Training Working Memory and Executive Control in Attention Deficit/Hyperactivity Disordered Children
Purpose: Children with attention-deficit hyperactivity disorder often experience difficulties in academic areas. Working memory, the cognitive system that allows for the maintenance and manipulation of information, is also affected in children with the disorder. Research has demonstrated an association between performance on working memory tasks and academic skills and learning outcomes. Given the importance of working memory for scholastic achievement, there has been increased interest in developing interventions that will improve working memory skills.
In this project, researchers are developing and testing a series of working memory interventions for elementary school-aged children with attention-deficit hyperactivity disorder. The interventions are designed to improve working memory in order to improve learning and academic outcomes.
Project Activities: Approximately 600 students (230 with attention-deficit hyperactivity disorder, 370 without attention-deficit hyperactivity disorder) 7–13 years of age will participate in this project. In Study 1, the team will examine different motivational factors within a video-game environment and their influence on students' task performance, and motivation. Those factors that are determined to have the most effect on task performance and motivation will be incorporated into the working memory and executive control interventions (Studies 2–4). In Studies 2 and 3, two working memory interventions will be developed and their effect on working memory performance and academic outcomes (e.g., reading, math) will be compared for typically developing children and children with attention-deficit hyperactivity disorder. Finally, in Study 4, an intervention that targets executive control functions that are considered to underlie the working memory difficulties in children with attention-deficit hyperactivity disorder will be developed. The primary dependent measures across all studies are working memory capacity, general reasoning skills, reading and math performance.
Products: The expected outcomes from this study include the development of a computer game that will improve working memory and executive control in children with attention-deficit hyperactivity disorder, published reports, and presentations.
Setting: Participating students will be from elementary schools in Michigan.
Population: Approximately 600 students (230 with attention-deficit hyperactivity disorder, 370 without attention-deficit hyperactivity disorder) 7–13 years of age will participate. Children with attention-deficit hyperactivity disorder will already have a diagnosis by a clinical psychologist.
Intervention: In Study 1, different features of computer games (e.g., task difficulty, type of feedback, reward structure) that can potentially influence motivation will be examined to determine the optimal training configuration for maximizing student engagement and motivation in the training interventions. In Study 2, two working memory interventions will be developed. One intervention (game) is intended to promote the maintenance of information in working memory; the other intervention is intended to promote the controlled processing of information in working memory. The features of the computer games will be determined by the outcome of Study 1. The games will be designed so that a session lasts for 15 to 20 minutes and would be played 5 days a week for 4 weeks. Three additional interventions will be developed that target related but distinct aspects of executive control: (a) the ability to inhibit a response and ignore distracting information, (b) the ability to efficiently and flexibly shift from one task to another, and (c) the ability to ignore irrelevant information. The games will be designed so that a session lasts 15–20 minutes and could be played every day. The interventions that produce the largest effects will then be combined into a single game that would take 15–20 minutes per session and could be played every day for four weeks.
Research Design and Methods: Across all studies, students will be randomly assigned to a training condition or a control group. In Study 1, students will be randomly assigned on one of five conditions. In Study 2 and 3, students will be randomly assigned to one of the two training conditions or a control group. In the first set of studies in Study 4, students will be randomly assigned to one of three training conditions or a control group. In the final study (Study 4c), students will be randomly assigned to the combined training intervention or control group.
Control Condition: Students in the control group will be tested from the same schools, afterschool programs, and clinical settings that treatment students are drawn. There is no control group for Study 1. In studies 2, 3, and 4, those students in the control condition will play a computer game that involves primarily visuomotor planning and is not designed to improve working memory or executive control.
Key Measures: The researchers will assess a number of cognitive and academic outcomes immediately after and 6 months following training. The cognitive assessments include measures of working memory capacity, reaction time, vigilance, and motivation. Students will also be assessed on general reasoning skills (e.g, Raven's coloured progressive matrices), reading (e.g., Woodcock Reading Mastery Test), and arithmetic performance (e.g, KeyMath).
Data Analytic Strategy: Multivariate analytic strategies, including multivariate analysis of variance and multiple regression, will be employed to determine the effects of the interventions for improving cognitive and academic skills in children with attention-deficit hyperactivity disorder.
Buschkuehl, M., Jaeggi, S.M., Shah, P., and Jonides, J. (2012). Working Memory Training and Transfer. In R. Subotnik, A. Robinson, C. Callahan, and P. Johnson (Eds.), Malleable Minds: Translating Insights from Psychology and Neuroscience to Gifted Education (pp. 101–115). Storrs, CT: National Center for Research on Giftedness and Talent, Institute of Education Sciences.
Katz, B., and Shah, P. (in press). Far Transfer may be Nearer Than you Think: Logical and Methodological Factors in Cognitive Training Research. In M. Bunting, J. Novick, M. Dougherty, and R. Engle (Eds.), Cognitive and Working Memory Training: Perspectives From Psychology, Neuroscience, and Human Development. Oxford, UK: Oxford University Press.
Katz, B., Jones, M., Shah, P., Buschkuehl, M., and Jaeggi, S.M. (2016). Individual Differences and Motivational Effects in Cognitive Training Research. In J. Karbach, and T. Strobach (Eds.), Cognitive Training: An Overview of Features and Activations. Springer International Publishing.
Journal article, monograph, or newsletter
Hullman, J., Adar, E., and Shah, P. (2011). Benefitting InfoVis With Visual Difficulties. IEEE Transactions on Visualization and Computer Graphics, 17(12): 2213–2222. doi:10.1109/TVCG.2011.175 Full text
Jaeggi, S.M., Buschkeuhl, M., Shah, P., and Jonides, J. (2014). The Role of Individual Differences in Cognitive Training and Transfer. Memory and Cognition, 42(3): 464–480. doi:10.3758/s13421–013–0364–z
Jaeggi, S.M., Buschkuehl, M., Jonides, J., and Shah, P. (2011). Short- and Long-Term Benefits of Cognitive Training. Proceedings of the National Academy of Sciences, 108(25): 10081–10086. Full text
Jaeggi, S.M., Buschkuehl, M., Jonides, J., and Shah, P. (2012). Cogmed and Working Memory Training Ś Current Challenges and the Search for Underlying Mechanisms. Journal of Applied Research in Memory and Cognition, 1(3): 211–213. doi:10.1016/j.jarmac.2012.07.002
Jonides, J., Jaeggi, S.M., Buschkuehl, M., and Shah, P. (2012). Building Better Brains. Scientific American Mind, 23: 59–63. doi:10.1038/scientificamericanmind0912–59
Katz, B., Buschkuehl, M., Jaeggi, S.M., Stegman, A., and Shah, P. (in press). Effects of GameŚLike Motivational Features on Cognitive Training. Frontiers in Neuroscience.
Katz, B., Jaeggi, S., Buschkuehl, M., Stegman, A., and Shah, P. (2014). Differential Effect of Motivational Features on Training Improvements in School-Based Cognitive Training. Frontiers in Human Neuroscience, 8. doi:10.3389/fnhum.2014.00242
Lustig, C., Shah, P., Seidler, R., and Reuter-Lorenz, P. (2009). Aging, Training, and the Brain: A Review and Future Directions. Neuropsychology Review, 19(4): 504–522. doi:10.1007/s11065–009–9119–9
Shah, P., Buschkuehl, M., Jaeggi, S.M., and Jonides, J. (2012). Cognitive Training for ADHD: The Importance of Individual Differences. Journal of Applied Research in Memory and Cognition, 1(3): 204–205. doi:10.1016/j.jarmac.2012.07.001
Wang, Z., Katz, B., and Shah, P. (2014). New Directions in Intelligence Research: Avoiding the Mistakes of the Past. Journal of Intelligence, 2(1): 16–20. doi:10.3390/jintelligence2010016 Full text
Wang, Z., Zhou, R., and Shah, P. (2014). Spaced Cognitive Training Promotes Training Transfer. Frontiers in Human Neuroscience, 8: 1–13. Full text
Nongovernment report, issue brief, or practice guide
Hullman, J., Rhodes, R., Rodriguez, F., and Shah, P. (2011). Research on Graph Comprehension and Data Interpretation: Implications for Score Reporting. Princeton, NJ: Educational Testing Service. Full text
Shah, P., and Rhodes, R. (2012). Educational Interventions, Socioeconomic Status, and the Brain. Sesame Workshop.
Hullman, J., Adar, E., and Shah, P. (2011). The Impact of Social Information on Visual Judgments. In Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems (pp. 1461–1470). New York: Association for Computing Machinery (ACM). doi:10.1145/1978942.1979157
Kupitz, C., Buschkuehl, M. Jaeggi, S. Jonides, J., Shah, P., and Vandekerckhove, J. (2015). A Diffusion Model Account of the Transfer-of-Training Effect. Retrieved from https://www.semanticscholar.org/paper/A-diffusion-model-account-of-the-transfer-of-Kupitz-Buschkuehl/934bfc81e6edhttps://www.semanticscholar.org/paper/A-diffusion-model-account-of-the-transfer-of-Kupitz-Buschkuehl/934bfc81e6ed02556b09c6a5d29a7e93b82fe592.