CoCoA: Conditional Correlation Models with Association Size Article Swipe
Danni Tu
,
Bridget W. Mahony
,
Tyler M. Moore
,
Maxwell A. Bertolero
,
Aaron Alexander‐Bloch
,
Ruben C. Gur
,
Dani S. Bassett
,
Theodore D. Satterthwaite
,
Armin Raznahan
,
Russell T. Shinohara
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.03.28.486098
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.03.28.486098
S ummary Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose CoCoA (Conditional Correlation Model with Association Size), a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale, while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semi-parametric estimators adapted from genome association studies, and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modelling and partitioned correlation analyses.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.03.28.486098
- https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdf
- OA Status
- green
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4221044482
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4221044482Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.03.28.486098Digital Object Identifier
- Title
-
CoCoA: Conditional Correlation Models with Association SizeWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-29Full publication date if available
- Authors
-
Danni Tu, Bridget W. Mahony, Tyler M. Moore, Maxwell A. Bertolero, Aaron Alexander‐Bloch, Ruben C. Gur, Dani S. Bassett, Theodore D. Satterthwaite, Armin Raznahan, Russell T. ShinoharaList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.03.28.486098Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdfDirect OA link when available
- Concepts
-
Estimator, Correlation, Statistics, Covariate, Econometrics, Regression analysis, Regression, Mathematics, Computer science, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4221044482 |
|---|---|
| doi | https://doi.org/10.1101/2022.03.28.486098 |
| ids.doi | https://doi.org/10.1101/2022.03.28.486098 |
| ids.openalex | https://openalex.org/W4221044482 |
| fwci | 0.0 |
| type | preprint |
| title | CoCoA: Conditional Correlation Models with Association Size |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11577 |
| topics[0].field.id | https://openalex.org/fields/32 |
| topics[0].field.display_name | Psychology |
| topics[0].score | 0.9807999730110168 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3205 |
| topics[0].subfield.display_name | Experimental and Cognitive Psychology |
| topics[0].display_name | Cognitive Abilities and Testing |
| topics[1].id | https://openalex.org/T10261 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9749000072479248 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1311 |
| topics[1].subfield.display_name | Genetics |
| topics[1].display_name | Genetic Associations and Epidemiology |
| topics[2].id | https://openalex.org/T13283 |
| topics[2].field.id | https://openalex.org/fields/32 |
| topics[2].field.display_name | Psychology |
| topics[2].score | 0.9710999727249146 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3205 |
| topics[2].subfield.display_name | Experimental and Cognitive Psychology |
| topics[2].display_name | Mental Health Research Topics |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C185429906 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6802396178245544 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1130160 |
| concepts[0].display_name | Estimator |
| concepts[1].id | https://openalex.org/C117220453 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5623860955238342 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5172842 |
| concepts[1].display_name | Correlation |
| concepts[2].id | https://openalex.org/C105795698 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5451790690422058 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[2].display_name | Statistics |
| concepts[3].id | https://openalex.org/C119043178 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5437704920768738 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q320723 |
| concepts[3].display_name | Covariate |
| concepts[4].id | https://openalex.org/C149782125 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5365495681762695 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[4].display_name | Econometrics |
| concepts[5].id | https://openalex.org/C152877465 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4494462013244629 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q208042 |
| concepts[5].display_name | Regression analysis |
| concepts[6].id | https://openalex.org/C83546350 |
| concepts[6].level | 2 |
| concepts[6].score | 0.444247841835022 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1139051 |
| concepts[6].display_name | Regression |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.3912610113620758 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.35091084241867065 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C2524010 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[9].display_name | Geometry |
| keywords[0].id | https://openalex.org/keywords/estimator |
| keywords[0].score | 0.6802396178245544 |
| keywords[0].display_name | Estimator |
| keywords[1].id | https://openalex.org/keywords/correlation |
| keywords[1].score | 0.5623860955238342 |
| keywords[1].display_name | Correlation |
| keywords[2].id | https://openalex.org/keywords/statistics |
| keywords[2].score | 0.5451790690422058 |
| keywords[2].display_name | Statistics |
| keywords[3].id | https://openalex.org/keywords/covariate |
| keywords[3].score | 0.5437704920768738 |
| keywords[3].display_name | Covariate |
| keywords[4].id | https://openalex.org/keywords/econometrics |
| keywords[4].score | 0.5365495681762695 |
| keywords[4].display_name | Econometrics |
| keywords[5].id | https://openalex.org/keywords/regression-analysis |
| keywords[5].score | 0.4494462013244629 |
| keywords[5].display_name | Regression analysis |
| keywords[6].id | https://openalex.org/keywords/regression |
| keywords[6].score | 0.444247841835022 |
| keywords[6].display_name | Regression |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.3912610113620758 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.35091084241867065 |
| keywords[8].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.1101/2022.03.28.486098 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nd |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2022.03.28.486098 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5079162823 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8153-1652 |
| authorships[0].author.display_name | Danni Tu |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I4210127693, https://openalex.org/I79576946 |
| authorships[0].affiliations[0].raw_affiliation_string | The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[0].institutions[0].id | https://openalex.org/I4210127693 |
| authorships[0].institutions[0].ror | https://ror.org/047939x15 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I102322052, https://openalex.org/I1335321130, https://openalex.org/I4210127693, https://openalex.org/I79576946 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Penn Center for AIDS Research |
| authorships[0].institutions[1].id | https://openalex.org/I79576946 |
| authorships[0].institutions[1].ror | https://ror.org/00b30xv10 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I79576946 |
| authorships[0].institutions[1].country_code | US |
| authorships[0].institutions[1].display_name | University of Pennsylvania |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Danni Tu |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[1].author.id | https://openalex.org/A5079355850 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8081-2791 |
| authorships[1].author.display_name | Bridget W. Mahony |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4210158500 |
| authorships[1].affiliations[0].raw_affiliation_string | Section on Developmental Neurogenomics, National Institutes of Mental Health, Bethesda, MD, USA |
| authorships[1].institutions[0].id | https://openalex.org/I4210158500 |
| authorships[1].institutions[0].ror | https://ror.org/04xeg9z08 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I1299022934, https://openalex.org/I1299303238, https://openalex.org/I4210158500 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | National Institute of Mental Health |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Bridget Mahony |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Section on Developmental Neurogenomics, National Institutes of Mental Health, Bethesda, MD, USA |
| authorships[2].author.id | https://openalex.org/A5024589723 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1384-0151 |
| authorships[2].author.display_name | Tyler M. Moore |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tyler M. Moore |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[3].author.id | https://openalex.org/A5014121556 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-2691-3698 |
| authorships[3].author.display_name | Maxwell A. Bertolero |
| authorships[3].countries | US |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I4210151747 |
| authorships[3].affiliations[1].raw_affiliation_string | Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA |
| authorships[3].institutions[0].id | https://openalex.org/I4210151747 |
| authorships[3].institutions[0].ror | https://ror.org/04yt1rh21 |
| authorships[3].institutions[0].type | healthcare |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210151747 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Lifespan |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Maxwell A. Bertolero |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA, Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA |
| authorships[4].author.id | https://openalex.org/A5021517716 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-6554-1893 |
| authorships[4].author.display_name | Aaron Alexander‐Bloch |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Aaron F. Alexander-Bloch |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[5].author.id | https://openalex.org/A5058433895 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9657-1996 |
| authorships[5].author.display_name | Ruben C. Gur |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Ruben Gur |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[6].author.id | https://openalex.org/A5110814853 |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Dani S. Bassett |
| authorships[6].countries | US |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I79576946 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[6].affiliations[1].institution_ids | https://openalex.org/I79576946 |
| authorships[6].affiliations[1].raw_affiliation_string | Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[6].affiliations[2].institution_ids | https://openalex.org/I79576946 |
| authorships[6].affiliations[2].raw_affiliation_string | Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[6].affiliations[3].institution_ids | https://openalex.org/I79576946 |
| authorships[6].affiliations[3].raw_affiliation_string | Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[6].institutions[0].id | https://openalex.org/I79576946 |
| authorships[6].institutions[0].ror | https://ror.org/00b30xv10 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I79576946 |
| authorships[6].institutions[0].country_code | US |
| authorships[6].institutions[0].display_name | University of Pennsylvania |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Dani S. Bassett |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA, Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA, Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[7].author.id | https://openalex.org/A5039906500 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-7072-9399 |
| authorships[7].author.display_name | Theodore D. Satterthwaite |
| authorships[7].countries | US |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I4210151747 |
| authorships[7].affiliations[0].raw_affiliation_string | Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA |
| authorships[7].affiliations[1].raw_affiliation_string | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA |
| authorships[7].institutions[0].id | https://openalex.org/I4210151747 |
| authorships[7].institutions[0].ror | https://ror.org/04yt1rh21 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I4210151747 |
| authorships[7].institutions[0].country_code | US |
| authorships[7].institutions[0].display_name | Lifespan |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Theodore D. Satterthwaite |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA, Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA |
| authorships[8].author.id | https://openalex.org/A5059250686 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-5622-1190 |
| authorships[8].author.display_name | Armin Raznahan |
| authorships[8].countries | US |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210158500 |
| authorships[8].affiliations[0].raw_affiliation_string | Section on Developmental Neurogenomics, National Institutes of Mental Health, Bethesda, MD, USA |
| authorships[8].institutions[0].id | https://openalex.org/I4210158500 |
| authorships[8].institutions[0].ror | https://ror.org/04xeg9z08 |
| authorships[8].institutions[0].type | facility |
| authorships[8].institutions[0].lineage | https://openalex.org/I1299022934, https://openalex.org/I1299303238, https://openalex.org/I4210158500 |
| authorships[8].institutions[0].country_code | US |
| authorships[8].institutions[0].display_name | National Institute of Mental Health |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Armin Raznahan |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Section on Developmental Neurogenomics, National Institutes of Mental Health, Bethesda, MD, USA |
| authorships[9].author.id | https://openalex.org/A5037974362 |
| authorships[9].author.orcid | https://orcid.org/0000-0001-8627-8203 |
| authorships[9].author.display_name | Russell T. Shinohara |
| authorships[9].countries | US |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I4210127693, https://openalex.org/I79576946 |
| authorships[9].affiliations[0].raw_affiliation_string | The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA |
| authorships[9].institutions[0].id | https://openalex.org/I4210127693 |
| authorships[9].institutions[0].ror | https://ror.org/047939x15 |
| authorships[9].institutions[0].type | facility |
| authorships[9].institutions[0].lineage | https://openalex.org/I102322052, https://openalex.org/I1335321130, https://openalex.org/I4210127693, https://openalex.org/I79576946 |
| authorships[9].institutions[0].country_code | US |
| authorships[9].institutions[0].display_name | Penn Center for AIDS Research |
| authorships[9].institutions[1].id | https://openalex.org/I79576946 |
| authorships[9].institutions[1].ror | https://ror.org/00b30xv10 |
| authorships[9].institutions[1].type | education |
| authorships[9].institutions[1].lineage | https://openalex.org/I79576946 |
| authorships[9].institutions[1].country_code | US |
| authorships[9].institutions[1].display_name | University of Pennsylvania |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Russell T. Shinohara |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | CoCoA: Conditional Correlation Models with Association Size |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11577 |
| primary_topic.field.id | https://openalex.org/fields/32 |
| primary_topic.field.display_name | Psychology |
| primary_topic.score | 0.9807999730110168 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3205 |
| primary_topic.subfield.display_name | Experimental and Cognitive Psychology |
| primary_topic.display_name | Cognitive Abilities and Testing |
| related_works | https://openalex.org/W2112143119, https://openalex.org/W31220157, https://openalex.org/W2312753042, https://openalex.org/W4289356671, https://openalex.org/W2389155397, https://openalex.org/W2165884543, https://openalex.org/W3186837933, https://openalex.org/W2368989808, https://openalex.org/W1969346022, https://openalex.org/W2034959125 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2022.03.28.486098 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nd |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2022.03.28.486098 |
| primary_location.id | doi:10.1101/2022.03.28.486098 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nd |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2022/03/29/2022.03.28.486098.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2022.03.28.486098 |
| publication_date | 2022-03-29 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2523999518, https://openalex.org/W2913875100, https://openalex.org/W2531874158, https://openalex.org/W2519484961, https://openalex.org/W3000251503, https://openalex.org/W3130555210, https://openalex.org/W2740108040, https://openalex.org/W2162280413, https://openalex.org/W2151596708, https://openalex.org/W3082093774, https://openalex.org/W2159928053, https://openalex.org/W2069724292, https://openalex.org/W2065759841, https://openalex.org/W2108748822, https://openalex.org/W2164411392, https://openalex.org/W2073576337, https://openalex.org/W1883157101, https://openalex.org/W1759606585, https://openalex.org/W2910532413, https://openalex.org/W2005900392, https://openalex.org/W2139279888, https://openalex.org/W2582743722, https://openalex.org/W2790916085, https://openalex.org/W2186502703, https://openalex.org/W2160654074, https://openalex.org/W2138034021, https://openalex.org/W2030561475, https://openalex.org/W2139860022, https://openalex.org/W3138691141, https://openalex.org/W2082693246, https://openalex.org/W2067272524 |
| referenced_works_count | 31 |
| abstract_inverted_index.A | 35 |
| abstract_inverted_index.S | 0 |
| abstract_inverted_index.a | 69, 91, 105, 190 |
| abstract_inverted_index.By | 198 |
| abstract_inverted_index.In | 80, 133 |
| abstract_inverted_index.We | 110 |
| abstract_inverted_index.as | 8, 47, 104, 202 |
| abstract_inverted_index.be | 6 |
| abstract_inverted_index.in | 15, 56, 189 |
| abstract_inverted_index.is | 38, 183 |
| abstract_inverted_index.of | 17, 30, 58, 68, 107, 114, 140, 205 |
| abstract_inverted_index.on | 43, 72, 124 |
| abstract_inverted_index.to | 64, 95, 121, 143, 212 |
| abstract_inverted_index.we | 82, 136, 177 |
| abstract_inverted_index.For | 13 |
| abstract_inverted_index.and | 19, 26, 60, 78, 102, 151, 159, 165, 216 |
| abstract_inverted_index.are | 62, 119 |
| abstract_inverted_index.can | 5 |
| abstract_inverted_index.for | 130, 196 |
| abstract_inverted_index.our | 207 |
| abstract_inverted_index.the | 22, 31, 66, 73, 97, 115, 125, 154, 173, 203 |
| abstract_inverted_index.two | 32 |
| abstract_inverted_index.Many | 2 |
| abstract_inverted_index.age. | 197 |
| abstract_inverted_index.bias | 158 |
| abstract_inverted_index.both | 162 |
| abstract_inverted_index.data | 171 |
| abstract_inverted_index.find | 152 |
| abstract_inverted_index.from | 147, 172 |
| abstract_inverted_index.such | 46 |
| abstract_inverted_index.task | 193 |
| abstract_inverted_index.that | 153, 179 |
| abstract_inverted_index.with | 88, 185 |
| abstract_inverted_index.CoCoA | 84 |
| abstract_inverted_index.Model | 87 |
| abstract_inverted_index.Using | 169 |
| abstract_inverted_index.about | 10 |
| abstract_inverted_index.ideal | 163 |
| abstract_inverted_index.lower | 157 |
| abstract_inverted_index.model | 166, 208 |
| abstract_inverted_index.novel | 112 |
| abstract_inverted_index.other | 44 |
| abstract_inverted_index.posit | 54 |
| abstract_inverted_index.size, | 117 |
| abstract_inverted_index.sizes | 123 |
| abstract_inverted_index.speed | 25, 77, 101 |
| abstract_inverted_index.study | 29 |
| abstract_inverted_index.terms | 57 |
| abstract_inverted_index.tests | 16 |
| abstract_inverted_index.third | 70 |
| abstract_inverted_index.under | 161 |
| abstract_inverted_index.which | 53, 118 |
| abstract_inverted_index.while | 128, 194 |
| abstract_inverted_index.Size), | 90 |
| abstract_inverted_index.effect | 67, 122 |
| abstract_inverted_index.former | 155 |
| abstract_inverted_index.genome | 148 |
| abstract_inverted_index.models | 55 |
| abstract_inverted_index.scale, | 127 |
| abstract_inverted_index.ummary | 1 |
| abstract_inverted_index.Cohort, | 176 |
| abstract_inverted_index.adapted | 146 |
| abstract_inverted_index.between | 24, 76, 100 |
| abstract_inverted_index.compare | 137 |
| abstract_inverted_index.complex | 191 |
| abstract_inverted_index.depends | 42 |
| abstract_inverted_index.greater | 180 |
| abstract_inverted_index.natural | 36 |
| abstract_inverted_index.outcome | 204 |
| abstract_inverted_index.propose | 83, 111 |
| abstract_inverted_index.whether | 39 |
| abstract_inverted_index.accuracy | 27, 103 |
| abstract_inverted_index.achieves | 156 |
| abstract_inverted_index.confound | 131 |
| abstract_inverted_index.coupling | 41, 188 |
| abstract_inverted_index.estimate | 96 |
| abstract_inverted_index.function | 106 |
| abstract_inverted_index.insights | 211 |
| abstract_inverted_index.measures | 113 |
| abstract_inverted_index.physical | 20 |
| abstract_inverted_index.provides | 209 |
| abstract_inverted_index.question | 37 |
| abstract_inverted_index.settings | 164 |
| abstract_inverted_index.stronger | 186 |
| abstract_inverted_index.studies, | 135, 150 |
| abstract_inverted_index.variable | 71 |
| abstract_inverted_index.variance | 160 |
| abstract_inverted_index.Classical | 50 |
| abstract_inverted_index.accuracy. | 79 |
| abstract_inverted_index.adjusting | 129 |
| abstract_inverted_index.analogous | 120 |
| abstract_inverted_index.analyses. | 219 |
| abstract_inverted_index.attention | 182 |
| abstract_inverted_index.cognitive | 18 |
| abstract_inverted_index.framework | 94 |
| abstract_inverted_index.instance, | 14 |
| abstract_inverted_index.interest, | 206 |
| abstract_inverted_index.modelling | 215 |
| abstract_inverted_index.motivates | 28 |
| abstract_inverted_index.outcomes, | 61 |
| abstract_inverted_index.questions | 4 |
| abstract_inverted_index.reasoning | 192 |
| abstract_inverted_index.response, | 81 |
| abstract_inverted_index.sustained | 48, 181 |
| abstract_inverted_index.symmetric | 74 |
| abstract_inverted_index.together. | 34 |
| abstract_inverted_index.trade-off | 23 |
| abstract_inverted_index.variables | 33 |
| abstract_inverted_index.additional | 108 |
| abstract_inverted_index.associated | 184 |
| abstract_inverted_index.assumption | 167 |
| abstract_inverted_index.attention. | 49 |
| abstract_inverted_index.covariates | 59 |
| abstract_inverted_index.estimators | 139, 145 |
| abstract_inverted_index.formulated | 7 |
| abstract_inverted_index.hypotheses | 9 |
| abstract_inverted_index.regression | 51, 214 |
| abstract_inverted_index.scientific | 3 |
| abstract_inverted_index.simulation | 134 |
| abstract_inverted_index.variables, | 45 |
| abstract_inverted_index.variables. | 109, 132 |
| abstract_inverted_index.Association | 89 |
| abstract_inverted_index.Correlation | 86 |
| abstract_inverted_index.association | 116, 149 |
| abstract_inverted_index.conditional | 11, 98, 141, 200 |
| abstract_inverted_index.controlling | 195 |
| abstract_inverted_index.correlation | 99, 126, 142, 218 |
| abstract_inverted_index.demonstrate | 178 |
| abstract_inverted_index.investigate | 65 |
| abstract_inverted_index.partitioned | 217 |
| abstract_inverted_index.statistical | 93 |
| abstract_inverted_index.techniques, | 52 |
| abstract_inverted_index.traditional | 213 |
| abstract_inverted_index.(Conditional | 85 |
| abstract_inverted_index.Philadelphia | 174 |
| abstract_inverted_index.correlations | 201 |
| abstract_inverted_index.highlighting | 199 |
| abstract_inverted_index.insufficient | 63 |
| abstract_inverted_index.performance, | 21 |
| abstract_inverted_index.relationship | 75 |
| abstract_inverted_index.complementary | 210 |
| abstract_inverted_index.correlations. | 12 |
| abstract_inverted_index.neurocognitive | 170 |
| abstract_inverted_index.speed-accuracy | 40, 187 |
| abstract_inverted_index.semi-parametric | 144 |
| abstract_inverted_index.likelihood-based | 92, 138 |
| abstract_inverted_index.misspecification. | 168 |
| abstract_inverted_index.Neurodevelopmental | 175 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5079162823 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I4210127693, https://openalex.org/I79576946 |
| citation_normalized_percentile.value | 0.03120014 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |