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Utilizing computerized cognitive training to improve working memory and encoding: piloting a school-based intervention.

A growing body of literature supports the use of computerized cognitive training in enhancing a variety of abilities related to improved cognitive performance. In fact, there are numerous publically available computer- and internet-based applications that are purported to support general cognitive function (e.g., Lumosity). The purpose of this project is to examine the effectiveness of a clinically-based computerized cognitive training program to enhance students' working memory and encoding capabilities.

Working Memory

Working memory (WM) is an individual's capacity for the short-term storage and manipulation of information (Holmes, et al., 2010). This ability is necessary for tasks such as learning, language comprehension, and reasoning (Baddeley, 1992). Working memoiy varies on the individual level and is associated with differences in controlled attention, fluid intelligence, and general cognitive functioning (Engle, Kane & Tuholski, 1999). Low WM capacity is associated with poor attention and the inability to override attentional capture from distracting information (Fukuda & Vogel, 2009). In a school setting, academic performance is significantly linked to both WM and attention (see Rabiner, Murray, Skinner & Malone, 2010).

Recently, functional magnetic resonance imaging (fMRI) technology has made it possible to observe the brain changes that take place as a result of WM training (Oleson, Westerberg, & Klingberg, 2004). These changes support the belief that WM can be trained (i.e., WM ability is not fixed). Moreover, the areas of the brain activated during WM training, the prefrontal and parietal cortices, show increased activity that can persist for months following training. These are also the brain areas known to be implicated in attention and learning disorders (Olesen, Macoveanu, Tegner & Klingberg, 2006; Westerberg, Hivikoski, Forssber, & Klingberg, 2004; Westerberg & Klingberg, 2007). Thus, there is a reasonable expectation that training may positively impact academic performance for students with learning difficulties.

Computerized Cognitive Interventions

Because working memory (and thus attention) is not fixed, a rapidly evolving aspect of research has focused on remediating WM weaknesses. Specifically, recent studies have examined the connections between an underlying deficit in WM and other executive functions and the resulting behaviors (see Westerberg et al., 2004; Gibson, Gondoli, Flies, Dobrzenski, & Insworth, 2010). One approach for addressing WM deficits is computer-assisted cognitive training. In computer-assisted cognitive training (CACT), a computer is used to present exercises; successful completion of these exercises requires maintaining attention in order to respond accurately based on defined rules that require the use of WM capabilities (Rabiner, Murray, Skinner, & Malone, 2010). Generally, programs of this sort are adaptive. That is, the difficulty of the exercises presented is adjusted based on the response of the user. Thus, if the user completes an exercise correctly the next exercise presented will be more difficult; conversely, if the exercise is completed incorrectly the next exercise will be less difficult.

Two recent notable studies of CACT are those of Shalev, Tsai, and Mevorach (2007) and Rabiner et al. (2010). The study conducted by Shalev et al. (2007) tested an independently-developed software program with the goal of determining to what extent Attention Deficit/Hyperactivity Disorder (ADHD) symptoms could be ameliorated through training of attentional networks. Results following the study suggested that CACT could positively affect both attention and academic achievement of students with ADHD. The study by Rabiner et al. (2010), while having a different goal (namely, to compare the effects of CACT and Computer Assisted Instruction on children evidencing symptoms of inattention), also suggested that computerized interventions could benefit students struggling with attention symptoms.

In an effort to clarify the relationship between WM deficits and attentional processes, Klingberg et al. (2005) conducted a randomized, controlled trial of Cogmed's RoboMemo software program. The goal of the study was to examine if systematic WM training would improve WM and other executive functions as well as to assess the impact of WM training on ADHD symptoms. In this study, 53 children (44 boys, 9 girls) ages 7-12 years (M=9.8 years) were randomly assigned to a treatment or control condition; all students had been previously diagnosed with an attention disorder. Participants in both groups completed the same WM exercises using RoboMemo; however, those in the treatment group completed an adaptive version of the program while those in the control group completed a non-adaptive version that remained at the same low-difficulty level for the duration of the training. The training addressed both verbal working memory (VWM) and visuospatial working memory (VSWM). At post-treatment assessment, Klingberg et al. (2005) found that treatment group subjects evidenced significant improvement in VSWM (d = 0.93), response inhibition and nonverbal reasoning. At the three-month follow-up, additional improvements were found in the VSWM measure and in the parent ratings of ADHD behaviors, with both evidencing a strong clinical effect resulting from the WM training (Klingberg et al., 2005).

While studies like those reviewed above indicate that WM training has potential as an intervention for children and adolescents, they do not suggest a way of conveniently implementing the training within a typical school day. For example, participants in the Klingberg et al. (2005) trial completed the training activities on their own time, either at home or at school. Likewise, Gibson, Serocynski, Gondoli, Braungart-Rieker, and Grundy (2007) reported on a study of adolescents that involved training prior to the school day.

One pilot study examining the efficacy of WM training in an easily accessible school-based program was conducted by Mezzacappa and Buckner (2010). In this study, the researchers implemented a small (N = 9; 6 boys, 3 girls) pilot study of Cogmed's RoboMemo software at a public school in an economically disadvantaged neighborhood (i.e. all students at that school qualified for free breakfast and lunch) in Boston, MA. The students ranged in age from 8-10.5 years; none had received previous clinical assessments or treatments (Mezzacappa & Buckner, 2010). Due to the small N and pilot nature of the study, it was carried out in a single group format with pre- and post-training comparisons of WM functioning. During the study, participants completed WM training activities (for 40-45 minutes per session) five days per week for five weeks; the sessions were implemented as a pull-out program from regular classes. At the end of the five weeks of training, it was shown that, on average, students improved on all the measures analyzed (Mezzacappa & Buckner, 2010). Teacher ratings of student attention-related behavior, using an ADHDRS-IV rating scale, reflected an average 26% improvement (d = 1.02). Student performance on the Finger-Windows task (a measure of VSWM) averaged 33% improvement (d = 0.73). The positive descriptive outcomes of this study suggest that WM training implemented in a school setting may be a feasible intervention to assist students. The researchers note that replicating the findings with a larger randomized and controlled study would help to assess the actual efficacy of the type of WM training program implemented in the pilot study (Mezzacappa & Buckner, 2010).

The Current Project

In the present study, we sought to determine if computerized cognitive training could increase WM and encoding capabilities within a school setting using a pre-test/post-test design that was integrated into the regular school day. It was hypothesized that students would demonstrate significant gains in verbal and visual working memory and encoding capabilities following training.

Methods

Participants

Thirty (14 females and 16 males) fourth through eighth graders from five parochial schools in suburban southern California participated; The students ranged in age from 9 to 13 years, with a mean age of 10.77, and all were enrolled in a resource support program at his/her respective school site. As such, each student was identified with learning-related delays that necessitated academic support services from school personnel. The majority of the students were of Caucasian descent and from families with middle to upper middle socio-economic status.

Materials

Wide Range Assessment of Memory and Learning -2 (WRAML-2). The WRAML-2 is a norm referenced clinical measure widely utilized in the identification of memory weaknesses. Four subtests from the WRAML-2 were utilized for pre- and post-testing. The Verbal Working Memory and Symbolic Working Memory tasks provide a measure of verbal and visual capabilities, respectively. The Finger Windows and Number Letter tasks provide an evaluation of visual and verbal encoding skills, respectively. Each of the four subtests were administered before and after training to all students. The WRAML-2 is a well normed and highly reliable measure; Cronbach's coefficient alphas range from .87 to .93 across the age groups included in this study.

Captains Log Cognitive Training Program. Captain's Log is an adaptive computerized cognitive training program utilized in a variety of settings (e.g., clinics and educational institutions) to remediate a variety of cognitive skills including working memory and encoding. Training modules within the program are presented as games in which the student's performance is recorded and "points" are earned based upon the accuracy of his/her performance. The different games that may be played are designed to specifically address individual cognitive abilities such as working memory. The adaptive approach used within the program tracks a participant's progress. If an individual is successful on a task, the following activity will increase in challenge. On the other hand, if a student is not successful on a particular task, the next activity will be automatically adjusted such that it is one level lower in challenge. In this way, there is an "optimally challenging" activity presented throughout training and each individual is able to progress through the activities at his/her own pace relative to his/her performance level.

Procedure

Five parochial schools were informed of the purpose of cognitive training programs and invited to participate in this project. Following each school's approval of the project, parents were apprised of the study and asked to provide consent for their son/daughter to engage in the training. Each student was pre-tested (by school personnel) with the four WRAML-2 subtests described above. Subsequently, all students were provided with 20 hours of cognitive training administered via Captain's Log; training occurred during the school day via a pull-out approach with all training supervised by school staff in a self-contained room on campus; student-school personnel ratio during training was no greater than 12:1 at any school site. Because five school sites were utilized, it was not possible to completely standardize the training due to schedule differences across schools. However, all students did receive the same amount of training (i.e., 20 hours). Additionally, because this program was implemented as part of the regular curricular program each day, the school administration expected that all participants received working memory training. As a result, no control group was utilized in this project. Following training, all students were administered the WRAML-2 subtests (by school personnel) in order to obtain post-training indices of working memory and encoding.

Results

Table 1 contains the means and standard deviations for each of the working memory and encoding subtests from the WRAML-2; the scores reported are scaled scores with a range of 1-19 and a mean of 10. As can be seen in the table, students demonstrated gains on all measures. The largest pre- to post-training differences were seen on the encoding subtests. Specifically, there was a 2.36 and 2.53 points rise in performance (following training) on the visual encoding (Finger Windows) and verbal encoding (Number Letter) tasks, respectively.

In order to examine the efficacy of cognitive training, a series of paired t-tests were conducted to compare pre- and post-training scores. In addition, effect size indices (Cohen's d) were calculated to indicate the practical utility of the cognitive training. These analyses indicated significant pre- to post-test changes on each of the working memory and encoding indices with corresponding effect sizes that ranged from small to large. Specifically, a significant increase in verbal working memory was observed with a moderate magnitude, t(29) = 4.12, p [less than or equal to] .01, d = .76. Likewise, there was a significant gain in visual working memory with a small effect, t(29) = 2.24, [p.bar] < .05, d = .38. Significant changes were observed on both measures of encoding, as well. Verbal encoding improved significantly following training with a large effect size, t(29) = 4.28, [p.bar] < .01, d = .87 while there was a moderate effect on the measure of visual encoding, t(29) = 3.83, [p.bar] < .01, d = .78.

Discussion

The primary purpose of this project was to examine the impact of cognitive training on working memory utilizing an in-school cognitive training program. Paired comparisons of pre- and post-training performance on indices of working memory and encoding provide evidence for a positive training effect. Moreover, effect size calculations (which provide a measure of practical utility for training approaches) yielded supportive estimates. As this study is one of the few in which cognitive training was integrated into the regular school day, it is especially valuable because it provides some much needed data to support the efficacy of including training into a school's daily curricular activities.

As noted earlier, there is an emerging body of literature to support the effectiveness of "training" working memory because it is not a fixed attribute (e.g., see Rabiner et al., 2010; Shalev et al., 2007) and that improvements in working memory can be sustained over a period of time (e.g., see Oleson, Westerberg, & Klingberg, 2004). However, a limitation of this existing research was that training generally occurred outside of the regular school day or within the home setting. Thus, it is conceivable that only those students who had greater flexibility in their days and/or students who came from higher socio-economic backgrounds were afforded access to training. Of course, this meant that it was entirely possible that the students most needing such interventions were not able to receive them. Mezzacappa and Buckner (2010) reported on one of the few school-based highly accessible programs. Results from their study provided evidence for the efficacy of a school-based training program. In particular, post-training testing indicated that visual encoding was significantly improved; moreover, teachers' ratings of attentiveness were also higher. More recently, Wong, Wiest, Pumaccahua, Nelson, and Niere (2012) found that a school-day training program produced positive changes in working memory for junior high school students. This study was similar to Mezzacappa and Buckner's (2010) in that a single group of students participated in training (i.e., there was no true control group). Nonetheless, the study provides additional support for in-school cognitive training. Given the above noted school-based studies and the results of the current project, there does appear to be a benefit to this type of training program being administered in a school setting. This is likely to be especially true when students with identified learning needs and/or with known delays in working memory and encoding are selected for intervention. Because working memory has been shown to be related to academic outcomes (see Rabiner, et al., 2010), the potential to remediate deficits in these types of abilities would appear to be especially important.

While the current study provides some much needed evidence for the effectiveness of a school-based training program, two limitations must be noted. First, a relatively small sample was employed and no true control group was present. Future studies will need to increasingly employ control groups to more fully examine the effectiveness of training programs. For example, in a small study reported by Patterson, Wong, Wiest, Lakamp, Saylor, and Armendarez (2011) seventeen students were randomly assigned to either a 20-hour cognitive training program or a 20hour out-of-class activity for a ten week period; students in the training group received 30 minutes of computer-based intervention four days per week while the non-training students participated in an activity for 30 minutes four days per week. The authors reported a significant improvement in verbal working memory for the training group with a large effect size; however, there was no significant difference in verbal working memory for the control group. On a measure of visual working memory, there was a marginally significant improvement following training with a moderate effect size; students in the control group did not show any change in visual working memory capabilities. Although the sample was small in the Patterson et al. (2011) study, the results do suggest that cognitive training is a viable intervention in the school setting and they are consistent with the results of the current project. A second limitation of the current study is that the training regimen was not standardized across school sites (due to differences in each school's daily schedule). Thus, although all students received 20 hours of training, it is not possible to say that all students experienced an identical training experience (in terms of how the training was administered).

Notwithstanding these concerns, this pilot study contributes to the emerging literature that supports the use of cognitive training programs. Within the context of a Response to Intervention (RTI) model of educational support and remediation, this type of intervention shows promise in remediating abilities that are significantly associated with academic performance. Assuming that such training programs can be implemented during the course of a school day (and there is demonstrated value in doing so), there is then the opportunity to reach those students most in need of remediation, to potentially improve academic achievement, and to possibly support engagement in school.

Author Note

Dudley J. Wiest, Eugene H.Wong and Tessy T. Pumaccahua are affiliated with the California State University, San Bernardino. Laura P. Minero is affiliated with California State University, Fullerton. We would like to thank the students and staff from each of the Lutheran schools for the participation in this project. Correspondence concerning this paper should be addressed to Eugene H. Wong, Department of Psychology, California State University, 5500 University Parkway, San Bernardino, California 92407 or at ewong@ csusb.edu.

References

Baddeley, A. (1992). Working memory. Science, 255, 556-559.

Engle, R. W., Kane, M., & Tuholski, S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 102-134), Cambridge: Cambridge University Press.

Fukuda, K., & Vogel, E. K. (2009). Human Variation in Overriding Attentional Capture. Journal Of Neuroscience, 29(27), 8726-8733. doi:10.1523/ JNEUROSCI.2145-09.2009

Gibson, B.S., Seroczynski, A., Gondoli, D.M., Braungart-Reiker, J., & Grundy, A. (2007). Working memory training for early adolescents with attention-deficit hyperactivity disorder. Poster presented at the Biennial Meeting of the Society for Research in Child Development, Boston, MA.

Gibson, B.S., Gondoli, D.M., Flies, A.C., Dobrzenski, B.A., & Unsworth, N. (2010). Application of the dual-component model of working memory to ADHD. Child Neuropsychology. 16, 60-79. doi: 10.1080/09297040903146958.

Holmes, J., Gathcrcolc, S., Place, M., Dunning, D., Hilton, K. & Elliott, J. (2010). Working memory deficits can be overcome: Impacts of training and medication on working memory in children with ADHD. Applied Cognitive Psychology. 24, 827-836. doi: 10.1002/acp.1589.

Klingberg, T., Femell, E., Olesen, P.J., Johnson, M., Gustafsson, P, Dahlstrom, K., ... Westerberg, H. (2005). Computerized training of working memory in children with ADHD--A randomized, controlled trial. Journal of American Academic Child and Adolescent Psychiatry. 44(2), 177-186. doi: 0890-8567/05/4402-0177.

Mezzacappa, E. & Buckner, J.C. (2010). Working memory training for children with attention problems or hyperactivity: A school-based pilot study. School Mental Health. 2(4), 202-208. doi: 10.1007/ s12310-010-9030-9.

Olesen P.J., Westerberg H., Klingberg T. (2004). Increased prefrontal and parietal activity after training working memory. Nature Neuroscience. 7(1), 75-79. doi: 10.1038/nn1165

Olesen, P.J., Macoveanu, J., Tegner, J., & Klingberg, T. (2006). Brain activity related to working memory and distraction in children and adults. Cerebral Cortex. 17, 1047-1054. doi: 10.1093/cercor/bh1014.

Patterson, A.M., Wong, E.H., Wiest, D.J., Lakamp, S., Saylor, A., & Armcndarcz, J. (2011). Using cognitive training to enhance working memory. Poster presented at the meeting of the Western Psychological Conference, Los Angeles, CA.

Rabiner, D., Murray, D., Skinner, A., & Malone, P. (2010). A randomized trial of two promising computer-based interventions for students with attention difficulties. Journal of Abnormal Child Psychology. 38, 131-142. doi: 10.1007/s10802-009-9353-x.

Shalev, L., Tsai, Y., & Mevorach, C. (2007). Computerized progressive attentional training (CPAT) program: Effective direct intervention for children with ADHD. Child Neuropsychology. 13, 382-388. doi: 10.1080/09297040600770787.

Westerberg, H., Hirvikoski, T., Forssbcrg, H., & Klingberg, T. (2004). Visuo-spatial working memory span: A sensitive measure of cognitive deficits in children with ADPff). Child Neuropsychology. 10(3), 155-161. doi: 10.1080/09297040490911014.

Westerberg, H., & Klingberg, T. (2007). Changes in cortical activity after training of working memory--a single-subject analysis. Physiology & Behavior, 92(1/2), 186-192. doi: 10.1016/j.physbeh.2007.05.041

Wong, E. H., Wiest, D. J., Pumaccahua, T. T., Nelson, C., & Niere, K. (2012). Using cognitive training to enhance attention and working memory for student with ADHD: A preliminary study. Poster presented at the meeting of the Western Psychological Conference, San Francisco, CA.

Dudley J. Wiest

California State University, San Bernardino

Eugene H. Wong

California State University, San Bernardino

Laura P. Minero

California State University, Fullerton

Tessy T. Pumaccahua

California State University, San Bernardino
Table 1. Pre- and Post-training Average
Scores for Working Memory and Encoding
Abilities

WRAML-2 Subtest   Pre-training   Post-training

Verbal Working    7.03 (2.46)     8.70 (1.88)
Memory

Visual Working    8.30 (2.45)     9.30 (2.78)
Memory

Number Letter     7.17 (2.72)     9.70 (3.08)
(Verbal
Encoding)

Finger Windows    6.87 (3.24)     9.23 (2.83)
(Visual
Encoding)

Note: standard deviations are reported within ()
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Author:Wiest, Dudley J.; Wong, Eugene H.; Minero, Laura P.; Pumaccahua, Tessy T.
Publication:Education
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Date:Dec 22, 2014
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