Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 Dec 1.
Published in final edited form as: Vision Res. 2012 Apr 13;74:2–9. doi: 10.1016/j.visres.2012.03.022

Inhibition of return in a visual foraging task in non-human subjects

Solmaz Shariat Torbaghan 1,4, Daniel Yazdi 1, Koorosh Mirpour 1, James W Bisley 1,2,3
PMCID: PMC3406263  NIHMSID: NIHMS370718  PMID: 22521511

Abstract

Inhibition of return is thought to help guide visual search by inhibiting the orienting of attention to previously attended locations. We have previously shown that, in a foraging visual search task, the neural responses to objects in parietal cortex are reduced after they have been examined. Here we ask whether the animals’ reaction times (RTs) in the same task show a psychophysical correlate of inhibition of return: a slowing of reaction time in response to a probe placed at a previously fixated location. We trained 3 animals to perform an RT version of the visual foraging task. In the foraging task, subjects visually searched through an array of 5 identical distractors and 5 identical potential targets; one of which had a reward linked to it. In the RT variant of the task, subjects had to rapidly respond to a probe if it appeared. We found that RTs were slower for probes presented at locations that contained previously fixated objects, faster to potential targets and between the two for behaviorally irrelevant distractors that had not been fixated. These data show behavioral inhibitory tagging of previously fixated objects and suggest that the suppression of activity seen previously in the same task in parietal cortex could be a neural correlate of this mechanism.

1. Introduction

In everyday behavior we are often burdened with the task of finding a particular object in a cluttered visual environment. Two key strategies help us perform this task efficiently: an ability to focus our attention on stimuli that have similar features to the target of our search; and a memory of where we have looked so that we do not waste time looking at the same places over and over again. It is thought that the memory component of this behavior is aided by inhibition of return, a mechanism used to make visual search efficient by inhibiting subjects from attending an object they have already attended in their search (Klein, 2000, Posner et al., 1985, Wang & Klein, 2010). Inhibition of return is usually thought of in terms of its psychophysical manifestation. This was first described by Posner and Cohen (1984) using their now classic cueing paradigm. Their results have been replicated countless times and neurophysiological correlates from the cueing paradigm have been studied extensively in the superior colliculus (Bell et al., 2004, Dorris et al., 2002, Fecteau et al., 2004, Fecteau & Munoz, 2005).

However, the behavioral relevance of inhibition of return, which can be thought of as tagging items that have already been examined (Klein, 1988, Wang & Klein, 2010), has been less well studied. Neurophysiologically, evidence for inhibition of return at a previously attended location comes from visual search data in the frontal eye field (FEF) across trials (Bichot & Schall, 2002). In this study, the authors found that when a target in visual search was placed in the same location in two sequential trials, the animals’ RTs were slower on the second trial than on the first trial. The time at which the neural activity in FEF identified the target was also later on the second trial; however inhibitory tagging or suppression at the target location was not illustrated. Psychophysically, a number of studies have shown that reaction times to probes flashed at locations that have previously been attended are slower than reaction times to probes flashed at novel locations (e.g. Klein, 1988, Klein & MacInnes, 1999, Thomas et al., 2006). This has resulted in its inclusion in a number of search models as a way to keep visual search efficient by reducing the number of eye movements necessary to find an object in a scene. We will focus on the saliency map model by Koch and colleagues (Itti et al., 1998, Koch & Ullman, 1985), which is conceptually similar to a number of original models of search in which the early visual areas pre-attentively process a visual scene and features or feature maps of the scene are extracted (Bundesen, 1990, Cave & Wolfe, 1990, Duncan & Humphreys, 1989, Treisman & Gelade, 1980). In this model, neurons at the early stages of visual processing are tuned to basic visual attributes such as color, intensity contrast, orientation, direction and velocity of motion and spatially compete for salience (Soltani & Koch, 2010). These feature maps then merge to form a unique saliency map that topographically encodes an aggregate measure of saliency that is not tied to any specific feature dimension. Through a winner-take-all process, attention is allocated to the peak of the saliency map, after which the activity in that location is suppressed by inhibition of return. This suppression is important as it allows attention to move on to the next highest point on the map and also keeps attention away until other areas of the scene have been attended.

Converging neurophysiological evidence suggests that a number of cortical and sub-cortical areas work together to drive the allocation of attention (Gottlieb & Balan, 2010, Lovejoy & Krauzlis, 2010, Noudoost et al., 2010). It is thought that these areas act as priority maps (Bisley & Goldberg, 2010), in which objects or locations in space are represented by activity that is proportional to their attentional priority. We use the term priority rather than saliency to highlight the fact that top-down cognitive influences play a significant role in the allocation of attention and to make it clear that attention is not only guided by bottom-up salience (Fecteau & Munoz, 2006, Serences & Yantis, 2006).

There are various candidates for the site of a biologically plausible priority map, including the lateral intraparietal area (LIP) of the posterior parietal cortex (Andersen et al., 1990, Bisley & Goldberg, 2010, Gottlieb et al., 1998), FEF (Moore & Armstrong, 2003, Thompson & Bichot, 2005), the inferior and lateral subdivisions of the pulvinar (Robinson & Petersen, 1992) and the superior colliculus (SC; Fecteau & Munoz, 2006, Lovejoy & Krauzlis, 2010). Consistent with a functional role of inhibition of return, we recently showed that LIP responses to stimuli that had been visited in a foraging visual search task were significantly lower than responses to the same stimuli that had not been looked at (Mirpour et al., 2009). This could be interpreted as a neurophysiological correlate of inhibitory tagging in a priority map, however no studies have examined whether non-human primates show behavioral inhibitory tagging in visual search. In fact, data from a task in which monkeys had to make saccades to a peripheral target, back to the fixation point and then back to a peripheral target showed that saccadic RTs to a target placed at a previously fixated stimulus location were no slower than to a target placed at an orthogonal location and were faster than to a target placed at a location opposite to the previously fixated point (Dorris et al., 1999). Given that the same animals showed inhibition of return to a peripheral pre-cue and that human subjects performing multiple-saccade tasks show slower RTs at previously fixated points (Hooge & Frens, 2000, Rafal et al., 1994), it is possible that monkeys may not utilize inhibitory tagging. However, the multiple-saccade tasks do not require a memory of previously fixated locations and there is some evidence that inhibition of return may only be utilized in active search (Dodd et al., 2009), so the lack of inhibition in the multiple-saccade task does not necessarily imply that monkeys do not exhibit inhibitory tagging and the behavioral data from Bichot & Schall (2002) indirectly argues against it. In this study, we explicitly ask whether monkeys exhibit inhibitory tagging by testing whether they show reaction time inhibition of return while performing a foraging visual search task.

2. Methods

All experiments were approved by the Chancellor’s Animal Research Committee at UCLA as complying with the guidelines established in the Public Health Service Guide for the Care and Use of Laboratory Animals. Three rhesus monkeys (monkeys D, E and H) weighing 8–13 kg were implanted with head posts, scleral coils and, in two cases (monkeys D and E), with a recording cylinder during sterile surgery under general anesthesia. Animals were initially anesthetized with ketamine and dexdomitor and maintained with isofluorane.

Stimuli were presented on a Samsung SyncMaster 1100DF CRT running at 100 Hz. The temporal precision of stimulus onset was captured by a photoprobe on the corner of the monitor. The behavioral paradigm was run using the REX/VEX system (Hays et al., 1982). Monkeys sat in a primate chair 57 cm from the monitor. Eye position information was recorded at 1 KHz using the DNI eye coil system (Newark, DE, USA). Data from REX was transferred to MATLAB (Mathworks) for further analysis.

2.1 Task

All monkeys were trained on an RT embedded version of the visual foraging task (Mirpour et al., 2009, Mirpour et al., 2010). Each trial started with the monkey maintaining his gaze within 1 deg of a fixation point presented on the left of the display. After a delay of 450–850 ms, a search array consisting of five potential targets (T) and five distractors (+) was presented, with one of the items appearing at the fixation point (Fig. 1). One of the Ts (the target) was loaded with reward and the animals had 8 s to fixate the target for 500 ms to get the reward. This resulted in a behavior in which the animals looked from T to T, waiting at each for approximately 600 ms (Mirpour et al., 2009), until they found the target and obtained the reward. This is represented in the first four panels in Fig. 1. On 50% of the trials, the search array disappeared during the trial and 27±2.9 ms (mean ± standard deviation) later a yellow probe flashed at one of ten locations (last two panels in Fig. 1). Six of the locations were in places previously occupied by a stimulus and four of the locations were in blank regions of space. The probe was equally likely to flash in each of the ten locations. On these trials, the animal had to make a rapid saccade to the probe to obtain the reward; none of the Ts were loaded with a reward, so the animal could not finish the trial before the probe appeared. Based on the monkeys’ behavior, we attempted to have the probe flash either after the first, second or third saccade by presenting it 985 ms, 1685 ms or 2485 ms after the onset of the foraging array. The number of trials and amount of reward were adjusted for each session based on the satiety level of the animal. On average, approximately 360 RT trials were collected within a session. Monkey D performed 21 sessions, monkey E performed 20 and monkey H performed 14, resulting in 7029, 7630 and 5451 RT trials from the monkeys D, E and H, respectively.

Fig. 1.

Fig. 1

Open in a new tab

The RT variant of the foraging task. Each trial started with the animal fixating a single point for 450–850 ms, after which the point disappeared and the foraging array appeared. The array consisted of 5 potential targets (T) and 5 distractors (+). One of the Ts was the target and the animal obtained a reward for fixating the target for 500 ms within 8 s of the array appearing. Black lines show an example eye movement path over time. On 50% of the trials in each session, a yellow probe flashed at one of 10 locations (dashed circles in last two panels) shortly after the array disappeared. In these trials, the animal had to make a saccade to the probe within 350 ms to receive the reward.

2.2 Data analysis

Reaction time was calculated as the latency from the probe onset to the initiation of the saccade, which was based on an eye velocity threshold. Saccades to the probe were accepted if they ended up within 2.5 deg of the probe. Reaction times from trials in which the probe appeared within 2 deg from the location where the animal was fixating were not included in the analysis. In addition, trials with RTs of less than 80 ms were excluded because the saccades occurred too early to be in response to the appearance of the probe. Trials with RTs greater than 300 ms were also excluded. These two RT criteria resulted in the exclusion of approximately 1.5% of the 20,110 trials recorded. An examination of the distribution of RTs (Fig. 2) shows that the removed RTs were not part of the main distribution and their removal did not qualitatively change the results.

Fig. 2.

Fig. 2

Open in a new tab

Distribution of reaction times. Histogram (2 ms bins) of all RTs from all animals from all 4 conditions. Hollow bins show data that was excluded from analyses: RTs < 80 ms and RTs > 300 ms.

Data were sorted depending on what was present at the location of the probe before its appearance. The main four groups analyzed were from trials in which the stimulus occupying the location of the probe was (1) a potential target that had not been fixated, (2) a T that had been fixated and ruled out as being the target (which we term a “visited T”), (3) a distractor that had not been fixated and (4) a blank region of the screen. Distractors were rarely fixated and we only recorded data from 226 trials in which the probe appeared at the location where a visited distractor had been presented. We used one-way ANOVAs to see if there was a significant difference among the four main classes of RTs and post-hoc Tukey’s HSD tests for pair wise comparisons when the ANOVA was significant. To test whether the time of probe presentation within the trial affected RT, we performed a two-way ANOVA with probe condition and presentation time as independent factors. Since the variance of the RTs was found to increase linearly with the mean, we first transformed the RTs by taking the reciprocal before performing any of the ANOVAs; this transformation made the variances more similar across conditions.

Intersaccadic intervals, average numbers of saccades and time to find targets for each session were examined to confirm that monkeys were not using an obviously different strategy when performing the RT embedded version of the task compared to when they performed the simple foraging task (Mirpour et al., 2009).

3. Results

In all three animals, reaction times were significantly slower to a probe flashed at a location that had been occupied by visited T than to a probe flashed at a location that had been occupied by a potential target (Fig. 3; p<0.05, post-hoc Tukey’s HSD tests, p≪0.001, ANOVAs). This appears to illustrate an inhibitory tagging form of inhibition of return. However, a close examination of the RT distribution shows a clear bi-modal distribution (Fig. 2), which appeared to primarily come from trials in which a potential target had occupied the location where the probe flashed (compare Figs. 4A & B). We hypothesized that the short RTs creating the mode around 100 ms came from trials in which the animal had planned a saccade to the potential target and, when the probe appeared in that location, was able to rapidly make a correct saccade to that location because the saccade had been pre-planned. Thus, it is possible that the shorter mean RT seen in the potential target condition compared to the visited T condition could be due to the fact that the potential target condition had more of these short-latency saccades because the monkeys planned and made more saccades to a potential target than to a visited T. If this were the case, then the means of the main distributions would be the same and it would suggest that there is no inhibitory tagging. To test this, we cut the RT data to remove the early mode and late tail (hollow columns; Figs. 4A & B) using times specific to each monkey based on the distribution in the potential target condition. For all 3 monkeys, ANOVAs performed on the cut distributions, which included all 4 conditions, were significant (p≪0.001), with a strong significant differences between the RTs in the visited T and potential target conditions (p<0.05, post-hoc Tukey’s HSD tests; Figs. 4C–E).

Fig. 3.

Fig. 3

Open in a new tab

Mean (±SEM) RTs for the 3 animals. Mean RTs to probes presented in a region of space that was unoccupied by a stimulus within the array (Blank), that was occupied by a T that the animal had not yet fixated (Potential target), that was occupied by a T that the animal had already fixated within the trial (Visited T) and by a distractor (Dist). One-way ANOVAs were significant for all 3 monkeys (p≪0.001) and a post-hoc Tukey’s HSD tests were used to show differences between mean RTs within individual animals. The diamonds indicate the mean RTs when the probe was presented in a region of space occupied by a visited distractor. Only 68 and 146 trials contribute to these points for monkeys D and H respectively. The mean from the 12 trials collected from monkey E is not shown.

Fig. 4.

Fig. 4

Open in a new tab

Reaction time differences were not due to the proportion of short-latency saccades. A & B. Reaction time distributions from monkey E when the probe appeared at a location occupied by a potential target (A) or by a T that the animal had already visited (B). To show that the difference in RTs between these two conditions was not due to more of the short-latency saccades, trials with RTs less than 125 ms or greater than 240 ms were removed (hollow columns) and the main distributions compared. C – E. The resulting RTs from the cut distributions are shown for monkey D (panel C; 125 ms < RTs < 300 ms), monkey E (panel D; 125 ms < RTs < 240 ms) and monkey H (panel E; 115 ms < RTs < 180 ms). In all cases, the ANOVAs, including all 4 conditions, were significant (p≪0.001) and the RTs in the visited T and distractor (Dist) conditions were significantly longer than in the Potential Target condition (p<0.05, post-hoc Tukey’s HSD tests).

It is also possible that the animals had planned multiple saccades to all or many of the potential target locations, which could mean that the difference in RTs between potential targets and visited Ts may be due to faster RTs to potential targets rather than slower RTs to visited Ts. To control for this, we compared the RTs in the visited T condition to the RTs in the distractor condition. The theory is that the animals know that distractors are never loaded with reward, so they should not pre-plan saccades to these objects. In two of the three monkeys (D and H), we found that RTs to a visited T were significantly slower than RTs to a distractor (p<0.05, post-hoc Tukey’s HSD tests). This was true both when short-latency saccades were included (Fig. 3) and when they were not (Fig. 4). In monkey E, there was a trend for slower RTs in the visited T condition, but this failed to reach significance in both analyses.

We looked to see whether there was any evidence for inhibitory tagging of distractors. The mean RTs from trials in which the probe appeared at a location previously occupied by a visited distractor are illustrated by the diamonds in Fig. 3. Overall, the monkeys fixated distractors on less than 20% of fixations: 15.7% for monkey D; 16.1% for monkey E; and 19.5% for monkey H. For monkey E, most of these fixations occurred in longer trials, so we only obtained 12 RTs in the visited distractor condition and have not included these data here. The data from monkey D and monkey H come from 68 and 146 trials respectively. In both cases, the RTs to a visited distractor appeared to be slower than to a distractor that had not been visited, but neither of these differences reached statistical significance (p>0.05, post-hoc Tukey’s HSD tests).

Reaction times to distractors were significantly slower than to potential targets. When all RTs were included (Fig. 3), all three monkeys had slower RTs in the distractor condition than in the potential target condition (p<0.05, post-hoc Tukey’s HSD tests). This was also the case when only the main distributions were compared (Fig. 4), although the removal of the short-latency saccades made this difference less striking in monkeys D and H.

In monkeys E and H, RTs to the probe flashed in a blank region of space were significantly slower than RTs to the probe flashed at a potential target location and significantly quicker than RTs to the probe flashed at a visited T (p<0.05, post-hoc Tukey’s HSD tests; Fig. 3). In monkey D, RTs to the probe flashed in a blank location were significantly slower than RTs to the probe flashed at a potential target location (p<0.05), but were not significantly different to the RTs to the probe flashed at a visited T.

The pattern of reaction times did not vary as a function of when the probe appeared. We first tested this by examining the mean RTs in 50 ms windows aligned by the time that the probe appeared relative to the end of the previous saccade (Fig. 5). We found that the mean RT varied substantially as a function of this time. Mean RTs were slowest when the probe flashed immediately after the end of the previous saccade, quickest when the probe flashed about 100 ms after the end of the previous saccade and were then mostly stable if the probe flashed 200 ms or more after the end of the previous saccade. Monkey H showed a trend of quickening RTs as time progressed from 200 ms following the end of the previous saccade. Importantly, the order of the RTs remained fairly constant. Reaction times in the potential target condition (green traces) were almost always faster than RTs in the other two conditions. Likewise, RTs to a visited T (blue traces) were predominantly slower and RTs to distractors (red traces) were generally between the two.

Fig. 5.

Fig. 5

Open in a new tab

The pattern of RTs did not vary as a function of when the probe was flashed relative to the time the previous saccade ended. Mean (±SEM) RTs were pooled over a 50 ms window every 5 ms after the end of the previous saccade. The data are plotted against the time in the middle of the 50 ms window. RTs to visited Ts are shown in blue, RTs to distractors are shown in red and RTs to potential targets are shown in green.

The pattern of reaction times was also similar when examined as a function of when the probe appeared in the trial. To test this, we examined the mean RTs for each monkey from each of the four conditions as a function of when the probe appeared relative to array onset. The data were analyzed using a 2-way ANOVA with condition (blank, potential target, visited T and distractor) and time of probe onset as independent variables. All three monkeys showed a significant main effect of condition (p≪0.001, ANOVA) and monkeys D and E showed a main effect of time (p=0.006, monkey D and p≪0.001, monkey E). Monkey H did not have a main effect of time (p=0.41). Monkeys D and H showed a significant interaction between condition and time (p=0.042, monkey D; p=0.005, monkey H), but this did not reach significance for monkey E (p=0.091). As in the above analysis, we found that RTs in the potential target condition (green traces) were almost always faster than in the other conditions and RTs to a visited T (blue traces) were almost always slower than in the other conditions (Fig. 6). Thus, these analyses show that there was a robust pattern of RTs under the different conditions, independent of when the probe flashed relative to array onset and when it flashed relative to the end of the last saccade.

Fig. 6.

Fig. 6

Open in a new tab

The pattern of RTs was similar at each of the three times the probe was flashed following array onset. Mean (±SEM) RTs are shown as a function of when the probe appeared after array onset. RTs to visited Ts are shown in blue, RTs to distractors are shown in red and RTs to potential targets are shown in green.

To test that RTs were not biased by the distance between gaze position when the probe appeared and the location of the probe, we plotted RTs as a function of this distance for each animal. We found no significant correlations in any of the animals (p>0.55, linear regressions). This means that the length of the saccade to the probe did not have an impact on the recorded RT.

4. Discussion

We found that when animals performed a visual foraging task, we saw a slowing of RT when probes appeared at visited Ts compared to when they appeared at visibly identical potential targets or at distractors that had not been visited. We also found that this pattern of RTs was maintained when short-latency saccades were excluded from the analyses and was present independent of when the probe appeared. This is reminiscent of the slowing of RTs seen in human psychophysical studies (Klein & MacInnes, 1999, Muller & von Muhlenen, 2000, Thomas et al., 2006, Thomas & Lleras, 2009), suggesting that a form of inhibitory tagging is occurring.

Inhibition of return is most often thought about in terms of the classic cueing paradigm. However, the psychophysical result itself is just a laboratory finding; it is the interpretation of the underlying mechanism that is important in everyday behavior. Posner and Cohen (1984) first interpreted their data as suggesting a mechanism for favoring eye movements to novel locations in space. Klein (1988) built on this premise to suggest that the mechanism helped facilitate visual search by tagging, with some form of inhibition, locations that have already been examined and the concept has been used in models driving the allocation of attention (Itti et al., 1998, Soltani & Koch, 2010). A number of studies in human subjects have confirmed the RTs to probes in previously fixated locations are slower than to other locations while subjects perform a visual search task (Dodd et al., 2009, Klein & MacInnes, 1999, MacInnes & Klein, 2003, Muller & von Muhlenen, 2000, Takeda & Yagi, 2000, Thomas et al., 2006). Our results are analogous to these, suggesting that monkeys utilize an inhibitory tagging mechanism during search. This finding, together with our previous study showing that LIP responses are attenuated to visited Ts (Mirpour et al., 2009), suggests that the inhibition in LIP may be a neural correlate of inhibitory tagging.

One subtle difference between the results of our study and a number of previous human studies is that we found inhibitory tagging even though the scene was extinguished about 27 ms before the probe appeared. A number of studies have shown that inhibitory tagging in humans appears to be linked to the scene or objects in the scene, because it is only found when the scene remains on the screen when the probe is presented (Klein & MacInnes, 1999, MacInnes & Klein, 2003, Takeda & Yagi, 2000). There are three possible reasons for this discrepancy. First, unlike the human observers, our animals are highly trained experts at this task. Each animal had performed thousands to many tens of thousands of trials over a 1–4 year period prior to being introduced to the RT version of the foraging task. This over-training could lead to a more persistent representation of the stimuli that affects RTs for a longer duration after the array is extinguished in these animals compared to non-experts. It could also have led the animals to adapt a slightly different strategy. Second, it is possible that the scene is still perceivable to the animals when the probe appears due to the fact that we used bright stimuli on a dark background in a dark room. We have measured the exponential phosphor decay on our CRTs to have a decay constant of 32 ms, so in addition to the perceptual persistence in the brain, there is an additional time that the stimuli are visible on the monitor. This is similar to the argument made in Klein and MacInnes (1999) for why inhibitory tagging was seen in Klein’s original study in which stimuli were extinguished before the probe appeared (Klein, 1988) and, together with the first possibility, may completely explain the discrepancy. Finally, it is possible that inhibitory tagging in monkeys is linked to locations rather than objects or scenes. We think this is unlikely as this might predict that inhibition would automatically occur at previously fixated locations in space which, given the lack of slow RTs in the multiple-saccade task (Dorris et al., 1999), does not appear to be the case.

There is a large and growing literature that suggests that activity in priority maps in parietal cortex (Andersen et al., 1992, Goldberg et al., 2006), frontal cortex (Bizzi, 1968, Bruce & Goldberg, 1985, Thompson et al., 1996) and the superior colliculus (Dorris et al., 1997, Horwitz & Newsome, 2001, Schiller & Stryker, 1972, Wurtz & Goldberg, 1971) may be used to target eye movements and that this activity can be used to explain RTs in simple saccade tasks (Bichot & Schall, 2002, Ipata et al., 2006, Roitman & Shadlen, 2002, Sato et al., 2001, Shen & Pare, 2007, Thomas & Pare, 2007). In these studies, it is believed that saccades are triggered when the accumulation of evidence reaches a threshold in discrimination tasks (Mazurek et al., 2003) or when a peak emerges in a detection task. Given this background, we were particularly interested in seeing how the RTs to distractors compared to the RTs to potential targets and visited Ts, given that responses in LIP are greatest for potential targets, lesser for visited Ts and lowest for distractors (Mirpour et al., 2009). It could be argued that the slowing of RTs at a previously attended location is due to a sensorimotor transformation independent of the reduction in activity we have seen in LIP (Mirpour et al., 2009). This would suggest that the inhibitory tagging we see behaviorally is not related to the reduction in neural activity. If this were true, it would predict that RTs to distractors that have not been visited should be as fast as to Ts that have not been visited, as they have not been tagged. However, we found that RTs to distractors were significantly slower than RTs to potential targets, even when the short-latency saccades were excluded from the analysis. Finding this result when excluding the short-latency saccades is important, because it suggests that this RT difference is not due to the pre-planning of saccades in the potential target condition. This result is consistent with the neural activity in LIP; the responses to potential targets are significantly greater than the responses to distractors, which we believe have reduced responses due to their behavioral irrelevance in the task. We believe this result, combined with the previous findings that activity in priority maps correlate with RTs, suggests that the behavioral inhibitory tagging we found could be due to the attenuated responses seen in LIP.

It is unclear whether the inhibitory tagging we have illustrated is driven by the same mechanism that drives the RT difference in the traditional cueing paradigm. We have already pointed out that inhibition of return usually describes a slowing of reaction times in a cueing paradigm and that it is the interpretation of its importance that has lead to the study of inhibitory tagging, which has also been referred to as inhibition of return. But it is possible that the neural mechanisms underlying these two behavioral phenomena may not be the same. We have suggested that the attenuation of activity in LIP to visited Ts could lead to reduced RTs by increasing the distance between the initial activity in LIP and the threshold that triggers a saccade. However, evidence from the superior colliculus suggests that the slowing of RTs in a cueing paradigm is due to reduced visual responses at a pre-cued location due to adaptation or habituation (Bell et al., 2004, Boehnke et al., 2011, Dorris et al., 2002, Fecteau et al., 2004, Fecteau & Munoz, 2005). The principle is that the initial faster RTs in the cueing paradigm are due to the visual response riding atop the initial neural response to the cue, so a shorter distance is needed until the activity reaches threshold. After a few hundred milliseconds the initial response to the cue has dropped, but habituation or adaptation causes the new response to the probe to be greatly attenuated, slowing the time it takes for the activity to reach threshold. Indeed, a number of models have put forward evidence that habituation could explain RTs in variations of the cueing paradigm in humans (Dukewich, 2009, Ludwig et al., 2009, Patel et al., 2010). If these mechanisms are truly different, then it is ironic that traditional inhibition of return has created a line of research into inhibitory tagging, but itself does not rely on the same neural mechanism that it suggested.

If the attenuated responses we seen in LIP explain the slower RTs observed in this study, then we believe that any variable that affects attentional priority, such as salience (Arcizet et al., 2011, Treisman & Gelade, 1980), habituation (Boehnke et al., 2011), reward information (Trommershauser et al., 2009), or any other form of top-down drive (Noudoost et al., 2010), will also affect RTs. While we have focused on saccadic reaction times and the study of LIP, we believe the same principles should hold true in other oculomotor areas and in other areas for other forms of reaction times. In these cases, the equivalent priority map to that found in LIP would most likely be in other parietal areas, such as AIP for fine finger movements or MIP for reaching movements.

  • Previous work has shown response inhibition in LIP to visited stimuli

  • We show reaction time inhibition to previously visited stimuli in the same task

  • Reaction times were slow to probes at non-visited distractor locations

  • We conclude that the inhibition seen in LIP is inhibitory tagging seen in IOR

Acknowledgments

This work was supported by the McKnight Foundation and the National Eye Institute (R01 EY019273-01). We thank Sabine Kastner, Ryan Mruczek and members of the Goldberg lab for their comments on a previous version of the manuscript and the members of the UCLA DLAM for their superb animal care.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Andersen RA, Asanuma C, Essick G, Siegel RM. Corticocortical connections of anatomically and physiologically defined subdivisions within the inferior parietal lobule. Journal of Comparative Neurology. 1990;296(1):65–113. doi: 10.1002/cne.902960106. [DOI] [PubMed] [Google Scholar]
  2. Andersen RA, Brotchie PR, Mazzoni P. Evidence for the lateral intraparietal area as the parietal eye field. Current Opinion in Neurobiology. 1992;2(6):840–846. doi: 10.1016/0959-4388(92)90143-9. [DOI] [PubMed] [Google Scholar]
  3. Arcizet F, Mirpour K, Bisley JW. A pure salience response in posterior parietal cortex. Cerebral Cortex. 2011;21(11):2498–2506. doi: 10.1093/cercor/bhr035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bell AH, Fecteau JH, Munoz DP. Using auditory and visual stimuli to investigate the behavioral and neuronal consequences of reflexive covert orienting. Journal of Neurophysiology. 2004;91(5):2172–2184. doi: 10.1152/jn.01080.2003. [DOI] [PubMed] [Google Scholar]
  5. Bichot NP, Schall JD. Priming in macaque frontal cortex during popout visual search: feature-based facilitation and location-based inhibition of return. Journal of Neuroscience. 2002;22(11):4675–4685. doi: 10.1523/JNEUROSCI.22-11-04675.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bisley JW, Goldberg ME. Attention, intention, and priority in the parietal lobe. Annual Review of Neuroscience. 2010;33:1–21. doi: 10.1146/annurev-neuro-060909-152823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bizzi E. Discharge of frontal eye field neurons during saccadic and following eye movements in unanesthetized monkeys. Experimental Brain Research. 1968;6(1):69–80. doi: 10.1007/BF00235447. [DOI] [PubMed] [Google Scholar]
  8. Boehnke SE, Berg DJ, Marino RA, Baldi PF, Itti L, Munoz DP. Visual adaptation and novelty responses in the superior colliculus. European Journal of Neuroscience. 2011;34(5):766–779. doi: 10.1111/j.1460-9568.2011.07805.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bruce CJ, Goldberg ME. Primate frontal eye fields. I. Single neurons discharging before saccades. Journal of Neurophysiology. 1985;53(3):603–635. doi: 10.1152/jn.1985.53.3.603. [DOI] [PubMed] [Google Scholar]
  10. Bundesen C. A theory of visual attention. Psychological Review. 1990;97(4):523–547. doi: 10.1037/0033-295x.97.4.523. [DOI] [PubMed] [Google Scholar]
  11. Cave KR, Wolfe JM. Modeling the role of parallel processing in visual search. Cognitive Psychology. 1990;22(2):225–271. doi: 10.1016/0010-0285(90)90017-x. [DOI] [PubMed] [Google Scholar]
  12. Dodd MD, Van der Stigchel S, Hollingworth A. Novelty is not always the best policy: inhibition of return and facilitation of return as a function of visual task. Psychological Science. 2009;20(3):333–339. doi: 10.1111/j.1467-9280.2009.02294.x. [DOI] [PubMed] [Google Scholar]
  13. Dorris MC, Klein RM, Everling S, Munoz DP. Contribution of the primate superior colliculus to inhibition of return. Journal of Cognitive Neuroscience. 2002;14(8):1256–1263. doi: 10.1162/089892902760807249. [DOI] [PubMed] [Google Scholar]
  14. Dorris MC, Pare M, Munoz DP. Neuronal activity in monkey superior colliculus related to the initiation of saccadic eye movements. Journal of Neuroscience. 1997;17(21):8566–8579. doi: 10.1523/JNEUROSCI.17-21-08566.1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dorris MC, Taylor TL, Klein RM, Munoz DP. Influence of previous visual stimulus or saccade on saccadic reaction times in monkey. Journal of Neurophysiology. 1999;81(5):2429–2436. doi: 10.1152/jn.1999.81.5.2429. [DOI] [PubMed] [Google Scholar]
  16. Dukewich KR. Reconceptualizing inhibition of return as habituation of the orienting response. Psychonomic Bulletin and Review. 2009;16(2):238–251. doi: 10.3758/PBR.16.2.238. [DOI] [PubMed] [Google Scholar]
  17. Duncan J, Humphreys GW. Visual search and stimulus similarity. Psychological Review. 1989;96(3):433–458. doi: 10.1037/0033-295x.96.3.433. [DOI] [PubMed] [Google Scholar]
  18. Fecteau JH, Bell AH, Munoz DP. Neural correlates of the automatic and goal-driven biases in orienting spatial attention. Journal of Neurophysiology. 2004;92(3):1728–1737. doi: 10.1152/jn.00184.2004. [DOI] [PubMed] [Google Scholar]
  19. Fecteau JH, Munoz DP. Correlates of capture of attention and inhibition of return across stages of visual processing. Journal of Cognitive Neuroscience. 2005;17(11):1714–1727. doi: 10.1162/089892905774589235. [DOI] [PubMed] [Google Scholar]
  20. Fecteau JH, Munoz DP. Salience, relevance, and firing: a priority map for target selection. Trends in Cognitive Sciences. 2006;10(8):382–390. doi: 10.1016/j.tics.2006.06.011. [DOI] [PubMed] [Google Scholar]
  21. Goldberg ME, Bisley JW, Powell KD, Gottlieb J. Saccades, salience and attention: the role of the lateral intraparietal area in visual behavior. Progress in Brain Research. 2006;155:157–175. doi: 10.1016/S0079-6123(06)55010-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gottlieb J, Balan P. Attention as a decision in information space. Trends in Cognitive Sciences. 2010;14(6):240–248. doi: 10.1016/j.tics.2010.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gottlieb JP, Kusunoki M, Goldberg ME. The representation of visual salience in monkey parietal cortex. Nature. 1998;391(6666):481–484. doi: 10.1038/35135. [DOI] [PubMed] [Google Scholar]
  24. Hays AV, Richmond BJ, Optican LM. Unix-based multiple-process system, for real-time data acquisition and control. WESCON Conference Proceedings. 1982;2:1–10. [Google Scholar]
  25. Hooge IT, Frens MA. Inhibition of saccade return (ISR): spatio-temporal properties of saccade programming. Vision Research. 2000;40(24):3415–3426. doi: 10.1016/s0042-6989(00)00184-x. [DOI] [PubMed] [Google Scholar]
  26. Horwitz GD, Newsome WT. Target selection for saccadic eye movements: prelude activity in the superior colliculus during a direction-discrimination task. Journal of Neurophysiology. 2001;86(5):2543–2558. doi: 10.1152/jn.2001.86.5.2543. [DOI] [PubMed] [Google Scholar]
  27. Ipata AE, Gee AL, Goldberg ME, Bisley JW. Activity in the lateral intraparietal area predicts the goal and latency of saccades in a free-viewing visual search task. Journal of Neuroscience. 2006;26(14):3656–3661. doi: 10.1523/JNEUROSCI.5074-05.2006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Itti L, Koch C, Niebur E. A model of saliencey-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998;20:1254–1259. [Google Scholar]
  29. Klein R. Inhibitory tagging system facilitates visual search. Nature. 1988;334(6181):430–431. doi: 10.1038/334430a0. [DOI] [PubMed] [Google Scholar]
  30. Klein RM. Inhibition of return. Trends in Cognitive Sciences. 2000;4(4):138–147. doi: 10.1016/s1364-6613(00)01452-2. [DOI] [PubMed] [Google Scholar]
  31. Klein RM, MacInnes WJ. Inhibition of return is a foraging facilitator in visual search. Psychological Science. 1999;10:346–352. [Google Scholar]
  32. Koch C, Ullman S. Shifts in selective visual attention: towards the underlying neural circuitry. Human Neurobiology. 1985;4(4):219–227. [PubMed] [Google Scholar]
  33. Lovejoy LP, Krauzlis RJ. Inactivation of primate superior colliculus impairs covert selection of signals for perceptual judgments. Nature Neuroscience. 2010;13(2):261–266. doi: 10.1038/nn.2470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Ludwig CJ, Farrell S, Ellis LA, Gilchrist ID. The mechanism underlying inhibition of saccadic return. Cognitive Psychology. 2009;59(2):180–202. doi: 10.1016/j.cogpsych.2009.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. MacInnes JW, Klein RM. Inhibition of return biases orienting during the search of complex scenes. Scientific World Journal. 2003;3:75–86. doi: 10.1100/tsw.2003.03. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mazurek ME, Roitman JD, Ditterich J, Shadlen MN. A role for neural integrators in perceptual decision making. Cerebral Cortex. 2003;13(11):1257–1269. doi: 10.1093/cercor/bhg097. [DOI] [PubMed] [Google Scholar]
  37. Mirpour K, Arcizet F, Ong WS, Bisley JW. Been there, seen that: a neural mechanism for performing efficient visual search. Journal of Neurophysiology. 2009;102(6):3481–3491. doi: 10.1152/jn.00688.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Mirpour K, Ong WS, Bisley JW. Microstimulation of posterior parietal cortex biases the selection of eye movement goals during search. Journal of Neurophysiology. 2010;104(6):3021–3028. doi: 10.1152/jn.00397.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Moore T, Armstrong KM. Selective gating of visual signals by microstimulation of frontal cortex. Nature. 2003;421(6921):370–373. doi: 10.1038/nature01341. [DOI] [PubMed] [Google Scholar]
  40. Muller HJ, von Muhlenen A. Probing distractor inhibition in visual search: inhibition of return. Journal of Experimental Psychology: Human Perception and Performance. 2000;26(5):1591–1605. doi: 10.1037//0096-1523.26.5.1591. [DOI] [PubMed] [Google Scholar]
  41. Noudoost B, Chang MH, Steinmetz NA, Moore T. Top-down control of visual attention. Current Opinion in Neurobiology. 2010;20(2):183–190. doi: 10.1016/j.conb.2010.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Patel SS, Peng X, Sereno AB. Shape effects on reflexive spatial selective attention and a plausible neurophysiological model. Vision Research. 2010;50(13):1235–1248. doi: 10.1016/j.visres.2010.04.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Posner MI, Cohen Y. Components of visual orienting. In: Bouma H, Bouwhuis D, editors. Attention and Performance. X. Erlbaum; 1984. pp. 531–556. [Google Scholar]
  44. Posner MI, Rafal RD, Choate LS, Vaughan J. Inhibition of return: neural basis and function. Cognitive Neuropsychology. 1985;2:211–228. [Google Scholar]
  45. Rafal R, Egly R, Rhodes D. Effects of inhibition of return on voluntary and visually guided saccades. Canadian Journal of Experimental Psychology. 1994;48(2):284–300. doi: 10.1037/1196-1961.48.2.284. [DOI] [PubMed] [Google Scholar]
  46. Robinson DL, Petersen SE. The pulvinar and visual salience. Trends in Neurosciences. 1992;15(4):127–132. doi: 10.1016/0166-2236(92)90354-b. [DOI] [PubMed] [Google Scholar]
  47. Roitman JD, Shadlen MN. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience. 2002;22(21):9475–9489. doi: 10.1523/JNEUROSCI.22-21-09475.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Sato T, Murthy A, Thompson KG, Schall JD. Search efficiency but not response interference affects visual selection in frontal eye field. Neuron. 2001;30(2):583–591. doi: 10.1016/s0896-6273(01)00304-x. [DOI] [PubMed] [Google Scholar]
  49. Schiller PH, Stryker M. Single-unit recording and stimulation in superior colliculus of the alert rhesus monkey. Journal of Neurophysiology. 1972;35(6):915–924. doi: 10.1152/jn.1972.35.6.915. [DOI] [PubMed] [Google Scholar]
  50. Serences JT, Yantis S. Selective visual attention and perceptual coherence. Trends in Cognitive Sciences. 2006;10(1):38–45. doi: 10.1016/j.tics.2005.11.008. [DOI] [PubMed] [Google Scholar]
  51. Shen K, Pare M. Neuronal activity in superior colliculus signals both stimulus identity and saccade goals during visual conjunction search. Journal of Vision. 2007;7(5):15, 11–13. doi: 10.1167/7.5.15. [DOI] [PubMed] [Google Scholar]
  52. Soltani A, Koch C. Visual saliency computations: mechanisms, constraints, and the effect of feedback. Journal of Neuroscience. 2010;30(38):12831–12843. doi: 10.1523/JNEUROSCI.1517-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Takeda Y, Yagi A. Inhibitory tagging in visual search can be found if search stimuli remain visible. Perception and Psychophysics. 2000;62(5):927–934. doi: 10.3758/bf03212078. [DOI] [PubMed] [Google Scholar]
  54. Thomas LE, Ambinder MS, Hsieh B, Levinthal B, Crowell JA, Irwin DE, Kramer AF, Lleras A, Simons DJ, Wang RF. Fruitful visual search: inhibition of return in a virtual foraging task. Psychonomic Bulletin and Review. 2006;13(5):891–895. doi: 10.3758/bf03194015. [DOI] [PubMed] [Google Scholar]
  55. Thomas LE, Lleras A. Inhibitory tagging in an interrupted visual search. Attention, Perception and Psychophysics. 2009;71(6):1241–1250. doi: 10.3758/APP.71.6.1241. [DOI] [PubMed] [Google Scholar]
  56. Thomas NW, Pare M. Temporal processing of saccade targets in parietal cortex area LIP during visual search. Journal of Neurophysiology. 2007;97(1):942–947. doi: 10.1152/jn.00413.2006. [DOI] [PubMed] [Google Scholar]
  57. Thompson KG, Bichot NP. A visual salience map in the primate frontal eye field. Progress in Brain Research. 2005;147:251–262. doi: 10.1016/S0079-6123(04)47019-8. [DOI] [PubMed] [Google Scholar]
  58. Thompson KG, Hanes DP, Bichot NP, Schall JD. Perceptual and motor processing stages identified in the activity of macaque frontal eye field neurons during visual search. Journal of Neurophysiology. 1996;76(6):4040–4055. doi: 10.1152/jn.1996.76.6.4040. [DOI] [PubMed] [Google Scholar]
  59. Treisman AM, Gelade G. A feature-integration theory of attention. Cognitive Psychology. 1980;12(1):97–136. doi: 10.1016/0010-0285(80)90005-5. [DOI] [PubMed] [Google Scholar]
  60. Trommershauser J, Glimcher PW, Gegenfurtner KR. Visual processing, learning and feedback in the primate eye movement system. Trends in Neurosciences. 2009;32(11):583–590. doi: 10.1016/j.tins.2009.07.004. [DOI] [PubMed] [Google Scholar]
  61. Wang Z, Klein RM. Searching for inhibition of return in visual search: a review. Vision Research. 2010;50(2):220–228. doi: 10.1016/j.visres.2009.11.013. [DOI] [PubMed] [Google Scholar]
  62. Wurtz RH, Goldberg ME. Superior colliculus cell responses related to eye movements in awake monkeys. Science. 1971;171(966):82–84. doi: 10.1126/science.171.3966.82. [DOI] [PubMed] [Google Scholar]

RESOURCES