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Children's ability to shift behavior in response to changing environmental demands is critical for successful intellectual functioning. While the processes underlying the development of cognitive control have been thoroughly investigated, its functioning in an ecologically relevant setting such as school is less well understood. Given the alarming number of children who face failure in the U.S. school system, the purpose of this project is to determine whether subtly different measures of cognitive control differentially predict academic achievement. Sixty-five kindergarten children were given two versions of a Dimensional Change Card Sort task—a geometric version followed by a linguistic version. Educational outcomes consisted of a standardized measure of academic achievement as well as assessments used by the school district. Results revealed that cognitive control, particularly as assessed by the linguistic variant, predicted children's academic performance on math and school-based assessments, thereby suggesting that deficient cognitive control negatively impacts educational success.
The ability to change one's behavior in response to varying environmental demands is essential for effective intellectual functioning. It is easy to imagine many situations in which one has a well learned response (e.g., driving to work), only to realize that the behavior must be altered due to a change in circumstances (e.g., it's Saturday—you do not have to go to work). To avoid such confusion, a higher-order rule (e.g., the day of the week) helps control where to drive (to work or to the park).
For the reason that being able to shift one's behavior flexibly is a skill that carries practical implications, the topic of cognitive control in children has become popular over the last 10 years. Incorrect continuation of the initial response (e.g., driving to work on Saturday even though you should be at the park) is called perseveration, and is typically found in individuals with neurologically based impairments such as schizophrenia, autism, attention deficit hyperactivity disorder (ADHD), and bipolar depression (Barkley, 1997; Dawson, Meltzoff, Osterling, & Rinaldi, 1998; Griffith, Pennington, Wehner, & Rogers, 1999). Being able to switch appropriately, on the other hand, is indicative of the mature prefrontal cortex performing a variety of executive functioning skills (Diamond, 2002; Hughes, 2002a, 2002b; Posner & DiGirolamo, 1998).
Cognitive control improves rapidly between the preschool and elementary school years (Bunge & Zelazo, 2006; Crone, Donohue, Honomichl, Wendelken, & Bunge, 2006; Moriguchi & Hiraki, 2009). A well-known task to document such development is the Dimensional Change Card Sort (DCCS) task, in which a child sorts cards by different criteria across two phases of a matching task (Zelazo & Frye, 1998). The child is instructed to sort cards by one dimension (form) in the pre-shift phase, and then the rule is changed to the other dimension (color) in the post-shift. Most 3-year-olds perseverate to the previous dimension (Rennie, Bull, & Diamond, 2004), while 4–5-year-old children switch quickly to the new dimension in post-shift trials (Zelazo, Muller, Frye, & Marcovitch, 2003). Cognitive Complexity and Control (CCC) theory explains this progression by positing that children eventually develop the ability to use a hierarchical rule structure to resolve conflicts in sorting (Diamond, Kirkham, & Amso, 2002; Zelazo & Frye, 1998; Zelazo, Jacques, Burack, & Frye, 2002; Zelazo et al., 2003). Children less than 3 years of age are able to take into account single-step if-then reasoning, whereas by 5 years of age children use higher-order or if-if-then rules to reflect upon the type of game being played and select the appropriate rule to resolve the incompatibility.
Although it has been widely assumed that cognitive control is fundamental for adaptive behavior in a variety of settings (Riggs, Blair, & Greenberg, 2003), the extent to which successful DCCS performance generalizes to other tasks is just beginning to be explored. Indeed, one setting that would manifestly require successful cognitive control is school. Consider some of the tasks that a child faces in a classroom. In a phonics-based approach to reading, for example, teachers often ask children to sort words by word families (e.g., cat, hat, mat, sat) and then may ask children to sort words by the beginning letter (e.g., cat, cup, car, can). In order to perform successfully, children must shift attention between either the beginning or root portions of the word as instructed. Also, in teaching math, children are frequently required to switch between addition and subtraction, or between multiplication and division, operations in a series of problems. When such problems are presented in a mixed format, children must pay attention to the operator to shift responding as needed. In either of these cases, failure to change leads to perseveration and ultimately task failure.
Indeed, understanding how cognitive control is involved when children process the demands of classrooms could improve methods of education. Given the alarming number of children who face educational failure in the U.S. school system (National Academy of Sciences, National Academy of Engineering, & Institute of Medicine, 2005; Rindermann & Ceci, 2009), there is a great deal of interest in whether discoveries about the executive functioning and behavioral regulation capabilities of the prefrontal cortex may be used to improve children's academic functioning (Burrage et al., 2008; Cameron Ponitz et al., 2008; Meltzer, 2007; see, however, cautions provided by Ansari & Coch, 2006; Bruer, 1997; Varma, McCandliss, & Schwartz, 2008).
There is a growing body of evidence attesting to the role that mental flexibility plays in education. Some studies, for example, reported that shifting skills are related to mathematical skills in school-age children (Bull, Johnston, & Roy, 1999; Bull & Scerif, 2001; van der Sluis, de Jong, & van der Leij, 2004), but not in preschool children (Blair & Razza, 2007; Espy et al., 2004). Another study found that shifting ability predicted both reading and math performance in each of the first three primary grades (Bull, Espy, & Wiebe, 2008). While these results are encouraging, they are difficult to compare against each other due to the variety of shift tasks employed across studies; for instance, across the studies just cited, measures of shifting or flexibility included the Flexible Item Selection task (FIST; Jacques & Zelazo, 2001), the Shape School task (Espy, 1997), the Wisconsin Card Sort task (Grant & Berg, 1948), and the Making Trails task (McLean & Hitch, 1999).
Recent findings regarding the relationship between educational measures and cognitive control as measured specifically by the DCCS task, however, have been quite promising. For example, in a large intervention study, the number of correct trials in the post-shift phase of the DCCS task by four-year-old children enrolled in Head Start classrooms was positively related to language skills such as phonological sensitivity and print awareness (Bierman, Nix, Greenberg, Blair, & Domitrovich, 2008) and to classroom and prosocial skills (Bierman, Torres, Domitrovich, Welsh, & Gest, 2009).
As valuable as these findings are for establishing a link between education and the construct of cognitive control, there is inconsistency in how cognitive control is measured in its relationship to educational outcome. Indeed, from the beginning of interest in executive functions, there have been concerns about its composition and measurement (Banich, 2009; Carlson, 2005; Miyake et al., 2000). The difficulty posed by task differences in assessing cognitive control is that it leads to uncertainty over how it may be used as a basis for intervention in classroom applications. When viewed from the perspective of education, with its preference for targeted and specific interventions (Hattie, Biggs, & Purdie, 1996), it is difficult to design appropriate procedures when the measurement of cognitive control is still being refined.
Therefore, in order to examine the role of cognitive control as it may be relevant to education, it makes sense to use multiple, converging measures of cognitive control and, in particular, by including an ecologically valid measure of this construct that is especially relevant to academic performance. To do this, the present experiment will use two measures of cognitive control to determine its relationship with education: One will be the typical geometric version of the DCCS task, and the other will be a new linguistic version of the DCCS task that requires children to shift between different portions of a word—a task that simulates the demands faced by children in a school setting. If it were found that the tasks are each related to educational measure, and are also correlated to each other, it would argue for DCCS task as a powerful index of cognitive control in educational measures.
In addition to relating performance on different versions of the DCCS task, this project also seeks to increase sensitivity of educational outcomes to variations in cognitive control by using a standardized achievement test (the Mini-Battery of Achievement [MBA] which includes math and reading subscales) and assessments already in place by the school district (Dynamic Indicators of Basic Early Literacy Skills [DIBELS]; Kindergarten Readiness Assessment—Literacy [KRAL], Kindergarten Exit Test).
Further because differences between individual children's cognitive control may be masked by the group performance, academic measures will be examined according to whether children pass or fail each DCCS task. CCC theory has taken a largely normative approach identifying that the vast majority of five-year-olds pass the DCCS task (approximately 75% in Zelazo et al., 2003), but providing little explanation for those children who fail. Differences in rates of neural development are plausible given that the PFC continues to develop into the adolescent years (Zelazo, Craik, & Booth, 2004), raising the possibility that some children take longer to reach the full cortical maturity necessary to pass the DCCS task. Individual differences in DCCS performance may thereby lead to concomitant delays in tasks such as education that depend upon executive control.