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
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),
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,
- 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,
- 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),
- 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
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.
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.
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
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:
- 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
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:
- Khng, K. H., Ang, S. Y., Bull, R., & Lee, K.
(2014). Trends in Applied Cognitive Development. Singapore.
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: