The effects of distractors and spatial precues on covert visual search in macaque

Vision Research 76 (2013) 43–49

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The effects of distractors and spatial precues on covert visual search in macaque Byeong-Taek Lee a, Robert M. McPeek b,⇑ a b

The Smith-Kettlewell Eye Research Institute, 2318 Fillmore Street, San Francisco, CA 94115, USA Graduate Center for Vision Research, SUNY Eye Institute, State University of New York, College of Optometry, 33 West 42nd Street, New York, NY 10036, USA

a r t i c l e

i n f o

Article history: Received 15 January 2012 Received in revised form 8 October 2012 Available online 23 October 2012 Keywords: Attention Transient Cueing Rhesus monkey Set-size effects

a b s t r a c t Covert visual search has been studied extensively in humans, and has been used as a tool for understanding visual attention and cueing effects. In contrast, much less is known about covert search performance in monkeys, despite the fact that much of our understanding of the neural mechanisms of attention is based on these animals. In this study, we characterize the covert visual search performance of monkeys by training them to discriminate the orientation of a briefly-presented, peripheral Landolt-C target embedded within an array of distractor stimuli while maintaining fixation. We found that target discrimination performance declined steeply as the number of distractors increased when the target and distractors were of the same color, but not when the target was an odd color (color pop-out). Performance was also strongly affected by peripheral spatial precues presented before target onset, with better performance seen when the precue coincided with the target location (valid precue) than when it did not (invalid precue). Moreover, the effectiveness of valid precues was greatest when the delay between precue and target was short (80–100 ms), and gradually declined with longer delays, consistent with a transient component to the cueing effect. Discrimination performance was also significantly affected by prior knowledge of the target location in the absence of explicit visual precues. These results demonstrate that covert visual search performance in macaques is very similar to that of humans, indicating that the macaque provides an appropriate model for understanding the neural mechanisms of covert search. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Most real-world visual scenes contain more information than can be consciously selected and processed. Visuo-spatial attention is, therefore, critical for filtering incoming information according to the demands of the task at hand. Visual attention has been studied extensively in human subjects using covert visual search tasks, in which subjects must make a response based on some aspect of a peripheral array of stimuli while remaining fixated. However, there have been comparatively fewer studies of covert visual search performance during fixation in the monkey (e.g., Balan et al., 2008; Buracas & Albright, 1999; Golla et al., 2004; Monosov & Thompson, 2009; Wardak et al., 2006; Wardak, Olivier, & Duhamel, 2004). Most studies of visual search in monkeys have instead focused on overt search, in which monkeys are rewarded for making an eye movement to a target stimulus among distractors (e.g., Arai, McPeek, & Keller, 2004; Bichot, Rossi, & Desimone, 2005; Bichot & Schall, 1999, 2002; McPeek & Keller, 2001), or on unconstrained search, in which monkeys are free to look anywhere in the scene while discriminating the target (e.g., Bisley et al., 2009; Motter & ⇑ Corresponding author. Address: SUNY Optometry, 33 West 42nd Street, New York, NY 10036, USA. Fax: +1 212 938 5537. E-mail address: [email protected] (R.M. McPeek). 0042-6989/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.visres.2012.10.007

Belky, 1998a, 1998b; Motter & Holsapple, 2000, 2007; Shen & Paré, 2006) In order to link the results of single-unit studies of attention in monkeys with human attentional performance, it is important to determine to extent to which the performance of monkeys in covert attention tasks is comparable to that of humans. Many studies of visual search have involved detecting the presence or absence of an odd target among distractors (e.g., Treisman & Gelade, 1980; Wolfe, 1994). However, it has been argued that when the target is easily discriminable from the distractors (e.g., pop-out search), such detection tasks require relatively few attentional resources (e.g., Braun & Sagi, 1990, 1991; Bravo & Nakayama, 1992; Sagi & Julesz, 1985), particularly in well-practiced observers (Braun, 1998). To examine pop-out in a more attentionallydemanding task, Bravo and Nakayama (1992) developed a visual search task in which the target is always present, and subjects must discriminate a fine shape detail of the target, rather than simply detecting it. They argued that this task is well suited for studying attention because discriminating the shape of the target requires subjects to focus attention on the target before responding. Furthermore, unlike conventional target present/absent search, this task allows one to decouple the difficulty of the required discrimination (in this case, of the target shape) from the difficulty of locating the target among the distractors. To date, only a few studies have examined the behavioral performance of

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monkeys in this type of search task (e.g., Balan et al., 2008; Bisley et al., 2009; Monosov & Thompson, 2009). Most other studies of covert search in monkeys have instead focused on target present/ absent search (e.g., Buracas & Albright, 1999; Wardak et al., 2006), or have examined tasks involving the discrimination of a target presented in isolation, without distractors (Golla et al., 2004). In this study, we used an adaptation of Bravo and Nakayama’s focal attention task to study covert attention during visual search in monkeys. Our goal was to systematically assess the performance of monkeys in a task similar to those used in humans (e.g., Bravo & Nakayama, 1992; Nakayama & Mackeben, 1989; Lu & Dosher, 2000; Maljkovic & Nakayama, 1994; Morgan, Ward, & Castet, 1998). In the first experiment, we compared the effect of varying the number of distractors on discrimination of a target which either does or does not pop-out from distractors. In the second experiment, we investigated the effects of peripheral cues on search performance, and mapped out the time course of the cueing effect. In the third experiment, we examined how prior knowledge of the target location influences search performance. In all three experiments, we used a limited-duration, masked target presentation, a procedure which captures variations in performance largely as changes in percentage correct performance (e.g., Braun, 1998; Braun & Sagi, 1990, 1991; Golla et al., 2004; Lu & Dosher, 2000; Morgan, Ward, & Castet, 1998; Nakayama & Mackeben, 1989; Sagi & Julesz, 1985) rather than as changes in reaction time (RT) or combined RT/error rate changes (e.g., Balan et al., 2008; Bravo & Nakayama, 1992; Buracas & Albright, 1999; Maljkovic & Nakayama, 1994; Monosov & Thompson, 2009; Treisman & Gelade, 1980; Wardak et al., 2006; Wolfe, 1994).

2. Experiment 1: Influence of distractors and pop-out Studies in humans and monkeys have shown that a target which is highly discriminable from distractors, such as a color oddball, tends to rapidly and automatically attract attention to itself (e.g., Burrows & Moore, 2009; Constantinidis & Steinmetz, 2005; Egeth & Yantis, 1997; Thompson, Bichot, & Schall, 1997; Treisman & Gelade, 1980; Wolfe, 1994). In classic present/absent search tasks, this has been observed as a flat or shallow slope in the function relating search performance to the number of distractors in the display. In humans, using a search task which required discriminating the shape of the target, rather than detecting its presence or absence, Bravo and Nakayama (1992) showed that when the target popped-out, either in color or spatial frequency, the time required to discriminate the shape of the target did not increase when more distractors were added. In particular, when the colors of the target and distractors remained the same from trial to trial (blocked condition), search times were independent of the number of distractors. Here, we examined the effects of color pop-out on covert search in monkeys, comparing target discrimination performance when the target was the same color as the distractors and when the target was a unique color. In the no pop-out condition, the colors of the target and distractors were identical, and we manipulated the number of distractors. We predicted that if the task requires monkeys to focus attention on the target, then target discrimination performance should decline when more distractors are present (e.g., Balan et al., 2008; Bisley et al., 2009). Presumably, this occurs because localization of the target is more difficult in the presence of same-colored distractors. In contrast, when the target has a unique color, and thus is easily discriminable from the distractors, we predict that attention will be shifted rapidly to the target regardless of the number of distractors, resulting in little or no decline in performance with more distractors (e.g., Bravo & Nakayama, 1992). This pattern of results

would support the conclusion that the attentional performance of humans and monkeys is similar. 2.1. Material and methods The experiments were conducted at the Smith-Kettlewell Eye Research Institute. All experimental protocols were approved by the Institutional Animal Care and Use Committee, and complied with the guidelines of the Public Health Service Policy on Humane Care and Use of Laboratory Animals. Two male rhesus monkeys (Macaca mulatta, H and F) weighing between 5 and 8 kg participated in the behavioral study. A scleral coil (Fuchs & Robinson, 1966; Judge, Richmond, & Chu, 1980) and a head holder system were implanted under isofluorane anesthesia and aseptic surgical conditions. At the completion of the surgery, animals were returned to their home cages, and then were trained for 6–8 months in the behavioral tasks. The monkeys were seated in a primate chair with their heads restrained for the duration of the testing sessions, which were performed in a dimly illuminated room. They executed behavioral tasks for juice reward and were allowed to work to satiation. Records of each animal’s weight and health status were kept, and supplemental water was given as necessary. The animals usually worked for 5 days a week and were allowed access to water on weekends. Data collection and storage was controlled by a real-time program running on a Macintosh computer. Horizontal and vertical eye position and velocity were sampled at 1 kHz and digitally stored on disk. The computer also generated the visual displays using Psychtoolbox (Brainard, 1997; Pelli, 1997) running in Matlab. Visual stimuli were presented on a 29 in. color CRT (Viewsonic GA29), in synchronization with the monitor’s vertical refresh. The monitor had a spatial resolution of 800 by 600 pixels and a noninterlaced vertical refresh rate of 75 Hz. The monitor was positioned 57 cm in front of the monkeys. 2.1.1. Search task The task was based on Bravo and Nakayama’s (1992) search paradigm, in which the shape of a target among distractors must be discriminated. As summarized in Fig. 1, monkeys initially fixated a central fixation point for a randomly-determined interval of 82–127 frames (approx. 1100–1700 ms). Next, a search array was presented for a brief duration (6 frames; 80 ms), followed by a high-contrast random-dot mask for 4 frames (53 ms). The search array contained one target and 0, 1, 2, or 5 distractors. Both target and distractors were Landolt-C stimuli, 2.5° in diameter. The target was distinguished by the fact that the opening of the C was

Initial display Target array Mask Response

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Fig. 1. Schematic representation of the stimulus sequence in Experiment 1. Each trial began with the presentation of a central white fixation point and two choice stimuli (above and below fixation). After a variably delay, a search array was briefly presented, followed by a masking stimulus. The target was defined as the Landolt-C with the gap aligned vertically, while the distractors were aligned horizontally. At the end of each trial, monkeys indicated the orientation of the target (up or down) by making a saccade to the corresponding choice stimulus.

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always aligned vertically (either upwards or downwards), while the distractors were aligned horizontally (left or right). The target was randomly presented in one of six possible locations (0°, 45°, 135°, 180°, 225°, and 315° from horizontal), and the distractors were spaced equidistantly around the array from the target. Both the target and distractors were located at an eccentricity of 10°. The luminance of the distractors was matched to that of the target. In blocks of pop-out trials, the target was blue and the distractors were yellow, while in separate blocks of no pop-out trials, the target and distractors were both blue in color. Monkeys were required to maintain fixation until after the offset of the mask, when the fixation point was extinguished. At this point, they indicated the direction of the target by making a saccade to one of two choice stimuli located above and below the central fixation position. An upward-aligned target was indicated by making a saccade to the upward stimulus, and vice-versa for a downward-aligned target. Monkeys were rewarded for correctly reporting the target orientation within 2 s of mask offset. An incorrect response was followed by a brief time-out period (1–3 s). 2.1.2. Data analysis We measured psychophysical performance as the percentage of targets correctly discriminated. Off-line analysis of the eye movement data was performed by algorithms using velocity and acceleration criteria to detect the beginning and end of saccades. The algorithm’s identification of saccades was visually inspected for every trial to verify its accuracy. 2.2. Results and discussion

Percent correct performance

In this experiment, we collected 5060 trials from Monkey F and 4895 trials from Monkey H. Both monkeys showed better target discrimination performance when the target appeared in the absence of distractors than when distractors were present (Fig. 2, unfilled vs. filled symbols). To verify the statistical significance of this difference, we performed a z-test for each monkey on the proportion of correct trials, with all the distractor conditions pooled together compared with the no distractor condition (Monkey F: p < 4.9  10 9; Monkey H: p < 8.8  10 4). Next, we analyzed the effects of increasing the number of distractors on search performance in the no pop-out and pop-out conditions. We found that, for both monkeys, target discrimination performance in the no pop-out condition became worse as the number of distractors increased (Fig. 2, left panel), similar to what was reported by Balan et al., (2008) and Bisley et al. (2009). The significance of this trend was verified using logistic regressions (Monkey H: logistic regression slope = 0.1556; p < 3.6  10 8;

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Monkey F: slope = 0.17; p < 1.8  10 7). On the other hand, when the target color popped-out, performance was only weakly dependent on the number of distractors (Fig. 2, right panel). Here, logistic regressions showed a non-significant trend for Monkey H (slope = 0.003; p = 0.92), indicating that performance was relatively independent of the number of distractors. On the other hand, for Monkey F, there was a significant decline in performance with more distractors (slope = 0.09; p < 0.02). To compare the extent to which distractors affected performance in the pop-out vs. no popout conditions for this monkey, we performed an additional logistic regression on the combined dataset (pop-out and no pop-out conditions), and found that there was a significant interaction effect between the number of distractors and the experimental condition (pop-out vs. no pop-out; p < 0.05), indicating a significant difference in the extent to which an increase in the number of distractors affected performance in the two conditions. This extended model also confirmed a significant interaction between the number of distractors and condition in Monkey H (p < 0.001). Experiments such as this one, in which a brief, masked target is discriminated, typically capture performance differences in the form of changes in percentage correct, rather then changes in RT. When we examined the mean RTs of our monkeys in this task, we found little variation among the different experimental conditions (ranging from 194 to 199 ms for Monkey F and 221 to 237 ms for Monkey H). In one-way ANOVAs with number of distractors as the factor, we found no significant differences in RT for either monkey in either the pop-out or no pop-out condition (all p > 0.09). The difference in the effect of distractors on search performance in pop-out vs. no pop-out search mirrors the classic set-size effects seen in visual search tasks in which subjects must indicate the presence or absence of a target among distractors (e.g., Buracas & Albright, 1999; Treisman & Gelade, 1980; Wardak, Olivier, & Duhamel, 2004; Wardak et al., 2006; Wolfe, 1994). However, our results indicate that even when the search task requires discrimination of the target’s shape, rather than detection, color pop-out facilitates covert search in monkeys. The pattern of results is similar to those of Bravo and Nakayama (1992), who found that in pop-out search, when the colors of the target and distractor remained the same across trials as they do here, target discrimination performance is relatively unaffected by the number of distractors in the display. The results are also consistent with the findings of Bisley et al. (2009), who showed similar set-size effects with pop-out and no pop-out targets in a task in which monkeys were not required to maintain fixation (as here), but were free to move their eyes while performing the search. Overall, these results point to a close congruence between human and monkey covert search performance.

3. Experiment 2: Precueing attention in visual search

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Number of distractors Fig. 2. Means and standard errors of percentage correct performance as a function of the number of distractors. The left panel shows data from both monkeys (F and H) in the no pop-out condition, and the right panel shows data in the pop-out condition. Unfilled symbols show performance in the absence of distractors. Lines show linear fits of the data, excluding the no distractor condition.

In this experiment, we examined the effects of peripheral spatial precues on covert search performance. In humans, it has been well-established that a peripheral cue, such as a sudden onset or color change, draws attention to the cued location in a variety of tasks. Valid cues, which correctly indicate the location of the target, generally improve performance, while invalid cues, which occur away from the target location, result in worse performance (e.g., Jonides, 1981; Posner, 1980). In monkeys, valid and invalid cueing effects have been shown both for isolated stimuli (e.g., Bowman et al., 1993; Golla et al., 2004) and for stimuli embedded in an array of distractors (e.g., Bisley et al., 2009; Monosov & Thompson, 2009; Thompson, Biscoe, & Sato, 2005). Fewer studies have examined the time-course of this precueing effect. In humans, Nakayama and Mackeben (1989) showed that performance that in a conjunction search task and in a shape-

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discrimination task varies with the SOA between a peripheral precue and the discrimination target. When a valid peripheral cue was presented simultaneously with the target and distractors, little cueing benefit was observed. When the SOA between the precue and the target was somewhat longer (80–100 ms), they found that discrimination performance was markedly improved, indicating a rapid shift of attention to the precued location. However, at longer SOAs, performance declined from its peak. They interpreted these results as indicating that peripherally-cued attention has a rapid transient component which provides peak attentional performance shortly after cue onset. A similar pattern of results was reported by Muller and Rabbitt (1989). In monkeys, Bisley and Goldberg (2006) showed that a sudden-onset stimulus can transiently capture attention (as well as modulate LIP activity), but the data from this study did not permit a fine-grained comparison of the time-course of this capture with the time-course found by Nakayama and Mackeben (1989). To look for further evidence of a transient component of peripherally-cued attention during covert search in monkeys, and to compare the time-course of the cueing effect with the results reported by Nakayama and Mackeben (1989) in humans, we examined the effect of varying the precue-target SOA in our task. Since the task requires the discrimination of the shape of a target among distractors, it is very similar to the tasks used by Nakayama and Mackeben (1989). Moreover, like Nakayama and Mackeben, we mapped the cueing time-course using a 100% valid precue. As such, the cue presumably triggers both an automatic (exogenous) capture of attention and a voluntary (endogenous) shift of attention. Thus, variations in performance as a function of cue-target SOA are likely to reflect the underlying timing of the covert attention system, and are unlikely to originate from a deliberate strategy on the part of the monkeys, since it was always in their best interests to maintain maximal attention at the cued location. If the allocation of attention by peripheral cues is similar in humans and monkeys, then we expect to see a transient time-course, with performance best at fairly short SOAs and then declining somewhat at longer SOAs, before leveling off at an asymptotic level, presumably due to the maintenance of sustained, endogenous attention at the cued location (e.g., Nakayama & Mackeben, 1989). 3.1. Methods The task was based on the search paradigm used in Experiment 1. However, a spatial precue array preceded the search array, as shown in Fig. 3. During initial fixation, six green boxes were presented at the six possible target locations. After a randomlyselected delay of 82–127 frames (1100–1700 ms), one of the boxes turned red while maintaining the same luminance as the green boxes. In these experiments, the target was always presented with five distractors, and colors of the target and distractors were identical. In the first set of experiments, the SOA between precue and target was fixed at 8 frames (107 ms), and the precue was valid (correctly predicting the location of the target) in 75% of trials, and invalid in the remaining 25% of trials. Invalid precues were always presented at a location 180° away from the target location (at an array location diametrically opposite the target). A separate set of experiments examined the effect of the SOA between the precue and target on performance. In these experiments, the precue was always valid and the SOA between the precue and target was varied, in separate blocks, from 6 to 105 frames (80–1400 ms) for Monkey H, and from 8 to 105 frames (107–1400 ms) for Monkey F. 3.2. Results and discussion First, we examined the effects of valid and invalid precues on search performance (Fig. 4). For these data, the precue-target

SOA was fixed at approximately 107 ms. Monkey F performed 1164 trials and Monkey H performed 884 trials. As expected, performance was significantly better with valid precues than with invalid precues (Monkey F, 77% vs. 61% at the valid vs. invalid conditions; Monkey H, 78% vs. 63%). The statistical significance of these differences was verified with z-tests (p < 0.001 in both cases). This pattern of results shows that peripheral cueing of attention significantly affects performance in our task. As before, there was little variation in mean RT for the two experimental conditions (Monkey F: 233 ms (valid) vs. 225 ms (invalid), t-test: p = 0.70; Monkey H: 267 ms (valid) vs. 277 ms (invalid), t-test: p = 0.33). In separate blocks of trials, we examined the effect of the SOA between a valid precue and the target (Fig. 5). Monkey F’s performance was based on 3342 trials, and Monkey H’s performance was based on 10,919 trials. When the precue was presented simultaneously with the target (SOA = 0 ms), performance was worse than at the other SOAs (z-tests comparing 0 ms SOA and all the other SOAs lumped together: Monkey F: p = 1.5  10 11; Monkey H: p = 6.8  10 12). However, when the precue appeared 80– 107 ms before the target, performance was substantially better in both monkeys. This SOA is similar to the optimal SOA for humans reported by Nakayama and Mackeben (1989) and Muller and Rabbitt (1989). As SOA increased further, performance declined from this peak, leveling off at the longest SOAs. We performed logistic regressions on the data collected with non-zero SOAs, and found that the decline in performance at longer SOAs was statistically significant in both monkeys (Monkey F: p = 3.3  10 7; Monkey H: p = 0.0008). Thus, the results indicate that the effects of peripheral cueing on covert search in monkeys exhibit a transient time course. This time course is similar to what is seen in humans (Muller & Rabbitt, 1989; Nakayama & Mackeben, 1989), again consistent with a close congruence in attentional performance between monkey and human. An analysis of the variation in RT with SOA showed no significant effect in Monkey F (range of mean RTs: 195–215 ms; one-way ANOVA with SOA as factor: p = 0.15), but did show a significant effect in Monkey H, with RTs increasing for longer SOAs (range: 209–237 ms, ANOVA p < 0.01). This is consistent with improved performance at the shorter SOAs, although the magnitude of the change in RT is small. Moreover, we caution that interpretation of RT differences in the task is not straightforward, since a longer RT did not provide a longer viewing time of the target.

4. Experiment 3: Influence of prior knowledge of target location In experiments with humans, attention is also often directed without the use of peripheral cues. Many studies have used symbolic or instructional cues which can be used by subjects to voluntarily shift attention to the target location in the absence of a peripheral cue (e.g., Jonides, 1981; Posner, 1980). Relatively fewer studies have used symbolic cues to shift attention in monkeys (e.g., Kustov & Robinson, 1996). However, Basso and Wurtz (1998) used a blocked target location manipulation in which the location of a saccade target repeated over many trials before shifting to a new location. When they compared saccadic performance in this blocked condition with performance in a random target location condition, they found that monkeys showed shorter saccade latencies in the blocked condition, indicating that they can make use of repeated target location information. Subsequently, Ciaramitaro, Cameron, and Glimcher (2001) suggested using prior knowledge of the likely location of the target based on the history of previous trials to endogenously cue covert attention. In a luminance discrimination task without distractors, they found that monkeys and humans are able to learn such location contingencies, and can use this knowledge to improve performance. Although it is un-

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Fig. 4. Illustration of the cueing effect in Experiment 2. The plots show the mean and standard error of percentage correct target discrimination for valid and invalid cue conditions for each monkey.

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The methods were identical to those used in the no pop-out condition of Experiment 1, except as noted. Trials were presented in alternating blocked and random blocks. Within blocks of the blocked condition, 30 consecutively trials were presented with the target fixed at one of the six possible locations. Following this, target location shifted to a new position, and another 30 trials were presented at this location. This continued until all six locations were probed, for a total of 180 trials per block. In the random blocks, the same 180 trials were presented, but their order was pseudo-randomly shuffled so that target location was unpredictable from trial to trial.

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Cue-Target SOA (ms) Fig. 5. Transient time-course of valid precue effects. Plots show, for each monkey, the mean and standard error of percentage correct target discrimination as a function of the SOA between the precue and the search array.

clear whether this endogenous cueing effect is volitional or is the result of an unconscious priming effect, it nonetheless shows that memory of prior target locations can affect the allocation of attention in monkeys. To examine the effects of this form of endogenous cueing in our search task, we tested whether monkeys’ performance improved with repetition of the target location. If so, it would indicate that this task is appropriate for studying memory-based endogenous attention shifts in monkeys. Specifically, we compared performance in two conditions: a ‘‘blocked’’ and a ‘‘random’’ condition. In both of these conditions, we presented 30 trials at each of the six possible target locations. The only difference between conditions was in the order of the tri-

Monkey H contributed 1064 trials and Monkey F contributed 1745 trials to this experiment. As Fig. 6 shows, discrimination of the target in the blocked (predictable target location) condition is superior to discrimination in the random (unpredictable target location) condition (mean: 77.4 % vs. 65.0 % for monkey F, and 72.5 % vs. 57.8 % for monkey H). This difference was significant in both monkeys (z-tests: Monkey F: p = 1.8  10 6; Monkey H: p = 4.6  10 5). This result indicates that in our search task, monkeys are able to use a memory of the target location to guide attention and improve performance. One such mechanism identified in prior studies in humans is the automatic priming of repeated target locations (e.g., Maljkovic & Nakayama, 1996). If location priming is occurring in our task, then we might expect to see an improvement in performance when the target location repeats (by chance) in the random condition. However, with six pseudo-randomly interleaved target locations, our random condition was not optimal for finding location priming. In Monkey H, performance in the random condition when the target location was the same as in the previous trials was

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Analyses of RTs in the blocked vs. random conditions revealed only small variations, which were not statistically significant. In Monkey F, the mean RT was 252 in the blocked condition vs. 265 ms in the random condition (t-test: p = 0.19), while in Monkey H, the mean RT was 251 ms in the blocked condition vs. 258 ms in the random condition (t-test: p = 0.31).

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Establishing homologies between human and monkey in attentional performance is crucial for relating single-unit studies of attention to human psychophysical and brain imaging studies. Even though covert visual search has been studied extensively in humans, most studies of visual search in the monkey have involved active search with eye movements, rather than covert search. Here, in a series of experiments, we studied covert visual search in monkeys using a task that requires discriminating the shape of a target presented with distractors. First, we found that the performance of monkeys depended strongly on the number of distractors in the search array when the color of the target and distractors was identical, similar to what was seen by Balan et al., (2008) and Bisley et al. (2009). In contrast, when the target popped-out in color, performance was relatively independent of the number of distractors. Second, we found that valid and invalid peripheral cues strongly affect covert search performance in monkeys, and that this cueing effect shows a transient time course similar to what has been seen in humans. Finally, we demonstrated that monkeys can use prior knowledge of the target location to improve covert search performance in the absence of peripheral cues. These results demonstrate a close congruence of search performance in monkeys and humans. Specifically, the pop-out results are reminiscent of what was found by Bravo and Nakayama (1992) in a similar task. Moreover, our results indicating that spatial cues follow a transient time-course is similar to what was found in humans by Nakayama and Mackeben (1989) and Muller and Rabbitt (1989). Previously, Golla et al. (2004) examined the effect of varying the precue-target SOA on discrimination of a Landolt-C target presented without distractors in both humans and monkeys. They found that human performance was significantly better at shorter SOAs. In monkeys there was a tendency for better performance at shorter SOAs, but the trend was not significant. However, the shortest SOA tested in monkeys was 250 ms. Our results, in agreement with human studies (Muller & Rabbitt, 1989; Nakayama & Mackeben, 1989), suggest that this duration is longer than the optimal SOA, which may account for the lack of a significant trend in their study. Along similar lines, Monosov and Thompson (2009) examined the effects of varying cue/target SOA on performance of monkeys in a shape discrimination task. They found that performance was worst when the cue appeared simultaneously with the target, but did not find any significant differences in performance at the other SOAs. However, their analysis involved gathering a range of different SOAs into discrete bins, which may have obscured a brief performance peak associated with transient attention. In the final experiment, we showed that, in the absence of exogenous spatial cues, discrimination performance improves in monkeys when the target location is repeated from trial to trial, presumably due to endogenous deployment of attention. One advantage of this manipulation when dealing with monkeys is that it requires little or no special training, in contrast to the relatively difficult task of training animals to interpret symbolic cues. Taken together, the results indicate that the attentional performance of monkeys is similar to what is seen in humans, and thus, provide a solid foundation for linking physiological evidence gained from primates with human psychophysics and neuroimaging studies.

Percentage Correct

100

100

Monkey H

Monkey F

50

50 Blocked Random

Blocked Random

Target Location Fig. 6. Effect of location predictability on search performance. For each monkey, the mean and standard error of percentage correct discrimination is shown in the blocked and random target location conditions.

slightly better (62% correct for same vs. 57% correct for different), but the difference was not statistically significant (z-test: p = 0.33), while in Monkey F, there was almost no difference at all (66% for same vs. 65% for different, z-test: p = 0.82). To gain a better window into how performance improves in the blocked condition, we examined the development of the location repetition effect across sequences of trials in the blocked condition with the same target location. In Fig. 7, we plot average performance as a function of the number of trials since the last switch in target location. Due to the smaller number of trials per data point in this analysis, we smoothed the percentage correct measure using a moving window that had a width of three trials in order to better visualize the trends. As shown in Fig. 7, percentage correct performance for both monkeys showed a gradual improvement with repeated target locations, although the time-courses seemed to vary. Improvement developed fairly quickly for Monkey H, reaching a plateau over approximately 8 trials, while it appeared to build more slowly across approximately 15–17 trials in Monkey F.

90 80

Percentage Correct

70 60 Monkey H 50 0

10

20

30

90 80 70 60 Monkey F 50 0

10

20

30

Trials since location switch Fig. 7. Buildup of location repetition effect in blocked condition. For each monkey, average percentage correct performance, across a window spanning from one trial in the past to one trial in the future, is plotted as a function of the number of trials since the last switch in target position. Error bars show standard error of the mean.

B.-T. Lee, R.M. McPeek / Vision Research 76 (2013) 43–49

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