analgesics were administered
postoperatively. Preoperative magnetic resonance imaging (MRI) was used
to identify the stereotaxic coordinates of V2 and V4. V4 recording
chambers were placed over the prelunate gyrus. Additional plastic
recording chambers were used for V2 recordings, centered 15 mm lateral
and 15 mm dorsal to the occipital pole. The skull remained intact during the initial surgery, and small holes (~3 mm in diameter) were
later drilled within the recording chambers under ketamine anesthesia
and xylazine analgesic to expose the dura for electrode penetrations.
Confirmation of recording sites
At the beginning of the study, several penetrations were made in
each chamber to ensure that the electrode was in the appropriate visual
area. This was determined by assessing receptive field sizes,
topographic organization, and feature preferences at each site. All
implants were nonferromagnetic (plastic recording chambers, titanium
screws, and brass head posts), so it was possible to verify the
locations of our recording sites using additional MRI scans. After our
experimental data were collected, we rescanned two monkeys with a
marker electrode (sharpened tungsten microelectrode; Frederick Haer & Co., Brunswick, ME) inserted in each recording chamber at the
coordinates used during recording. We used a plastic cylinder that fit
snugly inside the recording well to hold the marker electrode in place
during the scan. At each end of the cylinder was a grid that was
perforated with small holes, spaced 1 mm apart (Christ Instruments,
Damascus, MD). Each marker electrode was lowered through the grids and
into the brain to a depth of ~2 cm beneath the dura using the same
micropositioner and x-y stage that had been used
during recording. Before the micropositioner and
x-y stage were removed, a drop of glue was
applied to hold the marker electrode in the grid. After the
micropositioner and x-y stage were removed, the
end of the electrode that was protruding from the recording well was
then cut, and a plastic cap was placed over the recording chamber
during the scan.
These marker electrodes were clearly visible in each scan. The
positions of these markers, the positions of electrode tracks made
during recording, and the positions of the holes in the skull beneath
each recording chamber all verified that our recording sites were
appropriately located in areas V2 and V4. The third monkey, from which
eight neurons were recorded, has not been rescanned.
Recording technique
Recordings were obtained from a tungsten microelectrode that was
controlled by a hydraulic microdrive. We made no effort to select
neurons from a particular layer of cortex. We recorded from the first
neurons encountered that could be clearly isolated and had sufficiently
large receptive fields (see Receptive field mapping). The portion of
area V4 where we recorded was directly beneath the recording chamber,
so the first cells encountered were those in the superficial cortical
layers. Neurons in area V2 were recorded by passing the electrode
through V1 on the opercular surface, through the underlying white
matter, and into the portion of V2 that lies on the posterior bank of
the lunate sulcus. Therefore, the first cells encountered in V2
recordings were typically in the deep layers. Thus, there may be a bias
toward deeper recordings in V2 and more superficial recordings in V4.
In most cases, two neurons could be recorded simultaneously and
differentiated on the basis of the size and shape of the spike waveform, and an on-line spike-sorting computer was used to classify these spikes by means of a template-matching procedure. Although this
system allowed the concurrent recording of two neurons, spikes arising
from both neurons simultaneously (within a 1 msec interval) could not
be detected.
Stimuli
The stimuli used throughout all experiments in both cortical
areas were selected from a set of 16 stimuli composed of all combinations of four oriented bars (0, 45, 90, and 135°) presented in
four colors (red, blue, green, and yellow). The bars were 0.25° of
visual arc wide by 1° in length. The colors were chosen to be
photometrically equiluminant, with a luminance of 8.60 cd/m2, presented against a gray background of
luminance 0.65 cd/m2. In Experiment 1, all stimuli
were 250 msec in duration. In Experiment 2, stimulus duration ranged
from 50 to 250 msec.
Receptive field mapping
A manually (computer mouse) controlled flashing bar stimulus was
used to establish the outer boundaries of the multiunit receptive field. After cells were isolated, this same flashing bar stimulus was
used to estimate the position in the visual field where stimuli would
generate the strongest response (the "hot spot" of the cell). Each
of the 16 possible stimuli described above was then presented at this
position, and the stimulus that gave the greatest response was
identified. This stimulus was then repeatedly presented in a random
sequence at 11 positions falling at regular intervals along an arc of
equal eccentricity centered on the hot spot and extending bilaterally
into the surround of the receptive field. The responses of the cell at
these 11 positions constituted a one-dimensional profile of the
receptive field. Two of these 11 positions were used throughout the
rest of the recording session. These two positions were selected to
give approximately equivalent responses to the mapping stimulus and to
be clearly inside the receptive field. To achieve these two goals, we
found that it was necessary to place stimuli closer together for
neurons with smaller receptive fields. Therefore, the stimuli used to
test neurons in area V2 typically were closer together than were those used to test neurons in area V4. Because most receptive fields in both
V2 and V4 were approximately symmetric around the hot spot, the two
positions were typically approximately symmetric around the hot spot.
For neurons with small receptive fields (including most V2 neurons), it
was usually the case that the 11 positions used in this automatic
mapping procedure were spaced closely together. We were careful to
choose positions that were far enough apart to avoid overlap between
the stimulus pairs that would appear together in the main experiment.
If a receptive field was too small to fit two stimuli easily at equally
potent positions inside the receptive field, the neuron was excluded,
and we attempted to isolate a different neuron.
Experiment 1: characterization of V4 neurons' responses to
stimulus pairs
Stimulus configurations and experimental procedures.
The stimulus configuration used in Experiment 1 is shown in Figure
1A. The monkey was
rewarded for passively fixating a fixation spot at the center of the
computer screen while stimuli were presented within the receptive field
of the neuron under study. Stimuli could appear at one of two possible
locations within the receptive field, which were selected as described
above. At the beginning of a recording session, the stimulus that would
appear at position one, designated the reference stimulus, was chosen
from the set of 16 possible stimuli (four orientations × four
colors) described above. The identity of the reference stimulus was
fixed throughout the recording session. On each trial, the stimulus
that would appear at position two, designated a probe stimulus, was
selected at random from the same set of 16 possible stimuli. We sought to test neuronal responses to stimulus pairs across the full spectrum of possible reference-probe selectivity values. Therefore, the reference stimulus was chosen sometimes to be the stimulus (among the
set of 16 possible stimuli) eliciting the largest response, sometimes
to be that eliciting the smallest response, and sometimes to be that
eliciting a response that fell between the largest and the smallest
response. There is no reason to believe that the best stimulus of the
16 was the "optimal" stimulus for the cell.
View larger version (27K):
[in this window]
[in a new window]
|
Figure 1.
Stimulus configurations, Experiments 1 and 2, and
task, Experiment 2. A, In Experiment 1, stimuli could
appear at two locations within the receptive field (indicated by the
dotted outline). On a given trial, either (1) the
reference stimulus appeared at position 1, (2) a probe stimulus
appeared once at position 2, or (3) the reference appeared at position
1 and a probe appeared at position 2. B, In Experiment
2, stimuli could appear at four positions: two within the receptive
field and two across the vertical meridian. In the attend-away
condition, the monkey attended to one of the stimuli across the midline
from the receptive field. On each trial, the reference, the probe, or
the pair appeared within the receptive field. In the
attend-receptive-field-stimulus condition, stimuli appeared at all four
positions, and the monkey attended to the reference or probe stimulus
within the receptive field. C, Examples of stimulus
sequences. The monkey's task was to respond when a diamond-shaped
target appeared at the attended location, while ignoring distractor
targets, which occasionally appeared at the other locations. From trial
to trial, the length of the stimulus sequence varied at random, so the
monkey never knew when the target would appear. At the beginning of a
block of trials, there were a few instruction trials, in which a bright
cue box appeared at the location to be attended. After the monkey was
reliably responding to the targets appearing at the cued location and
ignoring distractors appearing at other locations, the cue was removed,
and the task continued in the absence of the cue. From block to block,
the monkey was recued to attend to a different location.
|
|
On any given trial, stimuli appeared in one of three possible
configurations. (1) the reference stimulus appeared at position one, 2)
a probe stimulus appeared at position two, or 3) the reference stimulus
appeared at position one together with a probe stimulus at position
two. Whenever a trial included a probe stimulus (i.e., configurations 2 or 3), the identity of the probe stimulus was selected for that trial
at random from the set of 16 possible stimuli. A recording session
consisted of 540 complete trials. These were composed of 60 trials in
which the reference stimulus appeared alone, 240 trials in which each
of the 16 possible probes appeared alone (15 repetitions of each
probe), and 240 trials in which each of the 16 possible probes appeared
with the reference stimulus (15 repetitions of each pair).
Data analysis. For each cell, we computed the average firing
rate over a 250 msec window (stimulus duration) beginning 70 msec after
stimulus onset (typical V4 neuron response onset). We chose this time
window to cover the neuron's full response period. To verify that our
results are not an artifact of this particular time window, we have
repeated this analysis using time windows that included only the first
100 msec of response, the second 100 msec of response, and the time
window that was used in Experiment 2. All of these analyses yielded
qualitatively similar results.
Averages were computed in three stimulus configurations: (1) the
reference stimulus appearing alone, (2) each of the 16 probe stimuli
appearing alone, and (3) each of the 16 resulting reference-probe pairs. We normalized all responses by dividing by the highest firing
rate observed within that time window in any stimulus condition. We
then computed the difference between the normalized response of the
cell to the reference stimulus (REF) and each probe
(PROBEi). This yielded 16 selectivity values,
denoted SEi, for each cell:
SEi = PROBEi  REF. This
selectivity index can range from 1 to +1, with negative values
indicating that the reference stimulus elicited the stronger response,
a value of 0 indicating identical responses to reference and probe, and
positive values indicating that the probe stimulus elicited the
stronger response.
We then computed an index that quantified the change in firing rate
that resulted from adding the probe stimulus to the reference stimulus.
This sensory interaction index (SIi) is the difference between the normalized response to the reference stimulus (REF) and the normalized response to the pair composed of the reference
stimulus and the ith probe stimulus
(PAIRi): SIi = PAIRi REF. Like the selectivity index, the
sensory interaction index can take on values from 1 to +1. Negative
values indicate that the response to the pair was smaller than the
response to the reference stimulus (i.e., adding the probe stimulus
suppressed the neuronal response). A value of 0 indicates that adding
the probe stimulus had no effect on the neuron's response. Positive
values indicate that adding the probe increased the neuron's response.
For each neuron, we quantified the relationship between selectivity and
sensory interactions by performing a linear regression on the 16 selectivity and sensory interaction indices. A criterion level of
p < 0.05 was used in all statistical analyses.
These indices were computed to test the first assumption of the
biased-competition model. According to the model, when two stimuli
appear within a neuron's receptive field, the pair response is
predicted to fall between the responses elicited when the stimuli appear individually. Thus, the response to a preferred reference stimulus (SE < 0) is predicted to be suppressed by the addition of a poor probe stimulus within the receptive field (SI < 0). Likewise, the response to a poor reference stimulus (SE > 0)
should be increased by the addition of a preferred probe stimulus
within the receptive field (SI > 0). Finally, if a reference
stimulus and a probe stimulus generate equivalent responses (SE = 0), then the pair response is predicted to be equal to either
individual stimulus response (SI = 0). Thus, according to the
biased-competition hypothesis, the relationship between sensory
interactions and selectivity should be positive and should pass through
the origin (SE = SI = 0).
The possibility that stimulus onset may have captured
attention. One possible concern is that the appearance of a
stimulus can capture attention, even if behaviorally irrelevant. If
this occurred for the stimuli used in the present experiment, it might have caused a change in neuronal response. Although we cannot eliminate
this possibility, we do not believe that this presents a serious
problem. The empirical relationship found between selectivity and
sensory interactions under passive fixation in Experiment 1 was
replicated in Experiment 2, in which the monkey actively attended to
stimuli presented simultaneously outside the receptive field.
Thus, any exogenous attention effects in Experiment 1 were evidently
small in magnitude, possibly because the monkey learned to ignore the
peripheral stimuli after thousands of stimulus presentations. Second,
our conclusions do not depend on the absolute magnitude of neuronal
responses. Instead, they depend on a comparison of responses to the
different stimuli. Provided that attention was not directed
preferentially to a particular stimulus, any effect of attention would
not be expected to influence the observed dependence of sensory
interactions on selectivity.
Experiment 2: characterization of the effect of attention on
sensory interactions in areas V2 and V4
Stimulus configurations and experimental procedures.
The attention task, which is similar to a task described previously
(Luck et al., 1997), is illustrated in Figure 1, B and
C. At the beginning of a recording session, a reference
stimulus and a probe were selected from the same set of 16 possible
stimuli used in Experiment 1. These two stimuli were used throughout a
recording session.
Stimuli could appear at four locations: two locations within the
receptive field and two other locations outside the receptive field. To
minimize the possibility that the extrareceptive field stimuli appeared
within the surround of the receptive field, we placed these stimuli
across the vertical meridian. As an added precaution, we avoided
recording from cells whose receptive fields were near the vertical
meridian. For the majority of recordings, the across-meridian stimuli
appeared at positions that were mirror images of the receptive field
locations, as depicted in Figure 1B. For a few
recordings, the across-meridian stimuli appeared at positions that were
above the horizontal meridian, 180° from the receptive field. The
results of the experiment did not seem to depend on which of these two
configurations was used. Nevertheless, it remains possible that for
some cells, the extrareceptive field stimuli may have fallen within the
surround of the receptive field. However, these stimuli appeared in all
attention conditions and all configurations of receptive field stimuli
(probe, reference, and pair). Therefore, any effect that they
potentially may have had on neuronal responses would not be expected to
bias our results. Also, similar patterns of attention effects were
observed in areas V2 and V4, despite the fact that V4 receptive fields
and surrounds are significantly larger.
During a block of trials, the monkey had to attend to one of the
positions and ignore the others to detect a target stimulus at the
attended location. In the "attend-away" condition, the monkey
attended to stimuli appearing at one of the two positions across the
vertical meridian from the receptive field while the reference
stimulus, the probe stimulus, or the pair appeared within the receptive
field. In the "attend-receptive-field-stimulus" condition, the
reference and probe stimuli both appeared together within the receptive
field, and attention was directed to either the reference stimulus or
the probe stimulus within the receptive field. Simultaneously,
unattended stimuli appeared at the two positions across the vertical
meridian from the receptive field.
We directed the monkey's attention to a given location as follows. At
the beginning of each block of trials, the monkey received a few
(typically three to five) "instruction trials" that indicated where
it was to attend for that block of trials. On these instruction trials,
a bright cue box appeared at the location to be attended (see Fig.
1C.) The monkey's task was to detect the presence of a
diamond-shaped target stimulus appearing at the cued location. This
target appeared at the end of a variable-length sequence of zero to six
nontarget (reference or probe) stimuli. The length of a given sequence
varied from trial to trial. Therefore, the monkey could not know in
advance when the target diamond would appear and had to attend to the
cued location throughout the trial to distinguish the target from the
nontargets, release the bar, and earn reward. Stimulus sequences
appeared synchronously at the other positions, and distractor targets
occasionally appeared embedded within them. If the monkey released the
response lever after the appearance of a distractor target, the trial
was aborted, and another trial began after a brief delay. After the
monkey was reliably releasing the response lever when the target
appeared at the cued location, the cue was removed, and the monkey had to continue performing the task without the cue.
It was rarely the case that the monkey would work long enough for us to
complete an experiment, find a new set of cells, and complete a second
experiment within a single recording session. However, it was often
possible to continue recording from the same neuron using an additional
stimulus pair. Therefore, to maximize the amount of data collected, we
recorded from a neuron with one or more additional reference-probe
pairs, whenever possible.
Data analysis. Neuronal responses were analyzed for trials
occurring after the spatial cue was removed (i.e., the instruction trials were excluded from the analysis). We measured neuronal responses
during a 150 msec time window beginning 120 msec after stimulus onset
(the period over which we typically observed attentional modulation).
To verify that our results are not an artifact of this particular time
window, we have repeated this analysis using time windows that depend
on stimulus duration and on response onset time and windows that varied
from cell to cell to cover the period of attentional modulation. All of
these analyses yielded qualitatively similar results. However, it is
worth noting that, as a result of variability in the timing of sensory
interactions and attention effects, we did miss some effects that fell
outside the time window. See, for example, the figure that shows the
response of a neuron for which sensory interactions and attention
effects began before the beginning of this time window (see Fig.
7).
Because nontarget stimuli greatly outnumbered target stimuli and
distractor targets, the nontarget responses could be measured more
reliably than the target responses. Therefore, our results are based on
analysis of responses to nontarget stimuli. However, under similar
experimental conditions, Luck et al. (1997) have compared the effect of
attention on neuronal responses to target versus nontarget stimuli.
They found that spatial attention has comparable effects on responses
to target and nontarget stimuli.
Responses were measured in five conditions. In the attend-away
condition, we measured responses to (1) the reference stimulus, (2) the
probe stimulus, or (3) the pair, while the monkey attended away from
the receptive field. In the attend-receptive-field-stimulus condition,
we measured the pair response, while attention was directed to (4) the
reference stimulus or (5) the probe.
As in Experiment 1, we normalized each cell's responses by dividing
all firing rates by the highest firing rate observed, for that cell, in
any of the five conditions. Using these normalized responses, we then
computed a selectivity index, SE, for each reference and probe: SE = PROBE REF. We then computed a sensory interaction index for
each of the three attentional conditions (attend away, attend to
reference, and attend to probe). This was the difference between the
response to the reference stimulus (REF) and the response to the pair
of stimuli, with attention directed either away from the receptive
field (PAIRa), toward the reference stimulus
(PAIRr), or toward the probe
(PAIRp): SIa = PAIRa REF, SIr = PAIRr REF, and SIp = PAIRp REF. These indices are comparable with
the indices derived in Experiment 1, except that
SIr and SIp correspond to
sensory interactions when attention was directed to the reference and probe stimulus, respectively.
Experiment 2 included many more experimental conditions than did
Experiment 1. Therefore, to avoid a combinatorial explosion, it was
necessary to reduce the number of probe stimuli from the 16 probes used
in Experiment 1. Typically the monkey worked long enough to enable us
to record from at most four different reference-probe pairs. For many
cells, only one pair could be tested completely. It was therefore
impossible, in Experiment 2, to quantify the relationship between
selectivity and sensory interactions across stimuli within single
cells. Instead, these comparisons were made across neurons within each
cortical area. As in Experiment 1, we quantified the relationship
between selectivity and sensory interactions in each cortical area by
performing linear regressions on the selectivity and sensory
interaction indices derived for each cell.
Model simulations
A simple model neural circuit, illustrated in Figure
2, was used to simulate the results of
Experiments 1 and 2. The model, which is a simple feedforward
competitive neural network, is defined by the four equations shown at
the bottom of Figure 2. The model includes two classes of cells. The
circle at the top of Figure 2 represents the neuron (output) being
measured. The two circles at the bottom of the diagram in Figure 2
represent populations of upstream neurons (inputs) that respond to the
reference (left) and probe (right) stimuli. Lines connecting the
inputs to the output represent feedforward synaptic connections.
Inhibitory inputs are assumed to depend on inhibitory interneurons (not
shown).
View larger version (17K):
[in this window]
[in a new window]
|
Figure 2.
Model circuit diagram. The circle
on top represents the neuron being recorded. The
variable y is the firing rate of this neuron. The
two circles at the bottom of the
diagram represent populations of "input" neurons
that respond to the reference (left) and probe
(right) stimuli and that project to the measured cell.
The average response of the ith input population is
designated xi. Black lines
indicate the excitatory projections from each input population to the
measured cell, and gray lines indicate the inhibitory
projections, which are assumed to depend on inhibitory interneurons
(not shown). The variable
wi+ is the
magnitude, or weight, of the excitatory projection from the
ith input population, and
wi is the weight
of the inhibitory projection from the ith input
population. For a complete description of the model, see Materials and
Methods.
|
|
Equations 1 and 2 (Fig. 2) describe the total excitatory and
inhibitory inputs, respectively, to the measured cell. The total excitatory input to the cell (E) is simply the sum of
the activities of the two input populations multiplied by their
respective excitatory weights, as shown in Equation 1. The total
inhibitory input to the cell (I) is the sum of the
activities of the two input populations multiplied by their respective
inhibitory weights, as shown in Equation 2.
Equation 3 (Fig. 2) describes how the firing rate of the output neuron
(y) changes over time. This equation was originally introduced by Grossberg (1973) to provide an explanation of how feedforward competitive neural networks can be constructed to avoid
saturating their responses to strong inputs (e.g., high-contrast stimuli) while remaining sensitive to weak inputs. See Grossberg and
Levine (1975) and Grossberg (1976, 1980) for further discussion.
The first term [(B y)E]
governs excitatory input. B is the maximum response of the
cell. Therefore, (B y) is always
positive. If excitatory input is greater than zero, then
(B y)E is positive, resulting
in an increase in response that grows smaller as the cell's response
y approaches its maximum rate. The second term ( yI) governs inhibitory input. If inhibitory input is
greater than zero, then yI is negative, resulting in
a decrease in response toward zero. The third term ( Ay)
is a passive decay term.
The net effect of excitatory and inhibitory input is described by
Equation 4 (Fig. 2), which is the equilibrium response of the output
neuron. The passive decay parameter A and the cell's maximum response B are constants. Therefore, the equilibrium
response depends on the relative contributions of the excitatory input E and the inhibitory input I. Large values of
E will drive the equilibrium firing rate toward the cell's
maximum firing rate B. Large values of I will
cause the cell to remain silent.
Attention is assumed to increase the strength of the signal coming from
the population of cells activated by the attended stimulus. The exact
mechanism by which this increase could occur is unknown. It is
implemented here by increasing the efficacy of synapses projecting to
the measured cell from the population activated by the attended
stimulus. Increasing the strength of the signal from the attended
stimulus population causes it to have a greater influence on the total
mix of excitation and inhibition. Consequently, the response of the
cell is driven toward the response that would be elicited if the
attended stimulus were presented alone.
For all simulations, the maximum neuronal firing rate B was
arbitrarily set to 1, and the passive decay parameter A was
set to 0.2. For each model neuron, the excitatory and inhibitory
weights projecting from the populations of neurons activated by the
reference and probe stimuli were selected at random from a uniform
distribution ranging from 0 to 1. To simulate the stochastic nature of
neural responses, ±10% random noise, selected from a uniform
distribution, was added to the response of the cell in each condition.
Attention was implemented by increasing by a factor of 5 the excitatory and inhibitory synaptic weights projecting from the input neuron population responding to the attended stimulus. No other parameters appear in the model.
Simulation of Experiment 1. The responses of each model
neuron to the reference stimulus, the 16 probes, and the corresponding 16 pairs were computed as follows. The reference stimulus and each of
the 16 probes were assumed to activate their own input populations.
Each of these input populations was assigned an excitatory and an
inhibitory weight at random from a uniform distribution ranging from 0 to 1. In the circuit diagram shown in Figure 2, the input on the left
is intended to correspond to the reference stimulus, which is constant
for a given cell in the simulation of Experiment 1. The input on the
right is intended to correspond to one of the 16 probe stimuli.
For each probe, the model was activated in three conditions. In the
reference condition, the reference input activity level was set to 1, and the probe input was set to 0. In the probe condition, the probe
input was set to 1, and the reference input was set to 0. In the pair
condition, the probe and reference inputs were both set to 1. In each
of the three conditions, the equilibrium response of the model neuron
was computed according to Equation 4 in Figure 2. The resulting
responses were then used to compute the indices of selectivity and
sensory interaction, as described for Experiment 1.
Simulation of Experiment 2. The model simulation of
Experiment 2 was conducted in the same manner as was the simulation of Experiment 1, with two changes. First, only one probe and one reference
were presented to each model neuron. So, in the circuit diagram shown
in Figure 2, the left input is intended to correspond to the reference
stimulus, and the right input is intended to correspond to the probe.
Second, to incorporate attention to the reference and probe stimulus,
the model was also simulated in two additional conditions. In both of
these conditions, the reference and probe input activity levels were
both set to 1. In the attend-reference condition, the strengths of
synaptic weights from the reference stimulus input were multiplied by a
factor of 5. In the attend-probe condition, the strengths of synaptic
weights from the probe stimulus input were multiplied by a factor of 5.
| |
RESULTS |
Experiment 1
Experiment 1 was designed to examine how the responses elicited by
a single stimulus within the receptive field (the reference stimulus)
are influenced by the addition of a second receptive field stimulus
(the probe), in the absence of attentional modulation. Depending on the
mechanisms that govern responses to stimulus pairs, adding the probe
might be expected to result in an increase, a reduction, or a more
complex change in the pair response, compared with the response
elicited by the reference stimulus. An increase in response could occur
as a result of additional recruitment of V1 afferents by the second
stimulus. A reduction in response could occur as a result of diminished
bottom-up or top-down excitatory drive. Response suppression by
extrareceptive field stimuli has been observed in area V1 (Knierim and
Van Essen, 1992; Levitt and Lund, 1997). Response suppression has also
been observed in higher-order areas that provide feedback to areas V2
and V4 (Miller et al., 1993b; Rolls and Tovee, 1995).
Alternatively, the individual stimulus responses might bear no
systematic relationship to the response elicited by the pair. For
example, the pair response might depend on factors other than the
firing rates elicited by the individual stimuli, such as the geometric
relationships between the stimuli (Kapadia et al., 1995; Sillito et
al., 1995) or their color contrast (Kiper et al., 1997). Or, V2 and V4
cells might simply treat the pair as a third, independent stimulus,
with its own arbitrary response.
In contrast to these alternatives, the biased-competition hypothesis
predicts that the pair response should fall between the responses to
the reference and probe stimuli. According to the hypothesis, stimuli
activate competing populations of neurons. To the extent that a probe
stimulus has any influence on the neuronal response, the probe should
move the pair response toward the response the probe would give if it
had been presented alone. Adding a low-firing rate probe should drive
down the response to a high-firing rate reference stimulus. Adding a
high-firing rate probe should drive up the response to a low-firing
rate reference stimulus. If probe and reference stimuli individually
elicit identical responses, then this same response should be generated
when they appear together as a pair.
We recorded the responses of 18 neurons from area V4 of one monkey. Our
results indicate that in area V4, the responses of neurons to pairs of
bar stimuli are a weighted average of the individual stimulus
responses. This is illustrated in Figure
3, which shows the responses of a typical
cell to the reference stimulus, the 16 different probes, and the
resulting 16 stimulus pairs. Figure 3, A-C, shows the
effect of adding three of the probe stimuli. For a probe that elicited
a lower mean response than did the reference (Fig. 3A), the
addition of the probe was suppressive. For a probe that elicited an
average response approximately equal to the response elicited by the
reference stimulus (Fig. 3B), the pair response was similar
to the responses to the probe and reference. For a probe stimulus that
elicited a stronger response than did the reference stimulus (Fig.
3C), the addition of the probe caused an increase in the
cell's mean response.
View larger version (44K):
[in this window]
[in a new window]
|
Figure 3.
Single cell, Experiment 1. A-C,
The response of a single V4 neuron to the reference, a probe, and the
corresponding pair is shown in each panel. Stimulus
conditions are indicated by the square icons in
A-C. The receptive field is indicated by the
dotted outline in each icon. The dot in
the top right corner of each icon represents the
fixation point. The x-axis shows time (in milliseconds)
from stimulus onset, and the thick horizontal bar
indicates stimulus duration. The vertical bar in the
upper left corner shows the SEM of the response
of this neuron, averaged over the three stimulus conditions for each
panel. The blue line that is constant
across all three panels shows the response to the
reference stimulus, which was a vertical green bar. The
response to the reference stimulus averaged over the defined time
window (70-320 msec after stimulus onset) was 11.75 spikes/sec.
A, The green line indicates the response
to a vertical yellow probe that drove the cell at a low
average rate (4.51 spikes/sec). The response to the pair, indicated by
a red line, was strongly suppressed by the probe
stimulus (5.31 spikes/sec). B, A 45° blue
bar probe, which elicited a response that was slightly smaller
than the response to the reference stimulus (mean response, 8.76 spikes/sec), caused a smaller suppression in the cell's response (mean
pair response, 8.82 spikes/sec). C, A 45° green
bar probe, which elicited a response that was larger than the
response to the reference (mean response, 17.80 spikes/sec), increased
the cell's response (mean response to pair, 13.81 spikes/sec).
D, Indices of selectivity (x-axis) and
sensory interaction (y-axis) for all 16 probe
stimuli are shown. The indices corresponding to each of the probes
illustrated in A-C are indicated by
squares and are labeled in D. A negative
selectivity index (indicating that the response to the probe was less
than the response to the reference stimulus) was typically paired with
a negative sensory interaction index (indicating that the addition of
the poor probe suppressed the response of the cell). Nonselective
reference-probe pairs showed little or no sensory interactions.
Preferred probes increased the response to the reference stimulus.
Ref, Reference stimulus.
|
|
This relationship held across all 16 probe stimuli, as illustrated in
Figure 3D. Each point corresponds to the indices of sensory
interaction (y-axis) versus selectivity
(x-axis) for each probe stimulus. Points labeled
A-C correspond to the examples shown in Figure 3,
A-C. The data were positively correlated
[r2 = 0.53;
r2 significantly different from 0, F(1,15) = 16.91 and p = 0.001] and fell along a line with a positive slope (+0.67), indicating that
the effect of adding a probe stimulus was proportional to the
selectivity of the cell's response to the reference and the probe
stimulus. Adding a probe tended to suppress the neuronal response if
the probe presented alone elicited a smaller response than did the
reference stimulus. A probe tended to increase the neuronal response if
the probe alone elicited a larger response than did the reference
stimulus. The most suppressive probes tended to be those that elicited
the smallest responses, when presented alone. The probes causing the
greatest increase in response tended to be those that elicited the
largest responses, when presented alone. The intercept (0.02) was not
significantly different from 0 [t(14) = 0.40;
p = 0.3486]. Thus, probes such as the one
corresponding to point B in Figure 3D, which gave
responses similar to that of the reference stimulus, had little or no
effect when added to the reference stimulus.
Sensory interactions are, for this cell, approximately proportional to
selectivity. Therefore, the slope of the best-fit line provides a
convenient way to quantify the relative influence exerted by the stimuli.
The equation of the best-fit line relating selectivity and sensory
interaction indices can be
written: SIi = w
SEi + offset, where w is
the slope of the regression equation, and offset is the increase or
decrease in response that is not accounted for by selectivity (i.e.,
the vertical intercept of the best-fit line). This equation can be
rewritten: PAIRi REF = w (PROBEi REF) + offset. Rearranging terms, this can be
expressed: PAIRi = w
PROBEi + (1 w)REF + offset.
Thus, the response to the pair (PAIRi)
is the average of the response to the probe
(PROBEi) and the response to the reference
stimulus (REF), weighted by the slope w, plus the offset
term. The slope of the best-fit line (w) indicates how
heavily the pair response is weighted toward the response to the probe.
The value (1 w) is the weighting factor of the reference stimulus. The slope w for the cell illustrated in
Figure 3 was 0.67, and the offset was not significantly different from 0. Therefore, for this cell, the pair response can be described as a
weighted average of the responses to the probe and reference stimuli,
with the reference stimulus exerting less influence (0.33) than the
probe (0.67). Note that, for this neuron, the reference stimulus
exerted less influence than the probe despite the fact that the
response to the reference was larger than the responses to 13 out of 16 probes (probes with negative selectivity values). Surprisingly, the
degree of influence exerted by a given stimulus does not seem to depend
only on the magnitude of the response elicited by that stimulus.
Although this weighting factor varied from cell to cell, responses to
the pair typically conformed to this pattern. Figure 4 shows examples of six cells (including
the example from Fig. 3D for comparison) that
illustrate the range of correlations we observed. Across the
population, sensory interactions were proportional to selectivity.
However, responses to the pair showed varying degrees of
reference-probe sensitivity. For some cells, such as the one
illustrated in Figure 4D, the pair response depended
strongly on reference-probe selectivity (intercept = 0.05;
slope = 0.8162), indicating that the pair response was determined
primarily by the probe stimulus and not by the reference. For others,
such as the cell illustrated in Figure 4F, the
responses to the pairs were approximately equal to the response to the
reference stimulus, regardless of the size of the responses to the
probes (intercept = 0.006; slope = 0.11). The cell was
selective for the probes, but there was no corresponding change in the
responses to the pairs, which were approximately equal to the response
to the reference stimulus. Sixteen of 18 cells (89%) had regression
slopes between 0 (pair response equal to the response to the reference
stimulus, regardless of the response to the probe) and 1 (pair response equal to the response to the probe). The two cells with slopes outside
this range had small slopes (0.07 and 0.06) that were not
significantly different from 0 [F(1,15) = 0.1728 and p = 0.6835; F(1,15) = 0.0669 and p = 0.80, respectively]. Across the
population, the mean regression intercept was 0.01, which was not
significantly different from 0 [t(17) = 0.5619;
p = 0.5815], indicating that, on average, the addition
of the probe stimulus did not result in a net change in neuronal
response that could not be attributed to the selectivity of the
cell for reference and probe.
View larger version (42K):
[in this window]
[in a new window]
|
Figure 4.
Six representative neurons, Experiment 1. A-F, The correlation between selectivity and sensory
interactions, across 16 probes, for one cell. A, The
same cell that appeared in Figure 3 shown for comparison.
B-D, Cells whose responses to pairs showed a greater
degree of probe control (slope > 0.5). E,
F, Cells for which the reference was the dominant
stimulus (slope < 0.5). In all cases, probe-reference
pairs for which the cell was nonselective showed little or no sensory
interactions.
|
|
When the probe influenced the neuronal response, it typically moved the
pair response toward the response that was elicited by the probe alone.
Across cells, the neuronal response was significantly changed by the
addition of the probe for 83 stimulus pairs tested (two-tailed
t test, p < 0.05). For 35 out of 83 (42%)
of these, the probe suppressed the pair response, and for the remaining 48 out of 83 (58%), the probe increased the pair response. Of the 35 pairs for which the probe was significantly suppressive, 34 out of 35 (97%) of these probes were less preferred than was the reference
stimulus (i.e., poor probes that suppressed the neuronal response). Of
the 48 pairs for which the probe increased the response, 37 out of 48 (77%) of these probes were relatively more preferred than was the
reference stimulus (i.e., preferred probes that increased the neuronal
response). Thus, when the probe caused a significant change in the
neuronal response, this change was toward the response elicited by the
probe 86% (71 out of 83 probes) of the time.
These data are incompatible with some possible models of sensory
processing in areas V2 and V4. In particular, we can eliminate models
in which the response to a pair of stimuli is greater than the response
to the preferred stimulus appearing alone or less than the response to
the poor stimulus alone. We can also eliminate models in which the pair
is treated as a third, independent stimulus, with its own arbitrary response.
Experiment 2
The second experiment was designed to examine the relationship
between (1) selectivity, (2) the sensory interactions resulting from
adding a probe stimulus within the receptive field of the cell, and (3)
the effect of directing attention to either the reference or probe
stimulus. In agreement with the finding of Luck et al. (1997), we often
observed increases in the spontaneous firing rate of neurons when
attention was directed to a location within the receptive field, during
the period before the stimulus appeared. However, in the present
experiment, we were interested in characterizing the effects of
attention on neuronal responses evoked by stimuli within the receptive
field. Therefore all of the results described below are based on
stimulus-evoked responses.
We recorded in areas V2 and V4, where previous studies have found
attentional modulation of neuronal responses. We measured the responses
of 158 neurons in three monkeys (86 in V2; 72 in V4). For some cells,
the monkey worked long enough to record responses from more than one
reference-probe pair. Responses were analyzed for all reference-probe
pairs for which reference and probe each gave significant responses,
relative to the neuron's spontaneous firing rate, with attention
directed away from the receptive field of the cell. A total of 208 stimulus pairs in 67 V2 cells and 138 stimulus pairs in 57 V4 cells
gave significant responses (two-tailed t test,
p < 0.05) for both reference and probe.
For these 124 neurons (346 stimulus pairs), we analyzed the
relationship between selectivity and sensory interactions. Consistent with the results of Experiment 1, the effect of adding a probe depended
on the cell's selectivity for reference and probe. This is illustrated
in Figure 5, which shows the relationship
between selectivity (x-axis) and sensory interactions
(y-axis) for cells in V2 (Fig. 5A) and V4
(Fig. 5B). In both cortical areas, there is a strong
correlation between selectivity and sensory interactions [V2,
r2 = 0.58;
r2 significantly different from 0, F(1,207) = 285.9 and p < 0.000001; V4, r2 = 0.61;
r2 significantly different from 0, F(1,137) = 213.3 and p < 0.000001]. This relationship appears linear and passes near the origin
(intercept, 0.01 and +0.08 for V2 and V4, respectively). In V2, the
intercept (0.01) is not significantly different from 0 [t(206) = 1.082; p = 0.14].
However, in V4, the intercept (0.08) is significantly >0
[t(136) = 6.1476; p < 0.000001], indicating that adding the probe stimulus within the
receptive field caused an increase in response that does not depend on
the cell's selectivity for the reference and probe stimuli. However,
the magnitude of this increase is very small, relative to the changes
in response that depend on selectivity for reference and probe. Both
populations have slopes near 0.5 (+0.53 and +0.55 for V2 and V4,
respectively) that are not significantly different from 0.5 [t(206) = 1.0395; p = 0.15;
t(136) = 1.2403; p = 0.11],
indicating that, on average, reference and probe stimuli exerted
approximately equivalent influence over pair responses. Thus, as in
Experiment 1, these results are incompatible with models in which the
response to a pair of stimuli falls outside the range of responses
defined by the two individual stimuli presented alone. As in Experiment
1, these results are also incompatible with models in which the pair is
treated as a third, independent stimulus, with its own arbitrary
response.
View larger version (23K):
[in this window]
[in a new window]
|
Figure 5.
Relationship between selectivity and sensory
interactions recorded in Experiment 2, with attention directed away
from the receptive field. A, B, Data from
cells in V2 and V4, respectively. Each point corresponds
to the indices of selectivity and sensory interaction computed for a
given reference-probe pair. Responses were computed using a time
window from 120 to 270 msec after stimulus onset. Cells tested with
more than one reference-probe pair appear more than once in the
figure. Consistent with the results of Experiment 1, a strong positive
correlation between selectivity and sensory interactions, in both
cortical areas, was found. Both best-fit lines passed close to the
origin (0.01 and 0.08), indicating that adding the second stimulus
had little effect on the pair response that was not accounted for by
selectivity. Slopes were not significantly different from 0.5, indicating that, across both populations, the reference and probes
exerted approximately equivalent control over responses to pairs
(slopes, 0.53 and 0.55).
|
|
When attention was directed to one of the stimuli, this caused a
substantial reduction in the influence of the nonattended stimulus. If
the neuronal response was reduced as a result of adding the probe, then
this suppressive effect was diminished when attention was directed to
the reference stimulus. Likewise, if adding the probe increased the
neuronal response, then directing attention to the reference stimulus
caused the response to move back toward the reference stimulus
response. This is illustrated in several figures (see Figs. 6-9) that
show responses of individual neurons in areas V2 and V4. Figure
6A shows the responses
of a V2 neuron for which adding the probe stimulus reduced the
response. Attention was directed away from the receptive field of the
cell (attend-away condition). The reference stimulus elicited a robust response (Fig. 6A, dotted line). The
pair response (Fig. 6A, dashed line)
was strongly suppressed by the presence of the probe stimulus. The
response to the probe (Fig. 6A, solid
line) is shown for comparison. Figure 6B
shows the responses of the same neuron, except that in this case, the
dotted line shows the response of the cell to the pair when
attention was directed to the reference stimulus. The majority of the
suppression caused by the probe stimulus was eliminated when attention
was directed to the reference stimulus.
View larger version (30K):
[in this window]
[in a new window]
|
Figure 6.
Attention filtering out the effect of a
suppressive probe in V2. A, B, The
x-axis shows time (in milliseconds) from stimulus onset,
and the thick horizontal bar indicates stimulus
duration. The y-axis shows instantaneous firing rate.
The vertical bar in the upper right
corner shows the SEM response for this neuron, averaged across
experimental conditions. A, Responses when attention was
directed away from the receptive field are shown. Small iconic
figures illustrate sensory conditions. Within each
icon, the dotted line indicates the receptive field, and
the small dot represents the fixation point. In this and
subsequent figures, we indicate the reference stimulus by a
vertical bar and the probe by a horizontal
bar. In fact, the identity of both stimuli varied from cell to
cell. The dotted line shows the response to the
reference stimulus. The solid line shows the response
elicited by the probe. The response to the pair (dashed
line) was suppressed by the addition of the probe.
B, The upper, dotted
line shows the pair response when attention (indicated by the
cone symbol) was directed to the reference
stimulus. The responses to the unattended probe (solid
line) and pair (dashed line), taken from
A, are repeated for comparison. Attention to the
reference stimulus caused the cell's response to move upward, toward
the response that was elicited by the unattended reference stimulus
presented alone (dotted line in A).
Att Away, Attend away; Att Ref, attend
reference.
|
|
For other neurons, adding the probe stimulus increased the neuronal
response, and this increase was eliminated by attention. This is
illustrated in Figure 7, A and
B. This V2 neuron gave a moderate response to the reference
stimulus that was substantially increased by the addition of the probe
stimulus. Attention to the reference stimulus filtered out most of the
increase that resulted from adding the preferred probe. Similar effects
were observed in area V4, as illustrated in Figures
8 and 9. As
in V2, when the response to the probe was lower than the response to
the reference, adding the probe typically suppressed the neuronal response (Fig. 8). When the response to the probe was higher than the
response to the reference stimulus (see Fig. 9), adding the probe
typically increased the neuronal response. In V4, as in V2, the effect
of attention was to filter out the effect of the nonattended
stimulus.
View larger version (29K):
[in this window]
[in a new window]
|
Figure 7.
Attention filtering out the effect of an enhancing
probe in V2. The format is identical to that in Figure 6.
A, With attention directed away from the receptive
field, this cell gave a moderate response to the reference stimulus
(dotted line). The response elicited by the probe
(solid line) was much higher, and the addition of the
probe drove up the response to the pair (dashed line).
B, When attention was directed to the reference
stimulus, the pair response (dotted line) was reduced to
a level comparable with the response to the unattended reference
stimulus (dotted line in A). The response
to the unattended pair (dashed line) and the probe
(solid line) are repeated from A for
comparison.
|
|
View larger version (29K):
[in this window]
[in a new window]
|
Figure 8.
Attention filtering out the effect of a
suppressive probe in V4. The format is identical to that in Figure 6.
A, With attention directed away, the response to the
reference stimulus (dotted line) was suppressed
(response to pair, dashed line) by the addition of the
probe (response to probe, solid line). B,
Attention to the reference stimulus drove the pair response
(dotted line) toward the response elicited by the
unattended reference stimulus presented alone (dotted
line in A).
|
|
View larger version (32K):
[in this window]
[in a new window]
|
Figure 9.
Attention filtering out the effect of an enhancing
probe in V4. The format is identical to that in Figure 6.
A, With attention directed away from the receptive
field, the moderate response to the reference stimulus (dotted
line) was increased (response to pair, dashed
line) by the addition of the probe (response to probe,
solid line). B, This increase was
diminished when attention was directed to the reference stimulus
(response to pair, with attention to reference stimulus, dotted
line).
|
|
Across neurons, adding the probe typically caused the neuronal response
to move toward the response elicited by the probe alone. This sensory
interaction could be magnified by directing attention to the probe or
reduced by directing attention to the reference stimulus. This is
illustrated in Figures 10 and
11, which show the relationship between
selectivity, sensory interactions, and attention in V2 and V4,
respectively. Figure 10, A and B, shows the
relationship between selectivity and sensory interactions for neurons
whose responses to a given reference-probe pair showed a statistically
significant change in response when attention was directed to the probe
stimulus, as determined by a two-tailed, unpaired t test
(p < 0.05). For neurons tested with more than one reference-probe pair, each pair was tested independently. A given
pair was included if the response elicited by the pair changed
significantly when attention was directed to the probe stimulus.
Therefore, some neurons appear more than once, if more than one pair
elicited a response that was significantly changed by attention to the
probe. A total of 55 out of 67 neurons (82%) tested with 96 out of 208 reference-probe pairs (46%) showed statistically significant changes
in their responses to pairs when attention was directed to the probe
stimulus.
View larger version (44K):
[in this window]
[in a new window]
|
Figure 10.
V2 neurons showing attention effects.
A, The relationship of sensory interaction indices
(y-axis) to selectivity indices
(x-axis) when attention was directed away from the
receptive field. All stimulus pairs were included that elicited a
response that changed significantly (two-tailed t test,
p < 0.05) when attention was directed to the probe
stimulus. B, Same population that is shown in
A. Directing attention to the probe stimulus caused the
probe to have enhanced influence over the pair response, as reflected
in the increased slope (slope, 0.69 vs 0.47 with attention directed
away from the receptive field in A). C,
The relationship of selectivity to sensory interaction indices when
attention was directed away from the receptive field. All stimulus
pairs were included that elicited a response that changed significantly
(two-tailed t test, p < 0.05) when
attention was directed to the reference stimulus. D,
Same population that is shown in C. Directing attention
to the reference stimulus caused the probe to have diminished influence
over the pair response, as reflected in the decreased slope (slope,
0.24 vs 0.55 with attention directed away from the receptive field in
C). Some cells were tested with more than one pair of
stimuli, so some cells appear more than once. All responses were
computed using a time window from 120 to 270 msec after stimulus
onset.
|
|
View larger version (41K):
[in this window]
[in a new window]
|
Figure 11.
V4 neurons showing attention effects.
A, The relationship of sensory interaction indices
(y-axis) to selectivity indices
(x-axis) when attention was directed away from the
receptive field. All stimulus pairs were included that elicited a
response that changed significantly (two-tailed t test,
p < 0.05) when attention was directed to the probe
stimulus. B, Same population that is shown in
A. Directing attention to the probe stimulus caused the
probe to have enhanced influence over the pair response, as reflected
in the increased slope (slope, 0.83 vs 0.49 with attention directed
away from the receptive field in A). C,
The relationship of selectivity to sensory interaction indices when
attention was directed away from the receptive field. All stimulus
pairs were included that elicited a response that changed significantly
(two-tailed t test, p < 0.05) when
attention was directed to the reference stimulus. D,
Same population that is shown in C. Directing attention
to the reference stimulus caused the probe to have diminished influence
over the pair response, as reflected in the decreased slope (slope,
0.21 vs 0.60 with attention directed away from the receptive field in
C). Some cells were tested with more than one pair of
stimuli, so some cells appear more than once. All responses were
computed using a time window from 120 to 270 msec after stimulus
onset.
|
|
The increased influence of the attended probe stimulus can be observed
by comparing data in Figure 10, A and B. Figure
10A shows the relationship between selectivity and
sensory interaction when attention was directed away from the receptive
field. The indices are correlated (r2 = 0.60), and the relationship appears linear. The slope was 0.47, which
was not statistically different from 0.5 [t(94) = 0.6704; p = 0.2521], indicating that, with
attention directed away from the receptive field, reference and probe
stimuli exerted approximately equal influence over neuronal responses.
The intercept of the best-fit line (0.04) was slightly but
significantly <0 [t(94) = 2.4731;
p = 0.0076]. For this subpopulation, adding a second stimulus within the receptive field caused a small (4% of maximum response) reduction in mean response, in addition to the larger changes
in firing rate that were related to selectivity.
When attention was directed to the probe stimulus, this magnified the
sensory interactions caused by the probe. This is reflected in a
steeper relationship between selectivity and sensory interaction indices. Figure 10B shows the indices for the same
cells shown in Figure 10A, but in this case, the pair
responses used to compute the sensory interaction indices were measured
when attention was directed to the probe. Attention increased the slope
of the regression line, from 0.47 to 0.69 (increase of 0.22). A
comparison of the regression slopes (Snedecor and Cochran, 1967)
indicated that this increase was significant
[F(1,190) = 14.4; p = 0.0002].
This increased slope was also significantly different from 0.5 [t(94) = 3.1946; p = 0.0001],
indicating that when attention was directed to the probe stimulus, the
probe exerted greater influence over the pair response. In addition to
this change of slope, there was a small (0.066) but significant
increase in the average response to the pair [unpaired t
test, t(95) = 2.3052; p = 0.02]. This is reflected in an upward shift in the best-fit line.
Thus, in addition to the magnified influence of the attended probe,
attention caused an increase in response that was unrelated to
selectivity. Selectivity and sensory interactions were still strongly
correlated (r2 = 0.59) with attention
directed to the probe stimulus.
When attention was directed to the reference stimulus, this caused the
pair response to move toward the response elicited by the reference
stimulus alone. This is illustrated in Figure 10, C and
D, which shows the relationship between selectivity and sensory interactions for neurons whose responses to a given
reference-probe pair changed significantly when attention was directed
to the reference stimulus. As in the previous analysis, significance was determined by a two-tailed, unpaired t test
(p < 0.05). A total of 56 out of 67 neurons
(84%) using 97 out of 208 reference-probe pairs (47%) showed
statistically significant changes in the pair response when attention
was directed to the reference stimulus.
Figure 10C shows, for this population, that the relationship
again appears to be linear. The intercept (0.04) was slightly but
significantly <0 [t(95) = 2.5228;
p = 0.0067]. The slope (0.55) was not significantly
different from 0.5 [t(95) = 1.2924; p = 0.0997], indicating that, across this
subpopulation, reference and probe exerted approximately equivalent
influence over neuronal responses with attention directed away from the
receptive field.
The increased influence of the attended reference stimulus is evident
in the reduced slope in Figure 10D. This graph shows indices for the same population of cells shown in Figure
10C, but in this case, the pair response used to
compute each sensory interaction index was recorded when attention was
directed to the reference stimulus. The slope dropped from 0.55 in the
attend-away condition (Fig. 10C) to 0.24 with attention
directed to the reference stimulus (Fig. 10D; a
reduction of 0.31). This reduction in slope was significant [F(1,192) = 22.16; p = 0.000005]. The reduced slope was also significantly different from 0.5 [t(95) = 4.4587; p = 0.00001], indicating that, with attention directed to the reference
stimulus, the reference exerted greater influence over the pair
response. The reduced influence of the probe stimulus is also reflected
in a diminished correlation coefficient, from
r2 = 0.67 with attention directed away
from the receptive field to r2 = 0.16 with attention directed to the reference stimulus. As with attention
directed to the probe (Fig. 10B), there was a small (0.04) increase in the mean response to the pairs, but this was not
statistically significant [unpaired t test,
t(96) = 1.3993; p = 0.16].
In summary, across V2 cells that showed significant attentional
modulation, attention strongly determined which stimulus drove the
cell's response to the pair. When attention was directed to the probe
stimulus, the pair response was a weighted average of 69% of the
response to the probe plus 31% of the response to the reference
stimulus. When attention was directed to the reference stimulus, the
pair response was a weighted average of 24% of the response to the
probe plus 76% of the response to the reference stimulus. In addition,
there was a small (4-6%) and marginally significant increase in mean
firing rate that was unrelated to the individual stimulus responses.
The average effect of attention was reduced when computed over the
entire population, including responses to pairs that were not
significantly modulated by attention. Across this entire population, the sensory interaction/selectivity slope with attention directed away
from the receptive field was 0.53. The slope increased by 0.14 to 0.67 when attention was directed to the probe and decreased by 0.19 to 0.34 when attention was directed to the reference stimulus. The total shift,
from 0.67 down to 0.34 (a shift of 0.33), was 27% smaller when
computed over the entire population than was the shift of 0.45 observed
for responses that showed significant attention effects.
Figure 11 shows comparable results for neurons recorded in area V4. A
total of 39 out of 57 neurons (68%) tested with 61 out of 138 reference-probe pairs (44%) showed a significant (two-tailed t test, p < 0.05) change in pair response
when attention was directed to the probe. The relationship between
selectivity and sensory interactions appears linear. With attention
directed away from the receptive field (see Fig.
11A), the selectivity and sensory interaction indices
for these neurons are correlated (r2 = 0.55), and the slope of the best-fit line was 0.49. This slope was not
significantly different from 0.5 [t(59) = 0.1663; p = 0.4343], indicating equivalent influence
of reference and probe with attention directed away from the receptive
field. The best-fit line was shifted slightly but significantly
[t(59) = 2.8226; p = 0.0032]
upward (+0.06), indicating that, in addition to sensory interactions
related to selectivity, the addition of the second stimulus caused a
small increase in response. With attention directed to the probe (Fig.
11B), the indices were still highly correlated (r2 = 0.60). As in V2, attention to the
probe significantly increased the slope, from 0.49 to 0.83 [increase
of 0.34; F(1,120) = 22.618; p = 0.000006]. The increased slope was also significantly different from a
slope of 0.5 [t(59) = 3.7178; p = 0.0002], indicating greater influence by the attended probe.
As in area V2, attending to the reference stimulus typically caused the
pair response to move toward the response that was elicited by the
reference stimulus. A total of 37 out of 57 neurons (65%) in 59 out of
138 stimulus configurations (43%) showed a significant effect of
directing attention to the reference stimulus. For these cells (Fig.
11C), the relationship between selectivity and sensory
interaction appears linear. The best-fit line was shifted slightly
upward (+0.06), indicating that the addition of the second stimulus
caused an increase in response that was unrelated to selectivity
[t(57) = 3.0362; p = 0.0018].
The slope of the best-fit line was 0.6, which was significantly >0.5
[t(57) = 1.8835; p = 0.0324].
This indicates that for this subpopulation, there was a small but
marginally significant bias in favor of the probe stimulus with
attention directed away from the receptive field. Note that probe and
reference stimuli were selected daily from the same stimulus set, and
it was impossible to know in advance which of two stimuli would exert
greater control over the pair response. Therefore, we can only assume
that for this subset of neurons, we happened to pick probes that
exerted, on average, slightly greater influence over responses to the
pairs than did the corresponding reference stimuli.
Attention to the reference stimulus overcame this bias, as reflected by
the reduced slope in Figure 11D. Directing attention to the reference significantly [F(1,116) = 20.796; p = 0.00001] decreased the slope from 0.60 down to 0.21 (decrease of 0.39). The reduced slope was also
significantly different from 0.5 [t(57) = 3.2059; p = 0.0011], indicating greater influence by
the attended reference stimulus. As in V2, the reduced influence of the
unattended probe stimulus was also reflected in a diminished
correlation coefficient, from r2 = 0.66 with attention directed away from the receptive field to
r2 = 0.09 with attention directed to the
reference stimulus. There were also small but significant increases in
the average response, when attention was directed to the probe stimulus
[mean shift = 0.13; unpaired t test,
t(60) = 3.8168; p = 0.0003] or
to the reference stimulus [mean shift = 0.10; unpaired
t test, t(58) = 3.7941;
p = 0.0004]. These increases are reflected in an
upward shift of the best-fit lines (Fig.
11B,D).
In summary, across V4 cells that showed significant attentional
modulation, we observed a shift of control that was comparable in
magnitude with the shift observed in V2. When attention was directed to
the probe stimulus, the pair response was a weighted average of 83% of
the response to the probe plus 17% of the response to the reference
stimulus. When attention was directed to the reference stimulus, the
pair response was a weighted average of 21% of the response to the
probe plus 79% of the response to the reference stimulus. In addition,
there was a small (10-13%) but significant increase in mean firing
rate that was unrelated to the individual stimulus responses.
As in V2, the average effect of attention was reduced when computed
over the entire population, including responses to pairs that were not
significantly modulated by attention. Across the entire V4 population,
the sensory interaction/selectivity slope with attention directed away
from the receptive field was 0.55. The slope increased by 0.19 to 0.74 when attention was directed to the probe and decreased by 0.21 to 0.34 when attention was directed to the reference stimulus. The total shift,
from 0.74 down to 0.34 (a shift of 0.40), was 35% smaller when
computed over the entire population than was the shift of 0.62 observed for responses that showed significant attention effects.
Although these data indicate that when attention effects were
observed, their direction and magnitude depended on the direction and
magnitude of underlying sensory interactions, it was not the case that
sensory interactions alone guaranteed the presence of attention
effects. In both areas, we regularly found stimulus pairs for which the
addition of the probe caused significant sensory interactions, with no
corresponding effect of directing attention to the reference stimulus.
A total of 48 out of 208 stimulus pairs tested in area V2 (23%) and 34 out of 138 stimulus pairs tested in area V4 (25%) caused sensory
interactions that were not accompanied by attention effects. Thus, when
attention is directed to a stimulus, the responses of some neurons
continue to be influenced by the presence of the unattended stimulus.
Model simulation results
A simple neural circuit that satisfies the constraints imposed by
the data from Experiments 1 and 2 is illustrated in Figure 2 and is
described in detail in Materials and Methods. To test whether the model
is consistent with the results of Experiment 1, we simulated a total of
100 model neurons, differing only in their randomly assigned weights.
For each model neuron, we computed the response to the reference
stimulus alone, the responses to each of the 16 probes, and the
responses to each of the resulting stimulus pairs. Selectivity and
sensory interaction indices were then computed for each probe. These
indices are shown for six representative model neurons in Figure
12 (compare with data in Fig. 4).
Across the population, the median slope was +0.506, indicating that, on
average, reference and probe exerted approximately equivalent influence
over the model neuron's responses to the pair. However, as we observed
for the cells recorded in Experiment 1, some model neurons (such as
those shown in Fig. 12B-D) had steeper slopes, whereas others (such as those in Fig.
12E,F) had shallower slopes, corresponding to a greater influence of the probe and reference, respectively.
View larger version (33K):
[in this window]
[in a new window]
|
Figure 12.
Model simulation of Experiment 1. Each
panel (A-F) shows the
relationship between sensory interactions
(y-axis) and selectivity (x-axis)
for a single model neuron tested with 16 probe stimuli. By varying only
randomly selected excitatory and inhibitory weights, the model
generates slopes that span the range observed in Experiment 1. Compare
with Figure 4. Simulations are fully described in Materials and
Methods.
|
|
According to the model, this range of slopes is the result of
differences in the strength of the (randomly chosen) projections from
the population of input neurons that respond to the reference stimulus.
Cells for which the reference stimulus had weak projections (such as
those illustrated in Fig. 12B-D) had steeper slopes
because the probe stimuli made up the majority of the input to the
cell. Cells for which the reference stimulus projections were stronger (such as those illustrated in Fig.
12E,F) had shallower slopes because responses to pairs were dominated by the inputs from the reference stimulus.
Figure 13 shows the results of
simulating Experiment 2 (i.e., the effects of attention). Again, 100 model neurons were simulated with the same parameters used to simulate
Experiment 1. As in the previous simulation, excitatory and inhibitory
weights were chosen at random for both the reference and the probe
stimuli. Figure 13A shows a scatter plot of these indices.
As in the recording data (compare with data in Figs.
10A,C, 11A,C), when
attention was directed away from the receptive field, the slope of the
best-fit line relating selectivity and sensory interactions for the
model neurons was ~0.5. The reference and probe had approximately
equal influence over the pair response. The best-fit line passes near the origin (+0.07), indicating that there was a small increase in
response beyond that depending on selectivity.
View larger version (22K):
[in this window]
[in a new window]
|
Figure 13.
Model simulation of Experiment 2. A, The sensory interaction and selectivity indices of
100 model neurons simulated with no attentional bias either to the
probe or to the reference stimulus. Compare with Figures 10,
A and C, and 11, A and
C. B, The indices of the same 100 model
neurons with an attentional bias added to the probe stimuli. Compare
with Figures 10B and 11B.
C, The same population of 100 model neurons with an
attentional bias added to the reference stimulus. Compare with Figures
10D and 11D. The magnitude
and direction of changes in slope and vertical offset are comparable
with those observed in Experiment 2. Simulations are fully described in
Materials and Methods.
|
|
To simulate Experiment 2, we modeled the effect of attention by
increasing the strength of inputs driven by the attended stimulus, as
described in Materials and Methods. Figure 13B shows the
indices of selectivity and sensory interaction that were computed when attention was directed to the probe stimulus. As in the experimental data (compare with data in Figs. 10B,
11B), attention caused a moderate increase in the
mean firing rate across model neurons. This is reflected in an upward
shift (+0.10) in the line relating sensory interactions to selectivity,
similar to the upward shifts of +0.06 and +0.11 that were observed in
V2 and V4, respectively. In contrast to the small change in intercept,
there was a large effect of attention on the slope, which increased
from 0.52 to 0.78 (increase +0.26), reflecting enhanced influence of
the attended probe stimulus. This increase in slope is comparable with
the increases measured in V2 (+0.22) and V4 (+0.34).
Figure 13C shows the effect of directing attention to the
reference stimulus. Attending to the reference resulted in a small (+0.04) upward shift in the line relating sensory interaction to
selectivity, which is comparable with upward shifts of +0.06 and +0.11
observed in V2 and V4, respectively. Again, the change in intercept was
small, compared with the reduction in slope that occurred when
attention was directed to the reference stimulus. Attention drove the
slope from 0.52 down to 0.18, reflecting the reduced influence of the
probe stimulus when attention was directed to the reference stimulus
(compare with data in Figs. 10D,
11D). This change in slope (0.34) is comparable
with the changes observed in V2 (0.31) and V4 (0.39).
These model predictions are robust in that they do not depend on many
parameters that are fine-tuned to achieve an adequate fit. Rather, the
magnitudes of attention effects relate directly to individual
parameters of the model. The predicted magnitude of the upward shift is
a function of the passive decay parameter A. If the model
neuron has a larger rate of passive decay, its equilibrium response
rate is further below its saturation response. Therefore a given
increase in input strength resulting from attention causes a
greater increase in response and a larger upward shift in the line
relating sensory interactions to selectivity. The size of the predicted
change in slope depends on the magnitude of the magnification factor
that is applied to the strength of the inputs from the attended
stimulus. A larger magnification factor causes the pair response to
move further toward the response elicited by the attended stimulus,
resulting in a larger change in slope.
| |
DISCUSSION |
In Experiment 1, we found that, during passive fixation, the
neuronal response to a pair of oriented bars depends linearly on the
responses to the individual bars. If reference and probe are selected
to give identical responses, then the pair response is typically
indistinguishable from the responses to the individual stimuli.
However, if the orientation and color of the probe are adjusted to
cause it to elicit a larger response than that of the reference
stimulus, the pair response typically increases. The magnitude of this
increase grows in proportion to the response elicited by the probe.
Changing the probe to a nonpreferred orientation or color typically
reduces the pair response. As the probe becomes more nonpreferred, it
typically becomes proportionally more suppressive. Thus, the degree of
influence exerted by a stimulus over the neuronal response to the pair
is not simply proportional to the magnitude of the response evoked by
that stimulus. Instead, the influence exerted by a stimulus and the
response it elicits when presented alone must be considered to be
separate variables. These findings were replicated in Experiment 2 with
attention directed away from the receptive field.
In Experiment 2, we found that when attention is directed to one of two
receptive field stimuli, its effect depends on these underlying sensory
interactions. In the absence of sensory interactions, attention to
either individual stimulus typically is limited to a moderate increase
in mean response. When sensory interactions do occur, the magnitude and
direction of the observed attention effects depend on the magnitude and
direction of the underlying sensory interactions. If the addition of
the probe suppresses the neuronal response, then attention to the
reference stimulus typically filters out some of this suppression. If
adding the probe facilitates the response, attention to the reference
typically filters out some of this facilitation. Attending to the probe magnifies the change that was induced by the addition of the probe.
These linear relationships between selectivity, sensory interactions,
and attention effects provide several constraints on the set of
possible models of ventral stream visual processing. Because of the
many stages of complex processing that occur between the retina and
cortical areas such as V2 and V4, these constraints narrowly
circumscribe the set of possible models. However, as our simulations
show, these results can be understood within the context of the
proposed model.
Model predictions
In addition to providing a way to satisfy these constraints, the
model also makes predictions about neuronal responses under conditions
that were not tested in the present experiments. First, it predicts the
conditions under which attention to a single receptive field stimulus
should result in an increase in neuronal response. According to the
model, attention increases the bottom-up drive reaching the measured
neuron, which forces the neuron's response upward, toward its maximum
firing rate for that particular stimulus. If the bottom-up inputs
driven by a particular stimulus are strong enough that the cell's
response has saturated, then attention is predicted to have no
influence on the response. However, if the response is not saturated,
then attention is predicted to increase it. Thus, a prediction of the
model is that attention should increase neuronal responses to stimuli
that elicit responses within the dynamic range of the cell. These would
include stimuli that activate populations of afferents that project
weakly to the measured cell or stimuli of low brightness or color contrast.
In addition, the model makes a novel prediction about how neuronal
responses should depend on the relative salience of two receptive field
stimuli, when attention is directed away from the receptive field.
Specifically, if the salience of one receptive field stimulus is
increased relative to the salience of another receptive field stimulus,
this should cause the pair response to move toward the response
elicited by the first stimulus. For example, suppose the response to a
preferred stimulus is suppressed by the addition of a less-preferred
stimulus. Then, according to the model, increasing the luminance
contrast of the less-preferred stimulus should increase the strength of
the inputs from that stimulus, resulting in greater suppression of the
response to the pair. This is predicted to occur even when the
less-preferred stimulus elicits a significant excitatory response on
its own. Finally, the model predicts that the increased influence of
the more salient stimulus can be offset if attention is directed to the
lower salience stimulus.
Baseline shift
The model can also account for a number of previously reported
results, such as the observation (Luck et al., 1997) that the spontaneous firing rate of V2 and V4 neurons increases when attention is directed to a location within the receptive field. According to the
model, attention increases the efficacy of synapses projecting from
afferent neurons whose receptive fields are at the attended location.
As a result of this increase, spontaneous activity among these
afferents is predicted to be better able to activate the measured
neuron, resulting in higher spontaneous activity in the measured
neuron. If the synapses of inputs projecting from the afferent neurons
are weak or sparse, this shift in baseline firing rate is predicted to
be small. It is predicted to be larger for afferents with stronger
projections to the measured cell. In agreement with this prediction,
Luck et al. (1997) found that the increase in spontaneous activity is
larger when attention is directed to the center of the receptive field
(stronger afferent projections) versus a position near the edge of the
receptive field (weaker afferent projections).
Attention to a single receptive field stimulus
The model is also consistent with previously reported spatial
attention effects in the ventral stream using single stimuli within the
receptive field (Haenny et al., 1988; Spitzer et al., 1988; Maunsell et
al., 1991). These studies have reported no change or small increases in
responsiveness with attention directed to the receptive field stimulus.
These findings are compatible with the model's prediction that
increases in response will be observed for a single stimulus, provided
the stimulus has not already saturated the neuronal response. With the
same parameters used to simulate the results of the present
experiments, the model predicts a mean increase of 17.5% in neuronal
response to a single stimulus with attention, which falls within the
range of effects reported in these studies.
According to the model, these increases in response should depend on
the magnitude of the attentional signal. Stronger top-down attentional
feedback is assumed to result in larger increases in input strength for
the attended stimulus. Therefore, the magnitude of the response
increase caused by attention to a single stimulus in a difficult task
is predicted to be equal to or greater than any increase observed in an
easy task, using identical stimuli. In agreement with this, Spitzer et
al. (1988) reported moderate (18%) increases in neuronal
responsiveness in V4 when attention was directed to a single stimulus
in a difficult discrimination task but not in a less-demanding task.
Three additional spatial attention studies conducted with a single
receptive field stimulus should be considered within the context of the
present results. Motter (1993) has reported that in the ventral stream,
attention to a stimulus inside the receptive field can cause increases
or decreases in response when stimuli appear outside the receptive
field. Connor et al. (1996, 1997) have reported that the
response to a single receptive field stimulus can increase or decrease,
depending on which of several extrareceptive field stimuli is attended.
One possible explanation for both of these findings is that attention
modulated sensory interactions resulting from the addition of the
extrareceptive field stimuli.
None of these studies compared the response of the receptive field
stimulus with and without the extrareceptive field stimuli, with
attention directed away from the receptive field. Therefore, it is
unknown whether the extrareceptive field stimuli induced sensory
interactions. However, extrareceptive field stimuli are known to
modulate the responses of cells in the areas examined in these studies.
Cells in V4, for example, have large, stimulus-selective, silent
surrounds that can be either inhibitory or excitatory (Schein and
Desimone, 1990). Because of the relationship between attention effects
and sensory interactions demonstrated in the present experiment, it
would be useful to know whether the attention effects observed in these
three studies were accompanied by sensory interactions resulting from
the presence of the extrareceptive field stimuli.
Comparing these results with those of previous experiments with
multiple receptive field stimuli
The attention effects observed in the present experiment are
compatible with those of previous studies that have examined the effect
of attention when multiple stimuli appeared within the receptive fields
of neurons in the ventral stream (Moran and Desimone, 1985; Luck et
al., 1997). These studies have found neuronal responses to be larger
when attention was directed to the preferred stimulus relative to when
the poor stimulus was attended. Among these studies, the experiment
that is more closely related to the present experiment is the study of
Luck et al. (1997), which used the same stimuli and the same behavioral
task. Luck et al. (1997) reported that, among V4 neurons that showed
attention effects, responses were, on average, 63% higher when
attention was directed to the preferred stimulus than when attention
was directed to the poor stimulus. Using the same selection criteria,
we find that, on average, responses were 69% higher. We find attention effects of comparable magnitude in area V2, where, on average, responses to stimulus pairs were 79% higher when attention was directed to the preferred stimulus relative to when attention was
directed to the poor stimulus.
The remaining studies of attention in the ventral stream did not
explicitly manipulate spatial attention. Instead, they manipulated nonspatial variables such as whether the stimuli matched the form of a
cue (Haenny et al., 1988; Chelazzi and Desimone, 1994; Ferrera et al.,
1994; Motter, 1994) or whether the monkey was engaged in a particular
task (Fischer and Boch, 1985). In these studies, the stimulus-evoked
responses and/or baseline firing rates of neurons were found to vary
depending on behavioral condition, but the relationship between such
nonspatial attention effects and the findings of the present study is
not yet clear.
The purpose and limitations of the model
The biased-competition model provides a unified, quantitative
framework within which to place a number of observed and predicted attention effects. However, our implementation of this model is not
intended to be an account of the actual neural circuitry underlying visual attention. Many of the details of this circuitry are simply unknown. For instance, the source of the biasing feedback is unknown, as are the neural elements that are the targets of feedback in the
cortex. In the absence of detailed knowledge of the circuitry underlying attention, it is not yet possible to distinguish between a
number of alternative models. These include models that implement competitive interactions using lateral inhibitory connections and that
assume that the attentional bias is mediated either by a direct
excitatory signal or by invoking synchronous discharge among cells
whose receptive fields overlap with the focus of attention (for
example, see Koch and Ullman, 1985; Anderson and Van Essen, 1987;
Niebur et al., 1993; Olshausen et al., 1993; Ferrera and Lisberger,
1995; Grossberg, 1995, 1999a,b; Stemmler et al., 1995; Pouget and
Sejnowski, 1997; Borisyuk et al., 1998). Our purpose in providing a
simple but mathematically complete implementation of the
biased-competition model is to provide a demonstration proof that
biased competition can satisfy the constraints imposed by the present
experiments while remaining compatible with results that have been
reported using single receptive field stimuli. Because it is simple,
has a closed-form solution, and depends on only three parameters, it is
possible to use the model to make quantitative predictions that can be
tested experimentally, to refute the model, or to determine better how
it is implemented in the brain.
Biased competition in the dorsal stream
Recent experiments suggest that similar mechanisms may be at work
in the dorsal stream. Ferrera and Lisberger (1995) have found that the
onset time of a smooth pursuit eye movement to a target moving in one
direction can be increased by the presence of a distractor moving in
the opposite direction or reduced by the presence of a distractor
moving with the target. They have modeled this result using a
winner-take-all competitive network that receives top-down feedback
that biases competition between the target and distractor. They have
also found that the responses of some neurons in areas MT and
MST to a moving stimulus depend on whether the stimulus is a
target of a smooth pursuit eye movement (Ferrera and Lisberger, 1997)
and have suggested that this might reflect a top-down biasing signal.
In related experiments, Treue and Maunsell (1996) have found that
attention modulates the responses of directionally selective neurons in
areas MT and MST. They found that the response to a single stimulus is
increased in magnitude when the stimulus is attended. However, larger
attention effects were observed when attention was directed to one of
two receptive field stimuli. When attention was directed to a dot
moving in the neuron's preferred direction of motion, the response was
greater than when attention was directed to a dot moving in the
opposite direction. Recanzone et al. (1997) have found that neurons in areas MT and MST respond to pairs of stimuli in a manner that is highly
consistent with what we have found in the ventral stream; namely, the
response to a stimulus moving in a nonpreferred direction was increased
by the addition of a second stimulus moving in the preferred direction.
Likewise, the response to a stimulus moving in a non-null direction for
the cell was suppressed by the addition of a stimulus moving in the
null direction. Taken together, these results seem to suggest that
biased competition may be a basic computational strategy that has been
adopted throughout the visual system and possibly in other modalities
as well.
| |
FOOTNOTES |
Received Aug. 18, 1998; revised Nov. 5, 1998; accepted Dec. 8, 1998.
This work was supported by the National Institute of Mental Health
Intramural Research Program. J.H.R. was supported by a fellowship from the McDonnell-Pew Foundation. L.C. was supported in
part by a grant from the Human Frontier Science Program Organization.
Correspondence should be addressed to Dr. Robert Desimone, Chief,
Laboratory of Neuropsychology, 49 Convent Drive MSC 4415, Building 49, Room 1B80, Bethesda, MD 20892-4415.
| |
REFERENCES |
-
Anderson CH,
Van Essen DC
(1987)
Shifter circuits: a computational strategy for dynamic aspects of visual processing.
Proc Natl Acad Sci USA
84:6297-6301[Abstract/Free Full Text].
-
Borisyuk RM,
Borisyuk GN,
Kazanovich YB
(1998)
The synchronization principle in modelling of binding and attention.
Membr Cell Biol
11:753-761[Medline].
-
Bushnell MC,
Goldberg ME,
Robinson DL
(1981)
Behavioral enhancement of visual responses in monkey cerebral cortex. I. Modulation in posterior parietal cortex related to selective visual attention.
J Neurophysiol
46:755-772[Free Full Text].
-
Chelazzi L,
Desimone R
(1994)
Responses of V4 neurons during visual search.
Soc Neurosci Abstr
20:1054.
-
Connor CE,
Gallant JL,
Preddie DC,
Van Essen DC
(1996)
Responses in area V4 depend on the spatial relationship between stimulus and attention.
J Neurophysiol
75:1306-1308[Abstract/Free Full Text].
-
Connor CE,
Preddie DC,
Gallant JL,
Van Essen DC
(1997)
Spatial attention effects in macaque area V4.
J Neurosci
17:3201-3214[Abstract/Free Full Text].
-
Desimone R,
Duncan J
(1995)
Neural mechanisms of selective visual attention.
Annu Rev Neurosci
18:193-222[Web of Science][Medline].
-
Ferrera VP,
Lisberger SG
(1995)
Attention and target selection for smooth pursuit eye movements.
J Neurosci
15:7472-7484[Abstract].
-
Ferrera VP,
Lisberger SG
(1997)
Neuronal responses in visual areas MT and MST during smooth pursuit target selection.
J Neurophysiol
78:1433-1446[Abstract/Free Full Text].
-
Ferrera VP,
Rudolph KK,
Maunsell JHR
(1994)
Responses of neurons in the parietal and temporal visual pathways during a motion task.
J Neurosci
14:6171-6186[Abstract].
-
Fischer B,
Boch R
(1985)
Peripheral attention versus central fixation: modulation of the visual activity of prelunate cortical cells of the rhesus monkey.
Brain Res
345:111-123[Web of Science][Medline].
-
Gottlieb J,
Kusunoki M,
Goldberg M
(1998)
The representation of visual salience in monkey parietal cortex.
Nature
391:481-484[Medline].
-
Grossberg S
(1973)
Contour enhancement, short-term memory, and constancies in reverberating neural networks.
Stud App Math
52:217-257.
-
Grossberg S
(1976)
Adaptive pattern classification and universal recoding. I. Parallel development and coding of neural feature detectors.
Biol Cybern
23:121-134[Web of Science][Medline].
-
Grossberg S
(1980)
How does a brain build a cognitive code?
Psychol Rev
87:1-51[Web of Science][Medline].
-
Grossberg S
(1995)
The attentive brain.
Am Scientist
83:438-449.
-
Grossberg S (1999a) The link between brain, learning,
attention, and consciousness. Conscious Cogn, in press.
-
Grossberg S (1999b) How does the cerebral cortex work?
Learning, attention, and grouping by the laminar circuits of visual
cortex. Spatial Vision, in press.
-
Grossberg S,
Levine DS
(1975)
Some developmental and attentional biases in the contrast enhancement and short term memory of recurrent neural networks.
J Theor Biol
52:341-380.
-
Haenny PD,
Maunsell JHR,
Schiller PH
(1988)
State dependent activity in monkey visual cortex. II. Retinal and extraretinal factors in V4.
Exp Brain Res
69:245-259[Web of Science][Medline].
-
Kapadia MK,
Ito M,
Gilbert CD,
Westheimer G
(1995)
Improvement in visual sensitivity by changes in local context: parallel studies in human observers and in V1 of alert monkeys.
Neuron
15:843-856[Web of Science][Medline].
-
Kiper DC,
Fenstemaker SB,
Gegenfurtner KR
(1997)
Chromatic properties of neurons in macaque area V2.
Vis Neurosci
14:1061-1072[Web of Science][Medline].
-
Knierim JJ,
Van Essen DC
(1992)
Neuronal responses to static texture patterns in area V1 of the alert macaque monkey.
J Neurophysiol
67:961-980[Abstract/Free Full Text].
-
Koch C,
Ullman S
(1985)
Shifts in selective visual attention: towards the underlying neural circuitry.
Hum Neurobiol
4:219-227[Web of Science][Medline].
-
Levitt JB,
Lund JS
(1997)
Contrast dependence of contextual effects in primate visual cortex.
Nature
387:73-76[Medline].
-
Luck SJ,
Chelazzi L,
Hillyard S,
Desimone R
(1997)
Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex.
J Neurophysiol
77:24-42[Abstract/Free Full Text].
-
Maunsell JHR,
Sclar G,
Nealey TA,
DePriest D
(1991)
Extraretinal representations in area V4 of the macaque monkey.
Vis Neurosci
7:561-573[Web of Science][Medline].
-
Miller EK,
Li L,
Desimone R
(1993a)
Activity of neurons in anterior inferior temporal cortex during a short-term memory task.
J Neurosci
13:1460-1478[Abstract].
-
Miller EK,
Gochin PM,
Gross CG
(1993b)
Suppression of visual responses of neurons in inferior temporal cortex of the awake macaque by addition of a second stimulus.
Brain Res
616:25-29[Web of Science][Medline].
-
Moran J,
Desimone R
(1985)
Selective attention gates visual processing in extrastriate cortex.
Science
229:782-784[Abstract/Free Full Text].
-
Motter B
(1993)
Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli.
J Neurophysiol
70:909-919[Abstract/Free Full Text].
-
Motter BC
(1994)
Neural correlates of attentive selection for color or luminance in extrastriate area V4.
J Neurosci
14:2178-2189[Abstract].
-
Mountcastle VB,
Motter BC,
Steinmetz MA,
Sestokas AK
(1987)
Common and differential effects of attentive fixation on the excitability of parietal and prestriate (V4) cortical visual neurons in the macaque monkey.
J Neurosci
7:2239-2255[Abstract].
-
Niebur E,
Koch C,
Rosin C
(1993)
An oscillation-based model for the neuronal basis of attention.
Vision Res
33:2789-2802[Web of Science][Medline].
-
Olshausen BA,
Anderson CH,
Van Essen DC
(1993)
A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information.
J Neurosci
13:4700-4719[Abstract].
-
Pouget A,
Sejnowski TJ
(1997)
A new view of hemineglect based on the response properties of parietal neurones.
Philos Trans R Soc Lond [Biol]
352:1449-1459[Abstract/Free Full Text].
-
Recanzone GH,
Wurtz RH,
Schwarz U
(1997)
Responses of MT and MST neurons to one and two moving objects in the receptive field.
J Neurophysiol
78:2904-2915[Abstract/Free Full Text].
-
Rolls ET,
Tovee MJ
(1995)
The responses of single neurons in the temporal visual cortical areas of the macaque when more than one stimulus is present in the receptive field.
Exp Brain Res
103:409-420[Web of Science][Medline].
-
Schein SJ,
Desimone R
(1990)
Spectral properties of V4 neurons in the macaque.
J Neurosci
10:3369-3389[Abstract].
-
Sillito AM,
Grieve KL,
Jones HE,
Cudeiro J,
Davis J
(1995)
Visual cortical mechanisms detecting focal orientation discontinuities.
Nature
378:492-496[Medline].
-
Snedecor G,
Cochran W
(1967)
Analysis of covariance.
In: Statistical methods, Sixth Edition, pp 419-446. Ames, IA: Iowa State UP.
-
Spitzer H,
Desimone R,
Moran J
(1988)
Increased attention enhances both behavioral and neuronal performance.
Science
240:338-340[Abstract/Free Full Text].
-
Stemmler M,
Usher M,
Niebur E
(1995)
Lateral interactions in primary visual cortex: a model bridging physiology and psychophysics.
Science
269:1877-1880[Abstract/Free Full Text].
-
Treue S,
Maunsell S
(1996)
Attentional modulation of visual motion processing in cortical areas MT and MST.
Nature
382:539-541[Medline].
Copyright © 1999 Society for Neuroscience 0270-6474/99/1951736-18$05.00/0
This article has been cited by other articles:
|
|
|
|
 
A. Thiele, A. Pooresmaeili, L. S. Delicato, J. L. Herrero, and P. R. Roelfsema
Additive Effects of Attention and Stimulus Contrast in Primary Visual Cortex
Cereb Cortex,
December 1, 2009;
19(12):
2970 - 2981.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. T. Buracas and T. D. Albright
Modulation of neuronal responses during covert search for visual feature conjunctions
PNAS,
September 29, 2009;
106(39):
16853 - 16858.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. Rotermund, K. Taylor, U. A. Ernst, A. K. Kreiter, and K. R. Pawelzik
Attention Improves Object Representation in Visual Cortical Field Potentials
J. Neurosci.,
August 12, 2009;
29(32):
10120 - 10130.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Grossberg
Cortical and subcortical predictive dynamics and learning during perception, cognition, emotion and action
Phil Trans R Soc B,
May 12, 2009;
364(1521):
1223 - 1234.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A.R. Mayer, A.R. Franco, J. Canive, and D.L. Harrington
The Effects of Stimulus Modality and Frequency of Stimulus Presentation on Cross-modal Distraction
Cereb Cortex,
May 1, 2009;
19(5):
993 - 1007.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. B. T. McMahon and C. R. Olson
Linearly Additive Shape and Color Signals in Monkey Inferotemporal Cortex
J Neurophysiol,
April 1, 2009;
101(4):
1867 - 1875.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. M. Ghose
Attentional Modulation of Visual Responses by Flexible Input Gain
J Neurophysiol,
April 1, 2009;
101(4):
2089 - 2106.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. C. Snow, H. A. Allen, R. D. Rafal, and G. W. Humphreys
Impaired attentional selection following lesions to human pulvinar: Evidence for homology between human and monkey
PNAS,
March 10, 2009;
106(10):
4054 - 4059.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. Poort and P. R. Roelfsema
Noise Correlations Have Little Influence on the Coding of Selective Attention in Area V1
Cereb Cortex,
March 1, 2009;
19(3):
543 - 553.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Borgers, S. Epstein, and N. J. Kopell
Gamma oscillations mediate stimulus competition and attentional selection in a cortical network model
PNAS,
November 18, 2008;
105(46):
18023 - 18028.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. Womelsdorf, K. Anton-Erxleben, and S. Treue
Receptive Field Shift and Shrinkage in Macaque Middle Temporal Area through Attentional Gain Modulation
J. Neurosci.,
September 3, 2008;
28(36):
8934 - 8944.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
L. B. Ekstrom, P. R. Roelfsema, J. T. Arsenault, G. Bonmassar, and W. Vanduffel
Bottom-Up Dependent Gating of Frontal Signals in Early Visual Cortex
Science,
July 18, 2008;
321(5887):
414 - 417.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Kayser, C. I. Petkov, and N. K. Logothetis
Visual Modulation of Neurons in Auditory Cortex
Cereb Cortex,
July 1, 2008;
18(7):
1560 - 1574.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. M. Ghose and J. H. R. Maunsell
Spatial Summation Can Explain the Attentional Modulation of Neuronal Responses to Multiple Stimuli in Area V4
J. Neurosci.,
May 7, 2008;
28(19):
5115 - 5126.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. I. Buia and P. H. Tiesinga
Role of Interneuron Diversity in the Cortical Microcircuit for Attention
J Neurophysiol,
May 1, 2008;
99(5):
2158 - 2182.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Fries, T. Womelsdorf, R. Oostenveld, and R. Desimone
The Effects of Visual Stimulation and Selective Visual Attention on Rhythmic Neuronal Synchronization in Macaque Area V4
J. Neurosci.,
April 30, 2008;
28(18):
4823 - 4835.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. de Hemptinne, P. Lefevre, and M. Missal
Neuronal Bases of Directional Expectation and Anticipatory Pursuit
J. Neurosci.,
April 23, 2008;
28(17):
4298 - 4310.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
W. K. Page and C. J. Duffy
Cortical Neuronal Responses to Optic Flow Are Shaped by Visual Strategies for Steering
Cereb Cortex,
April 1, 2008;
18(4):
727 - 739.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. Zoccolan, M. Kouh, T. Poggio, and J. J. DiCarlo
Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex
J. Neurosci.,
November 7, 2007;
27(45):
12292 - 12307.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. L. Shulman, S. V. Astafiev, M. P. McAvoy, G. d'Avossa, and M. Corbetta
Right TPJ Deactivation during Visual Search: Functional Significance and Support for a Filter Hypothesis
Cereb Cortex,
November 1, 2007;
17(11):
2625 - 2633.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. A. Stevens, M. Snodgrass, D. Schwartz, and K. Weaver
Preparatory Activity in Occipital Cortex in Early Blind Humans Predicts Auditory Perceptual Performance
J. Neurosci.,
October 3, 2007;
27(40):
10734 - 10741.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. A. McMains, H. M. Fehd, T.-A. Emmanouil, and S. Kastner
Mechanisms of Feature- and Space-Based Attention: Response Modulation and Baseline Increases
J Neurophysiol,
October 1, 2007;
98(4):
2110 - 2121.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Cadieu, M. Kouh, A. Pasupathy, C. E. Connor, M. Riesenhuber, and T. Poggio
A Model of V4 Shape Selectivity and Invariance
J Neurophysiol,
September 1, 2007;
98(3):
1733 - 1750.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Ardid, X.-J. Wang, and A. Compte
An Integrated Microcircuit Model of Attentional Processing in the Neocortex
J. Neurosci.,
August 8, 2007;
27(32):
8486 - 8495.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. J. Dubin and C. J. Duffy
Behavioral Influences on Cortical Neuronal Responses to Optic Flow
Cereb Cortex,
July 1, 2007;
17(7):
1722 - 1732.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. C. Alvarado, J. W. Vaughan, T. R. Stanford, and B. E. Stein
Multisensory Versus Unisensory Integration: Contrasting Modes in the Superior Colliculus
J Neurophysiol,
May 1, 2007;
97(5):
3193 - 3205.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Jacques and B. Rossion
Electrophysiological Evidence for Temporal Dissociation between Spatial Attention and Sensory Competition during Human Face Processing
Cereb Cortex,
May 1, 2007;
17(5):
1055 - 1065.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. Serre, A. Oliva, and T. Poggio
A feedforward architecture accounts for rapid categorization
PNAS,
April 10, 2007;
104(15):
6424 - 6429.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
A. Maier, N. K. Logothetis, and D. A. Leopold
Context-dependent perceptual modulation of single neurons in primate visual cortex
PNAS,
March 27, 2007;
104(13):
5620 - 5625.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. Fallah, G. R. Stoner, and J. H. Reynolds
Stimulus-specific competitive selection in macaque extrastriate visual area V4
PNAS,
March 6, 2007;
104(10):
4165 - 4169.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
N. W. Roach and J. H. Hogben
Impaired filtering of behaviourally irrelevant visual information in dyslexia
Brain,
March 1, 2007;
130(3):
771 - 785.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. T. Serences and S. Yantis
Spatially Selective Representations of Voluntary and Stimulus-Driven Attentional Priority in Human Occipital, Parietal, and Frontal Cortex
Cereb Cortex,
February 1, 2007;
17(2):
284 - 293.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. A. Silver, D. Ress, and D. J. Heeger
Neural Correlates of Sustained Spatial Attention in Human Early Visual Cortex
J Neurophysiol,
January 1, 2007;
97(1):
229 - 237.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. J. Geng, E. Eger, C. C. Ruff, A. Kristjansson, P. Rotshtein, and J. Driver
On-Line Attentional Selection From Competing Stimuli in Opposite Visual Fields: Effects on Human Visual Cortex and Control Processes
J Neurophysiol,
November 1, 2006;
96(5):
2601 - 2612.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
W. Suzuki, K. Matsumoto, and K. Tanaka
Neuronal Responses to Object Images in the Macaque Inferotemporal Cortex at Different Stimulus Discrimination Levels
J. Neurosci.,
October 11, 2006;
26(41):
10524 - 10535.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. F Balan and J. Gottlieb
Integration of Exogenous Input into a Dynamic Salience Map Revealed by Perturbing Attention
J. Neurosci.,
September 6, 2006;
26(36):
9239 - 9249.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Shomstein and M. Behrmann
Cortical systems mediating visual attention to both objects and spatial locations
PNAS,
July 25, 2006;
103(30):
11387 - 11392.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. Williford and J. H. R. Maunsell
Effects of spatial attention on contrast response functions in macaque area v4.
J Neurophysiol,
July 1, 2006;
96(1):
40 - 54.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
X. Li, B. Kim, and M. A. Basso
Transient Pauses in Delay-Period Activity of Superior Colliculus Neurons
J Neurophysiol,
April 1, 2006;
95(4):
2252 - 2264.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J.-M. Hopf, S. J. Luck, K. Boelmans, M. A. Schoenfeld, C. N. Boehler, J. Rieger, and H.-J. Heinze
The neural site of attention matches the spatial scale of perception.
J. Neurosci.,
March 29, 2006;
26(13):
3532 - 3540.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. Bauer, R. Oostenveld, M. Peeters, and P. Fries
Tactile Spatial Attention Enhances Gamma-Band Activity in Somatosensory Cortex and Reduces Low-Frequency Activity in Parieto-Occipital Areas
J. Neurosci.,
January 11, 2006;
26(2):
490 - 501.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
X. Li and M. A. Basso
Competitive Stimulus Interactions within Single Response Fields of Superior Colliculus Neurons
J. Neurosci.,
December 7, 2005;
25(49):
11357 - 11373.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. E. Rollenhagen and C. R. Olson
Low-Frequency Oscillations Arising From Competitive Interactions Between Visual Stimuli in Macaque Inferotemporal Cortex
J Neurophysiol,
November 1, 2005;
94(5):
3368 - 3387.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Moldakarimov, J. E. Rollenhagen, C. R. Olson, and C. C. Chow
Competitive Dynamics in Cortical Responses to Visual Stimuli
J Neurophysiol,
November 1, 2005;
94(5):
3388 - 3396.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. Hung, J. Driver, and V. Walsh
Visual Selection and Posterior Parietal Cortex: Effects of Repetitive Transcranial Magnetic Stimulation on Partial Report Analyzed by Bundesen's Theory of Visual Attention
J. Neurosci.,
October 19, 2005;
25(42):
9602 - 9612.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J.-D. Haynes, J. Tregellas, and G. Rees
Attentional integration between anatomically distinct stimulus representations in early visual cortex
PNAS,
October 11, 2005;
102(41):
14925 - 14930.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
E. P. Merriam and C. L. Colby
Active Vision in Parietal and Extrastriate Cortex
Neuroscientist,
October 1, 2005;
11(5):
484 - 493.
[Abstract]
[PDF]
|
|
|
|
|
|
|
D. Zoccolan, D. D. Cox, and J. J. DiCarlo
Multiple Object Response Normalization in Monkey Inferotemporal Cortex
J. Neurosci.,
September 7, 2005;
25(36):
8150 - 8164.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
K. Taylor, S. Mandon, W.A. Freiwald, and A.K. Kreiter
Coherent Oscillatory Activity in Monkey Area V4 Predicts Successful Allocation of Attention
Cereb Cortex,
September 1, 2005;
15(9):
1424 - 1437.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. Deco and E. T. Rolls
Neurodynamics of Biased Competition and Cooperation for Attention: A Model With Spiking Neurons
J Neurophysiol,
July 1, 2005;
94(1):
295 - 313.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Schwartz, P. Vuilleumier, C. Hutton, A. Maravita, R. J. Dolan, and J. Driver
Attentional Load and Sensory Competition in Human Vision: Modulation of fMRI Responses by Load at Fixation during Task-irrelevant Stimulation in the Peripheral Visual Field
Cereb Cortex,
June 1, 2005;
15(6):
770 - 786.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Borgers, S. Epstein, and N. J. Kopell
Background gamma rhythmicity and attention in cortical local circuits: A computational study
PNAS,
May 10, 2005;
102(19):
7002 - 7007.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
B. Krekelberg and T. D. Albright
Motion Mechanisms in Macaque MT
J Neurophysiol,
May 1, 2005;
93(5):
2908 - 2921.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Shipp
The importance of being agranular: a comparative account of visual and motor cortex
Phil Trans R Soc B,
April 29, 2005;
360(1456):
797 - 814.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
N. P. Bichot, A. F. Rossi, and R. Desimone
Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4
Science,
April 22, 2005;
308(5721):
529 - 534.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
F. H. Hamker
The Reentry Hypothesis: The Putative Interaction of the Frontal Eye Field, Ventrolateral Prefrontal Cortex, and Areas V4, IT for Attention and Eye Movement
Cereb Cortex,
April 1, 2005;
15(4):
431 - 447.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
E. A. Buffalo, G. Bertini, L. G. Ungerleider, and R. Desimone
Impaired Filtering of Distracter Stimuli by TE Neurons following V4 and TEO Lesions in Macaques
Cereb Cortex,
February 1, 2005;
15(2):
141 - 151.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
C. Constantinidis and M. A. Steinmetz
Posterior Parietal Cortex Automatically Encodes the Location of Salient Stimuli
J. Neurosci.,
January 5, 2005;
25(1):
233 - 238.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. B. Rowe, K. E. Stephan, K. Friston, R. S.J. Frackowiak, and R. E. Passingham
The Prefrontal Cortex shows Context-specific Changes in Effective Connectivity to Motor or Visual Cortex during the Selection of Action or Colour
Cereb Cortex,
January 1, 2005;
15(1):
85 - 95.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. T. Serences, S. Yantis, A. Culberson, and E. Awh
Preparatory Activity in Visual Cortex Indexes Distractor Suppression During Covert Spatial Orienting
J Neurophysiol,
December 1, 2004;
92(6):
3538 - 3545.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Shomstein and S. Yantis
Control of Attention Shifts between Vision and Audition in Human Cortex
J. Neurosci.,
November 24, 2004;
24(47):
10702 - 10706.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. Marois, M. M. Chun, and J. C. Gore
A Common Parieto-Frontal Network Is Recruited Under Both Low Visibility and High Perceptual Interference Conditions
J Neurophysiol,
November 1, 2004;
92(5):
2985 - 2992.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
B. W. Mel and J. Schiller
On the Fight Between Excitation and Inhibition: Location Is Everything
Sci. Signal.,
September 14, 2004;
2004(250):
pe44 - pe44.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. Ogawa and H. Komatsu
Target Selection in Area V4 during a Multidimensional Visual Search Task
J. Neurosci.,
July 14, 2004;
24(28):
6371 - 6382.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. A. Pinsk, G. M. Doniger, and S. Kastner
Push-Pull Mechanism of Selective Attention in Human Extrastriate Cortex
J Neurophysiol,
July 1, 2004;
92(1):
622 - 629.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
J. F.X. DeSouza and S. Everling
Focused Attention Modulates Visual Responses in the Primate Prefrontal Cortex
J Neurophysiol,
February 1, 2004;
91(2):
855 - 862.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. A. Schoenfeld, C. Tempelmann, A. Martinez, J.-M. Hopf, C. Sattler, H.-J. Heinze, and S. A. Hillyard
From the Cover: Dynamics of feature binding during object-selective attention
PNAS,
September 30, 2003;
100(20):
11806 - 11811.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
B. Zenger-Landolt and D. J. Heeger
Response Suppression in V1 Agrees with Psychophysics of Surround Masking
J. Neurosci.,
July 30, 2003;
23(17):
6884 - 6893.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
L. Pessoa, S. Kastner, and L. G. Ungerleider
Neuroimaging Studies of Attention: From Modulation of Sensory Processing to Top-Down Control
J. Neurosci.,
May 15, 2003;
23(10):
3990 - 3998.
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. R. Friedman-Hill, L. C. Robertson, R. Desimone, and L. G. Ungerleider
Posterior parietal cortex and the filtering of distractors
PNAS,
April 1, 2003;
100(7):
4263 - 4268.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Grossberg
How does the cerebral cortex work? development, learning, attention, and 3-D vision by laminar circuits of visual cortex.
Behav Cogn Neurosci Rev,
March 1, 2003;
2(1):
47 - 76.
[Abstract]
[PDF]
|
|
|
|
|
|
|
R. D. S. Raizada and S. Grossberg
Towards a Theory of the Laminar Architecture of Cerebral Cortex: Computational Clues from the Visual System
Cereb Cortex,
January 1, 2003;
13(1):
100 - 113.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
E.-M. Meftah, J. Shenasa, and C. E. Chapman
Effects of a Cross-Modal Manipulation of Attention on Somatosensory Cortical Neuronal Responses to Tactile Stimuli in the Monkey
J Neurophysiol,
December 1, 2002;
88(6):
3133 - 3149.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. S. Marcus and D. C. Van Essen
Scene Segmentation and Attention in Primate Cortical Areas V1 and V2
J Neurophysiol,
November 1, 2002;
88(5):
2648 - 2658.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
T. J. Gawne and J. M. Martin
Responses of Primate Visual Cortical V4 Neurons to Simultaneously Presented Stimuli
J Neurophysiol,
September 1, 2002;
88(3):
1128 - 1135.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. W. Spratling
Cortical region interactions and the functional role of apical dendrites.
Behav Cogn Neurosci Rev,
September 1, 2002;
1(3):
219 - 228.
[Abstract]
[PDF]
|
|
|
|
|
|
|
S. Corchs and G. Deco
Large-scale Neural Model for Visual Attention: Integration of Experimental Single-cell and fMRI Data
Cereb Cortex,
April 1, 2002;
12(4):
339 - 348.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Ben Hamed, J.-R. Duhamel, F. Bremmer, and W. Graf
Visual Receptive Field Modulation in the Lateral Intraparietal Area during Attentive Fixation and Free Gaze
Cereb Cortex,
March 1, 2002;
12(3):
234 - 245.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M Rizzo and S P Vecera
Psychoanatomical substrates of Balint's syndrome
J. Neurol. Neurosurg. Psychiatry,
February 1, 2002;
72(2):
162 - 178.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
S. Kastner, P. De Weerd, M. A. Pinsk, M. I. Elizondo, R. Desimone, and L. G. Ungerleider
Modulation of Sensory Suppression: Implications for Receptive Field Sizes in the Human Visual Cortex
J Neurophysiol,
September 1, 2001;
86(3):
1398 - 1411.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
L. Chelazzi, E. K. Miller, J. Duncan, and R. Desimone
Responses of Neurons in Macaque Area V4 During Memory-guided Visual Search
Cereb Cortex,
August 1, 2001;
11(8):
761 - 772.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Fries, J. H. Reynolds, A. E. Rorie, and R. Desimone
Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Science,
February 23, 2001;
291(5508):
1560 - 1563.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
D. L. Sheinberg and N. K. Logothetis
Noticing Familiar Objects in Real World Scenes: The Role of Temporal Cortical Neurons in Natural Vision
J. Neurosci.,
February 15, 2001;
21(4):
1340 - 1350.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
M. S. Worden, J. J. Foxe, N. Wang, and G. V. Simpson
Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific alpha -Band Electroencephalography Increases over Occipital Cortex
J. Neurosci.,
March 15, 2000;
20(6):
RC63 - RC63.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
G. H. Recanzone and R. H. Wurtz
Effects of Attention on MT and MST Neuronal Activity During Pursuit Initiation
J Neurophysiol,
February 1, 2000;
83(2):
777 - 790.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
E. Blaser, G. Sperling, and Z.-L. Lu
Measuring the amplification of attention
PNAS,
September 28, 1999;
96(20):
11681 - 11686.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
Top
Abstract
Introduction
