Children's Brains and Socioeconomic Status: A Selective-Attention ERP Study
Notice bibliographique
Résumé
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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Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».