Using machine learning to understand age and gender classification based on infant temperament

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dc.contributor.authorGartstein, Maria A.en
dc.contributor.authorSeamon, D. Erichen
dc.contributor.authorMattera, Jennifer A.en
dc.contributor.authorEnlow, Michelle Bosqueten
dc.contributor.authorWright, Rosalind J.en
dc.contributor.authorPerez-Edgar, Koralyen
dc.contributor.authorBuss, Kristin A.en
dc.contributor.authorLoBue, Vanessaen
dc.contributor.authorBell, Martha Annen
dc.contributor.authorGoodman, Sherryl H.en
dc.contributor.authorSpieker, Susanen
dc.contributor.authorBridgett, David J.en
dc.contributor.authorSalisbury, Amy L.en
dc.contributor.authorGunnar, Megan R.en
dc.contributor.authorMliner, Shanna B.en
dc.contributor.authorMuzik, Mariaen
dc.contributor.authorStifter, Cynthia A.en
dc.contributor.authorPlanalp, Elizabeth M.en
dc.contributor.authorMehr, Samuel A.en
dc.contributor.authorSpelke, Elizabeth S.en
dc.contributor.authorLukowski, Angela F.en
dc.contributor.authorGroh, Ashley M.en
dc.contributor.authorLickenbrock, Diane M.en
dc.contributor.authorSantelli, Rebeccaen
dc.contributor.authorSchudlich, Tina Du Rocheren
dc.contributor.authorAnzman-Frasca, Stephanieen
dc.contributor.authorThrasher, Catherineen
dc.contributor.authorDiaz, Anjoliien
dc.contributor.authorDayton, Carolynen
dc.contributor.authorModing, Kameron J.en
dc.contributor.authorJordan, Evan M.en
dc.date.accessioned2022-08-24T17:13:04Zen
dc.date.available2022-08-24T17:13:04Zen
dc.date.issued2022-04-13en
dc.description.abstractAge and gender differences are prominent in the temperament literature, with the former particularly salient in infancy and the latter noted as early as the first year of life. This study represents a meta-analysis utilizing Infant Behavior Questionnaire-Revised (IBQ-R) data collected across multiple laboratories (N = 4438) to overcome limitations of smaller samples in elucidating links among temperament, age, and gender in early childhood. Algorithmic modeling techniques were leveraged to discern the extent to which the 14 IBQ-R subscale scores accurately classified participating children as boys (n = 2,298) and girls (n = 2,093), and into three age groups: youngest (< 24 weeks; n = 1,102), mid-range (24 to 48 weeks; n = 2,557), and oldest (> 48 weeks; n = 779). Additionally, simultaneous classification into age and gender categories was performed, providing an opportunity to consider the extent to which gender differences in temperament are informed by infant age. Results indicated that overall age group classification was more accurate than child gender models, suggesting that age-related changes are more salient than gender differences in early childhood with respect to temperament attributes. However, gender-based classification was superior in the oldest age group, suggesting temperament differences between boys and girls are accentuated with development. Fear emerged as the subscale contributing to accurate classifications most notably overall. This study leads infancy research and meta-analytic investigations more broadly in a new direction as a methodological demonstration, and also provides most optimal comparative data for the IBQ-R based on the largest and most representative dataset to date.en
dc.description.notesMBE: R01HL095606, R01HD082078; National Institutes of Health https://www.nih.gov KPE, KB, & VL: NIH R01 MH109692; R21 MH103627 National Institutes of Health https://www.nih.gov MAB: R01 HD049878; R03 HD043057 National Institutes of Health https://www.nih.gov SG: 1P50 MH58922-01A1; 1P50 MH077928-01A1 National Institutes of Health https://www.nih.gov SS: 5R01HD080851-05 National Institutes of Health https://www.nih.gov AS: R01MH78033 National Institutes of Health https://www.nih.gov DL: 8P0GM103436; P20GM103436; 8P20GM103436 National Institutes of Health https://www.nih.gov TDRS: MFS 901; MFS 907 Western Washington University https://www.wwu.edu SAF: DK72996; M01RR10732 National Institutes of Health https://www.nih.gov None of the funders had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en
dc.description.sponsorshipNational Institutes of Health [R01HL095606, R01HD082078, NIH R01 MH109692, R21 MH103627, R01 HD049878, R03 HD043057, 1P50 MH58922-01A1, 1P50 MH077928-01A1, 5R01HD080851-05, R01MH78033, 8P0GM103436, P20GM103436, 8P20GM103436, DK72996, M01RR10732]; Western Washington University [MFS 901, MFS 907]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0266026en
dc.identifier.issn1932-6203en
dc.identifier.issue4en
dc.identifier.othere0266026en
dc.identifier.pmid35417495en
dc.identifier.urihttp://hdl.handle.net/10919/111621en
dc.identifier.volume17en
dc.language.isoenen
dc.publisherPLOSen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectcross-cultural differencesen
dc.subjectstates-of-americaen
dc.subjectunited-statesen
dc.subjectparent-reporten
dc.subject1st yearen
dc.subjectbehavioren
dc.subjectbirthen
dc.subjectfearen
dc.subjecttrajectoriesen
dc.subjectreliabilityen
dc.titleUsing machine learning to understand age and gender classification based on infant temperamenten
dc.title.serialPlos Oneen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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