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1The Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, Maryland; 2Department of Neurology, Georgetown University School of Medicine, Washington, DC; 3Mahoney Center for Brain and Behavior, Center for Neurobiology and Behavior, Columbia University College of Physicians and Surgeons, and the New York State Psychiatric Institute, New York; and 4Departments of Neurology and Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York
Submitted 11 August 2005; accepted in final form 1 December 2005
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONAPPENDIX AAPPENDIX BGRANTSACKNOWLEDGMENTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONAPPENDIX AAPPENDIX BGRANTSACKNOWLEDGMENTSREFERENCES |
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; Moran and Desimone 1985; Motter 1993; Reynolds et al. 2000; Treue and Maunsell 1999), posterior parietal cortex (Bushnell et al. 1981; Colby et al. 1996), prefrontal cortex (Everling et al. 2002), the superior colliculus (Dorris et al. 2002; Goldberg and Wurtz 1972; Robinson and Kertzman 1995), and the pulvinar nucleus of the thalamus (Petersen et al. 1985; Salzmann 1995). Generally, these studies have shown activity being modulated by attention. However, a growing number of studies have attempted to identify areas that may contribute to the allocation of visual attention by examining behavioral or physiological responses during microstimulation (Cutrell and Marrocco 2002; Moore and Fallah 2004) or reversible inactivation (Davidson and Marrocco 2000; Petersen et al. 1987; Wardak et al. 2004). These studies have shown that manipulations of posterior parietal cortex, the frontal eye fields, the superior colliculus, and the pulvinar nucleus of the thalamus can influence neuronal or behavioral measurements of attention. In this study, we focused on one of these areas, the lateral intraparietal area (LIP), whose neuronal responses are strongly modified by the behavioral relevance of the stimulus in the receptive field (Gottlieb et al. 1998; Toth and Assad 2002). Our aim was to correlate the activity from neurons in this area with a locus of attention measured behaviorally.
Attention can be defined in one of two ways: by showing enhanced sensitivity at an attended location (Bashinski and Bacharach 1980; Carrasco et al. 2000) or by measuring reaction times to visual events (Posner et al. 1980). We chose to define the locus of attention as the area of the visual field that has the best sensitivity to contrast. The benefit of measuring sensitivity is that it allows us to rule out the possibility that any advantage we see could be caused by influences in the motor system, a problem present when defining attention by changes in reaction time. Previous studies have shown that attentional benefits can be seen in the monkey both for changes in sensitivity (Ciaramitaro et al. 2001; Moore and Fallah 2001, 2004) and reaction time (Bowman et al. 1993; Witte et al. 1996).
Psychophysical experiments in humans have shown that attention is allocated to the endpoint of a planned saccade (Deubel and Schneider 1996; Shepherd et al. 1986) and that a flashed object (Egeth and Yantis 1997; Yantis and Jonides 1984, 1996) or a pop-out stimulus (Joseph and Optican 1996) can also attract attention. In this study, we used a modification of the memory-guided saccade task (Hikosaka and Wurtz 1983) to see whether attention is similarly modulated in the monkey and what the interactions were between exogenous and endogenous attention.
Some of the data presented here have appeared in brief form previously (Bisley and Goldberg 2003) and are appropriately labeled in the relevant figure legends.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONAPPENDIX AAPPENDIX BGRANTSACKNOWLEDGMENTSREFERENCES |
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All experiments were performed on two male macaque monkeys (Macaca mulatta) weighing 
9 and 10 kg, respectively. On weekdays, water was restricted before the experimental session, and the daily water ration was provided during the behavioral testing. On weekends, the monkeys were not tested behaviorally and received their water ration in their home cage. Food was continually available, and monkeys received supplements of fresh fruit daily. Body weights were recorded before every testing session to ensure good health and normal growth. The monkeys were implanted with scleral search coils and head restraint devices to monitor their eye position (Judge et al. 1980) and had recording cylinders placed above parietal cortex. All experimental protocols were approved by the NEI Animal Care and Use Committee as complying with the guidelines established in the Public Health Service Guide for the Care and Use of Laboratory Animals.
Surgical procedures
For all surgical procedures, which were performed under aseptic conditions, the animals were initially anesthetized with ketamine hydrochloride (15 mg/kg, im) and maintained with isofluorane through a closed rebreathing system. The concentration of isofluorane was varied from 1.5 to 3% depending on the animal's pulse, blood pressure, and degree of relaxation. After all invasive surgical procedures, the animals were given a short course of cephalexin (250 mg twice a day for 3–5 days) and allowed to recover for 1–2 wk before returning to the laboratory. Custom-made titanium head pieces were attached to the skull using 9–12 titanium screws. Eye coils, made from fine wire (AS 632, Cooner Wire, Chatsworth, CA), were surgically implanted beneath the conjunctiva of each eye and were attached to plugs (SM2S, Powel Electrics, Philadelphia, PA) held onto the head piece by dental acrylic. Craniotomies were made over parietal cortex, and a recording cylinder was implanted. The chamber was 20 mm in diameter and was attached to the skull by a ring of bone cement anchored by six to eight plastic under-the-skull bolts. The cylinder was placed above the intraparietal sulcus, allowing a dorsal approach to LIP. The precise location and shape of the sulcus for each monkey was determined from MRIs obtained with a small surface coil in a 1.5-T G.E. Signa 2 scanner. For this procedure, the monkeys were lightly anesthetized with a mixture of intramuscular ketamine hydrochloride (10 mg/kg), atropine (0.4 mg/kg), diazepam (0.2 mg/kg), and butorphanol (0.05–0.1 mg/kg) and placed in a specially constructed MRI compatible stereotaxic frame.
Stimuli and behavioral testing
Behavioral control and data collection were done on computers using the REX system (Hays et al. 1982). Visual stimuli were back-projected on a tangent screen by a NEC Multisync LT100 DLP projector calibrated with a Tektronix J17 Photometer. The background luminance was 15 cd/m2, and the projector refresh rate was 60 Hz. Stimulus timing was calculated by measuring a pulse from a photocell affixed to the back of the screen and illuminated by a small square on the corner of the same video frame as the appearance or disappearance of any stimulus. The monkey could not see the photocell or its illumination square.
The task was a variation of the memory-guided saccade task that allowed us to measure contrast thresholds (Fig. 1) and has been described briefly before (Bisley and Goldberg 2003; Bisley et al. 2004). The monkeys initiated a trial by fixating a small spot, and after a 1- to 2-s delay, a second spot (the saccade target) appeared for 100 ms at one of four positions equidistant from the fovea. The four positions changed from day to day but were constant on any given day. For the physiological studies, only two saccade target locations were used; one in the center of the receptive field (RF) and one opposite the center of the RF. At some time after the saccade target disappeared, a Landolt ring (the probe) and three complete rings of identical luminance flashed for one video frame (17 ms), with one ring at each of the four possible saccade target locations. The animals had to indicate the orientation of the Landolt ring by either maintaining fixation (when the gap was on the right, a nogo trial) or making a saccade (when the gap was on the left, a go trial) after the fixation point disappeared. There was a 500-ms delay between the probe and the disappearance of the fixation point, so that the animals had ample time to cancel the planned saccade (Hanes and Schall 1995). In one-half of the trials a task-irrelevant distractor appeared for 100 ms 600 ms after the onset of the saccade target. The distractor was identical in shape and luminance to the saccade target, and appeared either at the site of the saccade target (henceforth referred to as the saccade goal) or opposite it. The luminance of the rings varied methodically so that an equivalent number of trials began with each contrast level. We defined the contrast threshold as the contrast at which the animal could discriminate the orientation of the Landolt ring at the 75% correct level. The contrast of the fixation point, saccade target, and distractor was 55%. The saccade target and distractor were 0.2° in diameter, the rings were 0.2° thick and 2.2° in diameter, and the gap in the probe was 0.8°. All objects apart from the fixation spot were at 10° eccentricity for the psychophysics and ranged from 6° to 34° for the physiology depending on the location of the center of the RF of the cell being studied.
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). The Weibull function was of the form
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was the centering parameter of the function; 
was the steepness parameter of the function, 0.5 was the asymptotic minimum, and 
was the asymptotic maximum of the function. We defined the thresholds as the stimulus value corresponding to 75% correct.
We counted trials in which the animal broke fixation before the fixation point disappeared as fixation breaks, whereas we counted trials in which the monkey failed to make an appropriate saccade or failed to hold fixation in a nogo trial as errors. Therefore, although this is not a true two-alternative forced choice paradigm (i.e., the monkey could make an incorrect saccade on a go trial, which would still be counted as an error), we found that, below threshold, the animals ran at 50% correct (data described in RESULTS). This enabled us to limit the lower asymptote of the Weibull function to 50%. When we fitted the data with a Weibull function that had the lower asymptote as a free variable, the pattern of significant differences in thresholds was conserved.
Both monkeys' performances tended to vary slightly from day to day, so to visualize the data collected on different days, we normalized it on a daily basis to the monkey's performance on trials in which there was no distractor and the probe was not at the location of the saccade goal. We always confirmed statistical significance using the raw nonnormalized data and excluded an individual day's data from analysis when the nonnormalized threshold was >3 SDs from the mean calculated with that point included. This resulted in the removal of only 11 thresholds from the 732 thresholds collected.
Electrophysiological recordings
We recorded single-unit activity through tungsten microelectrodes (1.5–5.0 M
; FHC). We inserted the electrode through a guide tube positioned in a grid (Crist et al. 1988). Neurons were identified as being in LIP based on their responses during a memory guided saccade and their location within the intraparietal sulcus (Barash et al. 1991). In this study, we recorded from every LIP neuron with visual activity that we encountered. Once we identified a neuron and isolated it well, we carefully mapped the position and size of its receptive field using a memory-guided saccade task, controlling saccade target position by joystick.
Data analysis
We discriminated action potentials during the trial using a BAK Dual Window Discriminator, and pulses were time stamped and stored together with information about the current stimulus in REX. We only used well-isolated single units for analysis.
In the physiology experiments, a spike-density function (Richmond et al. 1987) was calculated for each trial by convolving the spike train, sampled at 1 kHz, with a Gaussian with a sigma of 10 ms. Neuronal responses were shown as the average of this spike density trace over the interval of interest, across all common correct trials. To create the population data, we normalized each spike density function in the following way. First, we took the square root of every data point from all the functions for that cell. We divided each value by the total mean of the square rooted values from all the functions. We also normalized by dividing the raw activity by the raw mean or by using the maximal firing rate as normalizing factor. We also summed the data rather than normalize. Each method gave a similar temporal pattern of activity. For quantitative analyses we compared neuronal responses by calculating the number of action potentials over a 100-ms epoch from different trial types. For single cells we used a t-test to compare the data statistically, and Wilcoxon signed rank tests were used for the population comparisons. All data analysis programs were written in Matlab (Mathworks, Natwick, MA) using its curve-fitting and statistical capabilities.
CROSSING POINT ANALYSIS. We were interested in calculating the "crossing point," the time at which the transient response to the task-irrelevant distractor fell below the level of delay period activity at the saccade goal. To determine the crossing point, we calculated the spike density functions from the cell or population using a sigma of 30 ms. In the cases in which there was more than one crossing point, we took the mean (and range) of all of the points on the functions that crossed between 100 and 700 ms after the distractor onset.
To see whether smaller populations of LIP neurons gave results similar to our complete population, we ran a shuffle analysis in which a limited number of neurons contributed to the population for each animal. In this analysis, the spike density functions created from these subpopulations were normalized and pooled, and the crossing point was calculated. This was repeated 2,000 times, providing us with a distribution of crossing points, each from a subpopulation of the neurons recorded.
DECAY OF ACTIVITY ANALYSIS.
To show a relationship between the relative level of delay activity and the decay of the visual response, we fit an exponential function using Matlab's lsqcurvefit function to the spike density functions smoothed with a sigma of 30 ms. We started our decay fit of the distractor response at the highest point of the spike density function after a minimum of the cell's latency plus 20 ms. We have previously shown that LIP neurons have a highly synchronous initial response that creates an apparent biphasic peak (Bisley et al. 2004), so by starting the fit after the first peak, we got a much more appropriate function. In one-half of the trials, the probe appeared 700 ms after the distractor onset, so we fit the decay over a 500-ms epoch. For the main analysis presented here, we fit the response after the distractor with an exponential function that decayed to zero, although we also used asymptotic levels equal to baseline or left as a free parameter.
CALCULATION OF POPULATION RESPONSE SEPARATION TIME.
We used a receiver operating characteristic (ROC)–based analysis (Bradley et al. 1987; Britten et al. 1992; Cohn et al. 1975; Green and Swets 1966; Tolhurst et al. 1983) to identify the time at which the activity from the population of neurons separated after the onset of the cue in different trial types. This analysis computes the time that an ideal observer could determine that the two trial types were different based on the distribution of responses. We performed the analysis on the normalized population response. At each millisecond of the function, we included the normalized responses from every trial from every cell. For each comparison, the responses at each ms in each function were compared by setting 62 evenly spaced threshold levels that covered the complete range of responses. For each level, the proportion of responses that were greater than the threshold was calculated for each distribution. The proportions for each distribution were plotted against each other to create an ROC curve. The area under this curve (aROC) gives the probability that an ideal observer could differentiate between the traces. An aROC of 0.5 indicates no difference in the distributions. A value of 0 or 1 indicates that the ideal observer could always tell which trial type the responses came from.
We ran a permutation test to calculate whether the aROC values were significantly different from 0.5. This was done by randomly distributing all the responses from both functions in a given 1-ms bin into two groups that were composed of the same number of data entries as in the original two distributions. We performed the same ROC analysis on the redistributed data and repeated this procedure 2,000 times, creating a distribution of aROCs. We determined where in this distribution the actual aROC lay (P value). We defined the time of separation as the first of 
100 bins (of 1 ms) in which all the aROCs had a P 
0.01.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONAPPENDIX AAPPENDIX BGRANTSACKNOWLEDGMENTSREFERENCES |
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In each session, the monkeys were run on the task with six contrast levels, four possible saccade target locations, six trial types, and two SOAs. The trial types were probe at saccade goal; probe away from saccade goal; probe and distractor at saccade goal; probe and distractor opposite saccade goal; probe at saccade goal and distractor opposite; and distractor at saccade goal and probe opposite.
TRIALS WITHOUT A DISTRACTOR. When no distractor appeared, the monkey's performance at the saccade goal was better than his performance away from the saccade goal. This is shown in Figure 2, which shows the monkey's performance in all the trials in a single session in which no distractor appeared. Figure 2A shows the monkey's performance when the probe was at the saccade goal. The squares show the percent correct at the six contrast levels from data pooled across saccade target locations. The solid line is the Weibull best fit, and the dotted line shows the threshold at 75% correct. Performance when the probe appeared away from the saccade goal is shown in Fig. 2B. In this example, we pooled the data from trials with all four saccade target locations and with the probe appearing at any of the three nontarget locations.
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Contrast thresholds were lower in trials in which the probe appeared at the saccade goal than in trials in which the probe was elsewhere. Considering that the performance at the three nontarget locations was the same, these data suggest that the monkey was better at performing the discrimination when the probe appeared at the saccade goal. We suggest that this improved performance represents an attentional benefit at the locus of the saccade goal. To test this, we compared the thresholds from the two task conditions across all the sessions for each monkey. We found that the mean thresholds were significantly less at the saccade goal for all three SOAs (P < 0.05, Wilcoxon signed rank test), suggesting that there is an attentional advantage at the goal of the memory guided saccade for 1,800 ms after the saccade target flashes (Fig. 3, A and C). We normalized the thresholds for each SOA in each session using the performance at the nontarget locations (e.g., in Fig. 2, we divided the threshold from Fig. 2A by the threshold in Fig. 2B). In this form, an attentional benefit occurs when the data lie significantly beneath the dashed line, which represents the normalized contrast threshold at the nontarget locations.
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). TRIALS WITH A DISTRACTOR OPPOSITE THE SACCADE GOAL. Although attention usually lay at the goal of the memory-guided saccade, the sudden appearance of a distractor in the opposite hemifield transiently drew the animals' attention. Figure 3, B and D, shows pooled normalized thresholds from trials in which the distractor appeared opposite the saccade goal. In these plots, the blue traces represent thresholds at the saccade goal and the red traces represent thresholds at the location where the distractor had flashed. Prenormalized thresholds that were significantly different from the normalizing thresholds (i.e., thresholds from trials with no distractor in which the probe appeared away from the saccade goal) are marked by the color-coded * (P < 0.05, Wilcoxon signed rank tests). In both monkeys, there was an attentional advantage at the distractor site 200 ms after the distractor, but not at the saccade goal. However, by 700 ms after the onset of the distractor, the attentional advantage had returned to the saccade goal and was no longer present at the distractor site. This suggests that there is a discrete locus of an attentional advantage that stays at the goal of a memory-guided saccade either until the monkey makes the saccade or until a distractor appears away from the goal. In this case, the locus of the attentional advantage briefly moves to the distractor site but within 700 ms moves back to the goal of the saccade.
Performance errors
The monkeys made five different types of errors during the trials. Two error types were simply consistent with the monkey's misinterpreting the probe: type 1, continuing to fixate on go trials; type 2, making a saccade to the saccade goal on nogo trials. The other three were trials in which the monkey made a wrongly targeted saccade, errors that could occur whether or not the monkey misinterpreted the probe: type 3, making a saccade to the distractor on go trials; type 4, making a saccade that did not land near the goal or distractor site on go trials; type 5, making a saccade that did not land near the goal or distractor site on nogo trials. The number of times the monkey made each of these types of error is plotted for each probe contrast and for each monkey in Fig. 4, A and C. In Fig. 4, B and D, these data are shown as a proportion of the total number of error trials for each contrast. Both monkeys made more errors as the contrast decreased and the task became more difficult. As the contrast neared threshold the monkeys' errors were predominantly making the wrong interpretation of the probe—either not making a saccade on a go trial or making the planned saccade on a nogo trial (classes 1 or 2). These data suggest that the animal was performing the task as a true two-alternative forced choice task. This is consistent with their chance performance at the lowest contrast, which was 51.6% correct for monkey B and 54.2% correct for monkey I.
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Activity of LIP neurons during the task
We recorded 41 neurons in the same two monkeys that performed the psychophysical experiments described above. We selected neurons for analysis if they had a visual response to the saccade target of a memory-guided saccade; 34/41 (83%) of these neurons had delay period activity (P < 0.05, t-test; 600–1,200 ms after saccade target onset) and 32/41 (78%) had perisaccadic activity (P < 0.05, t-test; 100-ms epoch centered on saccade onset). RFs ranged from 6° eccentricity to 34° eccentricity. Response latencies to the saccade target ranged from 42 to 76 ms (for details, see Bisley et al. 2004).
In keeping with previous studies, LIP neurons responded to the onset of the saccade targets and often maintained activity during the delay period. They also responded to the abrupt onset of a distractor in the RF when the monkeys were planning a saccade elsewhere. Figure 5 shows poststimulus time histograms (20-ms bins) of the responses of three example LIP neurons during two trial types: when the saccade target was flashed in the RF and the distractor was flashed opposite the RF (left plots) and when the distractor, but not the saccade target, was flashed in the RF (right plots). The presentation times of the saccade target (T), distractor (D), and probe (P) are indicated by the horizontal bars beneath the abscissa. Each neuron showed a visual burst to the saccade target, some degree of delay activity, and a second burst to the probe (we show trials with the same probe appearance delay to prevent averaging of probe responses across different SOAs). We also pooled trials in which either a Landolt ring or the complete ring appeared in the RF.
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The population average response resembled the illustrated cells. Figure 6 compares the population responses from trials in which the saccade target, but not the distractor, appeared in the RF (blue traces) with the responses from trials in which the distractor, but not the saccade target, appeared in the RF (red traces). In both monkeys, there was a clear visual response to the appearance of the saccade target, distractor, and probe when they appeared in the RF. In both monkeys, there was clear delay activity in the population and the level of this activity was dependent on whether the saccade target appeared in the RF. This is most evident in the delay before the appearance of the distractor in which the blue traces are much higher than the red traces.
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). The one exception was that, in monkey I, the delay activity after the saccade target (blue trace) appeared to dip after the distractor appeared outside the RF. To see whether this represented a significant drop in activity, we compared the response in trials in which the distractor appeared away from the RF to that obtained from trials in which the saccade target appeared in the RF, but no distractor appeared (Fig. 7, green traces). In monkey B, the distractor had no effect on the delay response (Fig. 7A); however, in monkey I, the blue and green traces diverged slightly (Fig. 7B). The divergence occurred 200 ms after the onset of the distractor and appeared to be caused by an increase in response of the delay activity rather than a suppression of the delay period activity by the distractor. These data were confirmed by plotting the responses during the epochs 0–200 and 300–500 ms after distractor onset against the same time (relative to saccade target onset) in trials in which no distractor flashed for the individual cells (Fig. 7, C and D). During both epochs, monkey B's data were not significantly different (
; P > 0.1, Wilcoxon signed rank test), but monkey I showed significantly more activity 300–500 ms after the distractor onset than the no distractor control (
; P << 0.001).
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The time-course of population activity in LIP correlated with the time-course of psychophysical performance. In dealing with the population responses, it can be easier to think of the responses from the two trial types in Fig. 6 as representing two populations of neurons with similar properties—one at the location where the saccade target was flashed (blue traces) and one at the location where the distractor was flashed (red traces). Thus we can think of these two traces as representing the responses at two locations on a salience map (Koch and Ullman 1985). Now if we compare the behavioral data in Fig. 3 with the normalized population responses of Fig. 6, we see a simple correlation. Whenever the blue trace (saccade target in RF) was higher than the red trace (distractor in RF), there was also an attentional benefit at the saccade goal. This was true 700 and 1,200 ms after the distractor was flashed and was true in trials in which no distractor was flashed—the population delay activity after the saccade target was always greater than baseline. Conversely, when the red trace was higher than the blue trace (immediately after the distractor flashed), an attentional benefit was present at the distractor location. This comparison is shown in Fig. 8, which shows both the psychophysical data (triangles) and the normalized population responses (bottom traces) for each monkey.
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). During the delay period, activity is greater at the goal of the memory-guided saccade, except when a distractor elsewhere evokes a transient response that rises above the delay period activity. This transient falls relatively quickly and soon crosses the delay period activity. For an epoch of 80 ms in each monkey, the two traces were not statistically significant (P > 0.05, Wilcoxon signed rank test on the activity of all the neurons over a 100-ms epoch measured every 5 ms), represented in Fig. 8 by the gray vertical column in each trace. We will refer to this time as the window of neuronal ambiguity. It spanned 415–495 ms in monkey B and 290–370 ms in monkey I. If attention is allocated to the spatial location evoking the greatest activity in LIP, we would predict that there should be no attentional advantage during this window. To test this, we stopped our recording sessions once we felt we had enough neurons to identify the window of neuronal ambiguity accurately and ran the monkeys on the psychophysics. We chose the time of the probe flash for each monkey to be in the middle of the window (455 ms for monkey B and 340 ms for monkey I) or 500 ms later (see APPENDIX A for an account of latency timing issues). Figure 8 shows these data (circles) together with the original psychophysical data (triangles), and the normalized population responses after the distractor onset. The data points in the vertical gray bar show that, in both monkeys, there was no attentional benefit at either location during the window of neuronal ambiguity. However, there was an attentional benefit at the saccade goal during trials in which the probe was flashed 500 ms later, suggesting that the lack of benefit in the window of neuronal ambiguity is real. This supports the hypothesis that the locus of attention is related to the site of greatest activity in LIP.
We can further test this hypothesis, because the activity in the populations of neurons from the two different monkeys gave us sufficiently different windows of neuronal ambiguity. If the activity in LIP were really correlated with the behavior, at the time attention appears to be shifting in monkey B (455 ms), attention should have already shifted in monkey I, because the two neural response traces in Fig. 8B have completely diverged by this time. Conversely, in the middle of monkey I's window of neuronal ambiguity (340 ms), the activity after the distractor is still significantly greater than the saccade goal delay activity in monkey B, so his attention should still be at the distractor site. We tested these hypotheses by presenting the probe to each monkey in the middle of the window for the other monkey. These data are included in Fig. 8 and confirm the predictions: in monkey B, the attentional benefit was still at the distractor site at 340 ms; and in monkey I, the attentional benefit was at the saccade goal at 455 ms.
Note that the absolute value of activity in LIP did not correlate with the locus of attention. Activity that was able to sustain the attentional advantage at the saccade goal could not do so when the distractor evoked much greater activity (1st 2 data points in Fig. 8A; 1st data point in Fig. 8B). The same psychophysical advantage occurred whether, as in the epoch of the distractor response, there was a huge difference between the activity at the distractor site and the activity at the saccade goal or when there was a small (but highly significant) advantage at the saccade goal as the distractor response decayed.
Controlling for task load
During the psychophysical experiments, the contrasts of the probe ranged from supra- to subthreshold, whereas during the majority of the physiological recordings, the probe had a constant suprathreshold contrast. Thus the task load was greater in the psychophysical experiments, and the monkey's attentional allocation may have been different. Because the underlying premise of our hypothesis is that the population response in LIP at the time of the probe presentation is correlated with the attentional benefit, it is possible that the activity before the probe presentation may actually be different when the monkey is performing the more difficult task. To test for this, we examined the responses from four neurons (2 from each monkey) for which we had activity both during the basic physiological task and during the psychophysical task with all probe contrasts. Figure 9A shows the responses of the neurons during six 100-ms epochs (200 and 800 ms after saccade target onset in trials with no distractor and 200, 340, 455, and 650 ms after the distractor) as recorded in the simple physiological task and the more difficult psychophysical task. We have plotted the responses from both the trials in which the saccade target appeared in the RF and trials in which the distractor appeared in the RF. This gives us a total of 48 points, all of which lie close to the dashed line (representing equal activity in the 2 cases). Although the mean responses from the two tasks were significantly different (P < 0.05, Wilcoxon signed rank test), the mean difference was 2.3 spikes/s, and responses were slightly less in the task with higher load. Furthermore, there was a strong correlation between responses (R2 = 0.90). Together these data suggest that, while there was a slight difference in response level during the two load levels, activity recorded in the suprathreshold task gives an excellent indication of activity during the psychophysical task in which the behavioral data were collected.
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Responses of single neurons in the population
Thus far, when discussing the responses of LIP neurons, we have only looked at the normalized population responses in the two monkeys. We will now show that the responses from single neurons have similar response properties to the population.
RESPONSES OF SINGLE NEURONS AT TIMES THAT PROBES APPEARED IN THE PSYCHOPHYSICAL TASK. We have already shown that the normalized population response of LIP neurons predicts where a monkey's attention is in space. The activity of single neurons in this task reflects the population response. Figures 10 and 11 show the activity of single neurons during five epochs in which we measured contrast thresholds. The responses of neurons 750–850 ms after saccade target onset from trials in which the target did not appear in the RF are plotted against the responses from trials in which the target did appear in the RF in Fig. 10. The population activity was significantly stronger when the saccade target was in the RF than when it was out of the RF in both monkeys (P << 0.001, Wilcoxon signed rank tests).
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For the most part, single cells had similar response profiles to the population. For instance, in all but one neuron, the response 800 ms into the delay was greater if the saccade target had appeared in the RF than if it had appeared elsewhere (Fig. 10), and these differences were significant in more than one-half of the neurons in each monkey (P < 0.0028 for monkey B, P < 0.0022 for monkey I, Bonferroni corrected t-test). Conversely, all but one neuron showed a greater response at the distractor site, 200 ms after the distractor, than at the saccade goal (Fig. 11, A and B), and these differences were also significant in more than one-half of the neurons in each monkey. As the response after the distractor waned, fewer neurons showed a significant difference in the conservative Bonferroni corrected t-test; however, in all of the epochs in which the population showed a significant difference, at least three neurons showed individual significant differences, and the difference was always in the same direction as the population difference. During the window of neuronal ambiguity, none of the neurons in monkey B (Fig. 11E) showed a significant difference. In monkey I (Fig. 11D), three neurons had significant differences during the window of neuronal ambiguity; however, the differences went both ways, with two neurons exhibiting more activity when the saccade target was in the RF and one neuron exhibiting more activity when the distractor was in the RF. In summary, we found that most of the neurons show the same pattern of activity as the population. Thus there is significantly more activity in single cells and the population as a whole at the saccade goal during a time at which we found a behavioral enhancement at that same location.
It is worth noting that, because the population activity in Figs. 6 and 8 contains neurons with both strong and weak delay activity, the difference in activity in the two traces is underplayed. Although the difference seen after the crossing point in Fig. 8A looks small, it is actually composed of a number of neurons that have firing rates that differ by 20–50 spikes/s during this epoch (Fig. 11G).
CROSSING POINTS FROM SINGLE NEURONS. The previous section suggests that individual neurons respond in a similar temporal manner during the task; however, we have already shown that neurons can have vastly different relative levels of visual and delay period activity (Fig. 5), with visual/delay ratios ranging from 1.38 to 9.17. To see whether neurons with different ratios do indeed follow the same temporal response characteristics during the trial, we calculated a metric, the crossing point, which represented the time it took for the activity after the distractor to decay to the level of activity maintained at the saccade goal and asked whether or not this metric was related to the visual/delay ratio.
We calculated the crossing point for each neuron by determining when the spike density function from trials in which the distractor appeared in the RF fell beneath the spike density function from trials in which the saccade target, but not the distractor, appeared in the RF (i.e., the time at which the red trace crosses to below the blue trace in Fig. 8). We limited crossing points by the minimum time at which the probe might appear (700 ms after distractor onset). To minimize the noise in the measurement, we used spike density functions smoothed with a 30 ms sigma. After this smoothing, only four neurons showed more than one crossing time. For these neurons, we defined the crossing point as the mean of all the times the activity after the distractor fell to less than the activity after the saccade target. In each case there were only two points, so we have plotted the range as well as the mean. One cell from monkey B showed no crossing point in this analysis; the response after the distractor fell to a level that was just above baseline, and the memory response 600–1,200 ms after the saccade target had already returned to baseline.
We found no clear relationship between the visual/delay ratio and the crossing point. The crossing points for each cell are plotted against their respective visual/delay ratios in Fig. 12. The gray bars show the window of neuronal ambiguity from the population data for each animal. The crossing points for individual cells are clustered near the window of neuronal ambiguity for each animal independent of the visual/delay ratio, and while there is some variance in the distribution of crossing points within the animals, there is little overlap between the two animals. Only five cells (28%) in monkey B had crossing points <400, whereas only four cells (17%) in monkey I had crossing points >400, and an ANOVA showed that the two means were significantly different (P < 0.02). This is important because the behavior predicted by this time was also different in the two animals (Fig. 8).
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This was in fact true: the crossing point was related more to the monkey than to the nature of the individual neurons in which it was measured. To show this, we first fit an exponential function to the decay in the response evoked by the appearance of the distractor. An example of such a fit is shown for a neuron with a visual/delay ratio of 3.6 in Fig. 13A. This neuron had the median R2 for that animal (0.97) and gives a good indication of how well the responses were fitted by the exponential. For most of the neurons (13/18 in monkey B, 20/23 in monkey I), the exponential fits were very good with R2 > 0.75. These neurons are represented by the filled symbols in Figs. 12–14. We chose not to use the remaining eight neurons in the analysis because the time constants calculated from these fits were not good enough approximations of the neural data and thus could have given misleading relationships between the ratio and the decay. However, neurons with good exponential fits covered the entire range of visual/delay ratios (Fig. 12) and, in fact, provided the upper and lower bounds for each sample. Thus by rejecting neurons with bad exponential fits we were not obviously discriminating against any particular range of visual/delay ratios.
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The estimated population crossing points, calculated from the slopes of the black lines, were 421 and 375 ms for monkeys B and I, respectively. These were both within the windows of neuronal ambiguity calculated from the population averages for each monkey and within 33 and 35 ms, respectively, of the times that we determined psychophysically that attention is shifting (Fig. 8). Note that with one exception, even the neurons that were not fit well by the exponential function (hollow circles) lay near to or within the population. The one exception from monkey I is not plotted because it lay well off the bounds of the axes.
The relationship we see between the visual/delay ratio and the decay rate of activity after the distractor was not caused by one of the two factors that make up the ratio. Figure 14 plots the ratio separated into its components against the decay rate constant for each monkey. The lines are fitted only to the solid circles, which again represent neurons that were well fit by the exponential function. In every case, the R2 from the component analysis is well below that obtained from the ratio in Fig. 13. This suggests that the visual response, delay activity, and decay rate after the distractor co-vary in such a way as to keep the crossing point constant.
To test whether the relationship between the visual/delay ratio and the decay rate is robust, we changed three parameters independently and redid the analyses. In the first of these tests, we changed the smoothing factor used to create the spike density functions. Normally we present data using a sigma of 10 ms (see Fig. 6), but for calculating the crossing point we used a relatively broad sigma (30 ms) to reduce the bumpiness of the trace and thus provide a single crossing point for most cells. Changing the sigma made little difference, as shown in Fig. 15A, which compares the crossing points obtained by smoothing the traces with these two kernels. Although the traces crossed more often in the 10-ms data, the mean crossing points were not different (P > 0.5, Wilcoxon signed rank test), suggesting that the mean crossing point calculation does not depend on the level of smoothing. Similarly, using a 10 ms sigma had only a small effect on the fit of the delay ratios to the time constants (Fig. 15, B and C). Unsurprisingly, the mean R2 for the exponential fits of the less smoothed data were not as high as for the more smoothed data (0.78 compared with 0.86), but the correlations between the ratio and decay rate were similar. Based on the linear fits to the neurons whose less smoothed data were fit well (R2 > 0.75; solid points) the estimated crossing points were 438 and 366 ms for monkeys B and I, respectively, within 17 and 26 ms of the behaviorally confirmed crossing points, respectively. Thus the level of smoothing applied to the raw data did not affect the relationship between the visual/delay ratio and the decay rate.
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We stated above that the estimated crossing point, taken from the slope of the linear fit through the solid points (i.e., the cells that had good exponential fits), would not be valid if we used the ratio from Fig. 12. There are two reasons for this: first, the original ratios were calculated with the visual response to the saccade target, not the distractor; second, the original memory response was taken from trials in which the distractor did not appear and we have already shown that in monkey I the response during this period is slightly different if the distractor appears (Fig. 7). Thus we expect that the estimated crossing point may be inaccurate. However, to show the robustness of the relationship between the ratio and decay rate, we estimated the crossing points anyway and found them to be 50 and 60 ms from the behaviorally confirmed crossing points in monkey B and I, respectively. Although these both lie just outside of the window of neuronal ambiguity, they are particularly close given the inherent inaccuracies expected using this ratio. We also ran this analysis using a number of different epochs (50–200, 50–150, 100–150 ms following either the saccade target or distractor) or using the peak of the exponential fit to the saccade target for the visual response. In each case, we obtained similar correlations. Thus we are confident that the correlation between the visual/delay ratio and the decay rate is robust to the epochs chosen to represent the visual and memory responses.
In the third reanalysis, we changed the parameters of the exponential fit so that the activity did not decay to 0, but reached an asymptotic level that was left as a free parameter. To maximize th
