Individual differences in working memory capacity determine the effects of oculomotor task load on concurrent word recall performance

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Research Report

Individual differences in working memory capacity determine the effects of oculomotor task load on concurrent word recall performance☆ Eun-Ju Lee a , Gusang Kwon b , Aekyoung Lee c , Jamshid Ghajar d, e , Minah Suh b, f,⁎ a

School of Business, Sungkyunkwan University, Seoul, South Korea Department of Biological Science, Sungkyunkwan University, Suwon, South Korea c Department of German Literature, Sungkyunkwan University, Seoul, South Korea d The Brain Trauma Foundation, New York, NY USA e Weill Medical College of Cornell University New York, NY USA f Samsung Advanced Institute for Health Science & Technology, Graduate Program for Health Science & Technology, Sungkyunkwan University Medical School, Seoul, South Korea b

A R T I C LE I N FO

AB S T R A C T

Article history:

In this study, the interaction between individual differences in working memory capacity,

Accepted 2 May 2011

which were assessed by the Korean version of the California Verbal Learning Test (K-CVLT),

Available online 7 May 2011

and the effects of oculomotor task load on word recall performance are examined in a dualtask experiment. We hypothesized that varying levels of oculomotor task load should result

Keywords:

in different demands on cognitive resources. The verbal working memory task used in this

Dual-task

study involved a brief exposure to seven words to be remembered, followed by a 30-second

Predictive smooth pursuit

delay during which the subject carried out an oculomotor task. Then, memory performance

Random smooth pursuit

was assessed by having the subjects recall as many words as possible. Forty healthy normal

Verbal working memory

subjects with no vision-related problems carried out four separate dual-tasks over four

Oculomotor task

consecutive days of participation, wherein word recall performances were tested under

Fronto-parietal network

unpredictable random SPEM (smooth pursuit eye movement), predictive SPEM, fixation, and

Cortico-cerebellar network

eyes-closed conditions. The word recall performance of subjects with low K-CVLT scores was significantly enhanced under predictive SPEM conditions as opposed to the fixation and eyes-closed conditions, but performance was reduced under the random SPEM condition, thus reflecting an inverted-U relationship between the oculomotor task load and word recall performance. Subjects with high K-CVLT scores evidenced steady word recall performances, regardless of the type of oculomotor task performed. The concurrent oculomotor performance measured by velocity error did not differ significantly among the K-CVLT groups. However, the high-scoring subjects evidenced smaller phase errors under predictive SPEM conditions than did the low-scoring subjects; this suggests that different resource allocation strategies may be adopted, depending on individuals' working memory capacity. © 2011 Elsevier B.V. All rights reserved.

☆

Competing interests: The authors have declared that no competing interests exist. ⁎ Corresponding author at: Laboratory of System Neuroscience, Department of Biological Science, Sungkyunkwan University, Suwon, South Korea. Fax: +82 31 299 4506. E-mail address: [email protected] (M. Suh). 0006-8993/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2011.05.003

60 1.

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Introduction

The ability to maintain transient information that is not currently available from the environment is a critical factor in high-level cognition, including working memory. Previous studies into working memory have shown sustained neural activities during the delayed period of a working memory task in the prefrontal areas (Funahashi et al., 1993), frontal eye fields (Curtis et al., 2004; Tark and Curtis, 2009) and posterior parietal cortex (Schluppeck et al., 2006); these neural activities are correlated strongly with memory performance. The neural pathways relevant to smooth pursuit eye movement (SPEM) largely overlap with those relevant to the modulation of working memory and attention (Corbetta et al., 1998), and include the fronto-parietal network (such as the frontal eye field, supplementary eye fields, and posterior parietal cortex) as well as the cortico-cerebellar network (Nagel et al., 2006). In particular, smooth pursuit tracking of a target stimulus moving with a predetermined periodicity and familiar sequence, such as predictive SPEM, increases the level of neuronal responsiveness of the cortico-cerebellar network (Lisberger, 2010; Poliakoff et al., 2005; Suh et al., 2000), where the cerebellum heavily processes information from the prefrontal cortex, and performs a pivotal role in cognitive learning (Hayter et al., 2007). The results of previous research have suggested an interdependency between SPEM and attention-related tasks (Chen et al., 2002; Drew and van Donkelaar, 2002; Kowler, 1989). Changes in SPEM performance have been observed in cases in which the subject's attention was divided between a tracking task and an unrelated secondary task (Kathmann et al., 1999; Van Gelder et al., 1995). Individual eye performance during SPEM has been found to depend on the secondary task type and the designated task priority (Souto and Kerzel, 2011). Pursuit performance is also known to be affected by the perturbation induced by a secondary object in proximity (Debono et al., 2010). Previous research results suggest that a secondary oculomotor task, i.e. SPEM, may improve word recall performances due to the synergic activation of shared neural networks. Furthermore, word recall performances under oculomotor task demands may reflect the dynamic allocation of cortical resources within a shared neural network. On the other hand, increased load on the shared neural network due to the addition of a secondary oculomotor task may not be consistently conducive of primary task performance. It is commonly known that working memory capacity, which reflects proficiency in the allocation of limited attentional resources (Minamoto et al., 2010), is extremely limited (Cowan et al., 2005; Luck and Vogel, 1997). How do individuals engaging in dual-tasking divide common neural resources between two strenuous tasks? In particular, imagine a case wherein the load from a secondary oculomotor task increases significantly, approaching a cortical capacity limit (Linden et al., 2003; Postle et al., 1999). In this case, two simultaneous brain functions relying on the common neural resource pool may begin to compete for limited resources (Sestieri et al., 2010). Spatial working memory can be impaired during smooth pursuit under another concurrent perceptual task demand (Kerzel and Ziegler, 2005). The dynamic competition between verbal memory and oculomotor functions may, then, induce a reduction in the performance of either (Hutton and Weekes, 2005; Meyer et al.,

2007). Any such detrimental effects are likely to be manifested most prominently in individuals with “low” cortical capacities. Individuals have been shown to evidence systematic differences in working memory capacity (Linden et al., 2003). In this paper, we assess the manner in which individual differences in working memory capacity, as assessed by the Korean version of the California Verbal Learning (K-CVLT) test, influence dual-task performance. The K-CVLT is a validated neuropsychological tool used to assess verbal learning and working memory (Kim and Kang, 1999). Herein, we introduced four types of oculomotor tasks that varied in terms of the level of shared neural network modulation required for the successful performance of the assigned task. For example, the eyes-closed condition entails the absence of any visual cues. Under the fixation condition, visual stimulation from retinal input is present at a constant level, but no eye movements are permitted. Under the predictive SPEM conditions, the cerebellum performs a dominant role in anticipating the periodicity of eye-tracking, which may reduce reliance on the activation of the fronto-parietal network. On the other hand, in the random SPEM, the system receives retinal input increasingly from the fronto-parietal network, in order to correct for the visual feedback errors deriving from continuously changing target positions. Such continual eye to-target calibration employs more cortical resources than are required for the visual tracking of a predictive target, and may thus constrain the working memory capacity of individuals with low cortical capacities. Therefore, we hypothesize that varying oculomotor task load levels should result in different demands on cortical resources. Under the increased cortical load induced by complex eye movements, individuals with high cortical capacities can efficiently divide common attentional resources between the oculomotor task and the verbal working memory (Awh and Vogel, 2008), and thus exhibit continued dual-tasking performance. However, the increased cortical load imposed by the secondary oculomotor task may exert detrimental effects on word recall performance, particularly in individuals with low cortical resources who may easily reach their cortical capacity limit under multiple-demand conditions.

2.

Results

2.1. task

Word recall performances after secondary oculomotor

Forty (average age 23.68 ± 2.07, 25 males and 15 females) healthy college students participated in this study, in return for extra course credits. All subjects underwent four separate dual-tasks over four consecutive days of participation, during which time word recall performances were tested under unpredictable random SPEM, predictive SPEM, fixation, and closed-eye conditions. The order of presentation was counterbalanced and we controlled for subject fatigue by having each subject participate in only one oculomotor paradigm per day. We pre-screened the subjects prior to each day's experiment after assessing any divergences from each subject's usual daily routine, such as sleep deprivation, alcohol consumption, and excessive caffeine intake. The verbal working memory task employed herein involved a brief exposure to seven words to be remembered,

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followed by a 30-second delayed period during which the subject carried out an oculomotor task. The subjects were seated 40 cm away from a computer screen in a darkened room. The subjects' heads were stabilized using a chin-rest system. Following the oculomotor task, subjects were instructed to recall as many words as possible. A total of four different oculomotor tasks – random SPEM, predictive SPEM, fixation and closed-eyes – were completed over four consecutive days of participation. We conducted a general linear model repeated measure ANOVA on verbal recall. Subjects' word recall performance differed according to the oculomotor task type assigned, resulting in significant oculomotor task load effects on correct recall (Wilks' λ = 0.761, F (3, 37) = 3.879, p = 0.017, η2 = 0.239) and recall error (Wilks' λ = 0.729, F (3, 37) = 4.594, p = 0.008, η2 = 0.271). Here, correct recall is the number of accurately recalled words. Omission and intrusion are identified as the two sources of recall error. Specifically, omission refers to those words on the original list which subjects completely failed to recall and did not mention; this is also obtainable as the reverse score of correct recall, i.e., 7-number of correct recalls. Intrusion refers to those words that subjects did recall, but incorrectly. The number of intrusions was very small ranging between 0.375 and 0.075 such that it is not worthwhile to carry out a statistical analysis. Therefore, we report the subject performance of correct recall only for the remainder of the paper. Correct recall occurred in the following order: predictive SPEM (M = 4.750), random SPEM (M = 4.500), fixation (M = 4.225), and closed-eye conditions (M = 4.175). Using paired t-test analysis, we determined that the number of correct recalls was significantly greater after predictive SPEM as compared to the fixation (t(39) = −2.82, p = 0.007) and closed-eye conditions (t(39) = − 2.88, p = 0.006) (Fig. 2). However, we detected no significant differences in correct recall between the predictive SPEM and random SPEM conditions (t(39) = −1.64, p = 0.151).

2.2. The effects of individual differences in working memory capacity on word recall We assessed the manner in which subjects' working memory capacity modulated the influence of eye movement on word recall by contrasting the “Low” Group, which consisted of 10 individuals whose K-CVLT scores were within the 0th to 25th percentiles, and the “High” Group, which included 10 individuals whose scores were above the 75th percentile on the K-CVLT.

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The general linear model repeated measure of a 2 (groups) × 4 (eye conditions: closed, fixation, random, predictive) mixedfactors ANOVA demonstrated significant interaction between eye movements and working memory groups on correct recall (Wilks' λ = 0.4742, F (3, 16) = 5.907, p = 0.007, η2 = 0.526). We compared the “High” and “Low” groups’ word recall performances via an independent sample Student's t-test. Correct recall in the “High” Group occurred in the following order: random SPEM (M= 5.300), predictive SPEM (M= 5.200), closed-eye (M= 4.700), and fixation (M= 4.300); correct recall in the “Low” Group was measured in the following order: predictive SPEM (M= 4.700), random SPEM (M= 4.100), fixation (M= 3.900), and closed-eye (M= 3.700) conditions. Fig. 3 shows that the correct recall of the “High” Group compared with that of the “Low” Group was significantly better under the random SPEM condition (t(18)= −2.939, p = 0.009), but no significant group differences in correct recall were detected among the closed-eye (t(18)= −1.928, p = 0.070), fixation (t(18)= −0.730, p = 0.475), and predictive SPEM conditions (t(18)= −1.282, p = 0.216).

2.3. The effects of individual differences in innate working capacity on oculomotor performance We analyzed the oculomotor data from the random SPEM, predictive SPEM, and fixation conditions in terms of eye velocity error, which tells us how stable the tracking was. As stated in the Experimental procedures section, the velocity errors were defined as the absolute difference between eye and target velocity. Velocity error in the “High” Group occurred in the following order: predictive SPEM (M = 11.813), fixation (M = 6.000), and random SPEM conditions (M = 5.841). Velocity error in the “Low” Group occurred in the following order: predictive SPEM (M = 9.438), random SPEM (M = 6.159), and fixation conditions (M = 5.283). However, no statistically significant differences in eye velocity errors between the “High” and “Low” working memory groups were detected for any oculomotor task conditions, as is shown in Fig. 4. The tendency of gaze-leading during predictive pursuit may have increased the overall velocity error. When the two groups were combined, velocity error under predictive SPEM conditions was significantly higher than that measured under the random SPEM and fixation conditions (t < −3.0, p < 0.01), as shown in Fig. 4. This result suggests that the subject's eye tracking may become automatic due to the high periodicity of the stimuli applied during predictive pursuit. High periodicity of the stimuli is also thought to be responsible for generating

Fig. 1 – Oculomotor task paradigms and eye movements.

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Fig. 2 – Correct recall after conducting oculomotor tasks. The error bars denote +/−1 standard errors. ** p < 0.01, * p < 0.05.

anticipatory pursuit, during which the eyes lead the target and predict its next position, despite the latency of the pursuit neural pathways (Leung et al., 1997). Because eye velocity error is calculated as the mere difference between eye and target velocity, anticipatory gaze-leading under the predictive SPEM is also counted as velocity error. Differences in predictive SPEM performance between the high- and low-capacity individuals were also analyzed using phase error. The phase error (as an average score of right and left eyes) under predictive SPEM was negative for the ‘High’ Group (M = −3.039) as compared to the ‘Low’ Group (M = 2.193), suggesting that the eyes of the low-capacity individuals were leading the target (t(18) = 2.127, p = 0.047).

3.

Discussion

3.1. Effects of different oculomotor tasks on word recall performances Our experimental results demonstrated that word recall performances were better when individuals were given a predictive SPEM task with a moderate cognitive load than when they fixated their eyes or kept them closed. This is consistent with existing research into working memory, which has generally found a strong correlation between activations in frontal eye fields and memory performance (Curtis et al., 2004; Tark and Curtis, 2009). Evidence of improvement in SPEM performance was also detected in a previous study in which the attention of normal subjects was forcibly divided between simultaneous tasks (Van Gelder et al., 1995). The results of previous clinical

Fig. 3 – K-CVLT group differences in correct recall. ** p < 0.01, * p < 0.05.

Fig. 4 – K-CVLT group differences in velocity error. Velocity errors under the three eye movement paradigms were compared between the ‘High’ and ‘Low’ Groups.

studies have also demonstrated that patients with mild traumatic brain injury (TBI), all of whom had suffered shearing injuries on the fronto-parietal and cortico-cerebellar tract, were subject to a detrimental effect of concurrent word recall load on oculomotor performance under predictive SPEM conditions, in contrast to the healthy control subjects who exhibited improvements in predictive SPEM under identical dual-task conditions (Suh et al., 2006b). Mild TBI patients are known to harbor disrupted cortico-frontal networks and shearing damage on prefrontal brain regions including the right ACR (Niogi et al., 2008; Suh et al., 2008). However, the SPEM augmentation of working memory performance may pertain only to the delayed recall of word stimuli which were auditorially provided. Other previous research on smooth pursuit and visual secondary tasks found the resource trade-off between the two visual task demands (Kerzel et al., 2008, 2009; Khurana and Kowler, 1987). Our results also demonstrated that word recall performances may deteriorate under random SPEM conditions relative to that measured under predictive SPEM conditions, possibly because different neuronal strategies might be relevant to generating the two different types of SPEM. The cerebellum is known to be an important brain region for the generation of predictive and anticipatory movement (Ivry, 2000; Thier and Ilg, 2005). During predictive SPEM, strong periodicity of target stimuli can increase reliance on cerebellar output (Suh et al., 2000; Zee et al., 1981), which might alleviate the oculomotor task load imposed on the fronto-parietal network. As subjects rely increasingly on the cerebellar network in order to carry out predictive pursuit, they may be able to devote those spared cortical resources to the word recall tasks. This efficient dynamic resource allocation strategy may be adopted by low working-memory individuals, as the phase error data demonstrated that these low-capacity individuals evidence a gaze-leading tendency. On the other hand, random SPEM necessitates systematic dependence on real-time retinal input from the fronto-parietal network in order to correct for the high levels of retinal slip errors induced by continuously changing random target positions (Krauzlis, 2004). Such continuous eye to-target calibration requires greater cortical resources than are required during predictive pursuit. Therefore, when the subject concurrently carries out dual-tasks of word recall and random pursuit, these two tasks may compete for the shared neural resources of the fronto-parietal neural network. Individuals with low-span working memory may employ inefficient strategies for the

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allocation of low cortical resources relative to high-capacity individuals. As a consequence, low-capacity individuals may recall fewer words than high-capacity individuals, particularly when they must concurrently carry out another eye-movement task. This is particularly true under the random SPEM conditions applied in our study. Under random SPEM conditions, a greater portion of cortical resources must be allocated to eye movement tasks, as compared to predictive SPEM or fixation conditions, in order to correct for the increased retinal-slip errors generated during random eye-tracking. Furthermore, cerebellar involvement is likely to be highest under predictive SPEM conditions, particularly toward the end of predictive pursuit, where the eye movements become automatic. As such, our results demonstrate a case of dynamic allocation strategy of limited neural resources under dual-task demands.

3.2. Interaction between working memory capacities and the level of concurrent oculomotor task load on word recall performances Individual differences in working memory capacity have been shown to perform an important role in determining the impact of secondary oculomotor task loads on primary task performance by affecting two factors. First, the total capacity of working memory appears to determine the overall performance on dualtasks. Individuals possessing greater working memory capacity outperformed low-capacity individuals in the working memory tasks under random SPEM conditions. Second, in the cases in which individuals are operating near the limits of their cortical capacity, the ability to reserve sufficient cognitive resources for the primary task appears to be of critical importance. Individuals possessing relatively higher working memory capacities demonstrated increased stability on primary word recall performance, regardless of the increasing resource demands of the oculomotor task; conversely, low-capacity individuals evidenced improved performance only under predictive SPEM conditions. The fidelity of memory of individuals with low cognitive capacity under random SPEM and eyes-closed conditions was relatively lower, indicating an inverted-U relationship between the oculomotor task load and the word recall performance. It is possible that, in this study, the greater cortical load usurped by the demands of complex random eye movement compromised verbal recall performance under random SPEM conditions. Additionally, the study subjects evidenced superior word recall performance under predictive SPEM conditions than under either the fixation or eyes-closed conditions; this suggests that it is the active eye tracking of a moving target, rather than the visual stimulation or the mere maintenance of the target image near the fovea per se, which caused the changes in verbal recall performance described herein. Collectively, the results of this study illustrated the dynamic allocation of limited cortical resources under dual-task demands. As the neural pathways for SPEM largely overlap with those relevant to attention and working memory, simultaneous verbal memory and oculomotor activities in a dual-task context may result in interdependent task outcomes. Dynamic allocation of cortical resources is a function of individual working memory capacity, and may be a principal determining factor in the outcomes, accuracy, and stability of performance under dual-task conditions.

4.

Experimental procedures

4.1.

Participants

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At the outset, the subjects (N= 40) completed a survey questionnaire that inquired about general factors that might influence the quality of individuals' oculomotor activities, including how frequently they played computer games, whether they wore contact lenses or eyeglasses, and whether they took any medicines. We removed any students with abnormal vision and/or vision-related activity from our subject pool. Subjects were also questioned regarding the last time they had eaten prior to the experiment, how long they had slept on the previous night, whether they took any caffeine or medicine, and how they were feeling on that day. These questions were included to identify factors that might affect the general physical conditions of the subjects. Subjects were instructed not to consume any substances that might affect their cognitive performance for five days. Subjects who were sleep-deprived or had consumed psychostimulatory substances – including caffeine – prior to the experiment were excluded from participation in this study. 4.2.

Word selection criteria and verbal working memory test

Seven words were randomly selected from a standard lexical database of the Korean language. The criteria for the seven word selections were based on a previous academic report (Cho, 2003) containing approximately 6000 words of varying usage frequencies and their ease of comprehension. Since each subject participated in four experimental sessions, four sets of seven words each were prepared in the subjects' native language, i.e., the Korean language (Table 1). Due to the random selection process, each word set was unique in composition, but the word sets were constructed to be relatively equal using the combined linguistic criteria of total number of syllables, word frequency, and level of ease. The phonological neighborhoods were maintained at constant numbers across the word sets, since we selected only oneword items from the Korean language database. Particularly, immediate recall might be directly influenced by the perceived word ease. We assessed the across-set word homogeneity in terms of perceived word ease by administering a separate survey with a separate sample (N = 184) obtained from the same student population. We instructed the survey respondents to evaluate the word sets in terms of perceived word ease on a 5-point Likert scale. No significant mean differences were detected across the four word sets in terms of the level of perceived word ease (F (3, 181) = 2.048, p = 0.109, η2 = 0.033). Finally, the selected word sets were recorded by the same professional announcer at Sungkyunkwan University and were auditorially administered to the 40 subjects immediately before they engaged in the directed secondary oculomotor task. 4.3.

Oculomotor tasks

Eye movements were recorded with a human infrared eye tracking system (Eyelink II) at a temporal resolution of 500 Hz (Kettner et al., 1996; Suh et al., 2000, 2006a). Calibrations were

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Table 1 – Word sets used in the verbal working memory task. Since all subjects were Korean nationals, words were prepared and presented in the subjects' native language (Korean). The Korean words are italicized with English translations in parentheses. Word set 1 Gaemi (Ant) Younghwajae (Movie Festival) Godeunghaksaeng (High School Student) Woocheguk (Post Office) Siksa (Meal) Pihaeja (Victim) Honam (South Province)

Word set 2

Word set 3

Word set 4

Gansup (Interference) Yagujang (Baseball Stadium) Jeonhwabeonho (Phone Number) Aircon (Air Conditioning) Suyoung (Swimming) Hangongi (Airplane) Hwebok (Restoration)

Gaesipan (Bulletin) Sunjang (Captain) Bumin (Criminal) Jamae (Sister) Imin (Immigrant) Chodeonghaksaeng (Elementary School Student) Fax (Fax)

Boohaejang (Vice President) Jeontongmunhwa (Forklore) Makgeolli (Rice Wine) Dongseonambook (Direction) Jaehwalyong (Recycle) Asium (Frustration) Seoyang (Western)

conducted based on 9 points, including the center and peripheral positions, thus ensuring that all subjects had a full range of oculomotor movements. Captured eye positions were stored in a computer for future data analysis by MatLab (MathWorks, Inc.). A total of four oculomotor tasks were carried out in this experiment: namely, predictive SPEM, random SPEM, fixation, and closed-eye conditions. Each of the oculomotor tasks lasted for 30 s. The random SPEM condition involved the presentation of a freely moving target with an unpredictable trajectory. The random stimulus was generated by changing the moving target trajectories in pseudo-random directions. Target speed was maintained at a constant 10°/s. The random SPEM trajectory is illustrated in Fig. 1. The predictive SPEM condition was created by combining horizontal and vertical sinusoidal stimuli. A moving red-point with a 10-degree radius was presented on a circular target trajectory. The circular trajectory was a simple 2-dimensional periodic trajectory, and was used as a basic predictive SPEM paradigm in this study (Kettner et al., 1996; Suh et al., 2000, 2006a). The predictive SPEM paradigm consisted of 12 clockwise circular trajectories with a speed of 0.4 Hz, and thus 2.5 s were required for the completion of one cycle. This simple paradigm can activate neural networks that modulate both horizontal and vertical eye movements, thus leading to a broader recruitment of the neural network than could be induced by stimuli restricted to one dimension. Under the fixated-eye conditions, the participants were instructed to fixate on a central red dot. In the closed-eye tasks, a black screen was presented, and subjects were instructed to close their eyes for 30 s. This condition provided no visual stimulation. Fig. 4 depicts the subjects' eye movements under each of the experimental conditions. 4.4.

Standardized measurement of verbal working memory

Immediate word recall was measured and analyzed, after which the Korean California verbal learning (K-CVLT) test was administered (Kim and Kang, 1999). The California verbal learning test (CVLT) is a well-established neuropsychological diagnostic test that identifies varying levels of working memory capacities among healthy individuals. Standard instructions were followed for the administration of the KCVLT test. We divided the 40 subjects into three groups based on their immediate word recall K-CVLT scores. The lowest K-CVLT group (Group ‘Low’) consisted of 10 individuals whose K-CVLT

scores were within the 0th to 25th scoring percentiles. The highest K-CVLT group (‘High’ Group) included 10 individuals whose scores were above the 75th K-CVLT score percentile. The rest of the subjects were in the medium (25–75 percentile) K-CVLT group (N = 20). 4.5.

Data analysis

All subjects participated in all four oculomotor task conditions, and four repeated measures of conditions were acquired. For the dependent variables of word recall performance, General Linear Model Repeated Measure ANOVA was employed. Independent sample t-tests were used to compare dual-task performance between subjects with high and low K-CVLT scores, and paired t-tests were employed to assess intraindividual variability between two secondary oculomotor task conditions. An alpha level of 0.05 was utilized for all statistical tests. The velocity errors were calculated for all oculomotor tasks, except for the closed-eye condition. Velocity error was defined as the absolute difference between eye and target velocity. The eye and target's x- and y-positional values were recorded at a temporal resolution of 500 Hz (Eyelink II, SR Research, Canada) and smoothed. Saccades were deleted according to velocity (60°/s) and acceleration (200°/s2) criteria. After the deletion of saccades, only the smooth pursuit portion of eye velocity error was summed for each of the oculomotor tasks. Phase error was calculated via the conversion of x- and y-coordinates of the target position into polar coordinates under the predictive SPEM condition. The phase error is defined as the difference in angle between the target and eye positions. A positive phase error is indicative of gazeleading. Due to the unpredictability of target movement under the random SPEM conditions, the phase error cannot be calculated under the random SPEM condition.

Acknowledgments This study was supported by a grant of the National Research Foundation (NRF) by the Korean government (MEST) (2009– 0074595) and of the Samsung Research Fund from Sungkyunkwan University (2010). The authors also appreciate all participants for their valuable contributions, as well as Drs. Myeongwon Choi and Haemoon Lee for their comments on the earlier version of this manuscript.

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REFERENCES

Awh, E., Vogel, E.K., 2008. The bouncer in the brain. Nat. Neurosci. 11, 5–6. Chen, Y., Holzman, P.S., Nakayama, K., 2002. Visual and cognitive control of attention in smooth pursuit. In: Hyona, J., Munoz, D. P., Heide, W., Radach, R. (Eds.), Brain's eye: neurobiological and clinical aspects of oculomotor research. : Progress in Brain Research, Vol. 140. Elsevier Science Bv, Amsterdam, pp. 255–265. Cho, N., 2003. Results report on selecting Korean words for study. The National Academy of the Korean Language. Corbetta, M., Akbudak, E., Conturo, T.E., Snyder, A.Z., Ollinger, J.M., Drury, H.A., Linenweber, M.R., Petersen, S.E., Raichle, M.E., Van Essen, D.C., Shulman, G.L., 1998. A common network of functional areas for attention and eye movements. Neuron 21, 761–773. Cowan, N., Elliott, E.M., Saults, J.S., Morey, C.C., Mattox, S., Hismjatullina, A., Conway, A.R.A., 2005. On the capacity of attention: its estimation and its role in working memory and cognitive aptitudes. Cogn. Psychol. 51, 42–100. Curtis, C.E., Rao, V.Y., D'Esposito, M., 2004. Maintenance of spatial and motor codes during oculomotor delayed response tasks. J. Neurosci. 24, 3944–3952. Debono, K., Schutz, A.C., Spering, M., Gegenfurtner, K.R., 2010. Receptive fields for smooth pursuit eye movements and motion perception. Vision Res. 50, 2729–2739. Drew, A., van Donkelaar, P., 2002. Allocation of attention during smooth pursuit eye movements. J. Cogn. Neurosci. B15. Funahashi, S., Bruce, C.J., Goldman-Rakic, P.S., 1993. Dorsolateral prefrontal lesions and oculomotor delayed-response performance: Evidence for mnemonic “scotomas”. J. Neurosci. 13, 1479–1497. Hayter, A.L., Langdon, D.W., Ramnani, N., 2007. Cerebellar contributions to working memory. Neuroimage 36, 943–954. Hutton, S., Weekes, B., 2005. The role of attention in smooth pursuit eye tracking: evidence from dual task and TMS. J. Cogn. Neurosci. 51. Ivry, R., 2000. Exploring the role of the cerebellum in sensory anticipation and timing: commentary on Tesche and Karhu. Hum. Brain Mapp. 9, 115–118. Kathmann, N., Hochrein, A., Uwer, R., 1999. Effects of dual task demands on the accuracy of smooth pursuit eye movements. Psychophysiology 36, 158–163. Kerzel, D., Ziegler, N.E., 2005. Visual short-term memory during smooth pursuit eye movements. J. Exp. Psychol. Hum. Percept. Perform. 31, 354–372. Kerzel, D., Souto, D., Ziegler, N.E., 2008. Effects of attention shifts to stationary objects during steady-state smooth pursuit eye movements. Vision Res. 48, 958–969. Kerzel, D., Born, S., Souto, D., 2009. Smooth pursuit eye movements and perception share target selection, but only some central resources. Behav. Brain Res. 201, 66–73. Kettner, R.E., Leung, H.C., Peterson, B.W., 1996. Predictive smooth pursuit of complex two-dimensional trajectories in monkey: component interactions. Exp. Brain Res. 108, 221–235. Khurana, B., Kowler, E., 1987. Shared attentional control control of smooth eye movement and perception. Vision Res. 27, 1603–1618. Kim, J.K., Kang, Y., 1999. Normative study of the Korean-California Verbal Learning Test (K-CVLT). Clin. Neuropsychol. 13, 365–369. Kowler, E., 1989. Cognitive expectations not habits control anticipatory smooth oculomotor pursuit. Vision Res. 29, 1049–1058. Krauzlis, R.J., 2004. Activity of rostral superior colliculus neurons during passive and active viewing of motion. J. Neurophysiol. 92, 949–958. Leung, H.C., Kettner, R.E., 1997. Predictive smooth pursuit of complex two-dimensional trajectories demonstrated by perturbation responses in monkeys. Vision Research. 37, 1347–1354.

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Linden, D.E.J., Bittner, R.A., Muckli, L., Waltz, J.A., Kriegeskorte, N., Goebel, R., Singer, W., Munk, M.H.J., 2003. Cortical capacity constraints for visual working memory: dissociation of fMRI load effects in a fronto-parietal network. Neuroimage 20, 1518–1530. Lisberger, S.G., 2010. Visual guidance of smooth-pursuit eye movements: sensation, action, and what happens in between. Neuron 66, 477–491. Luck, S.J., Vogel, E.K., 1997. The capacity of visual working memory for features and conjunctions. Nature 390, 279–281. Meyer, C., Gauchard, G.C., Deviterne, D., Perrin, P.P., 2007. Cognitive task fulfilment may decrease gaze control performances. Physiol. Behav. 92, 861–866. Minamoto, T., Osaka, M., Osaka, N., 2010. Individual differences in working memory capacity and distractor processing: possible contribution of top-down inhibitory control. Brain Res. 1335, 63–73. Nagel, M., Sprenger, A., Zapf, S., Erdmann, C., Kompf, D., Heide, W., Binkofski, F., Lencer, R., 2006. Parametric modulation of cortical activation during smooth pursuit with and without target blanking. An fMRI study. Neuroimage 29, 1319–1325. Niogi, S.N., Mukherjee, P., Ghajar, J., Johnson, C.E., Kolster, R., Lee, H., Suh, M., Zimmerman, R.D., Manley, G.T., McCandliss, B.D., 2008. Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. Brain 131, 3209–3221. Poliakoff, E., Collins, C.J.S., Barnes, G.R., 2005. Attention and selection for predictive smooth pursuit eye movements. Brain Res. Cogn. Brain Res. 25, 688–700. Postle, B.R., Berger, J.S., D'Esposito, M., 1999. Functional neuroanatomical double dissociation of mnemonic and executive control processes contributing to working memory performance. Proc. Natl. Acad. Sci. U. S. A. 96, 12959–12964. Schluppeck, D., Curtis, C.E., Glimcher, P.W., Heeger, D.J., 2006. Sustained activity in topographic areas of human posterior parietal cortex during memory-guided saccades. J. Neurosci. 26, 5098–5108. Sestieri, C., Shulman, G.L., Corbetta, M., 2010. Attention to memory and the environment: functional specialization and dynamic competition in human posterior parietal cortex. J. Neurosci. 30, 8445–8456. Souto, D., Kerzel, D., 2011. Attentional constraints on target selection for smooth pursuit eye movements. Vision Res. 51, 13–20. Suh, M., Leung, H.C., Kettner, R.E., 2000. Cerebellar flocculus and ventral paraflocculus purkinje cell activity during predictive and visually driven pursuit in monkey. J. Neurophysiol. 84, 1835–1850. Suh, M., Basu, S., Kolster, R., Sarkar, R., McCandliss, B., Ghajar, J., 2006a. Increased oculomotor deficits during target blanking as an indicator of mild traumatic brain injury. Neurosci. Lett. 410, 203–207. Suh, M., Kolster, R., Sarkar, R., McCandliss, B., Ghajar, J., Cognitive Neurobiological Res, C., 2006b. Deficits in predictive smooth pursuit after mild traumatic brain injury. Neurosci. Lett. 401, 108–113. Suh, M., Kolster, R.A., Niogi, S., McCandliss, B.D., Ivry, R.B., Voss, H.U., Sarkar, R., Ghajar, J., 2008. Degree of brain connectivity predicts eye-tracking variability. J. Korean Phy. Soc. 53, 3468–3473. Tark, K.J., Curtis, C.E., 2009. Persistent neural activity in the human frontal cortex when maintaining space that is off the map. Nat. Neurosci. 12, 1463–1468. Thier, P., Ilg, U.J., 2005. The neural basis of smooth-pursuit eye movements. Curr. Opin. Neurobiol. 15, 645–652. Van Gelder, P., Lebedev, S., Liu, P.M., Tsui, W.H., 1995. Anticipatory saccades in smooth pursuit: task effects and pursuit vector after saccades. Vision Res. 35, 667–678. Zee, D.S., Yamazaki, A., Butler, P.H., Gucer, G., 1981. Effects of ablation of flocculus and paraflocculus on eye movements in primate macaca-mulatta. Journal of Neurophysiology (Bethesda). 46, 878–899.