Children's Brains and Socioeconomic Status: A Selective-Attention ERP Study
Bibliographic record
Abstract
Children’s Brains and Socioeconomic Status: A Selective-Attention ERP Study Anthony T. Herdman (aherdman@tru.ca) & Amedeo D’Angiulli (adangiulli@tru.ca) The Centre for Early Education and Development, Thompson Rivers University Suite 103 – 1402 McGill Road, Kamloops, B.C., Canada V2C 1L3 Clyde Hertzman (clyde.hertzman@interchange.ubc.ca) Health Care and Epidemiology, University of British Columbia 5804 Fairview Avenue, Vancouver, B.C., Canada V6T 1Z3 David R. Stapells (stapells@audiospeech.ubc.ca) School of Audiology and Speech Sciences, University of British Columbia Room 205, 5804 Fairview Avenue, Vancouver, B.C., Canada V6T 1Z3 Introduction Children’s cognitive abilities, such as attention, are associated with their socioeconomic status (SES) (Noble, Norman, & Farah, 2005). Many everyday activities that children carry out at school, at home, and in the community require selective attention – i.e., attending to relevant information while ignoring irrelevant and distracting information. Although studies have shown that behavioural measures of cognitive functions are linked to SES and that attention-related brain responses can be recorded from children (Berman & Friedman, 1995), little is known about the relationships between the neural responses underlying selective attention and SES in children. The present study is a first attempt at verifying whether such relationships exist. Methods Thirty-four children (aged 11-12) volunteered from two schools in distinct SES areas. Individual SES was matched to school area using a composite measure of neighborhood, parents’ occupation, income and education. Children were then confirmed as belonging to a high or low SES group based on norms established by Statistics Canada. Electroencephalography (EEG) at F3, F4, Fz, FC3, FC4, Cz, Pz, VEOG sites was recorded during 2 blocks (either for 8- or 12-kHz tones) each consisting of 30 (10%) target- duration (either 100 or 250 ms) tones, 30 (10%) unattended target-duration tones with same the duration as target- duration tones but not frequency, 120 (40%) attended non- target duration tones with the same frequency as target tones but not duration, and 120 (40%) unattended non- target duration tones with different frequency and duration as target tones. Children listened to the tones randomly presented at an inter-stimulus interval of 1 second and were asked to press a button to target tones as accurately and as fast as possible. Reaction time and accuracy were measured from responses. Each participant’s EEG was epoched and averaged for each stimulus type. Event-related potential (ERP) differences between attended non-target-duration tones and unattended non-target-duration tones were calculated. Amplitudes of the attention-related Nd (difference negativity) wave were calculated as the maximum negative deflection between 100-400 ms in the ERP difference waveforms. Results Reaction times, accuracies, and false alarms were not significantly different between low (616 ms, 76%, 3%) and high (579 ms, 73%, 3%) SES children. The Nd amplitudes; however, were more negative for high SES (-3.2 µV ± 0.3 S.E.) than low SES (-2.4 µV ± 0.3 S.E.) children (p<.05). Discussion This is the first evidence that the Nd brain response, which reflects selective-attention, is related to SES. Because behavioural results were similar between SES groups, the group differences in Nd amplitudes could indicate that our behavioural results were insensitive to finding attentional differences between SES groups or that children with low SES are utilizing other neurocognitive systems to maintain similar performances as compared to children with high SES. In on-going studies, we are currently investigating which of these two possibilities is the correct one. Acknowledgement We thank Drs. Tim Oberlander and Joanne Weinberg for the help with this project. Grants from the Canadian Foundation for Innovation and the Human Early Learning Partnership supported this research. References Berman, S., & Friedman, D. (1995). The development of selective attention as reflected by event-related brain potentials. Journal of Experimental Child Psychology, Noble, K. G., Norman, M. F., & Farah, M. J. (2005). Neurocogntivie correlates of socioeconomic status in kindergarten children. Developmental Science, 8(1), 74-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".