Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 104

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The neuroscience of socioeconomic inequality

Noble, Kimberly G, Giebler, Melissa A·2020·Current Opinion in Behavioral Sciences
DOI 10.1016/j.cobeha.2020.05.007
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cobeha.2020.05.007

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 47

  1. Farah, 2017, The neuroscience of socioeconomic status: correlates, causes, and consequences, Neuron, 96, 56, 10.1016/j.neuron.2017.08.034 · DOI · Paper page
  2. Noble, 2015, Socioeconomic disparities in neurocognitive development in the first two years of life, Dev Psychobiol, 57, 535, 10.1002/dev.21303 · DOI
  3. Moorman, 2018, Childhood socioeconomic status and genetic risk for poorer cognition in later life, Soc Sci Med, 212, 219, 10.1016/j.socscimed.2018.07.025 · DOI
  4. Merz, 2019, Socioeconomic disparities in chronic physiologic stress are associated with brain structure in children, Biol Psychiatry, 86, 921, 10.1016/j.biopsych.2019.05.024 · DOI
  5. Merz, 2019, Socioeconomic disparities in language input are associated with children’s language-related brain btructure and reading skills, Child Dev
  6. McDermott, 2019, Longitudinally mapping childhood socioeconomic status associations with cortical and subcortical morphology, J Neurosci, 39, 1365, 10.1523/JNEUROSCI.1808-18.2018 · DOI · Paper page
  7. Hair, 2015, Association of child poverty, brain development, and academic achievement, JAMA Pediatr, 169, 822, 10.1001/jamapediatrics.2015.1475 · DOI
  8. Mackey, 2015, Neuroanatomical correlates of the income-achievement gap, Psychol Sci, 26, 925, 10.1177/0956797615572233 · DOI · Paper page
  9. Wrigglesworth, 2019, Brain-derived neurotrophic factor DNA methylation mediates the association between neighborhood disadvantage and adolescent brain structure, Psychiatry Res-Neuroimaging, 285, 51, 10.1016/j.pscychresns.2018.12.012 · DOI · Paper page
  10. Whittle, 2017, Role of positive parenting in the association between neighborhood social disadvantage and brain development across adolescence, JAMA Psychiatry, 74, 824, 10.1001/jamapsychiatry.2017.1558 · DOI
  11. Marshall, 2018, Socioeconomic disadvantage and altered corticostriatal circuitry in urban youth, Hum Brain Mapp, 39, 1982, 10.1002/hbm.23978 · DOI
  12. Tooley, 2019, Associations between neighborhood ses and functional brain network development, Cereb Cortex
  13. Tomlinson, 2020, Neighborhood poverty predicts altered neural and behavioral response inhibition, Neuroimage, 10.1016/j.neuroimage.2020.116536 · DOI
  14. Piccolo, 2016, Pediatric Imaging NGS: Age-related differences in cortical thickness vary by socioeconomic status, PLoS One, 11, 10.1371/journal.pone.0162511 · DOI
  15. Brito, 2017, Associations between cortical thickness and neurocognitive skills during childhood vary by family socioeconomic factors, Brain Cogn, 116, 54, 10.1016/j.bandc.2017.03.007 · DOI
  16. Leonard, 2019, Associations between cortical thickness and reasoning differ by socioeconomic status in development, Dev Cogn Neurosci, 36, 10.1016/j.dcn.2019.100641 · DOI
  17. Gur, 2019, Burden of environmental adversity associated with psychopathology, maturation, and brain behavior parameters in youths, JAMA Psychiatry, 76, 966, 10.1001/jamapsychiatry.2019.0943 · DOI · Paper page
  18. Betancourt, 2016, Effect of socioeconomic status (ses) disparity on neural development in female African‐American infants at age 1 month, Dev Sci, 19, 947, 10.1111/desc.12344 · DOI · Paper page
  19. Holz, 2015, The long-term impact of early life poverty on orbitofrontal cortex volume in adulthood: results from a prospective study over 25 years, Neuropsychopharmacology, 40, 996, 10.1038/npp.2014.277 · DOI
  20. Raffington, 2019, Stable longitudinal associations of family income with children’s hippocampal volume and memory persist after controlling for polygenic scores of educational attainment, Dev Cogn Neurosci, 10.1016/j.dcn.2019.100720 · DOI
  21. Dufford, 2018, Socioeconomic disadvantage, brain morphometry, and attentional bias to threat in middle childhood, Cogn Affect Behav Neurosci, 1
  22. Ellwood-Lowe, 2018, Time-varying effects of income on hippocampal volume trajectories in adolescent girls, Dev Cogn Neurosci, 30, 41, 10.1016/j.dcn.2017.12.005 · DOI
  23. Merz, 2018, Socioeconomic status, amygdala volume, and internalizing symptoms in children and adolescents, J Clin Child Adolesc Psychol, 47, 312, 10.1080/15374416.2017.1326122 · DOI
  24. Jenkins, 2020, Subcortical structural variations associated with low socioeconomic status in adolescents, Hum Brain Mapp, 41, 162, 10.1002/hbm.24796 · DOI
  25. Kim, 2019, Childhood poverty and the organization of structural brain connectome, Neuroimage, 184, 409, 10.1016/j.neuroimage.2018.09.041 · DOI
  26. Rosen, 2018, Socioeconomic disparities in academic achievement: a multi-modal investigation of neural mechanisms in children and adolescents, Neuroimage, 173, 298, 10.1016/j.neuroimage.2018.02.043 · DOI
  27. Gullick, 2016, Reading skill-fractional anisotropy relationships in visuospatial tracts diverge depending on socioeconomic status, Dev Sci, 19, 673, 10.1111/desc.12428 · DOI
  28. Dufford, 2017, Family income, cumulative risk exposure, and white matter structure in middle childhood, Front Hum Neurosci, 11, 547, 10.3389/fnhum.2017.00547 · DOI
  29. Takeuchi, 2018, The effects of family socioeconomic status on psychological and neural mechanisms as well as their sex differences, Front Hum Neurosci, 12, 543, 10.3389/fnhum.2018.00543 · DOI
  30. Ozernov-Palchik, 2019, The relationship between socioeconomic status and white matter microstructure in pre‐reading children: a longitudinal investigation, Hum Brain Mapp, 10.1002/hbm.24407 · DOI
  31. Brito, 2016, Associations among family socioeconomic status, eeg power at birth, and cognitive skills during infancy, Dev Cogn Neurosci, 19, 144, 10.1016/j.dcn.2016.03.004 · DOI
  32. Cantiani, 2019, Oscillatory gamma activity mediates the pathway from socioeconomic status to language acquisition in infancy, Infant Behav Dev, 57, 10.1016/j.infbeh.2019.101384 · DOI · Paper page
  33. Gao, 2015, Functional network development during the first year: relative sequence and socioeconomic correlations, Cereb Cortex, 25, 2919, 10.1093/cercor/bhu088 · DOI · Paper page
  34. Barch, 2016, Effect of hippocampal and amygdala connectivity on the relationship between preschool poverty and school-age depression, Am J Psychiatry, 173, 625, 10.1176/appi.ajp.2015.15081014 · DOI
  35. Gellci, 2019, Community and household-level socioeconomic disadvantage and functional organization of the salience and emotion network in children and adolescents, Neuroimage, 184, 729, 10.1016/j.neuroimage.2018.09.077 · DOI
  36. Younger, 2019, Brain lateralization of phonological awareness varies by maternal education, Dev Sci, 22, 10.1111/desc.12807 · DOI
  37. Conant, 2017, The relationship between maternal education and the neural substrates of phoneme perception in children: interactions between socioeconomic status and proficiency level, Brain Lang, 171, 14, 10.1016/j.bandl.2017.03.010 · DOI
  38. Romeo, 2018, Beyond the 30-million-word gap: children’s conversational exposure is associated with language-related brain function, Psychol Sci, 29, 700, 10.1177/0956797617742725 · DOI
  39. Ralph, 2020, Using the n400 event-related potential to study word learning from context in children from low-and higher-socioeconomic status homes, J Exp Child Psychol, 191, 10.1016/j.jecp.2019.104758 · DOI
  40. Sheridan, 2017, Dimensions of childhood adversity have distinct associations with neural systems underlying executive functioning, Dev Psychopathol, 29, 1777, 10.1017/S0954579417001390 · DOI · Paper page
  41. Finn, 2017, Functional brain organization of working memory in adolescents varies in relation to family income and academic achievement, Dev Sci, 20, 10.1111/desc.12450 · DOI
  42. White, 2019, Exposure to violence and low family income are associated with heightened amygdala responsiveness to threat among adolescents, Dev Cogn Neurosci, 40, 10.1016/j.dcn.2019.100709 · DOI
  43. Kim, 2017, Socioeconomic disadvantage, neural responses to infant emotions, and emotional availability among first-time new mothers, Behav Brain Res, 325, 188, 10.1016/j.bbr.2017.02.001 · DOI
  44. Farah, 2018, Socioeconomic status and the brain: prospects for neuroscience-informed policy, Nat Rev Neurosci, 19, 428, 10.1038/s41583-018-0023-2 · DOI
  45. von Stumm, 2019, Predicting educational achievement from genomic measures and socioeconomic status, Dev Sci
  46. Merz, 2019, Socioeconomic inequality and the developing brain: spotlight on language and executive function, Child Dev Perspect, 13, 15, 10.1111/cdep.12305 · DOI
  47. Romeo, 2018, Language exposure relates to structural neural connectivity in childhood, J Neurosci, 38, 7870, 10.1523/JNEUROSCI.0484-18.2018 · DOI

Cited by

See all 104

Metadata

Authors
Noble, K. G., & Giebler, M. A.
Venue
Current Opinion in Behavioral Sciences
Publisher
Elsevier BV
Published
2020-12
Volume
36
Pages
23-28
ISSN
2352-1546
DOI
10.1016/j.cobeha.2020.05.007
Cited by
104
References
2
Language
en
Licence
Funders
National Institutes of Health

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The neuroscience of socioeconomic inequality. https://app.cochat.ai/papers/10.1016/j.cobeha.2020.05.007
The paper
Noble, K. G., & Giebler, M. A. (2020). The neuroscience of socioeconomic inequality. Current Opinion in Behavioral Sciences. https://doi.org/10.1016/j.cobeha.2020.05.007

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.