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Abstract
Attention deficit hyperactivity disorder (ADHD) is characterized by deficient self-regulation, poor attentional control, and poor response inhibition. To date, however, the extent to which these deficits affect basic interference control remains a matter of controversy. Secondly, ADHD has been reported to be associated with arithmetic deficits. It remains unclear whether such deficits are a secondary consequence of the above-mentioned characteristics of ADHD or whether basic numerical magnitude representations are also affected. In the present study we attempted to investigate these issues using a basic numerical interference paradigm.
Nine- to twelve-year-old children with ADHD-C (attention-deficit hyperactivity-disorder combined type) and control children without ADHD (each n = 16) were presented with two digits of possibly different physical sizes (e.g., 3 7). This numerical Stroop task requires subjects to make a magnitude classification concerning either the physical or the numerical stimulus dimension. The irrelevant dimension can be congruent (same response), incongruent (different response), or neutral (no response association).
Children with ADHD-C performed worse than control children in most analyses. The most important finding was a significant interaction of congruity effects with group in the numerical comparison task. Children with ADHD-C tended to show larger congruity and interference effects than controls, and these were not attributable to a speed-accuracy trade-off.
The results might reflect differential processing speeds, or a different degree of automatic activation of physical and numerical magnitudes in children with and without ADHD-C. Alternative explanations, such as insufficient inhibition of selective (domain-specific) attention are also discussed.
The order of authorship is alphabetical, as the authors contributed equally to the study. We wish to thank the participating children, their parents, and their schools for contributing to this project. We are also grateful to Susanna Bloder and Maria Hoellwarth for data collection, and Elise Klein and Katharina Dressel for checking the references. In particular, we wish to thank Stuart Fellows for checking English style and grammar. This research was supported by funding of the European Community (Neuromath: HPRN-CT-2000-00076 and RTN NUMBRA proposal 504927), by grants of the RWTH Aachen (IZKF “BioMAT.”; VV N50, 51, 69c; START AZ 160/05) and the German Research Society (DFG: KFO110/TP2) supporting the research of Hans-Christoph Nuerk.
Notes
The order of authorship is alphabetical, as the authors contributed equally to the study. We wish to thank the participating children, their parents, and their schools for contributing to this project. We are also grateful to Susanna Bloder and Maria Hoellwarth for data collection, and Elise Klein and Katharina Dressel for checking the references. In particular, we wish to thank Stuart Fellows for checking English style and grammar. This research was supported by funding of the European Community (Neuromath: HPRN-CT-2000-00076 and RTN NUMBRA proposal 504927), by grants of the RWTH Aachen (IZKF “BioMAT.”; VV N50, 51, 69c; START AZ 160/05) and the German Research Society (DFG: KFO110/TP2) supporting the research of Hans-Christoph Nuerk.
1In Austria, performance levels are rated on a 5-graded scale: “1” corresponding to very good, “2” good, “3” satisfactory, “4” sufficient, and “5” not sufficient.
2We wish to thank Jacobus Donders for this suggestion of how to deal with the power issue.
3We wish to thank an anonymous reviewer for suggesting these analyses. Another possible source for the unexpected performance in the neutral/same condition could be learning effects (which may differentially affect different conditions in different groups) or that are due to unequal distribution of conditions. However, this did not seem to be the case. First, the conditions were equally distributed: When the 120 trials were subdivided into sub-blocks of 30 trials, the distribution of conditions did not differ from equality (chi2 (6) = 3.20; p = .78). Secondly, when learning effects were analyzed over these 4 blocks of 30 trials, we observed no systematic interaction of learning effects with group. However, there was a general speed-accuracy trade-off over time. for numerical comparisons, children became faster (linear trend over blocks F(1, 29) = 27.04, p < .01; f = .43, 1-β = 1.00), but less accurate over time (linear trend over blocks F(1, 29) = 9.43, p < .01; f = .23, 1-β = .84). for physical comparisons, children also became faster (linear trend over blocks F(1, 29) = 12.94, p < .01; f = .29, 1-β = .99) and less variable (F(1, 29) = 4.59, p < .05; f = .13, 1-β = .55). However, there was no significant learning effect in the accuracy analyses (F(1, 29) = 2.90, p = .09; f = .09, 1-β = .38) that had yielded the clearest results for the inferior performance in the neutral conditions. In sum, learning effects did not seem to be responsible for the inferior performance in the neutral condition. The fact that inferior performance of children with ADHD-C in the neutral/same condition seems neither due to few outliers nor to learning effects strengthens the impact of these data.