Task switching and shifting between stopping and going: Developmental change in between-trial control adjustments

Journal of Experimental Child Psychology 108 (2011) 484–503

Contents lists available at ScienceDirect

Journal of Experimental Child Psychology journal homepage: www.elsevier.com/locate/jecp

Task switching and shifting between stopping and going: Developmental change in between-trial control adjustments Mariëtte Huizinga ⇑, Maurits W. van der Molen Department of Developmental Psychology, University of Amsterdam, 1018 Amsterdam, The Netherlands

a r t i c l e

i n f o

Article history: Available online 18 November 2010 Keywords: Reaction time Task switching Development Inhibition Prefrontal cortex Cognitive flexibility

a b s t r a c t This study set out to investigate developmental differences in the ability to switch between choice tasks and to shift between Go/ NoGo and choice tasks. Three age groups (7-year-olds, 11-yearolds, and young adults) were asked to consider the shape or color of a bivalued target stimulus. The participants performed a switch task in which a cue signaled the task to be performed (i.e., respond to shape vs. respond to color) and a shift task in which a cue instructed them to make a choice reaction to the shape of the stimulus or to respond (Go) versus inhibit (NoGo) to the color of the stimulus. The ability to switch was examined by considering choice–choice switches versus choice–choice repeats. The ability to shift was examined by considering NoGo-to-choice shifts versus choice–choice repeats and NoGo-to-Go shifts versus Go–Go repeats. The results showed that responding on Go trials was delayed following response inhibition on a NoGo trial. This delay did not discriminate between age groups. Responding on choice trials was considerably slowed when following response inhibition on NoGo trials. This slowing decreased with advancing age. Finally, responses on switch trials were slower compared with repeat trials, and this slowing was disproportionately large in young children compared with the other two age groups. This pattern of findings was interpreted in terms of a generic mechanism involving between-trial control adjustments in the setting of response thresholds that are likely to be mediated by a complex neural network implicating the dorsolateral prefrontal cortex and the presupplementary motor cortex. Ó 2010 Elsevier Inc. All rights reserved.

⇑ Corresponding author. E-mail address: [email protected] (M. Huizinga). 0022-0965/$ - see front matter Ó 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.jecp.2010.10.004

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

485

Introduction The ability to perform goal-directed, flexible behavior displays a clear developmental trend. In children, this ability improves gradually (although adolescents may still display behavior that is both impulsive and shortsighted; for reviews, see Diamond, 2002; Welsh, 2002). In terms of experimental task performance, the behavior of young children is often stimulus-bound and impulsive (cf. Zelazo, Craik, & Booth, 2004). On cognitive flexibility tasks (i.e., tasks that require flexible switching between task demands), children display strongly perseverative behavior; that is, they persevere in a given mental set or activity and cannot shift easily from one set or activity to another (Cuneo & Welsh, 1992). Adults generally perform adequately on cognitive flexibility tasks and have little trouble in inhibiting responses that a change in task demand has rendered inappropriate (e.g., Monsell, 2003). These findings are usually interpreted in terms of developmental improvement in executive function, which in turn is associated with the maturation of the prefrontal cortex (e.g., Blakemore & Choudhury, 2006; Chugani, Phelps, & Mazziotta, 1987; Giedd et al., 1999; Gogtay et al., 2004; Huttenlocher, 1979; Pfefferbaum et al., 1994; Sowell et al., 2004; Yakovlev & Lecours, 1967; for reviews, see Amso & Casey, 2006; Casey, Tottenham, Liston, & Durston, 2005). Traditionally, cognitive flexibility has been assessed using neuropsychological tasks that are complex in the sense that they involve a variety of processes (e.g., problem solving, performance monitoring) in addition to cognitive flexibility itself. The role of various processes in complex tasks (e.g., Wisconsin Card Sorting Task) can render task performance difficult to interpret (Grant & Berg, 1948; Heaton, Chelune, Talley, Kay, & Curtis, 1993). The task switching paradigm was introduced to avoid the interpretational difficulties associated with complexity. This paradigm is a tool for assessing the component processes that underlie cognitive flexibility in the absence of problem solving (cf. Cepeda, Kramer, & Gonzalez de Sather, 2001). The task switching paradigm requires an individual to switch between two simple choice tasks such as deciding the color (e.g., red, blue) or the shape (e.g., circle, triangle) of a stimulus. The two tasks are presented in mixed blocks, allowing the comparison of performance on task repetitions and task alternations. Typically, responses are slower and less accurate on alternation trials than on repetition trials (e.g., Allport, Styles, & Hsieh, 1994; Meiran, 1996; Monsell, 2003). The differences in performance on task alternation and repetition trials are coined ‘‘switch costs.’’ These costs are attributed alternatively to the reconfiguration of task sets on alternation trials (De Jong, 2000; Meiran, 1996; Rogers & Monsell, 1995), to the passive decay of the previous task set from working memory (Meiran, Chorev, & Sapir, 2000; Spector & Biederman, 1976), or to proactive interference (Allport et al., 1994; Wylie & Allport, 2000). Studies of the development of the ability to switch flexibly between tasks during childhood and adolescence have revealed a decrease of task switch costs as children grow older (e.g., Cepeda et al., 2001; Chevalier & Blaye, 2009; Cragg & Nation, 2009; Crone, Bunge, van der Molen, & Ridderinkhof, 2006a; Davidson, Amso, Anderson, & Diamond, 2006; Deak, Ray, & Pick, 2004; Ellefson, Shapiro, & Chater, 2006; Gupta, Kar, & Srinivasan, 2009; Reimers & Maylor, 2005; but see Kray, Eber, & Lindenberger, 2004, for disparate results). This decrease is attributed to the improvement of cognitive flexibility, which in turn is attributed to development of its underlying neural substrate in the prefrontal cortex (e.g., Bunge & Wright, 2006; Diamond, 2002; Huizinga, Dolan, & van der Molen, 2006; Kharitonova, Chien, Colunga, & Munakata, 2009; Rougier, Noelle, Braver, Cohen, & O’Reilly, 2005; Zelazo, 2004). The current study set out to assess developmental differences in the ability to switch tasks, but its primary focus was on a related issue—the ability to shift from stopping to going. In adults, responses are delayed and the task switch effect was reduced or even annihilated when the immediately preceding trial required response inhibition (e.g., Gade & Koch, 2005; Hoffmann, Kiesel, & Sebald, 2003; Jamadar, Michie, & Karayanidis, 2010; Kleinsorge & Gajewski, 2004; Rieger & Gauggel, 1999; Rieger, Gauggel, & Burmeister, 2003; Schuch & Koch, 2003; Verbruggen & Logan, 2008). This finding is consistent with the observation that responses are delayed when the previous trial elicited a conflict between competing responses such as in Eriksen Flanker tasks (e.g., Gratton, Coles, & Donchin, 1992), Simon tasks (e.g., Ridderinkhof, Span, & Van der Molen, 2002), and Stroop tasks (e.g., Swick & Jovanovic, 2002). The response slowing on the postconflict trial has generated various interpretations

486

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

ranging from higher order conflict control (e.g., Botvinick, Nystrom, Fissell, Carter, & Cohen, 1999) to lower order priming (e.g., Nieuwenhuis et al., 2006). In contrast to studies of the ability to shift from going to stopping (e.g., Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Bédard et al., 2002; Durston et al., 2002; Klenberg, Korkman, & Lahti Nuuttila, 2001; Van den Wildenberg & Van der Molen, 2004), studies of the ability to shift from stopping to going are all but absent in the developmental literature. Therefore, we examined developmental differences in the ability to shift from stopping to going in the context of the task switching paradigm. That is, on half of the trials the participants were asked to exhibit or inhibit a response on the basis of the shape of the target stimulus, and on the other half of the trials they were asked to do the same on the basis of the color of the target. In the light of the stop signal task literature, we predicted that responses on trials following response inhibition would be slower than responses on trials following response exhibition. In adults, the between-trial adjustments following conflict, errors, and response inhibition have been interpreted in terms of executive control implicating frontal lobe areas of the brain (e.g., Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). Given that these areas are subject to protracted maturation (Amso & Casey, 2006; Casey et al., 2005), we predicted that the postinhibition slowing would decrease with advancing age.

The current study In this study, we had two major aims. First, we revisited the experimental procedures involving the task switching paradigm to determine developmental differences in the ability to switch between tasks. To this end, we employed a combination of the ‘‘alternating runs’’ and ‘‘task cueing’’ paradigms. Participants needed to perform on two simple choice tasks: responding to either stimulus color or stimulus shape. In the alternating runs paradigm, a predictable task change occurs on every nth trial, and the participant is assisted in keeping track of the current task by a cue such as the position of the stimulus on the screen (e.g., Rogers & Monsell, 1995). In the current paradigm, the tasks (i.e., color and shape) changed every 7th trial, and the requirement to switch tasks was indicated by a horizontal line that was continually present in the middle of the screen (see Fig. 1A). More specifically, stimuli presented at one of three positions above the line required a response based on color, and stimuli presented below the line required a response based on shape (or vice versa). Stimuli traveled from the uppermost box to the lowermost box and back. In the task cueing paradigm, each stimulus is preceded or accompanied by a cue. The cue informs the participant which task must be performed on the upcoming or current trial (e.g., Meiran, 1996; Sudevan & Taylor, 1987). In the current study, a task cue preceded the target stimulus by either 150, 600, or 1500 ms. The cue-to-target interval was fixed within trial blocks. The cue-to-target interval was varied to assess developmental change in the time needed for switching between tasks (e.g., Cepeda et al., 2001; Meiran, 1996). Three age groups were studied: 7-year-olds, 11-year-olds, and young adults. We predicted that switch costs would decrease with advancing age and with lengthening of the cue-to-target interval. An interaction between age group and cue-to-target interval would suggest that at least part of the developmental gain is due to an age-related increase in efficiency related to the lengthening of the cue-to-target interval. In other words, we expected that the age-related increase in performance would be positively related to the lengthening of the cue-to-target interval. Our second, and principal, aim was to assess developmental differences in the ability to shift from stopping to going vis-à-vis the developmental trend in task switching. Participants were presented with a hybrid choice/Go–NoGo paradigm in which they were required to shift between stopping (NoGo) and going (choice or Go). In this paradigm, one task (e.g., ‘‘respond to shape’’) required a choice response, and the other task (e.g., ‘‘respond to color’’) required a Go versus NoGo decision. For example, one color (e.g., yellow) required response inhibition (stopping), whereas the other color (e.g., blue) required response execution (going). Note that here the term switching refers to transitions between choice reactions, and the term shifting refers to transitions between stopping and going. A cue preceded the target stimulus to indicate the color (or shape) that signaled response execution (or inhibition) (see Fig. 1B). We expected choice responses on trials, which are preceded by a NoGo trial, to be delayed relative to repeated choice responses (e.g., Hoffmann et al., 2003). The data emerging from

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

487

Fig. 1. (A) Schematic presentation of a trial sequence in the mixed choice task blocks. The figure displays a sequence of the first 8 trials. The leftmost display reflects the first trial, which required shape discrimination. The flanking triangle and the circle served as cues that instructed the participant to respond by pressing the left key (‘‘z’’) when the target was a circle and by pressing the right key (‘‘/’’) when the target was a triangle. Trials 2 and 3 also required shape discrimination. Trial 4 required a switch to color discrimination, where the participant should press the left key for a blue target and the right key for a yellow target. Trials 5, 6, 7, and 8 also required color discrimination, and when trial 10 was reached a switch to shape discrimination was required and so on. (B) Schematic presentation of a trial sequence in the hybrid choice/NoGo task blocks. The figure displays a sequence of the first 8 trials. The task description is similar to the task switching paradigm (see panel A). The three arrays at the bottom of the display were characterized by a Go/NoGo task; that is, a cue indicated whether a response should be executed (Go) or inhibited (NoGo). On these trials, the square was flanked by a cue through which a cross was drawn, indicating that no response should be made for that particular task (i.e., the response to ‘‘blue’’ should be executed, and the response to ‘‘yellow’’ should be withheld).

this hybrid choice/Go–NoGo paradigm allowed us to assess the development in children’s ability in shifting between stopping and going vis-à-vis age-related changes in task switching. Method Participants Three groups of healthy participants took part in this experiment. The first group comprised 16 7year-olds (8 girls and 8 boys) ranging in age from 6.22 to 8.21 years (M = 7.11 years, SD = 0.65, mean Raven quartile = 3.25, SD = 0.93), the second group comprised 16 11-year-olds (6 girls and 10 boys) ranging in age from 10.25 to 12.25 years (M = 11.59, SD = 0.59, mean Raven quartile = 2.62, SD = 0.87), and the third group comprised 15 young adults (4 women and 11 men) ranging in age from 19.15 to 30.17 years (M = 24.08, SD = 3.73, mean Raven quartile = 2.63, SD = 0.52). The gender distribution did not differ between age groups, v2(2) = 1.79, p = .409. To assess intelligence, we administered a nonverbal IQ test, the Standard Progressive Matrices (SPM) (Raven, Court, & Raven, 1985), in 7- and 11-year-olds and the Raven Advanced Progressive Matrices (APM) (Raven, Raven, & Court, 1998) in young adults. IQ test scores were missing in three children and seven young adults. Raw scores were converted to quartile scores following the norms

488

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

for each age group. Mean Raven quartiles did not discriminate between age groups, F(2, 34) = 2.57, p = .092. The children were recruited from regular local schools with the help of their teachers. Permission to participate was ascertained from the children’s primary caretakers. The young adults were recruited from the University of Amsterdam by means of flyers. Informed consent was obtained, and experimental procedures were approved by the ethical committee of the Department of Psychology. The children received a small gift for their participation, and the young adults received course credit. All participants had normal or corrected-to-normal vision.

Apparatus and stimuli Task administration was computerized. The tasks were presented on a Toshiba Satellite 1600 laptop (Intel Celeron 800-MHz processor, 15-inch 60-Hz monitor, 1024  768 pixels). All tasks required only left- and right-hand responses. The response button for the left hand was the ‘‘z’’ key on the computer keyboard, and the response button for the right hand was the ‘‘/’’ key (responses were counterbalanced across participants). Target stimuli were a blue circle, a yellow circle, a blue triangle, and a yellow triangle. The size of the target stimuli covered 12.23° visual angle (horizontally and vertically). Cue stimuli differed in shape or color and consisted of either a pair of squares (one blue and the other yellow) or a pair comprising a red circle and a red triangle. The size of each member of the cue stimulus covered 12.23° visual angle (horizontally and vertically). A black horizontal line was presented continuously through the center of the computer screen against a light gray background. Perpendicular to this line, six boxes covering 16.70° visual angle (horizontally and vertically) were presented, with three boxes above the line and three boxes below the line. The distance between the boxes covered 18.43° visual angle, and the distance between a box and the horizontal line covered 14.04° visual angle. Target stimuli were presented in these boxes, and cue stimuli were presented adjacent to the boxes. The distance between the center of the cue and the center of the target stimulus covered 26.57° visual angle. The first target was always presented in the uppermost box, and the first cue stimulus was always adjacent to this box. The next target was presented in the box just below the uppermost box, and the cue was presented adjacent to it. The lowermost box was occupied twice, and then the target traveled through the boxes to the uppermost box that was then occupied twice. Next, the target traveled to the lowermost box, which was occupied twice, and so on. A schematic of the display and the travel of the stimuli over the successive trials comprising the task is shown in Figs. 1A and B.

Design Participants performed a task switching paradigm and a hybrid choice/Go–NoGo shifting paradigm. We used the task switching paradigm to assess the ability to switch between choice tasks, and we used the hybrid switching/shifting paradigm to assess the ability to shift from stopping to going in addition to switching between choice tasks.

Task switching paradigm This paradigm required participants to respond to the shape (respond-to-shape task) or the color (respond-to-color task) of the target stimulus with either a left- or right-hand button press (i.e., choice responses), depending on the dimension indicated by the cue stimulus. The respond-to-shape task was presented only above the horizontal line, and the respond-to-color task was presented only below the horizontal line (or vice versa). The presentation of the tasks, either above or below the horizontal line, was counterbalanced across participants and kept fixed across the experiment. In addition, the mapping of stimuli onto responses (S–R mapping) was counterbalanced across participants and kept fixed across the experiment. Half of the trials required a right-hand response, and the other half required a left-hand response.

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

489

Respond-to-shape task. A cue consisting of a triangle–circle pair indicated the respond-to-shape task. In this task, cues had the same shape as the target stimulus but had a different color (i.e., red circle and red triangle). Thus, a red circle presented to the left of the box and a red triangle presented to the right indicated that the target stimulus required a left-hand response when it was a circle and required a right-hand response when it was a triangle. Respond-to-color task. A pair of blue and yellow squares cued the respond-to-color task. In this task, cues had the same color as the target stimulus but had a different shape. Thus, a blue square presented to the left of the target box and a yellow square presented to the right indicated that the target stimulus required a left-hand response when it was blue and required a right-hand response when it was yellow. Hybrid choice/Go–NoGo shifting paradigm This paradigm required the execution of either a left- or right-hand response (choice trials) or the execution or inhibition of a response (Go vs. NoGo trials). The choice tasks presented in the hybrid paradigm were identical to those presented in the task switching paradigm (i.e., respond-to-shape and respond-to-color). The trials above the horizontal line required choice reactions, and the trials below the horizontal line required Go–NoGo reactions (or vice versa). Go–NoGo trials were cued by a black ‘‘X’’ placed on the cue stimuli that appeared adjacent to the target box. Thus, crossing the yellow square right to the target box, but not the blue square appearing to the left, indicated that the right-hand response should be inhibited (NoGo trial) if the target was yellow and that the left-hand response should be executed (Go trial) if the target was blue. A quarter (25%) of the trials were NoGo trials. The S–R mapping was counterbalanced across participants and kept fixed across the experiment. Half of the trials required a right-hand response, and the other half required a left-hand response. For a single participant, only one task (e.g., respond-to-shape) required a Go–NoGo decision, and this was kept constant for each participant. Thus, this task allowed shifting from a NoGo trial to a choice trial (i.e., from a NoGo trial below the horizontal line to a choice trial above the horizontal line [or vice versa if the Go–NoGo trials were presented above the horizontal line]) and allowed shifting from a NoGo trial to a Go trial (i.e., from a NoGo trial below the horizontal line to a Go trial also below the horizontal line [or vice versa if the Go–NoGo trials were presented above the horizontal line]). The travel of stimuli was identical to the task switching paradigm. A schematic of the display and the travel of the stimuli over the successive trials comprising the hybrid task is shown in Fig. 1B. Procedure Each participant performed two ‘‘pure task’’ blocks: a series of six blocks from the task switching paradigm and a series of six blocks from the hybrid switching/shifting paradigm. First, the participant was presented with two pure task blocks. One pure block consisted of 60 trials requiring only choice responses to the respond-to-shape task, and the other pure block consisted of 60 trials requiring only choice responses to the respond-to-color task (counterbalanced across participants). The respond-toshape task was presented only above the horizontal line, and the respond-to-color task was presented only below the horizontal line (or vice versa) (counterbalanced across participants). The cue-to-target interval in the pure task blocks was fixed at 600 ms. Before each pure block, the participant received a practice block consisting of 60 trials with the cue-to-target interval kept constant at 600 ms. The pure task blocks served to familiarize the participant with the respond-to-shape task and the respond-tocolor task. Next, the participant completed a series of the task switching blocks and a series of the hybrid switching/shifting task blocks. The order of the task switching series and the hybrid switching/shifting task series was randomized. A series of task switching blocks consisted of six blocks comprising 80 trials, and a series of hybrid switching/shifting task blocks consisted of six blocks comprising 100 trials. Within each series of blocks, the cue-to-target interval was 150, 600, or 1500 ms and was varied between blocks of trials (i.e., the participant received two blocks with a 150-ms cue-to-target interval, two blocks with a 600-ms cue-to-target interval, and two blocks with a 1500-ms cue-to-target interval in random order). Before a series of the task switching blocks and a series of the hybrid switching/

490

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

shifting task blocks, the participant received a practice block consisting of 60 trials with the cue-totarget interval kept constant at 600 ms. A short rest of 2 min separated test blocks, and a longer rest of 5 min followed the third test block. A rest of 15 min separated the task switching blocks and hybrid task blocks. There was a fixed delay of 1000 ms between the start of a trial block and the first cue. The response-to-cue interval was varied pseudo-randomly between 900 and 1100 ms in steps of 10 ms. Instructions First, the travel of the stimuli through the boxes (from top to bottom and back) was explained to the participants. Second, participants were told that a cue appears before the appearance of the target stimulus. Third, the two-dimensionality of a stimulus was explained (i.e., ‘‘The stimulus is either a triangle or a circle and is either yellow or blue’’). Before the pure blocks, the S–R mappings were introduced by explaining the use of the cue: respond-to-shape instruction—‘‘If the cue consists of a circle on the left side of the box and a triangle on the right side of the box, you respond to the shape of the stimulus and press left if the stimulus is a circle or right if the stimulus is a triangle’’; respond-to-color instruction—‘‘If the cue consists of a blue square on the left side of the box and a yellow square on the right side of the box, you press left if the stimulus is blue or right if the stimulus is yellow.’’ The S–R mappings were introduced separately for each pure block. Before the task switching blocks, participants were instructed that respond-to-shape and respondto-color tasks would alternate, with the respective tasks blocked above and below the horizontal line: ‘‘Now the tasks will alternate, and the cue will tell you which task to perform. As before, if the cue consists of a triangle and a square, you decide on the shape of the stimulus; if the cue consists of a yellow and a blue square, you decide on the color of the stimulus. Color [shape] tasks will only appear above the horizontal line, and shape [color] tasks will only appear below the line.’’ Before the hybrid choice/Go–NoGo shifting task blocks, participants received a similar instruction as before the task switching blocks with the addition of NoGo trials: ‘‘A cross is drawn through one half of one of the cues. For example, if you see a cross drawn through the yellow part of the cue, you should not press a button if the stimulus is yellow. But if you see a cross drawn through the blue part of the stimulus, you should not press a button when the stimulus is blue.’’ Participants were instructed to balance speed and accuracy. Care was taken to ensure that the participants understood the instructions, as indexed by verbal report, response accuracy, and stable reaction times (RTs). The tasks were administered individually in a dimly lit and quiet room. The duration of the total test session was approximately 2 h. Because we feared that a 2-h session was quite long for the children, we planned the first hour of the children’s session before a 20-min break in the morning and the second half of the session after the break. This 20-min break is standard at primary schools in The Netherlands. During this break, the children usually play outside and have a snack and drink. Coding For the pure choice task blocks, we compared performance on the respond-to-color task with performance on the respond-to-shape task (i.e., the rule effect). For the mixed choice task blocks, we focused on two different trial sequences (i.e., the trial effect) and two different response sequences (i.e., the response effect). To examine the trial effect, we compared choice reactions that could be preceded by a trial requiring the same choice task (i.e., choice–choice repeat trials) and choice reactions requiring a switch to the alternative choice task (i.e., choice–choice switch trials). The difference between choice–choice repeats and choice–choice switches provides an estimate of the costs involved in switching between choice tasks. For the response effect, we compared performance on trials where the current response was similar to the previous response (response repetition) and performance on trials where the current response was different from the previous response (response alternation). Previous studies indicated that task switching effects are sensitive to response effects; that is, the task switching effect is greatly reduced when responses alternate compared with when responses repeat (e.g., Crone et al., 2006a; Gupta et al., 2009).

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

491

For the hybrid choice/Go–NoGo task blocks, we focused on two types of trial effects: one related to choice trials and the other related to Go trials. The trial effect related to choice trials involved the comparison of choice–choice repeats versus NoGo-to-choice shifts. The trial effect related to Go trials involved the comparison of Go–Go repeats versus NoGo-to-Go shifts. For the trial effect related to Go trials, the response effect could not be analyzed because Go–Go repeats always involve response repetitions, whereas NoGo-to-Go shifts always involve response alternations. Results We excluded from the RT analyses all incorrect responses, all responses that were preceded by an incorrect response, all responses with RTs shorter than 120 ms, and all responses with a latency exceeding the mean by more than 2.5 standard deviations (we established this for each age group and task separately). This amounted to less than 1% of all RTs. We considered the first five responses as warm-up trials and excluded these from the analyses. Prior to all analyses, RTs were transformed by taking the natural logarithm. This was done to minimize the influence of outliers (Ratcliff, 1993) and to reduce the influence of differences between age groups in baseline performance (Meiran, 1996). In this vein, switch costs are expressed in terms of ratio scores instead of absolute differences. Hence, an interaction between age group and an experimental variable will indicate a disproportional difference in RT between age groups in one condition relative to another condition. The results are reported in terms of the antilogs of the mean nlogRTs (i.e., the geometric means). In addition, we applied a square root transformation of the accuracy to improve normality of this variable. The results are reported as the back transformation of the square root of accuracy. We performed three sets of repeated measures analyses of variance (ANOVAs). The variables of interest were response accuracy (square root of the percentage correct) and response latency (nlog RT [ms]). The first set of ANOVAs focused on the pure blocks and served to examine age-related change in performing the respond-to-shape and respond-to-color tasks separately. The second set focused on the choice–choice switch blocks and hybrid choice–Go/NoGo shifting blocks and served to examine age-related change in switching and shifting. The third set examined developmental differences in switching between choice reactions (switch task) versus shifting from NoGo to choice reactions (hybrid shift task). For all ANOVAs, age group (7-year-olds vs. 11-year-olds vs. young adults) was included as a between-participants variable. Greenhouse–Geisser correction was applied when sphericity was violated. Pure choice task blocks In the first set of analyses, we compared performance on the respond-to-shape block with performance on the respond-to-color block (the rule effect). The ANOVAs revealed a main effect of age group on both response accuracy and latency. These effects reflected an increase of response accuracy with advancing age (93% [SE = 1.2] in 7-year-olds, 97% [SE = 1.3] in 11-year-olds, and 96% [SE = 1.3] in young adults), F(2, 44) = 3.43, p = .041, g2p = .135. In addition, RTs were shorter in 11-year-olds and young adults compared with 7-year-olds (770 ms [SE = 25] in 7-year-olds, 509 ms [SE = 16] in 11-year-olds, and 361 ms [SE = 12] in young adults), F(2, 44) = 136.35, p < .0001, g2p = .861. Main or interaction effects involving rule were absent, indicating that performances on the respond-to-shape and respond-to-color tasks were comparable and did not differ between age groups. Therefore, the factor rule was not included in the remaining analyses of choice blocks and hybrid choice–Go/NoGo shifting blocks. Mixed choice task blocks In the second set of analyses, we examined developmental differences in the ability to switch between choice tasks. Therefore, we focused on the trial effect, that is, the difference between choice–choice repeats and choice–choice switches. In addition, we examined the response effect by

492

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

comparing performance on trials where the current response was similar to the previous response (response repetition) and performance on trials where the current response was different from the previous response (response alternation). Furthermore, we included cue-to-target interval (150 vs. 600 vs. 1500 ms) as a within-participants factor. We performed Trial (2)  Response (2)  Cue-to-Target Interval (3)  Age Group (3) repeated measures ANOVAs on response accuracy and latencies. See Table 1 for an overview of accuracy and response latencies as a function of age group, trial, response, and cue-to-target interval. Response accuracy The ANOVA revealed a main effect of trial, reflecting switch costs, as indicated by a smaller proportion of errors on choice–choice repeats as compared with choice–choice switches (93 vs. 94%), F(1, 44) = 13.26, p = .001, g2p = .232. The ANOVA also revealed a main effect of cue-to-target interval, reflecting more accurate responding with longer time to prepare, as indicated by increased accuracy on longer cue-to-target intervals compared with shorter cue-to-target intervals (95% on the 1500-ms cue-to-target interval, 93% on the 600-ms cue-to-target interval, and 92% on the 150-ms cue-to-target

Table 1 Median response latency (geometric means of nlog RTs) and response accuracy (percentages of correct responses) on the mixed choice task per age group as a function of cue-to-target interval, trial (choice–choice repeats vs. choice–choice switches), and response (repetition vs. alternation). Age group

Cue-to-target interval (ms)

Trial

Response

RT (and SE) (ms)

Percentage correct (and SE)

7-year-olds

150

Choice–choice repeat

Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation

794 850 967 981 693 737 828 777 797 812 857 847

(34) (37) (50) (52) (30) (31) (44) (43) (33) (35) (44) (40)

91 90 90 89 93 91 89 92 94 93 93 92

(2.1) (1.7) (2.2) (2.6) (1.6) (1.8) (3.0) (3.0) (1.4) (1.8) (1.7) (2.6)

Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation

542 560 615 648 503 508 546 546 577 577 603 586

(23) (24) (32) (35) (21) (21) (29) (30) (24) (25) (31) (28)

93 93 92 87 93 94 94 93 97 96 97 93

(2.1) (1.8) (2.2) (2.6) (1.6) (1.8) (3.0) (3.0) (1.4) (1.8) (1.7) (2.6)

Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation Repetition Alternation

369 399 428 417 364 370 383 389 390 401 414 412

(16) (18) (23) (23) (16) (16) (21) (22) (17) (18) (22) (20)

97 95 89 96 95 96 92 93 97 96 97 93

(2.2) (1.9) (2.3) (2.8) (1.7) (1.9) (3.1) (3.1) (1.5) (1.9) (1.8) (2.7)

Choice–choice switch 600

Choice–choice repeat Choice–choice switch

1500

Choice–choice repeat Choice–choice switch

11-year-olds

150

Choice–choice repeat Choice–choice switch

600

Choice–choice repeat Choice–choice switch

1500

Choice–choice repeat Choice–choice switch

Young adults

150

Choice–choice repeat Choice–choice switch

600

Choice–choice repeat Choice–choice switch

1500

Choice–choice repeat Choice–choice switch

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

493

interval), F(2, 88) = 8.37, p < .0001, g2p = .160. Additional main effects were absent. There was one significant interaction involving age group, trial, response, and cue-to-target interval, F(4, 88) = 2.72, p = .034, g2p = .110. In general, response accuracy was highest in young adults given the 1500-ms cue-to-target interval on choice–choice repeat trials and on response repetition trials.

Response latency The ANOVA yielded a significant main effect of age group, indicating shorter RTs with advancing age (825 ms in 7-year-olds, 566 ms in 11-year-olds, and 394 ms in young adults), F(2, 44) = 75.55, p < .0001, g2p = .774. There was also a main effect of trial, reflecting switch costs, as indicated by shorter RTs on choice–choice repeats compared with choice–choice switches (545 vs. 594 ms), F(1, 44) = 117.18, p < .0001, g2p = .727. In addition, there was a main effect of cue-to-target interval, indicating a decrease in RT from 597 to 531 ms when the cue-to-target interval was prolonged from 150 to 600 ms and an increase to 581 ms when the cue-to-target interval was lengthened to 1500 ms, F(1.75, 88) = 36.69, p < .0001, g2p = .455. The main effect of response was not significant. A Trial  Cue-to-Target Interval interaction indicated that switch costs decreased with the lengthening of the cue-to-target interval (95, 46, and 25 ms for the 150-, 600-, and 1500-ms cue-to-target intervals, respectively), F(1.84, 88) = 16.20, p < .0001, g2p = .269. As anticipated, switch costs were larger on response repetition trials (60 ms) than on response alternation trials (38 ms), F(1, 44) = 11.03, p = .002, g2p = .200. As expected, age groups differed with respect to the effect of trial, as indicated by a decrease of switch costs with age (95 ms in 7-year-olds, 46 ms in 11-year-olds, and 25 ms in young adults), F(2, 44) = 3.57, p = .037, g2p = .140 (see left panel of Fig. 2). Follow-up analyses showed that switch costs in 7-year-olds differed from 11-year-olds (p = .027), who did not differ from young adults (p = .999). Further interaction effects were absent. In a broad outline, the pattern of results that emerged from the mixed choice blocks is consistent with the literature on task switching in adults (e.g., De Jong, 2000; Meiran, 1996; Rogers & Monsell, 1995) and with the results of previous developmental studies that addressed task switching in participants in the current age range (e.g., Cepeda et al., 2001; Chevalier & Blaye, 2009; Deak et al., 2004; Ellefson et al., 2006; Gupta et al., 2009; Reimers & Maylor, 2005). The results showed disproportional switch costs for young children. In addition, the results indicated that switch costs were larger for response repetitions compared with alternations. It should be noted, however, that the current analysis did not show the Age Group  Trial  Response interaction reported by Crone and colleagues (2006a) or the Age Group  Trial  Cue-to-Target Interval interaction reported by Cepeda and colleagues (2001).

Fig. 2. (Left panel) Switch costs on the mixed choice task. Middle panel: shift costs on the choice/Go–NoGo task (NoGo-to-Go shifts). (Right panel) Shift costs on the choice/Go–NoGo task (NoGo-to-choice shifts). Switch and shift costs are expressed in terms of difference scores of the geometric means of nlog RTs.

494

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

Hybrid choice/Go–NoGo task blocks The main goal of this study was to examine the developmental pattern in the ability to shift from stopping (i.e., successfully inhibited NoGo trials) to going. Therefore, we focused on the trial effect and the response effect for two different trial sequences. That is, we focused on (a) the difference between Go–Go repeats and NoGo-to-Go shifts and on (b) the difference between choice–choice repeats and NoGo-to-choice shifts. In one set of ANOVAs, we compared Go–Go repeats and NoGo-to-Go shifts. Obviously, response repetitions could not be observed on Go trials because all NoGo trials were mapped to one hand. Thus, the design of this repeated measures ANOVA on both response accuracy and latency consisted of trial (2) and cue-to-target interval (3) as within-participants factors and age group (3) as a between-participants factor. Another set of ANOVAs compared choice–choice repeats and NoGo-to-choice shifts. Because all participants committed a considerable amount of errors on the NoGo trials (i.e., failed inhibits), the number of response repetition and response alternation trials after a successfully inhibited NoGo trial was too small to perform ANOVA. Therefore, we collapsed the data over cue-to-target interval. The design of the repeated measures ANOVA on both response accuracy and latency consisted of trial (2) and response (2) as within-participants factors and age group (3) as a between-participants factor. See Tables 2 and 3 for overviews of accuracy and response latencies obtained from the two sets of analyses. Response accuracy Errors of commission on NoGo trials. First, we analyzed the proportion of commission errors (i.e., failed inhibits) on NoGo trials. The ANOVA included as factors trial (NoGo–NoGo repeats vs. choice-to-NoGo shifts vs. Go-to-NoGo shifts), cue-to-target interval (3), and age group (3). The results showed that the ability to refrain from responding increased with advancing age. The proportions of failed inhibits were 19% in 7-year-olds, 6% in 11-year-olds, and 7% in young adults, F(2, 44) = 5.51, p = .007, g2p = .200. All other main effects or interactions were not significant. Go–Go repeats versus NoGo-to-Go shifts. The ANOVA revealed a significant main effect of age group, indicating an increase of accuracy with advancing age (95% in 7-year-olds, 98% in 11-year-olds, and 99% in young adults), F(2, 44) = 5.74, p = .006, g2p = .207. The main effect of trial just failed to reach an acceptable significance level, reflecting a slightly higher accuracy on NoGo-to-Go shifts compared with Go–Go repeats (96 vs. 97%), F(1, 44) = 3.23, p = .079, g2p = .068. Choice–choice repeats versus NoGo-to-choice shifts. The ANOVA revealed a significant main effect of age group, indicating an increase of accuracy with age (93% in 7-year-olds, 98% in 11-year-olds, and 99% in young adults), F(2, 42) = 11.21, p < .0001, g2p = .348. Additional main or interaction effects were absent. Table 2 Mean response latency (geometric means of nlog RTs) and response accuracy (percentages of correct responses) on the hybrid choice/Go–NoGo task per age group as a function of trial (choice–choice repeats vs. NoGo–choice shifts) and response. Age group

Trial

Response

RT (and SE) (ms)

Percentage correct (and SE)

7-year-olds

Choice–choice repeat

Repetition Alternation Repetition Alternation

785 807 950 953

(29) (32) (58) (60)

95 92 94 93

(1.0) (1.1) (1.7) (1.8)

Repetition Alternation Repetition Alternation

577 582 704 684

(21) (23) (43) (43)

99 99 97 98

(1.7) (1.9) (1.1) (1.2)

Repetition Alternation Repetition Alternation

401 408 459 466

(15) (16) (29) (30)

95 92 94 93

(1.0) (1.1) (1.7) (1.8)

NoGo–choice shift 11-year-olds

Choice–choice repeat NoGo–choice shift

Young adults

Choice–choice repeat NoGo–choice shift

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

495

Table 3 Mean response latency (geometric means of nlog RTs) and response accuracy (percentages of correct responses) on the hybrid choice/Go–NoGo task per age group as a function of cue-to-target interval and trial (Go–Go repeats vs. NoGo–Go shifts). Age group

Cue-to-target interval (ms)

Trial

RT (and SE) (ms)

Percentage correct (and SE)

7-year-olds

150

Go–Go repeat NoGo–Go shift Go–Go repeat NoGo–Go shift Go–Go repeat NoGo–Go shift

744 801 716 776 768 798

(27) (33) (27) (31) (30) (33)

96 92 96 95 96 94

(0.9) (1.5) (1.1) (0.8) (1.1) (1.2)

Go–Go repeat NoGo–Go shift Go–Go repeat NoGo–Go shift Go–Go repeat NoGo–Go shift

538 574 522 543 560 582

(20) (24) (20) (22) (22) (24)

96 98 97 98 98 98

(0.9) (1.6) (1.1) (0.8) (1.1) (1.2)

Go–Go repeat NoGo–Go shift Go–Go repeat NoGo–Go shift Go–Go repeat NoGo–Go shift

372 409 354 381 385 406

(14) (17) (14) (16) (16) (17)

99 98 98 98 99 99

(0.9) (1.6) (1.2) (0.8) (1.1) (1.2)

600 1500 11-year-olds

150 600 1500

Young adults

150 600 1500

Response latency Go–Go repeats versus NoGo-to-Go shifts. The ANOVA revealed a significant main effect of age group, indicating longer RTs among 7- and 11-year-olds compared with young adults (767 ms in 7-year-olds, 553 ms in 11-year-olds, and 384 ms in young adults), F(2, 44) = 92.75, p < .0001, g2p = .808. There was a main effect of trial, reflecting shift costs, as indicated by faster responses on Go–Go repeats compared with NoGo-to-Go shifts (530 vs. 563 ms), F(1, 44) = 48.33, p < .0001, g2p = .523. In addition, there was a main effect of cue-to-target interval, as indicated by a decrease in response speed (551 to 526 ms) when the cue-to-target interval was lengthened from 150 to 600 ms and an increase to 561 ms with a lengthening of the cue-to-target interval to 1500 ms, F(1.68, 44) = 9.98, p < .0001, g2p = .185. A Trial  Cue-to-Target Interval interaction reflected a decrease in shift costs when the cue-to-target interval was lengthened (43, 34, and 24 ms on the 150-, 600-, and 1500-ms cue-to-target intervals, respectively), F(1.93, 88) = 4.88, p = .010, g2p = .100. The NoGo-to-Go shift costs for each age group are plotted in the middle panel of Fig. 2. This figure reveals that shift costs seem greater in the youngest children compared with the other two groups. However, the apparent developmental trend was far from significant, as indicated by the current analysis on the nlog transformed data (ps > .49) and a post hoc analysis on median RTs (ps > .55).

Choice–choice repeats versus NoGo-to-choice shifts. The ANOVA revealed a significant main effect of age group, reflecting longer RTs among 7- and 11-year-olds compared with young adults (870 ms in 7year-olds, 634 ms in 11-year-olds, and 433 ms in young adults), F(2, 44) = 61.67, p < .0001, g2p = .737. There was a main effect of trial, reflecting shift costs, as indicated by shorter RTs on choice–choice repeats compared with NoGo-to-choice shifts (571 vs. 673 ms), F(1, 44) = 66.77, p < .0001, g2p = .603. Importantly, this effect did not differ between the age groups given that we failed to observe an Age Group  Trial interaction (F < 1). In addition, similar to the choice–choice switch block, we failed to observe a main effect of response, and this effect did not differ between age groups (Fs < 1). The NoGo-to-choice shift costs for each age group are plotted in the right panel of Fig. 2. It can be seen that shift costs are considerable and differ significantly between age groups, as confirmed by a post hoc analysis on median RTs, F(2, 40) = 3.96, p = .027, g2p = .165. Follow-up analyses indicated that shift costs did not differ between 7-year-olds and 11-year-olds (p = .490) or between 11-year-olds and young adults (p = .470); however, 7-year-olds did differ from young adults (p < .0001).

496

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

In sum, the results that emerged from the hybrid choice/Go–GoNo shifting task showed that the need to shift from NoGo to choice reactions is associated with a pronounced delay relative to choice–choice repeats. The costs associated with the need to shift from a NoGo trial to a Go trial were considerably lower. Importantly, both NoGo-to-Go and (in particular) NoGo-to-choice shift costs showed considerable differences between age groups, as indicated by post hoc analyses on median RTs. The differences between age groups were absent following the nlog transformation of the data, indicating that the apparent differences between age groups were not disproportional. Finally, an additional ANOVA on NoGo-to-choice shifting, including cue-to-target interval (but not response), indicated that shifting costs decreased when the cue-to-target interval was lengthened, F(1.97, 80) = 3.36, p = .040, g2p = .078. Thus, both NoGo-to-Go and NoGo-to-choice shifting were influenced by the cue-to-target interval. On all three cue-to-target intervals, shifting costs were considerably larger on NoGo-to-choice shifts compared with NoGo-to-Go shifts (135 vs. 43 ms, 115 vs. 34 ms, and 78 vs. 24 ms on the 150-, 600-, and 1500-ms cue-to-target intervals, respectively). Differences between switching and shifting A final set of ANOVAs was performed to assess developmental differences in switching between choice reactions (mixed choice switch task) and shifting from NoGo-to-Choice reactions (hybrid choice/Go–NoGo shifting task). The ANOVAs consisted of Task (switch vs. shift), Trial (repeat vs. nonrepeat), and Response (repetition vs. alternation) as within-participants factors and age group (3) as a between-participants factor. The ANOVAs on response accuracy and latency yielded robust task differences for task, reflecting somewhat lower accuracy but faster responses on the choice trials in the mixed choice task compared with choice trials in the hybrid task (accuracy: 93% [SE = 1.0] vs. 96% [SE = 0.5]; latency: 569 ms [SE = 14] vs. 620 ms [SE = 16]), F(1, 44) = 12.55, p = .001, g2p = .226, and F(1, 44) = 34.88, p < .0001, g2p = .232, respectively. The ANOVA on accuracy yielded a significant Trial  Task interaction, F(1, 44) = 8.31, p = .006, g2p = .159. For the switching task, we observed a higher accuracy on task repeat trials compared with task switch trials (94% [SE = 0.9] vs. 92% [SE = 1.1]). We observed the opposite result for the shifting task (96% [SE = 0.6] vs. 97% [SE = 0.8]). This interaction did not interact with age group or response. The ANOVA on response latency also yielded a Trial  Task interaction, F(1, 44) = 14.58, p < .0001, g2p = .249. This interaction reflected both switching and shifting costs, as indicated by shorter RTs on task repeat trials (545 ms [SE = 12] for the switching task and 571 ms [SE = 12] for the shifting task) compared with task switching/shifting trials (594 ms [SE = 16] for the switching task and 673 ms [SE = 22] for the shifting task). Note that switching costs were much smaller compared with shifting costs (102 vs. 49 ms). This effect did not interact with age group or response. In sum, the comparison between choice–choice switches (switch task) and NoGo–choice shifts (hybrid task) revealed that the delay in responding was much larger for shifts than for switches. Discussion In this study, we set out to assess developmental trajectories in the ability to switch between tasks and to shift from stopping to going. Three different age groups (7-year-olds, 11-year-olds, and young adults) performed both a choice–choice switching task and a hybrid choice–Go/NoGo shifting task in which participants were cued to consider the color or shape of a target stimulus. In the choice–choice switching task, participants needed to execute a choice response; on some trials they could use the same rule, but on other trials they needed to switch between rules. In the hybrid choice–Go/NoGo shifting task, participants were required to make a choice reaction on some trials, but on other trials they were required to determine whether they needed to respond (Go trials) or inhibit (NoGo trials). The choice–choice switching task allowed us to assess developmental differences in the efficiency of switching between choice tasks. The hybrid choice–Go/NoGo shifting task allowed us to assess developmental differences in the efficiency of shifting from stopping to going. First, we discuss the results obtained from the choice–choice switching task to set the stage for our discussion of the results obtained with the hybrid choice–Go/NoGo shifting task.

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

497

Switching between choice tasks Consistent with the task switching literature, we found that choice responses were reliably slower on switch trials compared with the responses on repeat trials (e.g., Allport et al., 1994; Meiran, 1996; Rogers & Monsell, 1995). In addition, we observed that switch costs were reduced by an increase in the time available to prepare for a task change prior to target onset (Meiran, 1996; Rogers & Monsell, 1995). Moreover, we found the switch costs to be substantially larger for response repetitions compared with response alternations (e.g., Hübner & Druey, 2006; Kleinsorge, 1999; Mayr & Kliegl, 2003; Meiran et al., 2000; Schuch & Koch, 2003). Most important, we replicated previous findings showing that the ability in switching between tasks improves as children mature (e.g., Cepeda et al., 2001; Chevalier & Blaye, 2009; Crone et al., 2006a; Davidson et al., 2006; Deak et al., 2004; Ellefson et al., 2006; Gupta et al., 2009; Reimers & Maylor, 2005). The current findings add to this literature in that they show that the switch costs in the youngest children are disproportionately larger compared with the older children and young adults (see also Cragg & Nation, 2009, Experiment 1). Our results are only partly consistent with the results reported in Crone and colleagues (2006a; see also Gupta et al., 2009). Our results agree with Crone and colleagues’ findings in showing that switch costs are larger for response repetitions than for alternations. But our results disagree in that Crone and colleagues observed that the response effect (repetition vs. alternation) on switch costs decreased with advancing age. Here we observed no such developmental trend. Crone and colleagues interpreted the larger sensitivity of young children to response repetitions, when switching between tasks, in terms of carryover effects from the previously activated S–R link. These carryover effects interfere with young children’s ability to switch to currently required actions. More specifically, this account assumes that young children experience a greater beneficial effect on choice–choice repeats, including response repetition due to automatic carryover effects. It is not clear why the higher order interaction of the effects of Age Group  Trial (repeat vs. switch)  Response (repetition vs. alternation) was not obtained in the current study using comparable age groups. One obvious difference between studies relates to the size of the task switch effect. This effect was considerably larger in Crone and colleagues’ report than it was in the current study. Crone and colleagues reported a developmental decrease in switch costs from 237 to 129 to 72 ms as compared with 95 to 46 to 25 ms in the current study. Thus, in the current study, there might have been too little room for the higher-order interaction to show up. Alternatively, it should be noted that Crone and colleagues performed their analyses on mean RT, whereas in the current study RTs were nlog transformed to acknowledge baseline differences in the speed of performance across age groups (e.g., Kray et al., 2004). Indeed, a reanalysis of the current data focusing on median RTs rather than nlog transformed data yielded a four-way interaction consisting of age group, trial (repeat vs. switch), response (repetition vs. alternation), and cue-to-target interval.1 This pattern was basically similar to the one reported by Crone and colleagues in that it showed that switch costs are higher for response repetitions, in particular for the youngest children when the cueto-target interval was smallest. Accordingly, the current data replicate the sensitivity of young children to carryover effects from previous S–R links, but the current data demonstrate that this sensitivity is developmentally proportional. Finally, our results seem to deviate from the findings reported previously by Cepeda and colleagues (2001), who showed that children needed more time to prepare for switching task rules compared with young adult participants. The current analysis, however, failed to reveal a higher order interaction of the effects of trial, age group, and cue-to-target interval. Thus, the current findings suggest that the time needed to prepare does not differ between children and young adults. This apparent discrepancy can be resolved by a reanalysis of the current data using median RTs rather than the nlog transformed data. This analysis did show a higher order interaction consisting of age group, trial,

1 The ANOVA performed on median RTs yielded a significant interaction consisting of age group, trial (repeat vs. switch), response (repetition vs. alternation), and cue-to-target interval, F(4, 88) = 2.77, p = .032, g2p = .112. A decomposition of this complex interaction showed that on both response repetition and response alternation trials, there were significant interactions of age group and trial, indicating a decrease of switch costs when children grow older, F(2, 44) = 13.00, p < .0001, g2p = .372, and F(2, 44) = 7.16, p = .002, g2p = .246, respectively. Basically, the pattern of results showed that switch costs were higher for response repetitions than for response alternations, especially for the youngest children.

498

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

and cue-to-target interval. More specifically, the outcome of this analysis revealed that young children do need more time to prepare for a task switch relative to young adults.2 Thus, the results of the combined analyses indicate that children need more time to prepare for a task switch (or, alternatively, the decay of the previous task set takes longer in children relative to young adults) but that children do not need disproportionately more time than adults when preparing for a task switch. Shifting from stopping to going The hybrid choice–Go/NoGo shifting task was similar to the choice–choice switching task with the exception that the shifting task included Go–NoGo trials. In both tasks, participants needed to consider shape (or color) for the selection of choice reactions above the horizontal line (see Fig. 1). The major difference between the tasks is that in the choice–choice switching task shape (or color) needed to be used to select a choice reaction below the horizontal line, whereas in the hybrid choice–Go/NoGo shifting task shape (or color) needed to be used to decide whether a response needed to be executed (Go trials) or inhibited (NoGo trials). The analysis of the data from the hybrid choice–Go/NoGo shifting task focused on three trial transitions: (a) from responding to NoGo, (b) from NoGo to Go versus Go to Go, and (c) from NoGo to choice versus choice to choice. The analysis of transitions from responding to NoGo focused on the proportion of commission errors (i.e., failed inhibits). Overall, young children failed to inhibit more frequently compared with older children and young adults, (19, 6, and 7%, respectively). The developmental trend in the ability to inhibit to NoGo signals is similar to results reported previously using Go–NoGo tasks in which probabilities were biased toward Go trials (e.g., Durston et al., 2002). The developmental decrease in the proportion of failed inhibits is consistent with the literature of age-related changes in the ability to inhibit using a variety of paradigms (for reviews, see Dempster, 1992; Van der Molen, 2000; see also Huizinga et al., 2006). The results obtained with the hybrid choice–Go/NoGo shifting task showed that responses on Go trials were approximately 30 ms delayed when trials followed a NoGo trial relative to a Go trial. This observation is compatible with the stop signal task literature, which shows that choice reactions are delayed on trials following a trial where participants successfully inhibited their reaction to the choice stimulus when a stop signal required them to refrain from responding (e.g., Rieger & Gauggel, 1999; Verbruggen, Logan, Liefhooghe, & Vandierendonck, 2008; but see Verbruggen, Liefooghe, Szmalec, & Vandierendonck, 2005). Some authors interpret the delay in responding following inhibited responses in terms of negative priming a phenomenon that occurs when a stimulus, which should be ignored or not responded to on the previous trial, is presented again on the current trial (e.g., Hoffmann et al., 2003; Rieger & Gauggel, 1999). This interpretation is less appropriate in the current data in that both the target stimulus and the response changed from Go to NoGo trials. Rieger and Gauggel (1999) offered an alternative account of the delay in responding following inhibits. They postulated that between-trial adjustments are made following response inhibition, which involves setting the response threshold higher on the subsequent trial. This then results in a slower response. This idea resembles the conflict monitoring theory proposed by Botvinick and colleagues (1999), which stipulates that an evaluation device (the anterior cingulate cortex) detects a conflict (e.g., the conflict between planning a response and the need to inhibit its execution) and signals for an adjustment in cognitive control via a regulative device (the dorsolateral prefrontal cortex) (see also Miller & Cohen, 2001). Given the protracted development of the prefrontal cortex, one would predict that the between-trial adjustments are sensitive to developmental change. However, the current NoGo-to-Go data seem to suggest that they are not. We return to this apparent discrepancy below. 2 The higher order interaction consisting of age group, trial, and cue-to-target interval showed a decrease of switch costs when the cue-to-target interval was lengthened, and this effect was most pronounced for 7-year-olds, F(4, 80) = 2.47, p = .052, g2p = .110. Follow-up analyses indicated that the age-related changes in the trial effect were not significant for the 600- and 1500-ms cue-totarget intervals. For the shortest cue-to-target interval, the trial effect was significantly different between 7- and 11-year-olds (p = .011), whereas 11-year-olds and young adults did not differ (p = 1.000).

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

499

The transition of greatest interest concerns the shift from NoGo to choice. We examined this shift relative to choice–choice trial sequences. The results showed a pronounced delay when participants shifted from NoGo to choice (122 ms), which is considerably larger than the current switch costs (49 ms). Which factors contribute to the sizable delay in responding when participants need to shift from a NoGo trial to a choice trial? First, NoGo-to-choice transitions involve a switch from considering the color (or shape) of the target to responding to its shape (or color). In this respect, NoGo-to-choice transitions are similar to choice–choice transitions; thus, part of the delay may result from the need to inhibit the previous color (or shape) rule and to implement the current shape (or color) rule. It should be noted, however, that this possibility is unlikely given recurrent observations that switch costs on the current trial are reduced considerably (or even disappear) when the response on the previous trial is countermanded by a stop signal (Astle, Jackson, & Swainson, 2006; Jamadar et al., 2010; Philipp, Jolicoeur, Falkenstein, & Koch, 2007; Verbruggen, Liefooghe, & Vandierendonck, 2006). Second, NoGo-to-choice transitions involve a shift from a Go–NoGo task to a choice task. It is well known that responding is faster on Go–NoGo tasks compared with choice tasks (e.g., Luce, 1986; Woodworth & Schlosberg, 1954). The current data show that the average speed of responding on the Go–NoGo task was 530 ms compared with 545 ms on the choice task (choice–choice repeats). Thus, shifting from the Go–NoGo task to the choice task involves adjusting speed to maintain accuracy. Several computational models of the speed–accuracy tradeoff postulate that, with the onset of the stimulus, evidence accumulates over time from some baseline until it hits a dynamically adjustable response threshold (e.g., Laming, 1968; Ratcliff, 1978). Within this context, raising the response threshold will result in more stimulus evidence, and thus more accurate responses, and slower responding. Accordingly, part of the delay in responding on choice trials following a NoGo trial could be due to a transient increase in the response threshold. Third, the between-trial adjustment following the conflict between responding and going, discussed above for NoGo-to-Go transitions, is likely to be involved in transitions from NoGo to choice trials. At this point, we hypothesize that the pronounced delay in responding on choice trials following a NoGo trial arises from between-trial control adjustments involving a higher setting of the response threshold. For both NoGo-to-Go and NoGo-to-choice transitions, the results indicate that shift costs decreased with a lengthening of the cue-to-target interval. This suggests that fine-tuning of the response threshold is better given the availability of more preparation time. The current interpretation of shift costs in terms of between-trial adjustments in the setting of response thresholds is consistent with the brain potential data reported by Jamadar and colleagues (2010). This study showed similar delays in responding on choice trials following a NoGo trial. Importantly, this shift effect emerged in response-related brain potentials well before any response selection could occur. In line with the current interpretation, Jamadar and colleagues suggested that response inhibition on the previous trial must have reduced the preparedness to response on the current trial, requiring the reactivation of the response system. The current analyses suggest that the delay in responding following a NoGo trial is due to a decrease in response activation resulting from the inhibition of the response on a previous trial. This decrease in response activation is likely to be similar on Go trials and on choice trials following a NoGo trial, and it results in a modest delay in the speed of responding on the subsequent trial. The current results indicate that children and adults are equally sensitive to the decrease in response activation following response inhibition. If shifting from stopping to going involves a transition from a Go–NoGo task to a choice task, the decrease in activation following response inhibition on the NoGo trial is accompanied by an increase in response threshold to maintain response accuracy. Most likely, the increase in response threshold is responsible for most of the delay observed when participants are required to shift from response inhibition to response choice and execution. In this regard, shifting from stopping to choice seems to involve a generic mechanism involved in response selection, preparation, and execution (e.g., Niemi & Näätänen, 1981). The notion of a developmental trend in the fine-tuning of the response threshold is compatible with the observation that children’s responses are typically slower and more variable than they are in older individuals (e.g., Bédard et al., 2002). Simmonds and colleagues (2007) observed that this variability is positively correlated with the proportion of commission errors on a Go/NoGo task and negatively correlated with the activity in the presupplementary motor area (pre-SMA). Furthermore,

500

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

several functional magnetic resonance imaging (fMRI) studies reported that the pre-SMA is involved in task switching (e.g., Rushworth, Walton, Kennerley, & Bannerman, 2004), most notably in the inhibition of previous S–R links (Crone, Wendelken, Donohue, and Bunge (2006c), Importantly, Crone, Donohue, Honomichl, Wendelken, and Bunge (2006b) observed an immature pattern in pre-SMA activation in children relative to adolescents and adults during task switching. This pattern of findings suggests that the pre-SMA is included in the common final pathway implicated in the ability to inhibit in between-trial control adjustments and in the setting of the speed–accuracy tradeoff. One final issue remains, namely the apparent discrepancy between our NoGo-to-Go and NoGo-tochoice findings. The delay in responding on Go trials following a NoGo trial was on average 33 ms and did not discriminate between age groups. We identified the setting of the response threshold as a critical factor contributing to both delays. Moreover, we postulated more precision in the setting of the response threshold with advancing age. Thus, the question that needs to be answered is why the delay on Go trials following a NoGo trial does not decrease as children are grow older. Unfortunately, we do not have a satisfactory account for the apparent absence of a developmental difference in shifting from NoGo trials to Go trials. But we point to a similar discrepancy reported in a developmental study of posterror slowing by Hogan, Vargha-Khadem, Kirkham, and Baldeweg (2005). In their study, a sample of adolescents (12- to 18-year-olds) and a sample of adults (18- to 22-year-olds) performed two tasks: a two-choice task and a four-choice task. In the two-choice task, participants were required to respond in the direction of left- versus right-pointing arrows. In the four-choice task, they needed to make a spatially compatible response to a green arrow (75% occurrence) but an incompatible response to a red arrow (25% occurrence). The results showed that post-error slowing was small on the two-choice task and did not differ between age groups (5 vs. 12 ms for adolescents and adults, respectively). Posterror slowing on the four-choice task was much larger for incompatible reactions on the four-choice task, and this slowing discriminated between age groups (79 vs. 109 ms for adolescents and adults, respectively). A speculative interpretation of this pattern of findings is that the mechanisms involved in the selection of a response and the setting of a response threshold are linked in a capacity-sharing relation. Thus, a higher demand on response selection negatively affects the precision in the setting of the response threshold. Obviously, further research is indicated to assess whether this interpretation is plausible. Conclusions In the current study, we examined developmental differences in task switching and shifting from stopping to going. In line with previous developmental studies of task switching, we observed that the ability in switching between choice reactions improved considerably with age. In addition, our analysis indicated that young children experience a disproportional difficulty in switching between tasks. Finally, our results add to previous findings indicating that response repetition is a critical factor in task switching and that the delay in responding on switch trials is reduced considerably when there is more time to prepare for the new task. The results that emerged from the hybrid Go–NoGo-to-choice shifting paradigm showed developmental gains in the ability to inhibit (indexed by a reduction in commission errors) and in the ability to shift from stopping to going on choice trials (indexed by the difference between choice reactions following inhibits vs. choice reactions). Importantly, our analysis indicated that, although young children are considerably slower than young adults in shifting from response inhibition to the selection and execution of a choice response, young children are not disproportionately slower when shifting from stopping to choice. Based on the current findings, we propose that trial-by-trial control adjustments in the setting of response thresholds provide a critical mechanism implicated in the cognitive control that is exercised when there is a need to select and execute a choice response, to switch from one task to another, and to shift from stopping to going. The neural substrate, the pre-SMA, involved in setting response thresholds is part of a larger network, including the dorsolateral prefrontal cortex. There is a considerable literature examining this network vis-à-vis developmental change in the ability to inhibit. It would be of considerable interest to assess how this network is operating in tasks requiring a shift from stopping to going and how maturational neural constraints may limit the performance of young children.

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

501

Acknowledgments This study was supported by a grant from the Netherlands Organization of Scientific Research (NWO–MaGW Grant 575-63-082C/575-25-005). Maurits van der Molen gratefully acknowledges the support in residence at the Netherlands Institute for Advanced Study (NIAS), Wassenaar, the Netherlands, which was also critical for the research presented in this article. The authors thank Birte U. Forstmann for helpful comments.

References Allport, D. A., Styles, E. A., & Hsieh, S. (1994). Shifting intentional set: Exploring the dynamic control of tasks. In C. Umilta & M. Moscovitch (Eds.), Attention and performance XV: Conscious and nonconscious information processing (pp. 421–452). Cambridge, MA: MIT Press. Amso, D., & Casey, B. J. (2006). Beyond what develops when: Neuroimaging may inform how cognition changes with development. Current Directions in Psychological Science, 15, 24–29. Astle, D. E., Jackson, G. M., & Swainson, R. (2006). Dissociating neural indices of dynamic cognitive control in advance task–set preparation: An ERP study of task switching. Brain Research, 1125, 94–103. Bédard, A. C., Nichols, S., Barbosa, J. A., Schachar, R., Logan, G. D., & Tannock, R. (2002). The development of selective inhibitory control across the life span. Developmental Neuropsychology, 21, 93–111. Blakemore, S. J., & Choudhury, S. (2006). Brain development during puberty: State of the science. Developmental Science, 9, 11–14. Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S., & Cohen, J. D. (1999). Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature, 402, 179–181. Bunge, S. A., & Wright, S. B. (2007). Neurodevelopmental changes in working memory and cognitive control. Current Opinion in Neurobiology, 17, 243–250. Bunge, S. A., Dudukovic, N. M., Thomason, M. E., Vaidya, C. J., & Gabrieli, J. D. E. (2002). Development of frontal lobe contributions to cognitive control in children: Evidence from fMRI. Neuron, 33, 301–311. Casey, B. J., Tottenham, N., Liston, C., & Durston, S. (2005). Imaging the developing brain: What have we learned about cognitive development? Trends in Cognitive Sciences, 9, 104–110. Cepeda, N. J., Kramer, A. F., & Gonzalez de Sather, J. C. M. (2001). Changes in executive control across the life span: Examination of task-switching performance. Developmental Psychology, 37, 715–730. Chevalier, N., & Blaye, A. (2009). Setting goals to switch between tasks: Effect of cue transparency on children’s cognitive flexibility. Developmental Psychology, 45, 782–797. Chugani, H. T., Phelps, M. E., & Mazziotta, J. C. (1987). Positron emission tomography study of human brain functional development. Annals of Neurology, 22, 487–497. Cragg, L., & Nation, K. (2009). Shifting development in mid-childhood: The influence of between-task interference. Developmental Psychology, 45, 1465–1479. Crone, E. A., Bunge, S. A., van der Molen, M. W., & Ridderinkhof, K. R. (2006a). Switching between tasks and responses: A developmental study. Developmental Science, 9, 278–287. Crone, E. A., Donohue, S. E., Honomichl, C., Wendelken, C., & Bunge, S. A. (2006b). Brain regions mediating flexible rule use during development. Journal of Neuroscience, 25, 11239–11247. Crone, E. A., Wendelken, C., Donohue, S. E., & Bunge, S. A. (2006c). Neural evidence for dissociable components of task-switching. Cerebral Cortex, 16, 475–486. Cuneo, K., & Welsh, M. C. (1992). Perseveration in young children: Developmental and neuropsychological perspectives. Child Study Journal, 22, 73–92. Davidson, M. C., Amso, D., Anderson, L. C., & Diamond, A. (2006). Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia, 44, 2037–2078. De Jong, R. (2000). An intention–activation account of residual switch costs. In S. Monsell & J. Driver (Eds.), Attention and performance XVIII: Control of cognitive performance (pp. 357–376). Cambridge, MA: MIT Press. Deak, G. O., Ray, S. D., & Pick, A. D. (2004). Effects of age, reminders, and task difficulty on young children’s rule-switching flexibility. Cognitive Development, 19, 385–400. Dempster, F. N. (1992). The rise and fall of the inhibitory mechanism: Toward a unified theory of cognitive development and aging. Developmental Review, 12, 45–75. Diamond, A. (2002). Normal development of prefrontal cortex from birth to young adulthood: Cognitive functions, anatomy, and biochemistry. In D. T. Stuss & R. T. Knight (Eds.), Principles of frontal lobe function (pp. 466–503). New York: Oxford University Press. Durston, S., Thomas, K. M., Yang, Y. H., Ulug, A. M., Zimmerman, R. D., & Casey, B. J. (2002). A neural basis for the development of inhibitory control. Developmental Science, 5, F9–F16. Ellefson, M. R., Shapiro, L. R., & Chater, N. (2006). Asymmetrical switch costs in children. Cognitive Development, 21, 108–130. Gade, M., & Koch, I. (2005). Linking inhibition to activation in the control of task sequences. Psychonomic Bulletin & Review, 12, 530–534. Giedd, J. N., Blumenthal, J., Jeffries, N. O., Castellanos, F. X., Lui, H., Zijdenbos, A., et al (1999). Brain development during childhood and adolescence: A longitudinal MRI study. Nature Neuroscience, 2, 861–863. Gogtay, N., Giedd, J. N., Lusk, L., Hayashi, K. M., Greenstein, D., Vaituzis, A. C., et al (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America, 101, 8174–8179.

502

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

Grant, D. A., & Berg, E. A. (1948). A behavioral analysis of degree of reinforcement and ease of shifting to new response in a Weigl-type card sorting problem. Journal of Experimental Psychology, 34, 401–411. Gratton, G., Coles, M. G. H., & Donchin, E. (1992). Optimizing the use of information: Strategic control of activation of repsonses. Journal of Experimental Psychology: General, 121, 480–506. Gupta, R., Kar, B. R., & Srinivasan, N. (2009). Development of task switching and post-error slowing in children. Behavioral and Brain Functions, 5, 38. Heaton, R. K., Chelune, G. J., Talley, J. L., Kay, G. G., & Curtis, G. (1993). Wisconsin Card Sorting Test manual: Revised and expanded. Odessa, FL: Psychological Assessment Resources. Hoffmann, J., Kiesel, A., & Sebald, A. (2003). Task switches under Go/NoGo conditions and the decomposition of switch costs. European Journal of Cognitive Psychology, 15, 101–128. Hogan, A. M., Vargha-Khadem, F., Kirkham, F. J., & Baldeweg, T. (2005). Maturation of action monitoring from adolescence to adulthood: An ERP study. Developmental Science, 8, 525–534. Hübner, R., & Druey, M. (2006). Response execution, selection, or activation: What is sufficient for response-related repetition effects under task shifting? Psychological Research, 70, 245–261. Huizinga, M., Dolan, C. V., & van der Molen, M. W. (2006). Age-related change in executive function: Developmental trends and a latent variable analysis. Neuropsychologia, 44, 2017–2036. Huttenlocher, P. R. (1979). Synaptic density in human frontal cortex: Developmental changes and effects of aging. Brain Research Bulletin, 163, 195–205. Jamadar, S., Michie, P. T., & Karayanidis, F. (2010). Sequence effects in cued task switching modulate response preparedness and repetition priming processes. Psychophysiology, 47, 365–386. Kharitonova, M., Chien, S., Colunga, E., & Munakata, Y. (2009). More than a matter of getting ‘‘unstuck’’: Flexible thinkers use more abstract representations than perseverators. Developmental Science, 12, 662–669. Kleinsorge, T. (1999). Response repetition benefits and costs. Acta Psychologica, 103(3), 295–310. Kleinsorge, T., & Gajewski, P. D. (2004). Preparation for a forthcoming task is sufficient to produce subsequent shift costs. Psychonomic Bulletin & Review, 11, 302–306. Klenberg, L., Korkman, M., & Lahti Nuuttila, P. (2001). Differential development of attention and executive functions in 3- to 12year-old Finnish children. Developmental Neuropsychology, 20, 407–428. Kray, J., Eber, J., & Lindenberger, U. (2004). Age differences in executive functioning across the lifespan: The role of verbalization in task preparation. Acta Psychologica, 115, 143–165. Laming, D. (1968). Information theory of choice reaction times. New York: Academic Press. Luce, R. D. (1986). Response times: Their role in inferring elementary mental organization. Oxford, UK: Oxford University Press. Mayr, U., & Kliegl, R. (2003). Differential effects of cue changes and task changes on task–set selection costs. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 362–372. Meiran, N. (1996). Reconfiguration of processing mode prior to task performance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1423–1442. Meiran, N., Chorev, Z., & Sapir, A. (2000). Component processes in task switching. Cognitive Psychology, 41, 211–253. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7, 134–140. Niemi, P., & Näätänen, R. (1981). Foreperiod and simple reaction time. Psychological Bulletin, 89, 133–162. Nieuwenhuis, S., Stins, J. F., Posthuma, D., Polderman, T. J. C., Boomsma, D. I., & de Geus, E. J. (2006). Accounting for sequential trial effects in the flanker task: Conflict adaptation or associative priming? Memory & Cognition, 34, 1260–1272. Pfefferbaum, A., Mathalon, D. H., Sullivan, E. V., Rawles, J. M., Zipursky, R. B., & Lim, K. O. (1994). A quantitative magnetic resonance imaging study of changes in brain morphology from infancy to late adulthood. Archives of Neurology, 51, 874–887. Philipp, A. M., Jolicoeur, P., Falkenstein, M., & Koch, I. (2007). Response selection and response execution in task switching: Evidence from a go-signal paradigm. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 1062–1075. Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108. Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological Bulletin, 114(3), 510–532. Raven, J. C., Court, J. H., & Raven, J. (1985). Raven’s Progressive Matrices. London: Author. Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for Raven’s Progressive Matrices and Vocabulary Scales. Oxford, UK: Oxford Psychologists Press. Reimers, S., & Maylor, E. A. (2005). Task switching across the life span: Effects of age on general and specific switch costs. Developmental Psychology, 41, 661–671. Ridderinkhof, K. R., Span, M. M., & Van der Molen, M. W. (2002). Perseverative behavior and adaptive control in older adults: Performance monitoring, rule induction, and set shifting. Brain and Cognition, 49, 382–401. Ridderinkhof, K. R., Ullsperger, M., Crone, E. A., & Nieuwenhuis, S. (2004). The role of the medial frontal cortex in cognitive control. Science, 306, 443–447. Rieger, M., & Gauggel, S. (1999). Inhibitory after-effects in the stop signal paradigm. British Journal of Psychology, 90, 509–518. Rieger, M., Gauggel, S., & Burmeister, K. (2003). Inhibition of ongoing responses following frontal, nonfrontal, and basal ganglia lesions. Neuropsychology, 17, 272–282. Rogers, R. D., & Monsell, S. (1995). Costs of a predictible switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124, 207–231. Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005). Prefrontal cortex and flexible cognitive control: Rules without symbols. Proceedings of the National Academy of Sciences of the United States of America, 102, 7338–7343. Rushworth, M., Walton, M., Kennerley, S., & Bannerman, D. (2004). The role of medial frontal cortex in making decisions and changing tasks. International Journal of Psychology, 39, 514. Schuch, S., & Koch, I. (2003). The role of response selection for inhibition of task sets in task shifting. Journal of Experimental Psychology: Human Perception and Performance, 29, 92–105. Simmonds, D. J., Fotedar, S. G., Suskauer, S. J., Pekar, J. J., Denckla, M. B., & Mostofsky, S. H. (2007). Functional brain correlates of response time variability in children. Neuropsychologia, 45, 2147–2157.

M. Huizinga, M.W. van der Molen / Journal of Experimental Child Psychology 108 (2011) 484–503

503

Sowell, E. R., Thompson, P. M., Leonard, C. M., Welcome, S. E., Kan, E., & Toga, A. W. (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. Journal of Neuroscience, 24, 8223–8231. Spector, A., & Biederman, I. (1976). Mental set and mental shift revisited. American Journal of Psychology, 89, 669–679. Sudevan, P., & Taylor, D. A. (1987). The cuing and priming of cognitive operations. Journal of Experimental Psychology: Human perception and performance, 13, 89–103. Swick, D., & Jovanovic, J. (2002). Anterior cingulate cortex and the Stroop task: Neuropsychological evidence for topographic specificity. Neuropsychologia, 40, 1240–1253. Van den Wildenberg, W. P. M., & Van der Molen, M. W. (2004). Developmental trends in simple and selective inhibition of compatible and incompatible responses. Journal of Experimental Child Psychology, 87, 201–220. Van der Molen, M. W. (2000). Developmental changes in inhibitory processing: Evidence from psychophysiological measures. Biological Psychology, 54, 207–239. Verbruggen, F., Liefooghe, B., Szmalec, A., & Vandierendonck, A. (2005). Inhibiting responses when switching: Does it matter? Experimental Psychology, 52, 125–130. Verbruggen, F., Liefooghe, B., & Vandierendonck, A. (2006). Selective stopping in task switching: The role of response selection and response execution. Experimental Psychology, 53, 48–57. Verbruggen, F., & Logan, G. D. (2008). Response inhibition in the stop-signal paradigm. Trends in Cognitive Sciences, 12, 418–424. Verbruggen, F., Logan, G. D., Liefhooghe, B., & Vandierendonck, A. (2008). Short-term aftereffects of response inhibition: Repetition priming or between-trial control adjustments? Journal of Experimental Psychology: Human Perception and Performance, 34, 413–426. Welsh, M. C. (2002). Developmental and clinical variations in executive functions. In D. L. Molfese & V. J. Molfese (Eds.), Developmental variations in learning: Applications to social, executive function, language, and reading skills (pp. 139–185). Mahwah, NJ: Lawrence Erlbaum. Woodworth, R. S., & Schlosberg, H. (1954). Experimental psychology (rev. ed.). Oxford, UK: Holt. Wylie, G., & Allport, A. (2000). Task switching and the measurement of ‘‘switch costs’’. Psychological Research, 63, 212–233. Yakovlev, P. I., & Lecours, A. R. (1967). The myelogenetic cycles of regional maturation of the brain. In A. Minkovsky (Ed.), Regional development of the brain in early life (pp. 3–70). Oxford, UK: Blackwell. Zelazo, P. D. (2004). The development of conscious control in childhood. Trends in Cognitive Sciences, 8, 12–17. Zelazo, P. D., Craik, F. I. M., & Booth, L. (2004). Executive function across the life span. Acta Psychologica, 115, 167–183.