Applied Cognitive Development

Description of Task Force

This research area will continue with OER’s strength in cutting-edge scientific research with a focus on translation. The overarching objective of the work conducted within the Applied Cognitive Development group is to understand the domain-specific, cognitive and affective predictors of academic achievement, and to develop effective interventions for low-achieving children. To achieve this, we focus on the integration of a range of measurement methods including behavioural, physiological, and neurological techniques. Specific research foci include examining the interaction between cognition, emotion and classroom pedagogy, the relationship between higher cognitive functions (e.g., maintaining attention, holding information in memory) and academic achievement, development of more sensitive measures of these higher cognitive functions, investigating the interplay between emotional and/or genetic factors and higher cognitive functions, investigating the degree to which these relationships differ between normally achieving children and those with difficulties in mathematics and reading, and developing intervention protocol (cognitive, physical, and attentional) for helping children at-risk. Our research also focuses on the early pre-literacy and pre-numeracy skills that result in low achievement in literacy and mathematics, and the possibilities for early intervention.

List of team members:

List of team members' publications related to the research theme:

  • Rebecca Bull
    • Bull, R., Espy, K. A., & Senn, T. E. (2004). A comparison of performance on the Towers of London and Hanoi in young children.Journal of Child Psychology and Psychiatry, 45, 743–754
    • Bull, R., Espy, K. A., Wiebe, S. A., Sheffield, T., & Nelson, J. M. (2011). Using confirmatory factor analysis to understand executive control in preschool children: Sources of variation in emergent mathematic achievement. Developmental Science, 14, 679–692.
    • Bull, R., Espy, K. A., & Wiebe, S. (2008). Short-term memory, working memory and executive functioning: Longitudinal predictors of mathematics achievement at age 7. Developmental Neuropsychology, 33, 205–228
    • Bull, R., & Johnston, R. S. (1997). Children’s arithmetical difficulties: Contributions from processing speed, item identification, and short term memory. Journal of Experimental Child Psychology, 65, 1–24.
    • Bull, R., Johnston, R. S., & Roy, J. A. (1999). Exploring the roles of the visual-spatial sketch pad and central executive in children’s arithmetical skills: Views from cognition and developmental neuropsychology. Developmental Neuropsychology, 15, 421–442
    • Bull, R., & Lee, K. (2014). Executive functioning and mathematics achievement. Child Development Perspectives, 8, 36–41.
    • Bull, R., & Scerif, G. (2001). Executive functioning as a predictor of children’s mathematics ability: Shifting, inhibition, and working memory. Developmental Neuropsychology, 19, 273–293.
    • Espy, K. A., & Bull, R. (2005). Inhibitory processes in young children and individual variation in short-term memory. Developmental Neuropsychology, 28, 669–688.
    • Espy, K. A., Bull, R., & Martin, J., & Stroup, W. (2006). Measuring the development of executive control with the Shape School.Psychological Assessment, 18, 373–381.
    • McKenzie, B., Bull, R., & Gray, C. (2003). The effects of visual-spatial and phonological disruption on children's arithmetical skills.Educational and Child Psychology, 20, 93–108
    • Rennie, D. A. C., Bull, R., & Diamond, A. (2004). Executive functioning in preschoolers: Reducing the inhibitory demands of the Dimensional Change Card Sort task. Developmental Neuropsychology, 26, 423–443.
    • Whyte, J. C., & Bull, R. (2008). Number games, magnitude representation, and basic number skills in preschoolers. Developmental Psychology, 44, 588–596.
  • Kerry Lee
    • Lee, K. (2004). Age, neuropsychological, and social cognitive measures as predictors of individual differences in susceptibility to the misinformation effect. Applied Cognitive Psychology, 18(8), 997–1019.
    • Lee, K., Bull, R., & Ho, R. M. (2013). Developmental differences in the structure of executive functions. Child Development, 84, 1933–1953.
    • Lee, K., & Bussey, K. (2001). Children’s susceptibility to retroactive interference: The effects of age and degree of learning. Journal of Experimental Child Psychology, 80, 372–391.
    • Lee, K., Khng, F., Ng, S. F., & Ng, J. L. K. (2013). Longer bars for bigger numbers? Children’s usage and understanding of graphic representations of algebraic problems. Frontline Learning Research, 1, 81–96.
    • Lee, K., Lim, Z. Y., Yeung, S., Venkatraman, V., Ng, S. F., & Chee, M. W. L. (2007). Strategic differences in algebraic problem solving: Neuroanatomical correlates. Brain Research, 1155(June), 163–171.
    • Lee, K., Ng, E. L., & Ng, S. F. (2009). The contributions of working memory and executive functioning to problem representation and solution generation in algebraic word problems. Journal of Educational Psychology, 101(2), 373–387.
    • Lee, K., Ng, S. F., Ng, E. L., & Lim, Z. Y. (2004). Working memory and literacy as predictors of performance on algebraic word problems. Journal of Experimental Child Psychology, 89(2), 140–158.
    • Lee, K., Ng, S.-F., Bull, R., Le Pe, M., & Moon, R. H. (2011). Are patterns important? An investigation of the relationships between proficiencies in patterns, computation, executive functioning, and algebraic word problems. Journal of Educational Psychology, 103, 269–281.
    • Lee, K., Pe, M. L., Ng, S-W., Ang, S. Y., Hasshim, M.N.A.M, & Bull, R. (2012). The cognitive underpinnings of emerging mathematical skills: executive functioning, patterns, numeracy, and arithmetic. British Journal of Educational Psychology, 82, 82–99.
    • Lee, K., Pe, M. L., Ang, S. Y., & Stankov, L. (2009). Do Measures of Working Memory Predict Academic Proficiency Better Than Measures of Intelligence? Psychological Science Quarterly, 51(4), 403–419.
    • Ng, S. F., & Lee, K. (2009). Model method: Singapore children’s tool for representing and solving algebra word problems. Journal for Research in Mathematics Education, 40(3), 282–313.
    • Ng, S. F., Lee, K., Ang, S. Y., & Khng, F. (2007). Model Method: Obstacle or bridge to learning symbolic algebra. In W. Bokhorst-Heng & L. Osborne (Eds.). Redesigning Pedagogies. New York, NY: Sense Publishing
  • Beth Ann O’Brien
    • Kroeger, L. A., Brown, R. D., & O’Brien, B. A. (2012). Connecting neuroscience, cognitive, and educational theories and research to practice: A review of mathematics intervention programs. Early Education and Development, 23, 37–58.
    • O’Brien, B. A., Wallot, S., Haussmann, A., & Kloos, H. (in press). Using complexity metrics to assess silent reading fluency: A cross-sectional study comparing oral and silent reading. Scientific Studies of Reading. doi: 10.1080/10888438.2013.862248.
  • Ang Su Yin
    • Ang, S. Y., & Lee, K. (2008). Central executive involvement in children’s spatial memory. Memory, 16, 918–933.
    • Ang, S. Y., & Lee, K. (2010). Exploring developmental differences in visual short-term memory and working memory. Developmental Psychology, 46, 279–285.
  • Khng Kiat Hui
    • Khng, K., & Lee, K. (2011). Neural mechanisms underlying stroop and stop-signal inhibition: Modulation by susceptibility to prepotent interference from prior knowledge. Front. Hum. Neurosci. Conference Abstract: XI International Conference on Cognitive Neuroscience (ICON XI). doi: 10.3389/conf.fnhum.2011.207.00420
    • Khng, K. H., Ang, S. Y., Bull, R., & Lee, K. (2014). Trends in Applied Cognitive Development. Singapore.
    • Khng, K. H., & Lee, K. (2009). Inhibiting interference from prior knowledge: Arithmetic intrusions in algebra word problem solving. Learning and Individual Differences, 19(2), 262–268.
  • Ng Ee Lynn
    • Ng, E. L., & Lee, K. (2010). Children’s task performance under stress and non-stress conditions: A test of the processing efficiency theory. Cognition and Emotion, 24, 1229–1238.

List of projects from this research group: