Associations between Socioeconomic Status, Cognition, and Brain Structure: Evaluating Potential Causal Pathways through Mechanistic Models of Development

Autor/inn/en	Thomas, Michael S. C.; Coecke, Selma
Titel	Associations between Socioeconomic Status, Cognition, and Brain Structure: Evaluating Potential Causal Pathways through Mechanistic Models of Development
Quelle	In: Cognitive Science, 47 (2023) 1, (55 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Thomas, Michael S. C.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.13217
Schlagwörter	Socioeconomic Status; Cognitive Processes; Brain; Cognitive Development; Causal Models; Neurological Organization; Language Acquisition; Environmental Influences; Prenatal Influences; Response to Intervention; Individual Differences + Suchen Sie Ihr Suchwort? Socio-economic status; Sozioökonomischer Status; Cognitive process; Kognitiver Prozess; Gehirn; Kognitive Entwicklung; Kausalanalyse; Sprachaneignung; Spracherwerb; Environmental influence; Umwelteinfluss; Pränataler Einfluss; Individueller Unterschied
Abstract	Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01