The development of multitasking in children aged 7–12 years: Evidence from cross-sectional and longitudinal data

Journal of Experimental Child Psychology 161 (2017) 63–80

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The development of multitasking in children aged 7–12 years: Evidence from cross-sectional and longitudinal data Tian-Xiao Yang a,b, Weizhen Xie c, Chu-Sheng Chen d, Mareike Altgassen e, Ya Wang a,b, Eric F.C. Cheung f, Raymond C.K. Chan a,b,⇑ a Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China b Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China c Department of Psychology, University of California, Riverside, Riverside, CA 92521, USA d Department of Psychology, Sun Yat-Sen University, Guangzhou 510275, China e Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands f Castle Peak Hospital, Hong Kong Special Administrative Region

a r t i c l e

i n f o

Article history: Received 1 August 2016 Revised 5 April 2017

Keywords: Multitasking Children Development Longitudinal Cross-sectional Six Element Test for Children Battersea Multitask Paradigm

a b s t r a c t This study investigated the development of multitasking ability across childhood. A sample of 65 typically developing children aged 7, 9, and 11 years completed two multitasking tests across three time points within a year. Cross-sectional and longitudinal data consistently indicated continuous linear growth in children’s multitasking ability. By the age of 12 years, children could effectively perform a simple multitasking scenario comprising six equally important tasks, although their ability to strategically organize assorted tasks with varied values and priorities in a complex multitasking setting had not reached proficiency yet. Cognitive functions underlying a complex multitasking scenario varied in their developmental trajectories. Retrospective memory developed continuously from 7 to 12 years of age, suggesting its supporting role in the development of multitasking. Planning skills developed slowly and showed practice effects for older children but not for younger children. The ability to adhere to plans also

⇑ Corresponding author at: Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, 16 Lincui Road, Chaoyang District, Beijing 100101, China. Fax: +86 (0)10 64836274. E-mail address: [email protected] (R.C.K. Chan). http://dx.doi.org/10.1016/j.jecp.2017.04.003 0022-0965/Ó 2017 Elsevier Inc. All rights reserved.

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developed slowly, and children of all age groups benefited from practice. This study offers a preliminary benchmark for future comparison with clinical populations and may help to inform the development of targeted interventions. Ó 2017 Elsevier Inc. All rights reserved.

Introduction Humans are restricted by a limited amount of cognitive resources (Cowan, 2001; Miller, 1956). Nowhere is this limitation more evident than in situations where people need to concurrently perform multiple tasks and manage several different ongoing tasks in a short period of time (Logie, Trawley, & Law, 2011). Everyday examples include domestic scenarios like cooking a meal (Craik & Bialystok, 2006) and work settings like running a hotel (Manly, Hawkins, Evans, Woldt, & Robertson, 2002). Empirical evidence indicates that the ability to multitask is highly correlated with the executive control and attention network (Burgess, Veitch, de Lacy Costello, & Shallice, 2000; Kievit et al., 2014; Roca et al., 2011) and is supported by the prefrontal cortex (Burgess, 2000; Burgess et al., 2000; Shallice & Burgess, 1991), a brain area that continuously develops from childhood to adolescence (Tsujimoto, Genovesio, & Wise, 2011). Consequently, the ability to handle multiple tasks should also develop significantly during this period. However, no study to date has examined how multitasking skills typically develop across childhood. This is surprising considering the ubiquity of multitasking in everyday life—not least due to the explosion of information technology (e.g., multiscreen electronic devices; Ophir, Nass, & Wagner, 2009). Current knowledge of multitasking and neuropsychological functions builds on Shallice and Burgess’s (1991) seminal investigation of three patients with prefrontal lesions. These authors developed the Six Element Test (SET) and the Multiple Errands Test to capture these patients’ abilities to manage several tasks within a limited time (Shallice & Burgess, 1991). Despite normal IQ, these patients failed to switch between tasks and followed the rules less closely compared with healthy controls. Importantly, these multitasking deficits could not be fully explained by the patients’ performance in other cognitive functions (e.g., perception, memory, language, intelligence, executive functions; Shallice & Burgess, 1991). Rather, their impaired multitasking performance reflected an inability to remember and execute delayed intentions that were not directly prompted in the environment. These deficits were postulated to be associated with a deficient supervisory attentional system (Norman & Shallice, 1980), which is responsible for creating and activating delayed intentions in novel situations and may be central to multitasking abilities. Burgess (2000) postulated the key features of multitasking: (a) discrete tasks to be attempted/completed, (b) one task at a time, (c) interleaved working on subtasks, and (d) switching to another subtask without any external reminders. To understand the cognitive mechanisms involved in more complex multitasking scenarios, Burgess et al. (2000) developed the Greenwich test, which requires effective organization of tasks with varied values and priorities. Various aspects of mulitasking performance were assessed, including the ability to memorize the subgoals and rules of the task, form a reasonable plan, follow the plan when performing the task, complete the goals without breaking the rules, recount one’s own performance, and recall the subgoals and rules afterward. Burgess and colleagues administered the test to a large sample of patients with brain lesions and healthy controls, and they identified three crucial cognitive constructs that support multitasking: retrospective memory, planning, and prospective memory. Retrospective memory refers to remembering past information (e.g., memory for words and experiences people encountered in the past; Baddeley, Eysenck, & Anderson, 2009) and helps to memorize and retain the rules and subgoals in a multitasking scenario. Planning refers to the cognitive process of forming thoughts to achieve goals and assists in making optimal arrangements for the multiple tasks with varied priorities. Prospective memory is defined as the ability to implement delayed intentions on one’s own initiative (Craik, 1986) and, according

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to Burgess et al. (2000), reflects three aspects of performance: plan adherence, completion of task goals without breaking the rules, and the ability to recount one’s performance afterward. Thus, in Burgess and colleagues’ model, retrospective memory plays a fundamental role and supports both planning and prospective memory, and it is even considered a precursor of prospective memory. Across middle childhood (7–12 years of age), the cognitive functions that are assumed to underlie multitasking go through rapid development. From 7 years of age to early adolescence, retrospective memory capacity further increases (Gathercole, 1998; Maylor & Logie, 2010). Similarly, prospective memory shows continuous improvement across childhood and adolescence (Mahy, Moses, & Kliegel, 2014; Yang, Chan, & Shum, 2011). The development of prospective memory between 7 and 12 years of age has been associated with executive functions, especially working memory and inhibition (Kerns, 2000; Mäntylä, Carelli, & Forman, 2007; Yang et al., 2011). From childhood to adolescence, the cost of task switching continuously decreases, reflecting growing efficiency of shifting ability (Huizinga, Dolan, & van der Molen, 2006). Children also become more adaptive and strategic in time monitoring from 5 to 14 years of age (Mackinlay, Kliegel, & Mäntylä, 2009; Voigt et al., 2014). The ability to make effective plans for each response to solve a problem develops throughout the period from 7 to 12 years of age (Anderson, Anderson, & Lajoie, 1996; Baker, Segalowitz, & Ferlisi, 2001; Huizinga et al., 2006) and matures during adolescence (Asato, Sweeney, & Luna, 2006). It should be noted that the developmental patterns of these cognitive functions are based on different tests with varied cognitive demands and task goals. It remains unknown how these cognitive functions interact when they need to reach one goal in a multitasking scenario. From a neurological point of view, middle childhood represents a period when the key brain areas for multitasking (i.e., the prefrontal cortex) rapidly develop (Dumontheil, Burgess, & Blakemore, 2008). The rostral prefrontal cortex (Brodmann’s Area [BA] 10), which is involved in top-down control and multitasking (Shallice & Burgess, 1991), shows prolonged development throughout childhood and across adolescence (Dumontheil et al., 2008). Importantly, the rostral prefrontal cortex plays a key role in integrating multiple cognitive operations to achieve a higher goal (Ramnani & Owen, 2004) and helps to coordinate the cognitive functions underlying multitasking, including prospective memory (Burgess, Scott, & Frith, 2003) and planning (Koechlin, Basso, Pietrini, Panzer, & Grafman, 1999). The maturation of the rostral prefrontal cortex during this period, therefore, may provide the biological basis for the development of multitasking. However, it might not be the only one given that multitasking scenarios require the seamless cooperation of various cognitive functions and, thus, of multiple brain areas. Besides the rostral prefrontal cortex (BA 10), other brain structures/areas including the prefrontal cortex (BA 8 and BA 9), the anterior cingulate cortex, the posterior cingulate cortex, and the frontostriatal tract have also been associated with multitasking performance (Burgess et al., 2000; Kievit et al., 2014; Roca et al., 2011; Zhang et al., 2016). Notably, most of these brain areas belong to the executive attention and control system (Petersen & Posner, 2012; Yeo et al., 2011), which shows increasing efficiency at around 7 years of age and continues to develop during middle and late childhood (Pozuelos, Paz-Alonso, Castillo, Fuentes, & Rueda, 2014). Moreover, there is evidence for a shift from relatively separate local networks to a more distributed functionally connected network across childhood and adolescence, implying growing efficiency of communication between distant brain regions as children develop (Fair et al., 2009). The improved synergy of these remote brain areas can be particularly beneficial in dealing with complex situations like multitasking scenarios and may play a critical role in the refinement of multitasking ability. Given the rapid change in brain structures and cognitive functions associated with multitasking, multitasking itself should also go through significant change during this period. However, to the best of our knowledge, few studies have examined multitasking ability in children and all studies (except one) have focused on clinical populations (Chan et al., 2006; Mackinlay, Charman, & Karmiloff-Smith, 2006; Siklos & Kerns, 2004). For instance, Siklos and Kerns (2004) modified the Six Element Test for Children (C-SET) and tested children with attention deficit/hyperactivity disorder (ADHD) between 7 and 13 years of age. They found that children with ADHD attempted fewer tasks and checked the clock less frequently compared with healthy controls, indicating impairment in self-monitoring and strategy generation. To evaluate children’s multitasking ability in a more complex multitasking situation, Mackinlay et al. (2006) adapted the Greenwich test (Burgess et al., 2000) into a children’s version, the Battersea Multitask Paradigm (BMP). They found that children with autism were less efficient

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in planning and task switching and attempted fewer tasks than healthy controls, indicating a specific impairment in multitasking in children with autism. These studies are informative in showing deficits of multitasking in clinical populations of children, but understanding the nature of multitasking impairment in children with developmental disorders requires knowledge of its developmental trajectories in typically developing children. To date, only one study has investigated multitasking in typically developing children. In a lifespan study, Kliegel, Mackinlay, and Jäger (2008) tested 7- and 10-year-olds with a modified SET to which a prospective memory component was added. To explore the role of inhibitory control for multitasking, individuals either needed to actively interrupt the current subtask to switch to another subtask or needed to newly select the category of the subtask each time after making a response. Overall, 10year-olds outperformed 7-year-olds in measures of planning and prospective memory, whereas the two age groups showed comparable performance in retrospective memory. Importantly, 7-year-olds demonstrated reduced task switching (representing multitasking ability) in both inhibitory control conditions, but the difference between 7- and 10-year-olds was larger in the interruption condition than in the no-interruption condition, indicating that inhibitory control may be a critical factor for the development of multitasking ability. Although this study provides evidence for the development of multitasking in 7- and 10-year-olds, these findings were based only on cross-sectional data and only two age groups were tested. Moreover, compared with young adults, 10-year-olds recalled plans similarly well but showed poorer planning and task switching performance. Other studies also indicated that after 10 years of age cognitive functions underlying multitasking ability (e.g., time monitoring, prospective memory, planning) continue to develop (Anderson et al., 1996; Voigt et al., 2014; Yang et al., 2011). For example, at around 10 years of age, children start to gradually apply more adaptive time-monitoring behavior by recruiting more working memory resources compared with a previously more reactive style (Voigt et al., 2014). Prospective memory goes through a second wave of qualitative development at around 10 or 11 years of age after a first wave of quantitative development at around 7 or 8 years (Yang et al., 2011). Similarly, rapid development of planning skills occur at around 11 or 12 years of age after an initial developmental spurt between 7 and 9 years (Anderson et al., 1996). Therefore, including children older than 10 years may provide new insights into multitasking development and its underlying processes during late childhood. Moreover, applying a longitudinal approach in addition to cross-sectional testing may further delineate the development of multitasking across middle childhood. The current study aimed to assess children’s multitasking abilities across three age groups—7-, 9-, and 11-year-olds—at three time points over the course of a year, thereby covering an age range from 7 to 12 years. Two multitasking paradigms, namely the C-SET (Siklos & Kerns, 2004) and the BMP (Mackinlay et al., 2006), were employed to provide a more comprehensive evaluation. Both tasks tap into multitasking but have different emphases. The C-SET involves the coordination and attempt of six subtasks of equal importance within 10 min while following one simple rule (i.e., two parts of the same task cannot be performed consecutively). Therefore, reliable memory for rules and simple time allocation skills are sufficient for good performance in this task. In contrast, the BMP comprises three subtasks, but each of them includes items with varied values (e.g., yellow items get higher points than blue items). Thus, strategically prioritizing certain items over others would lead to more points. However, given that this requires strategic planning and advanced time allocation, the BMP might put higher demands on cognitive resources compared with the C-SET. In addition, the BMP provides an assessment of different cognitive functions that underlie multitasking (e.g., planning, prospective memory, retrospective memory) and enables the detection of key cognitive functions for skillful multitasking. The goals of the current study were threefold. First, we aimed to investigate the developmental trajectory of multitasking across childhood. Given the ongoing cognitive and brain development across middle childhood, we expected that typically developing children would show significant improvement in overall multitasking ability, reflected in an increased total profile score of the C-SET and in the ‘‘perform” score of the BMP across age groups. Moreover, to examine standard measures of performance, we analyzed intra-individual variability of performance because it represents an additional indicator of development (Van Geert & Van Dijk, 2002). It has been suggested that when young children learn a sophisticated skill or deal with a complex task, they tend to try out various strategies,

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which may result in higher variability of performance. As children grow older and become more adept at the skill/task, they tend to apply more uniform and advanced strategies, leading to lower variability of performance (Siegler, 2007). Thus, larger intra-individual variability in performance may indicate a period of rapid change, whereas smaller variability of performance may imply a relatively stable period (Siegler, 2007). In this study, intra-individual variability was measured as individual standard deviations in performance across the three time points of assessments (Schmidt & Teti, 2006). We predicted that intra-individual variability in performance would decrease with increasing age across the three age groups. Second, we aimed to explore whether the developmental course of multitasking would vary with the complexity of specific multitasking tests. Given the higher cognitive demands of the BMP compared with the C-SET, we expected that multitasking ability would show longer ongoing development and reach the optimal level of performance later when measured with the BMP than when measured with the C-SET. Third, we examined the developing patterns of different aspects of multitasking ability (e.g., rule learning, planning, plan adherence, overall task performance, monitoring, memory of rules) in a complex multitasking scenario (i.e., the BMP). Because the BMP was adapted from the Greenwich test (Burgess et al., 2000), the cognitive components supporting multitasking performance in the two tests should be similar, including retrospective memory (reflected by rule learning and memory of rules), planning, and prospective memory (i.e., plan adherence, overall task performance, and monitoring). Investigating the development of these underlying cognitive functions may help us to understand which cognitive functions are precursors, restricting or assisting factors in the development of multitasking ability. In terms of learning and maintaining memory of rules, as children’s retrospective memory capacity continues to increase from 7 years of age to early adolescence (Gathercole, 1998), we expected to see its continuous improvement throughout the examined age period. Given that 10-year-olds outperformed 7-year-olds in making plans for a simple multitasking setting (Kliegel et al., 2008), and given evidence for continuous development of planning in problem-solving tasks from 7 to 12 years of age (Anderson et al., 1996; Baker et al., 2001; Huizinga et al., 2006), we predicted that children’s ability to make sophisticated plans for a multitasking scenario (i.e., the BMP) would also improve from 7 to 12 years of age. The ability to monitor one’s multitasking performance in the BMP was measured by how well one can recount what one has done after completing the task. This ability may be more reliant on retrospective memory than on active monitoring of task progress that involves executive function. However, direct empirical evidence of the ability to recount what had happened in a multitasking scenario is scarce; there is only one study that indicates ceiling performance for typically developing children at 12 years of age (Mackinlay et al., 2006). Similarly, children’s ability to adhere to their own plans has rarely been investigated. For these reasons, no hypotheses relating to the development of monitoring and plan adherence ability were made. Method Participants A total of 65 children from 7 to 11 years of age were recruited from a primary school in China. Based on their age at the time of recruitment, these children were assigned to three age groups: 7-, 9-, and 11-year-olds. The local research ethics committee approved this study, and we obtained written informed consent from the children’s parents and teachers prior to the study. Exclusion Table 1 Demographic information of three age groups at baseline time.

Gender (M:F) IQ (SD)

7-year-olds (n = 21)

9-year-olds (n = 24)

11-year-olds (n = 20)

v2/F

p

8:13 95.93 (11.00)

11:13 98.01 (11.21)

9:11 100.45 (11.11)

0.32 0.85

.853 .432

Note. M, male; F, female. The statistic value was the value of Pearson chi-square test for gender and F value of one-way ANOVA for IQ.

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criteria included any personal or family history of developmental disorders such as autism, ADHD, and neurological disorders (e.g., epilepsy). Demographic information, including gender ratio and mean estimated IQ scores of the three age groups, is presented in Table 1. Measures IQ tests To control for the impact of general cognitive ability on task performance, children’s IQ scores were estimated using the abbreviated version of the Wechsler Intelligence Scale for Children on recruitment (Chinese adaptation: Gong & Cai, 1993). This test included four subtests: Block Design, Picture Completion, Information, and Vocabulary. The estimated IQ scores of this short version test have been shown to be highly correlated with the full scale test IQ scores (r = .95; Goh, 1980). The C-SET The Chinese version of the Six Element Test for Children (adapted by Chan et al., 2006; Siklos & Kerns, 2004) consists of six tasks: (a) using jigsaw puzzle pieces to build animals, (b) using Lego blocks to build a small catapult by following a construction picture, (c) making clown faces with pieces of face features, (d) attempting mazes, (e) finding specific frog pictures out of 30 similar frogs, and (f) finding differences between two similar pictures. The first three tasks were placed on a red plate, and the other three tasks were put on a yellow plate. The experimenter first introduced the six tasks and the function of a timer for countdown to the children. The experimenter then emphasized that the main goal of the task was to attempt, rather than complete, all six tasks within 10 min. Children were also instructed to adhere to one rule—not performing two tasks from the same color plate consecutively. Then children were questioned to make sure that they understood the goal, the rule, and the time limit of the test. Finally, the experimenter asked children to describe their plan to perform these tasks. For all children, we recorded the duration of every attempted task, the number of tasks attempted, the number of rule breaks, and the number of time checks. These records formed three variables: (a) the number of attempted tasks (from 0 to 6), which indicated children’s ability to use strategies and allocate time; (b) the number of rule breaks (from 0 to no limit), which captured self-monitoring and inhibitory abilities; and (c) the total profile score (from 0 to 4), which was an overall measurement of multitasking performance considering the numbers of attempted tasks, rule breaking, and excessive time spent on a single task (see Appendix for details). The BMP The Battersea Multitask Paradigm (Mackinlay et al., 2006) consists of three interleaved tasks: bead sorting, counter sorting, and caterpillar coloring. In each task, the children were asked to sort yellow and blue items (beads, counters, and caterpillar circles) to fill a ‘‘cluster” (a pot of beads, a grid of counters, or a chunk of caterpillar). Children were required to attempt (not necessarily complete) all three tasks within 3 min while obeying four rules, namely that (a) all three tasks must be attempted before the alarm rang, (b) yellow items give more points than blue ones, (c) full clusters give extra points, and (d) items must be picked up or colored one by one. There were six stages of task administration, and each stage generated a behavioral variable. Given that scoring of each variable was complex and lengthy, here we describe only what each variable represented. The detailed method for scoring can be found in the original article (Mackinlay et al., 2006). In the first stage, the experimenter introduced the children to the tasks and informed them of the rules. After a brief familiarization period, children were asked to recall the rules until all of them were recalled correctly (i.e., free recall). Then children were asked nine questions about the rules (i.e., cued recall). The sum of free recall and cued recall generated the composite score of ‘‘rule learn” (8–18 points), which indicated children’s ability to learn and understand the multitasking rules. The lowest score for rule learning was 8 because children were required to learn all four rules correctly (2 points for each rule) before continuing. Next children were asked to formulate a plan on how to achieve high scores without breaking the rules (the second stage). The score of ‘‘plan” (0–12 points) was calculated based on children’s consideration of attempting all three tasks, prioritizing yellow items, and filling up clusters.

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Thereafter, children were first reminded of the rules and time limit and then asked to perform the tasks within 3 min (the third stage). Three aspects of behavior were rated: strategic performance (i.e., the ability to use rules to gain higher points), penalty performance (i.e., rule breaking), and task switch performance (i.e., efficiency in switching tasks). These three scores formed a composite score of ‘‘perform” (3–20 points), which was an overall index of multitasking efficiency and capability. At the end of their performance (the fourth stage), children was evaluated on how well they had adhered to their original plans. A composite score of ‘‘plan follow” (0–12 points) was generated in favor of plan completion and with a penalty to plan deviation. Shortly afterward, the children were asked to recount or demonstrate what they had done and to explain why (the fifth stage). The correct recall of tasks attempted and the order of these attempted tasks were assessed and formed the score of ‘‘monitor” (0–9 points). According to Mackinlay et al. (2006), this score reflects children’s ability to describe what they have done and is an indication of how well children monitor their achievements during performance on the multitasking paradigm. Alternatively, recounting what one has done may also reflect the ability to retrieve what has happened from retrospective memory. It should be noted that although this score was named ‘‘monitor” (Mackinlay et al., 2006), it does not reflect what is typically called monitoring behavior during multitasking such as checking the time (Burgess et al., 1996; Manly et al., 2002; Siklos & Kerns, 2004). Thus, the ‘‘monitor” score in the BMP is different from the time-monitoring behavior measured in the SET tasks (Burgess et al., 1996; Siklos & Kerns, 2004), which was indicated by individuals’ clock checking behavior during multitasking. In the last (sixth) stage, children’s memory of the rules was tested again by the above-described free recall and cued recall procedure, which yielded a composite score of ‘‘rule memory” (0–18 points), reflecting participants’ memory of the rules. Given that the BMP was developed based on the Greenwich test, performance of both tests should rely on the same cognitive mechanisms (Burgess et al., 2000), specifically retrospective memory (reflected by the ‘‘learn” and ‘‘memory” scores), planning (i.e., the ‘‘plan” score), and prospective memory (i.e., the ‘‘plan follow,” ‘‘perform,” and ‘‘monitor” scores). Procedure All children were tested at three time points: baseline, Time 1 (T1), and Time 2 (T2). To reduce potential practice effects, the minimum time interval between testing was 3 months. The actual average intervals between time points were 5.11 months (SD = 0.73) for baseline to T1 and 5.89 months (SD = 0.73) for T1 to T2. The baseline session included the IQ test and the two multitasking tests (the C-SET and the BMP). In the follow-up sessions (T1 and T2), children were tested only with the C-SET and the BMP. The task sequence of the C-SET and the BMP was counterbalanced across participants for all sessions. All of the children understood and complied with the task requirements and completed the tests in all sessions. Children’s performance was scored during the task or at the end of the session by a trained experimenter who strictly followed the task procedure and the scoring manuals based on Mackinlay et al.’s (2006) study and the testing and scoring protocol of the Chinese version of the C-SET (Chan et al., 2006). Data analysis Cross-sectional comparisons at baseline The age effect of overall multitasking skills at baseline across the three age groups was examined using separate multivariate analyses of variance (MANOVAs) for the C-SET and BMP tests. Age effects on individual variables in the two tests were examined with a series of univariate tests, followed by post hoc comparisons with Bonferroni corrections. Longitudinal contrast analyses Although latent growth modeling is appropriate in capturing inter- and intra-individual growth in a longitudinal design (e.g., Finkel, Reynolds, McArdle, Gatz, & Pedersen, 2003), it requires a larger sample size with more sampling occasions than the current study to ensure enough statistical power.

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Thus, it was not used in this study. However, we applied a contrast analysis for repeated-measures data (Rosenthal, Rosnow, & Rubin, 2000), which outweighs the traditional analysis of variance (ANOVA) approach in its precision of significance testing, higher statistical power, and less imposed assumptions on the data (Rosenthal et al., 2000). First, for each variable of each participant, a composite L score was calculated with the contrast weights set as 1, 0, and 1 for baseline, T1, and T2 measures [L = Baseline  ( 1) + T1  0 + T2  1]. These L scores indicated the goodness of fit of the data with the predicted pattern (linear growth over time). For a given variable, the composite L score of each age group was then tested using a one-sample t test, and a significant p value would indicate that the data in a particular group fit the linear growth trend prediction (Rosenthal et al., 2000). Second, for a given multitasking variable, its critical age period of development was discerned by comparing the linear growth patterns (L scores) across the three age groups. Intra-individual variability across age groups The comparison of average scores essentially assumes that either the behaviors of interest are stable over time in human development or the change that occurs is similar for all persons across age groups (Hultsch & MacDonald, 2004). In contrast, the analysis of intra-individual variability avoids this assumption by taking into account the variability across age groups. More important, this approach is also informative with regard to estimating sensitive periods in development because reduction in variability could indicate stabilized levels of performance in the particular age group (Siegler, 2007). Each child’s intra-individual variability for each multitasking variable across the three time points of assessment were calculated and represented as the individual standard deviation (iSD; Van Geert & Van Dijk, 2002). Here, to delineate the profile of intra-individual variability across the three age groups, contrast analysis was applied instead of traditional ANOVA (Rosenthal et al., 2000). With the prediction that intra-individual variability of multitasking variables would decrease as children grow older, a set of linear decrease contrast weights (+1, 0, and 1) were applied to the 7-, 9-, and 11-year-old groups to test whether iSDs would diminish across age groups. Results The means and standard deviations of the variables in the C-SET and the BMP measured at baseline, T1 (6 months), and T2 (12 months) are summarized in Table 2 and presented in Figs. 1 and 2. A series of one-sample t tests were conducted to test the gender effect on all variables in the C-SET and the BMP at the three time points. There was no significant gender effect in any of the variables at any time point (ps > .10); thus, gender was not considered further in the following analyses. Cross-sectional comparisons at baseline The C-SET The overall age effect on this task was significant, Wilks’ lambda = .702, F(6, 120) = 3.89, p = .001, g2p = .162. This effect was mostly driven by the difference in total profile scores, F(2, 62) = 8.37, p = .001, g2p = .213, and the number of tasks attempted across age groups, F(2, 62) = 6.72, p = .002, g2p = .178, but not the total number of rule breaks, F(2, 62) = 2.26, p = .113, g2p = .068. Significant improvements occurred mainly between 7- and 11-year-olds, as reflected in the total profile score and the number of tasks attempted (ps < .05). In addition, significant improvement in the total profile score was observed between 9- and 11-year-olds (ps < .05). The BMP There was a significant overall age effect on this task, Wilks’ lambda = .492, F(12, 114) = 3.87, p < .001, g2p = .284, driven by the scores of ‘‘rule learn,” ‘‘memory,” and ‘‘monitor” across age groups (ps < .05). The age effect was marginally significant for the ‘‘perform” score, F(2, 62) = 3.06, p = .054, g2p = .090, and was absent for the ‘‘plan” and ‘‘plan follow” scores (ps > .10). The significant improvement of ‘‘rule learn” occurred between 7-year-olds and the other two age groups (ps < .05). For ‘‘memory,” significant differences were observed in all three paired group comparisons

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T.-X. Yang et al. / Journal of Experimental Child Psychology 161 (2017) 63–80 Table 2 Means (and standard deviations) of C-SET and BMP scores across three measurements and results of contrast analysis. Measurement

7-year-olds

9-year-olds

11-year-olds

Linear model

Baseline

6 months

12 months

L

t

p

C-SET Total profile Tasks attempted Rule breaks

1.95 (1.61) 4.24 (1.61) 1.10 (1.92)

2.90 (1.18) 5.33 (1.32) 0.52 (0.98)

3.19 (0.98) 5.52 (0.98) 0.14 (0.36)

1.24 (1.22) 1.28 (1.42) 0.95 (2.01)

4.66 4.13 2.17

<.001 <.001 .043

BMP Learn Plan Plan follow Perform Monitor Memory

10.95 (3.07) 7.67 (2.65) 6.38 (3.26) 12.33 (5.88) 7.48 (2.42) 11.38 (2.62)

13.76 (2.77) 7.90 (2.49) 7.29 (2.76) 14.57 (4.52) 8.05 (1.94) 12.90 (2.57)

14.67 (2.20) 8.29 (2.67) 7.95 (2.69) 15.43 (3.41) 8.52 (1.03) 14.52 (2.58)

3.71 0.62 1.57 3.10 1.05 3.14

5.47 0.87 2.17 2.86 2.07 5.83

<.001 .392 .042 .010 .052 <.001

C-SET Total profile Tasks attempted Rule breaks

2.42 (1.06) 4.79 (1.02) 0.71 (0.86)

3.46 (0.78) 5.75 (0.61) 0.13 (0.34)

3.75 (0.53) 6.00 (0.00) 0.25 (0.53)

1.33 (1.17) 1.20 (1.02) 0.45 (0.83)

5.57 5.76 2.66

<.001 <.001 .014

BMP Learn Plan Plan follow Perform Monitor Memory

13.46 (2.72) 6.67 (2.46) 6.17 (2.76) 14.04 (3.97) 8.75 (1.23) 13.58 (2.63)

14.79 (2.41) 8.00 (2.72) 7.88 (2.79) 15.71 (2.93) 8.58 (0.93) 14.33 (2.65)

15.96 (1.76) 9.21 (2.43) 9.12 (2.36) 16.63 (2.18) 8.58 (1.02) 15.21 (1.93)

2.51 (2.79) 2.54 (2.81) 2.96 (2.56) 2.58 (4.40) 0.17 (1.09) 1.63 (2.57)

4.41 4.41 5.66 2.88 0.76 3.11

<.001 <.001 <.001 .009 .453 .005

C-SET Total profile Tasks attempted Rule breaks

3.30 (0.98) 5.60 (0.82) 0.25 (0.72)

3.20 (0.89) 5.90 (0.31) 0.90 (1.37)

3.80 (0.41) 6.00 (0.00) 0.00 (0.00)

0.50 (1.19) 0.40 (0.82) 0.25 (0.72)

1.86 2.18 1.55

.076 .042 .137

BMP Learn Plan Plan follow Perform Monitor Memory

15.20 (1.54) 8.25 (2.81) 7.65 (3.03) 15.95 (4.00) 8.55 (1.10) 16.10 (1.45)

16.70 (1.56) 9.45 (1.96) 8.65 (2.91) 16.80 (3.46) 8.40 (1.57) 16.20 (1.91)

17.30 (1.30) 10.30 (1.95) 10.45 (3.33) 17.95 (3.79) 8.50 (1.67) 16.65 (1.73)

2.10 (1.25) 2.05 (2.98) 2.80 (3.19) 2.00 (3.61) 0.05 (1.50) 0.55 (1.57)

7.51 3.07 3.93 2.48 0.15 1.57

<.001 .006 <.001 .023 .883 .134

(3.33) (3.25) (3.31) (4.97) (2.33) (2.47)

Note. Contrast weights of 1, 0, and +1 were applied to baseline, T1, and T2 scores, respectively, representing a linear growth pattern of multitasking functions within a year.

(ps < .05). For ‘‘monitor,” significant improvement occurred only between the 7- and 9-year-old groups. For ‘‘perform,” significant enhancement occurred between 7- and 11-year-old groups (ps < .05). Longitudinal contrast analyses The results of longitudinal contrast analysis for each variable in the C-SET and the BMP are presented in Table 2. We first tested the linear growth pattern in each age group and then compared the growth patterns across the three age groups. The C-SET In both the 7- and 9-year-old groups, the data for all variables across the three time points of assessment fit the linear growth model (ps < .05). In contrast, for the 11-year-old group, the total profile score and the number of rule breaks did not fit a linear developing pattern (ps > .05), whereas the number of tasks attempted did (p = .042). Further analysis of the L scores across the age groups

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Fig. 1. Mean C-SET scores of three age groups across three time points. The scores included total profile scores, number of tasks attempted, and number of rule breaks. Error bars indicate the standard errors. The values of the y axis indicate the possible range of the data (note that the number of rule breaks ranged from 0 to no limit). The significance of cross-sectional comparisons is indicated by asterisks with brackets, and longitudinal results are represented next to the legend of each age group. * p < .05; n.s., nonsignificant.

revealed a marginally significant group difference in the total profile score, F(2, 62) = 3.07, p = .054, g2p = .090. and a significant group difference in the number of tasks attempted, F(2, 62) = 4.00, p = .023, g2p = .114. These group differences could be best captured by a linear decreasing pattern (+1, 0, and 1 corresponding to the 7-, 9-, and 11-year-old groups, respectively); for total profile score, t(62) = 1.98, p = .052, and for number of tasks attempted, t(62) = 2.56, p = .013, indicating that younger children showed a more linear growth pattern compared with older children.

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Fig. 2. Mean BMP scores of three age groups across three time points. The scores included ‘‘learn,” ‘‘plan,” ‘‘perform,” ‘‘plan follow,” ‘‘monitor,” and ‘‘memory.” Error bars indicate the standard errors. Cross-sectional comparisons are indicated by asterisks with brackets, and longitudinal results are represented next to the legend of each age group. * p < .05; m, marginally significant (.05 < p < .10); n.s., nonsignificant.

The BMP Among the six variables, ‘‘rule learn,” ‘‘plan follow,” and ‘‘perform” showed significant linear development across the three time points in all three age groups (ps < .01). Other variables varied in their linear development trends across the three age groups. For ‘‘memory,” a linear increment was observed only in 7- and 9-year-old children (ps < .05) but not in 11-year-old children (p = .134). For ‘‘plan,” linear growth was observed in both 9- and 11-year-olds (ps < .05) but not in 7-year-olds (p = .392). For ‘‘monitor,” a trend of linear development was observed in 7-year-olds (p = .052) but not in 9- and 11-year-olds (ps > .10). Given these differences, developmental patterns were further compared among the three age groups. There were significant differences in ‘‘monitor” score, F(2, 62) = 3.34, p = .042, g2p = .097, and

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in ‘‘memory” across age groups, F(2, 62) = 6.74, p = .002, g2p = .179. These differences were best captured by a linear decreasing pattern (+1, 0, and 1 corresponding to the 7-, 9-, and 11-year-old groups, respectively); for ‘‘monitor,” t(62) = 2.07, p = .043, and for ‘‘memory,” t(62) = 3.46, p = .001, suggesting more linear growth in younger children than in older children. There was a marginally significant linear decreasing trend in the ‘‘learn” score across age groups, t(62) = 1.96, p = .055. However, for ‘‘plan,” ‘‘performance,” and ‘‘plan follow,” no significant differences were obtained in either ANOVA or contrast analysis (ps > .10), suggesting a similar growth pattern across the three age groups for these variables. Intra-individual variability across age groups The C-SET As shown in Fig. 3, intra-individual variability generally decreased across age groups. The linear reduction (+1, 0, and 1 for the 7-, 9-, and 11-year-old groups, respectively) in intra-individual

Fig. 3. Intra-individual variability for C-SET (A) and BMP (B) scores across a year in three age groups. Intra-individual variability was calculated as the individual standard deviation (iSD) across three time points of assessments for each participant. The figure shows the averaged group data. The broken line with the arrow shows a linear decrease prediction (+1, 0, 1) as the age increases. Significance tests were applied to examine whether the iSD data followed this linear decrease prediction across age groups. Error bars indicate the standard errors. * p < .05; ** p < .01; n.s., nonsignificant.

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variability was significant for total profile scores, t(62) = 2.11, p = .039, and number of tasks attempted, t(62) = 2.70, p = .009, but not for number of rule breaks (t < 1). The BMP Intra-individual variability was relatively stable across age groups. Significant effects of a linear decrease in intra-individual variability were observed in ‘‘rule learn,” t(62) = 3.46, p = .001, and ‘‘memory,” t(62) = 2.85, p = .006, but not in ‘‘plan” (t < 1), ‘‘perform,” t(62) = 1.41, p = .164, ‘‘plan follow,” t (62) = 1.50, p = .140, or ‘‘monitor,” t(62) = 1.46, p = .149. Discussion The current study examined the development of multitasking ability in typically developing children using a combination of cross-sectional and longitudinal design. Specifically, the study aimed to (a) describe the developmental trajectory of multitasking ability across childhood, (b) investigate whether this trajectory varies with the complexity of the multitasking test, and (c) examine and compare the developmental trajectories of different cognitive constructs underlying multitasking ability. Consistent with our first hypothesis, cross-sectional data indicate that, overall, the ability to multitask (as reflected in the total profile score of the C-SET and in the ‘‘perform” score of the BMP) improves across the three age groups. This finding is consistent with the superior multitasking ability found in 10-year-old children as compared with 7-year-old children by Kliegel et al. (2008) using a complex prospective memory paradigm. The observed overall improvement of multitasking across middle childhood is also in line with the changes in the key brain structure underlying multitasking (i.e., rostral prefrontal cortex; Dumontheil et al., 2008) and the development of cognitive functions associated with multitasking across the examined age period (Anderson et al., 1996; Gathercole, 1998; Huizinga et al., 2006; Mackinlay et al., 2009; Mahy et al., 2014). Consistent with our second hypothesis that the developing course of multitasking may vary with the complexity of the applied multitasking test, development of overall multitasking ability was much faster in the less complex C-SET than in the more demanding BMP. Specifically, in the C-SET crosssectional data indicate that development was relatively slow between 7 and 9 years of age but accelerated from 9 to 11 years of age. The rapid development of multitasking ability was further indicated by the continuous linear reduction in intra-individual variability across the three age groups, suggesting relative stability with increasing age. Given that there is evidence that children often shift from using diverse strategies to applying more uniformed and advanced strategies when dealing with complex cognitive tasks, resulting in reduced intra-individual variability across time (Siegler, 2007), the increased stability may reflect a growing sophistication in applying more efficient and homogeneous strategies during multitasking. Longitudinal data further indicate that, in contrast to 7- and 9-yearolds, 11-year-olds failed to show ongoing linear development, suggesting a slowing down of development. In addition, children of all age groups rarely broke task rules, which is consistent with previous research using the C-SET (Siklos & Kerns, 2004). This finding indicates that children could remember the rules well and could refrain from breaking them during testing. However, whereas cross-sectional data showed no age effects on the number of rule breaks, longitudinal data indicated reduced rule breaks in 7- and 9-year-olds, but not in 11-year-olds, across time. This discrepancy may reflect a growing familiarization with the task requirements with multiple testing, which may be particularly beneficial for younger children. In the BMP, whereas cross-sectional comparisons and longitudinal data indicate a continuous development of overall multitasking, intra-individual variability of BMP performance failed to show a significant reduction across age groups and children did not reach ceiling performance by 12 years of age. These findings suggest that the multitasking ability measured by the BMP are not fully developed yet. Compared with the C-SET, the BMP involves strategic planning like prioritizing tasks with high values and making efficient switches between subtasks during performance. These abilities develop late in childhood and continue to improve across adolescence (Asato et al., 2006; Gathercole, Pickering, Ambridge, & Wearing, 2004; Huizinga et al., 2006; Maylor & Logie, 2010). In our study, 12-year-old children might not yet be fully equipped with these abilities to successfully

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perform a complex multitasking scenario (e.g., the BMP) despite their proficiency in handling tasks of equal importance in a simple multitasking setting (e.g., the C-SET). The prolonged development of advanced multitasking may be related to the extended maturation of the crucial brain area for multitasking (BA 10) in children (Dumontheil et al., 2008). Therefore, these findings confirm our second hypothesis that the development of multitasking varies with the cognitive complexity of the specific task. The third goal of the current study was to explore the development of the underlying cognitive functions of multitasking performance in a complex scenario (i.e., the BMP). Retrospective memory is reflected in the ability to learn/memorize the goals and rules of the task, to maintain them during performance, and to recall them after completion of the task. Empirical evidence based on patients with cerebral lesions suggests that retrospective memory plays a fundamental role in multitasking and is a prerequisite for the normal function of prospective memory in multitasking (Burgess et al., 2000). For instance, remembering and maintaining the rule (e.g., all tasks must be attempted before the alarm rings) during multitasking performance is crucial for individuals to remember to switch to another subtask while engaging in the current subtask. Results based on the cross-sectional, longitudinal, and intra-individual variability analyses consistently point to a linear development of retrospective memory, indicating an increase in children’s capacity to learn and memorize rules and goals of tasks in a multitasking setting from 7 to 12 years of age. These results are consistent with our hypothesis and the gradual quantitative improvement of retrospective memory reported by previous studies (Fivush, 2011; Gathercole, 1998; Maylor & Logie, 2010; Ofen et al., 2007). Importantly, the developmental trajectory of retrospective memory resembles that of overall multitasking (i.e., ‘‘perform” score; see Fig. 2), which is in line with its assumed supporting role in the development of multitasking skills in Burgess’s (2000) neurocognitive model. In contrast to the continuous improvement of retrospective memory from 7 to 12 years of age, planning displayed a different developmental pattern. Based on developmental studies on planning for simple multitasking scenarios (Kliegel et al., 2008) and for problem-solving tasks (Anderson et al., 1996; Baker et al., 2001; Huizinga et al., 2006), we predicted continuous development of planning ability across the examined age period. In contrast to this hypothesis, cross-sectional data and intraindividual variance showed little development of planning across the three age groups, indicating that the ability to make plans for complex multitasking scenarios actually develops relatively slowly during this age period. This finding suggests that, unlike making plans in a relatively simple multitasking scenario (Kliegel et al., 2008) or planning each response step to solve a specific problem (Anderson et al., 1996; Baker et al., 2001; Huizinga et al., 2006), the ability to formulate complex plans for multiple tasks with varied importance/priorities develops relatively slowly during childhood. Compared with planning for simple multitasking scenario and problem solving, making effective plans for specific tasks with varied priorities can be cognitively more demanding and difficult for children in this age range (e.g., exceeding their working memory capacity). Therefore, the developmental trajectory of planning ability may vary with the complexity/cognitive demands of tasks. This inference is supported by an earlier study in which development of planning for problem solving was delayed when the complexity and difficulty of the task increased (Luciana & Nelson, 1998). Longitudinal data of planning, however, revealed significant improvement of planning in 9- and 11-year-old children, but not in 7-year-old children, across a year. The significant improvement of planning may reflect refinement in the use of strategies during the second and third testing sessions given that children may have optimized their plans after having already completed the same complex multitasking scenario during baseline testing. Importantly, this improvement in planning occurred only in older children (9- and 11-year-olds) but not in younger ones (7-year-olds). This finding indicates that older children can benefit from the prior experience of planning and performing the multitasking task, implying that the ability to make effective plans for complicated situations may be trainable for children older than 9 years. Overall, these findings suggest that the ability to make plans for a complex multitasking scenario develops slowly in children from 7 to 12 years of age. Because the willingness to spend time on planning is crucial for achieving goals effectively (Asato et al., 2006), the slow development of planning skills might have limited younger children in achieving high-level performance in multitasking. The last and most critical construct in Burgess et al.’s (2000) multitasking model is prospective memory, which reflects three aspects of performance: completion of the task goals without breaking

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the rules, the ability to follow plans, and the ability to recount what one has done. Goal completion and rule-keeping behavior are considered to be the most fundamental aspect of multitasking and are used to represent the overall performance of the BMP, the developmental pattern of which has already been discussed. The ability to implement previous plans in a complex multitasking scenario is most similar to the original concept of prospective memory (carrying out previously formed intentions), and adherence to the individual’s original plan is important for successful multitasking. For instance, previous research has shown that plan followers showed better multitasking performance than plan alterers regardless of the efficiency of the original plan, indicating the importance of plan following in multitasking (Logie et al., 2011). In our study, both cross-sectional data and intraindividual variance showed little improvement, whereas longitudinal data pointed to linear growth across the year in all three age groups. On the one hand, this finding may suggest that the ability to follow one’s plan in a complex multitasking scenario develops slowly from 7 to 12 years of age. On the other hand, it may also indicate that children’s plan adherence improved as children became more familiar with the multitasking scenario in the second and third testing sessions, implying that this ability may be trainable and may benefit from practice. Thus, these findings add to the largely ignored area of the development of plan adherence in a multitasking scenario in children from 7 to 12 years of age. In terms of the ability to recount what one has done after performance, children were able to recall the subtasks and the order of their performance on these subtasks well. Both cross-sectional and longitudinal data showed that the ability measured by the monitor score developed early, reaching ceiling level at 9 years of age and showing little improvement afterward. The intra-individual variability of recounting was small and did not change across time. These findings suggest that this ability develops early in this examined age period. In an earlier study using the BMP test, typically developing children (12-year-olds) also showed ceiling performance in the recount measure (Mackinlay et al., 2006). In Burgess et al.’s (2000) model, plan following and recounting are thought to support the same cognitive construct (i.e., prospective memory) given that both involve the activation of original plans. In the BMP, Mackinlay et al. (2006) took a further step to name the ‘‘recount” score as the ‘‘monitor” score, which was thought to reflect children’s awareness of their achievements and was an executive monitoring process. Although these speculations might be true, in children recounting what one has done may simply involve retrieval of events from episodic memory. Studies on memory for sequential events indicate that children as young as 1 or 2 years show accurate memory capacity for three-event sequences (Bauer & Mandler, 1992). Therefore, it is conceivable that 9-year-old children in the current sample could easily remember the sequence and content of the tasks they had performed just a moment earlier. Thus, it seems that the detection of potential development of task monitoring ability may have been affected by this indirect way of measuring this ability in the BMP. To clarify the developmental pattern of task monitoring ability in multitasking, future studies should consider including direct measures of monitoring during multitasking performance. For instance, in the C-SET task (Siklos & Kerns, 2004), time-monitoring behavior is assumed to reflect how people monitor their online progress during multitasking performance. Time-monitoring behavior can be measured by the number of times that people check the clock during the task. Apart from the cognitive functions assumed to underlie multitasking, the development of multitasking may also reflect advances in iterative processing. According to Zelazo (2015), iterative processing is a neurocognitive skill characterized by deliberate and sustained consideration to solve goaldirected problems by formulating and maintaining hierarchical rule systems in working memory. Improvement in iterative processing is necessary for the construction of complex representations of task rules and verbally mediated attentional control. Iterative processing/reflection can typically be observed when people actively interrupt the ongoing stream of actions and step back to consider the current situation. In the complex multitasking scenarios (i.e., BMP), the age-related improvement in the ability to learn more complex rules and monitor task progress may reflect advances in iterative processing, which may have helped to represent the complex rules and goals involved in multitasking and to reflect the current status during task performance. In addition, Zelazo predicted that constructive practice of iterative processing improves executive functions skills. In the current study, repeated testing may provide a chance for practice in iterative processing, which may help children to form more advanced plans and effectively follow them during performance.

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The current study had four limitations. First, different cognitive functions such as planning, retrospective memory, and prospective memory were generated from the same task and might not be totally independent from each other. Future studies should apply individual tests for these abilities to explore their contributions to the development of multitasking. Second, the same multitasking tests were used across the three time points, which may have caused practice effects. However, unlike longitudinal data, the baseline measures were not contaminated by practice effect. Therefore, the comparisons of baseline performance across age groups (cross-sectional data) could still provide some insights into age-related development in multitasking ability. Future studies should consider using parallel versions of tests. Third, children’s performance was rated by a single experimenter based on a strict scoring sheet, and so no inter-rater reliability data were available. Finally, the longitudinal contrast analyses in this study assumed a linear growth pattern across time within the three age groups; this may have prevented detection of the actual (if nonlinear) developmental pattern of multitasking. Nonetheless, the linear growth model captures a large amount of variance shared with other nonlinear monotonically growing models and, therefore, is useful and informative in revealing the underlying developmental pattern (Rosenthal et al., 2000). Thus, our results may pave the way for future research to adopt a model-free approach with more data points within each participant to allow a more detailed profiling of the underlying developmental trajectory. Notwithstanding these limitations, this is the first study involving both cross-sectional and longitudinal data to examine the developmental trajectory of multitasking ability in typically developing children between 7 and 12 years of age. Overall, 7- to 12-year-olds showed continuous and linear development of multitasking ability. Children as young as 7 years showed good understanding of the requirements of multitasking scenarios and could allocate time to some of the required tasks. Children attempted more tasks as they grew older, and most children by 8 years of age attempted the majority of the required tasks (e.g., five of six tasks in the C-SET). At 9 years of age, children could easily remember the sequence and content of the tasks they had performed just a moment earlier. At 11 years of age, the developmental pace of basic multitasking ability began to slow down, whereas sophisticated multitasking ability continued to show linear growth. By 12 years of age, children were adept in managing multiple tasks of equal importance, whereas they were still inadequate and unsophisticated in strategically prioritizing and switching between multiple tasks with varied values. In addition, developmental trajectories of cognitive constructs underlying multitasking also varied. Specifically, retrospective memory (e.g., remembering the rules) showed a similar linear development as multitasking ability, indicating its facilitating role. The ability to make effective plans for complex multitasking scenarios developed slowly across the examined age period, implying that it may be a bottleneck for development of overall multitasking ability during middle childhood, but it can also be a potential driving force for further development. The ability to follow plans developed slowly from 7 to 11 years of age, but this ability improved after repeated testing in all age groups, suggesting that plan adherence may benefit from practice and familiarity with the multitasking scenario. Future studies should continue to investigate these interesting hypotheses using independent measures of multitasking ability and the associated cognitive functions. Taken together, these findings not only show the developmental trajectory of multitasking and its associated cognitive functions from 7 to 12 years of age but also allow us to discern the age-specific characteristics of multitasking ability. Identification of these cognitive functions may yield useful information for the development of targeted cognitive training to improve multitasking ability in typically and atypically developing children. Acknowledgments This study was supported by grants from the Beijing Training Project for the Leading Talents in S & T (No. Z151100000315020), the National Natural Science Foundation of China (No. 31400873), the CAS Key Laboratory of Mental Health, Institute of Psychology (No. KLMH2015ZG02), and the CAS-SAFEA International Partnership Program for Creative Research Teams (No. Y2CX131003). The funding agents had no further role in the study design; in the collection, analysis, and interpretation of the data; in the writing of the manuscript; or in the decision to submit the manuscript for publication.

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Appendix The calculation of total profile score of the C-SET followed the scoring method of the Modified Six Element Test in the Behavioural Assessment of the Dysexecutive Syndrome for adults (Wilson, Alderman, Burgess, Emslie, & Evans, 1996). First, a raw score was generated by subtracting the number of rule breaks from the number of attempted tasks. Given that the number of rule breaks can be high and lead to a negative number for raw score, the maximum score of rule breaks is limited to 3. Second, this raw score was transformed to a profile score according to the rule in the following table. In addition, 1 point was taken from the profile score if more than 271 s was spent on one task. Raw score

Profile score

6 4 or 5 2 or 3 1 or less

4 3 2 1

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