Peripheral target identification performance modulates eye movements

Vision Research 133 (2017) 81–86

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Vision Research journal homepage: www.elsevier.com/locate/visres

Peripheral target identification performance modulates eye movements Min-Suk Kang a,b,⇑, Sori Kim b, Kyoung-Min Lee c a

Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon, Republic of Korea Department of Psychology, Sungkyunkwan University, Seoul, Republic of Korea c Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea b

a r t i c l e

i n f o

Article history: Received 9 March 2016 Received in revised form 26 December 2016 Accepted 27 December 2016

Keywords: Eye movement Inhibition

a b s t r a c t We often shift our eyes to an interesting stimulus, but it is important to inhibit that eye movement in some environments (e.g., a no-look pass in basketball). Here, we investigated participants’ ability to inhibit eye movements when they had to process a peripheral target with a requirement to maintain strict fixation. An array of eight letters composed of four characters was briefly presented and a directional cue was centrally presented to indicate the target location. The stimulus onset asynchrony (SOA) between the cue and the stimulus array was chosen from six values, consisting of pre-cue conditions ( 400 and 200 ms), a simultaneous cue condition (0 ms), and post-cue conditions (200, 400, and 800 ms). We found the following: 1) participants shifted their eyes toward the cued location even though the stimulus array was absent at the onset of eye movements, but the eye movement amplitude was smaller than the actual location of the target; 2) eye movements occurred approximately 150 ms after the onset of stimulus array in the pre-cue conditions and 250 ms after cue onset in the simultaneous and post-cue conditions; and 3) eye movement onsets were delayed and their amplitudes were smaller in correct trials than incorrect trials. These results indicate that the inhibitory process controlling eye movements also compete for cognitive resources like other cognitive processes. Ó 2017 Elsevier Ltd. All rights reserved.

1. Introduction Cognitive operations processing visual information are closely linked to eye movements. If an interesting object captures an individual’s attention when walking along a street, the person tends to make an eye movement towards that object. However, it is important to inhibit such eye movements in some situations. For example, basketball players tend to inhibit their eye movements so that defenders cannot determine where the ball will be passed. Furthermore, it is common for volunteers in laboratory experiments to maintain fixation for an extended period of time while processing visual information in the periphery. Nevertheless, the ability to inhibit eye movements interacts with cognitive processes. First, the mere presence of a fixation point influences saccadic eye movements, such that simply removing the fixation point (the gap condition) decreases saccadic latency substantially (Fischer & Ramsperger, 1984). The influence of fixation in controlling eye movements is more dramatically portrayed in anti-saccade tasks (Hallett, 1978; Munoz & Everling, 2004). In the anti-saccade tasks, participants have to inhibit their ⇑ Corresponding author at: Department of Psychology, Sungkyunkwan University, Seoul, Republic of Korea. E-mail address: [email protected] (M.-S. Kang). http://dx.doi.org/10.1016/j.visres.2016.12.020 0042-6989/Ó 2017 Elsevier Ltd. All rights reserved.

eye movements and shift their eyes to the opposite side of the target position. Yet, participants occasionally shift their eyes erroneously to the target location and these erroneous saccades to the target location increase in frequency and decrease in latency with the extinction of the fixation point (Dorris & Munoz, 1995; Fischer & Weber, 1997). Second, microsaccades refer to very small saccadic eye movements that we are not even aware of (Steinman, Haddad, Skavenski, & Wyman, 1973). Recent studies have provided evidence that microsaccades reflect cognitive processes and even serve our vision (Rolfs, 2009). For example, microsaccades modulate perceptual processing (Martinez-Conde, Macknik, Troncoso, & Dyar, 2006; Poletti, Listorti, & Rucci, 2013) and reflect shifts in covert attention (Engbert & Kliegl, 2003; Hafed & Clark, 2002; Yuval-Greenberg, Merriam, & Heeger, 2014) and task load (Kang & Woodman, 2014, Siegenthaler et al., 2014). In a related vein, eye movements including microsaccades are inhibited following external visual or auditory stimulation (Engbert & Kliegl, 2003; Hafed & Clark, 2002; Hafed & Ignashchenkova, 2013; Pastukhov & Braun, 2010; Rolfs, 2009). Third, eye movements to a particular location are inhibited when inhibiting visual or memory representations occurring at that location (Belopolsky & Theeuwes, 2011; Theeuwes, Olivers, & Chizk, 2005). Taken together, these studies provide reasons to analyze the ability to inhibit eye movements

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in relation to cognitive operations involved in processing visual information. One particular aspect of the interaction is that the inhibitory control of eye movements is weakened with task demand. Increasing working memory load leads to higher errors in anti-saccade tasks due to reduced inhibitory control for reflexive eye movements (Mitchell, Macrae, & Gilchrist, 2002; Roberts, Hager, & Heron, 1994) and so do other dual-tasks (e.g., perceptual judgment tasks) for unpracticed participants (Evens & Ludwig, 2010). Halliday and Carpenter (2010) also concluded that inhibitory control is weakened with task demand. In their study, participants had to move their eyes to a green, peripheral stimulus (go-trial) but maintain fixation for a red, peripheral stimulus (no-go trial). With an additional task to perform, the error rate as well as saccades with very short latency in the no-go trials increased. Theoretically, these results indicate that the inhibition of eye movements compete for the same cognitive resources like other cognitive processes (e.g. attention); however, its implication is limited because overt eye movements were required to perform the task and the inhibition of eye movements was evaluated when eye movements were made erroneously. In the present study, we asked the same question without requiring eye movements to perform the task to establish the conclusion and its theoretical implications over settings that are more relevant to many laboratory experiments. Participants reported the target item among an array of eight letters indicated by a centrally presented arrow cue under the instruction of strict fixation (Fig. 1). We used a central cue to indicate the target location because such cues are less potent in eliciting eye movements in a particular direction than exogenous, peripheral cues. In addition, we varied the stimulus onset asynchrony (SOA) between the cue and the stimulus array to manipulate visual processing of the cued stimulus. The SOA was chosen from six values, consisting of pre-cue conditions ( 400 and 200 ms), a simultaneous cue condition (0 ms), and post-cue conditions (200, 400, and 800 ms). According to previous studies, despite instructing participants to maintain fixation throughout the trials, participants would shift their eyes toward the target location irrespective of whether the target was available in the pre-cue conditions (Engbert & Kliegl, 2003; Hafed & Clark, 2002) or in the post-cue conditions (Kang & Woodman, 2014). Post-cue conditions are particularly critical to the present study. If the inhibition of eye movements and other cognitive processes (e.g. attention) compete for the same cognitive resources, the shift toward the target should be larger in the incor-

rect trials than the correct trials. This is because attention or decision-making processes could have been involved for a longer period of time when the target representation was unavailable or ambiguous than when the target stimulus was available with confidence and, thus, the task demand should be higher in the incorrect trials than correct trials. Instead of directly manipulating task demand by increasing memory load or another task to perform, we decided to compare the correct and incorrect trials mainly because changing stimulus configuration can also modulate reflexive and microsaccadic eye movements (Hafed & Ignashchenkova, 2013; Kang & Woodman, 2014).

2. Methods 2.1. Participants The experiment was carried out in accordance with the Declaration of Helsinki. Seventeen volunteers (age: 19–28 years, average 23.3 years; 12 females) participated in the present study after providing informed consent in advance for procedures approved by Sungkyunkwan University’s Institutional Review Board. The participants received monetary compensation (approximately 10 USD per hour). They declared that they had normal color vision, visual acuity, and no neurological history. Data from two participants were excluded from the analysis because too many trials were lost due to eye blinks (50%), and thus it was difficult to obtain meaningful data from each condition. As a result, 83.8% of trials were used for the analysis on an average (range 59.8–95.7%). 2.2. Apparatus The participant was seated with his or her head positioned on a chin rest, 60 cm from a computer screen, in a dimly illuminated room. All stimuli were presented on a CRT monitor (1024  768 pixels resolution; 31  24 cm size; 85 Hz refresh rate; 70.69 cd/ m2 mean luminance) using the Psychophysics Toolbox-3 (Brainard, 1997; Pelli, 1997) running on a Mac Mini (Apple, Cupertino, CA, USA). The participants’ eye movements were recorded using an Eyelink II (SR Research, Ontario, Canada) video-based eye tracker with the video camera attached to the chin-head rest. For all participants, the positions of both eyes were recorded at a 500 Hz sampling rate.

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2.3. Stimuli and procedures

Fig. 1. Illustrations of the stimulus sequence of a pre-cue condition, simultaneouscue condition, and post-cue condition.

Fig. 1 illustrates three stimulus sequences. A stimulus array consisted of eight black capital letters, namely D, F, J, and K (subtending approximately 0.5°  0.7° of visual angle), each of which appeared twice, in randomized positions (Lu, Neuse, Madigan, & Dosher, 2005). The eight letters were equally spaced, occupying four cardinal locations and four diagonal locations, and separated from the fixation point by 3.0°. The fixation point was a small dot (0.1° in diameter) and the cue was a short clock hand (0.3°) pointing to one of the eight locations. The stimulus sequence consisted of a brief fixation (500 ms), cue (100 ms), and stimulus array (100 ms). SOA determining the interval between the cue and stimulus array was chosen from six values ( 400, 200, 0, 200, 400, and 800 ms). In the pre-cue conditions, the cue preceded the stimulus array ( 400 and 200 ms SOAs). In the simultaneous cue condition (0 ms), the cue and stimulus array were presented at the same time. In post-cue conditions (200, 400, and 800 ms SOAs), the cue was presented after the stimulus array. The fixation point was presented throughout the trial to facilitate the participant’s fixation.

M.-S. Kang et al. / Vision Research 133 (2017) 81–86

Participants performed a cued identification task with the instruction to maintain strict fixation. The participant pressed the D, F, J, or K key to register the target stimulus at the cued location. If he or she did not know what the target was, he or she was asked to guess among the four alternatives. Although the speed of response was not emphasized, if there was no response within the 2 s interval following the stimulus array offset in the pre-cue conditions, or cue offset in the other conditions, the trial was aborted and considered incorrect. The aborted trials were not used for eye movement analysis. This time deadline was set to increase the number of incorrect trials. Each session started with a calibration phase, in which the participant looked toward the four corners (in the cardinal directions) and the center of the screen. Drift was corrected every 6 trials and brief breaks were allowed every 24 trials. In each session, each participant completed 672 trials (8 locations  4 letters  7 cue SOAs  3 repetitions).

2.4. Data analysis The eye movement data of each trial were extracted differently depending on the SOA. Specifically, eye movement data of the left eye were extracted from 400 ms to 800 ms with respect to the stimulus array onset in the pre-cue and simultaneous cue conditions, and from 400 ms of the stimulus array onset to 800 ms after the cue onset in the post-cue conditions. To increase the number of trials in each condition, data from the eight locations were collapsed by rotating the eye movement vector of each trial to the 3 o’clock target position. For example, the eye movement vector (x, y) obtained from the 12 o’clock target trial was rotated by 90° and a vector obtained from the 6 o’clock target trial was rotated by 90° so that these vectors were aligned as if the targets were presented at the 3 o’clock position. Nevertheless, we also examined whether there were idiosyncratic patterns contingent upon target direction. After the vector rotation, the baseline was corrected in the interval between 200 ms and 0 ms of the last stimulus (the stimulus array in the pre-cue and simultaneous cue conditions and the cue in the post-cue conditions) instead of the pre-stimulus array interval because eye movements occurred with the array and cue information. The onset of eye movements was estimated as follows, which is less susceptible to random fluctuations in the eye movement data than applying a threshold for a speed of a trace of eye movement. First, the collapsed time course data were extracted from the eye movement vector and then the vector component along the fixation point and target location axis was modeled using a logistic function f(t) = a(1 + e (t-d)/s) 1, where a, d, and s were free parameters, denoting amplitude, temporal delay, and a time constant defining slope, respectively. Since the eyes tended to move back to the fixation point, we applied the logistic function to the temporal window between the onset of the stimulus array and 600 ms post cue onset in the post-cue conditions, and 400 ms post cue onset in the pre-cue and simultaneous cue conditions. Second, if participants rarely move their eyes, onset cannot be correctly estimated and some participants indeed rarely move their eyes. We considered that onset was not correctly estimated when the slope parameter was smaller than 0.01 ms and did not include such data in the analysis. Finally, a curve with large s deviates from the fixation point more slowly compared to a curve with small s for the same temporal delay parameter d. Therefore, if the onset is estimated from the delay parameter d, it cannot reflect the onset of the eye movements. To reduce this problem, we obtained the onset when the slope reached 10% of its maximum amplitude. Amplitude a was used for all conditions.

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3. Results Participants’ performance decreased as a function of SOA between the stimulus array and the location cue (Fig. 2A), which is consistent with previous studies (Lu et al., 2005; Sperling, 1960). A one-way ANOVA with the factor SOA yielded a significant main effect [F(1,14) = 506.4, p < 0.001]. Response times (RTs) showed several characteristics (Fig. 2B). Because the number of incorrect trials was small in the pre-cue (3.3% ± 6.0) and simultaneous cue (6.9% ± 6.0) conditions, we only analyzed the post-cue conditions to obtain RTs of incorrect trials. We found that, first, RTs associated with correct trials increased with SOA [F(1,14) = 51.61, p < 0.001]. This result was expected because the pre-cue is informative in directing the participant’s attention ahead of the target array presentation. Second, in the post-cue conditions, RTs were slower in the incorrect trials than in the correct trials. A two-way ANOVA with factors of trial type (correct vs. incorrect) and SOA (200, 400, and 800 ms) confirmed this observation by yielding a significant main effect of trial type, but no other significant effects [trial type: F(1,14) = 66.78, p < 0.001; SOA: F(1,14) = 3.62, p = 0.078; trial type  SOA: F(1,14) = 1.89, p = 0.191]. Regarding the eye movement data, Fig. 3A shows the eye positions locked to the stimulus array onset for the pre-cue and simultaneous-cue conditions and to the cue onset for the postcue conditions. We collapsed the position vector for the eight target locations by rotating the vector of each trial at a given location by the difference between the target direction of the trial and the 3 o’clock location. In the figure, achromatic colors indicate eye movements to the target direction and purple colors indicate eye movements orthogonal to the target direction. For the post-cue conditions, we separated the eye-movement traces of the correct (dark colors) and incorrect trials (light colors). Green vertical bars indicate the onset of the cue. Visual inspection of Fig. 3A shows several noticeable patterns. First, on an average eye movements occurred to the target location without any movement in the orthogonal directions. The onset of eye movements was also systematic in that they occurred approximately 200 ms after the stimulus array onset in the pre-cue conditions and approximately 300 ms after the cue onset in the post-cue and simultaneous cue conditions. Second, the amplitude of eye movements averaged less than 1.0° for all SOA conditions; however, the amplitudes were larger for the incorrect trials than the correct trials. Third, for the pre-cue conditions, corrective eye movements occurred such that the averaged eye movement amplitudes were reduced approximately 400 ms post-stimulus onset. We further analyzed the eye movements and found that, first, it is not only the averaged direction in eye movements that changed over time. Fig. 3B shows the aggregate distribution of the eye movement amplitudes. The left panels show distributions of the amplitudes obtained between 0 ms to 200 ms post stimulus array onset for the six different SOA conditions and they are almost indistinguishable. On the other hand, if we measure the amplitudes at different temporal windows (200–400 ms, blue shaded region of Fig. 3A in the middle panels and 400–600 ms, green shaded region of Fig. 3A in the right panels), the amplitudes are more broadly distributed and skewed toward the target locations over time. It is very likely that a skewed distribution, especially the pre-cue conditions, is a mixture of two distributions in which one is near the fixation at the center and the other is shifted toward the target location. These results indicated that large eye movements that actually landed near the target did exist; however, averaging overall eye movements captured the pattern of eye movements toward the target direction for all six SOA conditions, as shown in Fig. 3A. Second, we found that eye movements occurred in all target directions. To generate Fig. 3C, we sorted the eye-movement vectors

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Fig. 2. Behavioral results. A) Accuracy as a function of SOA. B) Response time (RT) as a function of SOA. Black bars and gray bars represent correct and incorrect trials, respectively. Error bars represent ± 1 S.E.M.

Fig. 3. Eye movements across 6 SOA conditions. A) Time courses of eye positions locked to the stimulus array onset (pre-cue and simultaneous cue conditions) or cue onset (post-cue conditions). Different SOA conditions are arranged vertically. Achromatic colors indicate eye movements toward to target direction and purple colors indicate eye movements orthogonal to the target direction. The green bars indicate cue onset. Dark lines indicate eye movements in the correct trials and light colors eye movements in the incorrect trials. B-C). Eye movement amplitudes are averaged within the shaded region of A and correct and incorrect trials are combined. B) Distributions of the eye movement amplitudes. Shaded regions indicate ± 1 S.E.M. C) eye movement amplitudes for the eight target locations are plotted along the axes between the fixation and target locations. The dashed circles mark 0.5° from the fixation point and the error bars indicate ± 1 S.E.M.

aligned with the target direction and plotted their amplitudes according to target location for the blue and green shaded temporal windows shown in Fig. 3A. The dashed circles have a 0.5° radius. It appears that the horizontal eye movements of the precue conditions are mainly responsible for the corrective eye movements shown in Fig. 3A; however, eye movements occurred toward all target directions rather equally in the post-cue conditions. Taken together, eye movements occurred randomly near the fixation point in a majority of trials and the horizontal eye movements were particularly stronger than other directions at least in the pre-cue conditions. Nevertheless, it is our best strategy to quantify eye movements of each individual by averaging one’s eye movements over time, as shown in Fig. 3A, to answer the ques-

tion whether the inhibition of eye movements interact with cognitive processes by comparing the correct and incorrect trials of the post-cue conditions. This is because it is difficult to isolate eye movements toward the target from random movements near fixation, and the horizontal bias was only stronger at early phase of the pre-cue conditions. To statistically confirm the visual evidence in the plot, we quantified the onset and amplitude of the eye movements by modeling their trace vectors using a logistic function (see Section 2). Fig. 4A shows the onset as a function of SOA, where the onsets for incorrect trials are plotted for only the post-cue conditions. When the onset could not be estimated because a participant rarely moved his or her eyes during a particular condition, that participant’s data

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Fig. 4. Estimated eye-movement onsets and amplitudes. A) Onset of eye movements as a function of SOA. Black bars represent correct trials and gray bars incorrect trials. Error bars represent ± 1 S.E.M. Numbers within the bars indicate the number of data points excluded from the analysis due to a lack of eye movements. B) Amplitude of eye movements as a function of SOA. Other aspects are identical to A.

for a given trial type were excluded from statistical analysis. The amount of data excluded from the analysis for each condition is shown within the bar. To address the unequal sample sizes in the repeated measures analysis, a linear mixed-effects model was applied, with a different intercept for each participant. A likelihood-ratio test was then carried out to compare a model with SOA as a factor and a model without the SOA factor (Winter, 2013). We first submitted the onset times of the correct trials of all SOA conditions to the model and found a significant effect of SOA [v2 (1) = 57.529, p < 0.001], indicating that onsets were significantly faster in the pre-cue conditions than in simultaneous and post-cue conditions. However, when the likelihood test was applied to the onsets of the pre-cue conditions and the post-cue conditions separately, it did not yield a significant effect of SOA in either case [pre-cue conditions: v2 (1) = 0.0006, p > 0.5, post-cue conditions: v2 (1) = 0.313, p > 0.5], meaning that onsets were similar within the pre-cue and the post-cue conditions. We then compared the onsets for the correct and incorrect trials of the post-cue conditions. The onsets for correct trials were significantly slower than the onsets for incorrect trials [v2 (1) = 6.531, p = 0.0106], and the modulating effect of SOA on their differences approached significance [v2 (1) = 3.549, p = 0.0595]. Second, we compared the eye movement amplitudes for the six SOA conditions (Fig. 4B). Unlike the onset analysis, we did not exclude any data from the ANOVA because the amplitude parameter should be close to 0 in the absence of eye movements. We found that the amplitudes of correct trials were similar across all six SOA conditions [F(1,14) = 0.916, p = 0.355]. However, when a two-way ANOVA with factors of trial type (correct vs. incorrect) and SOA was applied to the amplitude of the post-cue conditions, we found a significant effect of trial type [F(1,14) = 14.64, p < 0.0018], but no other effect was significant [SOA: F(1,14)  0, p > 0.5, trial type  SOA: F(1,14) = 1.134, p = 0.305]. 4. Discussion The cognitive resources for processing visual information are limited and previous studies indicate that the inhibitory control process of eye movements also competes for resources like other cognitive processes. Here, we extended this conclusion with a setting that is more similar to laboratory experiments that do not require overt eye movements. We found that eye movements occurred earlier in the pre-cue conditions than in the post-cue conditions by approximately 100 ms. More importantly, the eye move-

ments occurred earlier and were larger in the incorrect trials than in the correct trials of the post-cue conditions. The difference between pre-cue and post-cue conditions is not surprising because these two conditions of the present study differ in several respects. Participants could register only the target item in the pre-cue conditions because they were aware of the target location ahead of its appearance, while participants had to remember as many items as possible and select one of them in the postcue conditions. That is, cognitive processes (e.g., attention) could have operated differently in the two conditions: attention is deployed to the target location in the pre-cue conditions and then it operates immediately on a perceptual representation. In contrast, attention is directed to the cued location by accessing a memory representation in the post-cue conditions. As a result, eye movements in the pre-cue condition are expected to be faster than those in the post-cue conditions. However, it is inconsistent or even counterintuitive that large eye movements were elicited in the incorrect trials than in the correct trials. When eye movements are allowed, eye movements toward the location of a target item facilitate memory performance (Lara & Wallis, 2012). Socially important information (e.g., the gaze of another person) also elicits eye movements (Deaner & Platt, 2003). Considering that neural responses are higher in the correct trials than in the incorrect trials of visual memory experiments (Buschman, Siegel, Roy, & Miller, 2011; Sligte, Scholte, & Lamme, 2009) and brain areas involved in controlling eye movements are also involved in shifting attention (Goldberg & Wurtz, 1972, Lovejoy & Krauzlis, 2010), it is reasonable that eye movements should occur toward a location where target information is available. Nevertheless, we found the opposite pattern, which demands explanation. One plausible explanation is that eye movements interferes with the task, but we discounted that explanation with reasons described below. In this account, eye movements, whether they are elicited voluntarily or involuntarily, are hypothesized to disrupt visual working memory, and thus, large eye movements disrupted memory representations more than small eye movements, resulting in poor memory performance. However, previous studies go against this account. First, involuntary eye movements did not influence both spatial and verbal working memory. Postle, Idzikowski, Sala, Logie & Baddeley (2006) found no difference in the memory performance between a condition where involuntary eye movements elicited by optokinetic nystagmus, a vestibulocular reflex, and the other condition where fixation is needed. Kang and Woodman (2014) found that eye movements

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were elicited toward the visual field where the memoranda were presented but the memory performance was indistinguishable when trials were separated between the small and large eye movements. Second, however, as mentioned above, eye movements do influence memory performance by shifting attention. Microsaccades made to target location resulted in higher memory performance than the microsaccades made in the opposite direction (Lara & Wallis, 2012). Similarly, memory performance was higher when eye movements were made to the target during the retention interval than when fixation was required (Williams, Pouget, Boucher & Woodman, 2013). On the other hand, task-irrelevant eye movements, periodically moving eyes between the left and right, disrupted spatial working memory (Postle et al., 2006). It means that overt attention accompanied with eye movements aided maintenance of spatial information during the retention interval. Specifically, according to those studies, eye movements occurring closer to the target should facilitate memory maintenance better, but we found the results to be opposite. On the other hand, previous studies have shown that increasing task demand leads to higher errors in anti-saccade tasks due to reduced inhibitory control for reflexive eye movements (Mitchell et al., 2002; Roberts et al., 1994). In addition, these erroneous saccades occurred faster due to reduced inhibition. Similarly, it is plausible that the task demand should increase in the incorrect trials because attention or decision-making processes could have been involved for a longer period of time when the target representation was unavailable or ambiguous than when the target stimulus was available with confidence. Consistently, in visual search, RTs are faster when the target is present among distractors than when it is absent. This means that participants spend more time in searching for the target until they conclude that the target is absent (Chun & Wolfe, 1996; Wolfe, 1994). RTs are also slower when the choice is difficult because of ambiguity or uncertainty (Luce, 1986). It is therefore that the reduced inhibitory control of eye movements’ hypothesis remains a viable account as fast and large eye movements of the incorrect trials occurred due to reduced inhibitory control of eye movement. Note that the results are different from those of a previous study in which the eye movement did not differ between the correct and incorrect trials if the eye movements were obtained during the retention interval of a change-detection because those additional cognitive processes were unlikely to be deployed before the test stimulus (Kang & Woodman, 2014). Taken together, the present study provides converging evidence that the inhibitory control of eye movements is weakened with task demand under an experimental setting that does not require overt eye movements to perform tasks. Acknowledgments We appreciate Suk Won Han and anonymous reviewers for their helpful comments. This paper was supported by Sungkyun Research Fund, Sungkyunkwan University, 2014. References Belopolsky, A. V., & Theeuwes, J. (2011). Selection within visual memory representations activates the oculomotor system. Neuropsychologia, 49(6), 1605–1610. Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10(4), 433–436. Buschman, T. J., Siegel, M., Roy, J. E., & Miller, E. K. (2011). Neural substrates of cognitive capacity limitations. Proceedings of the National Academy of Sciences USA, 108(27), 11252–11255. Chun, M. M., & Wolfe, J. M. (1996). Just say no: How are visual searches terminated when there is no target present? Cognitive Psychology, 30(1), 39–78. Deaner, R. O., & Platt, M. L. (2003). Reflexive social attention in monkeys and humans. Current Biology, 13(18), 1609–1613.

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