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Drift diffusion modeling (DDM) can clarify the cognitive mechanisms underlying risk for depression by decomposing cognitive task performance into latent components, such as drift rate, boundary separation, and non-decision time. However, limited research has applied DDM to adolescent depression or examined whether these cognitive processes interact with pubertal timing, a key biological risk factor. Participants were 103 racially\/ethnically and socioeconomically diverse youth (M\n                    <jats:sub>age<\/jats:sub>\n                    = 12.58, 48.5% female) who completed cognitive and affective inhibitory control tasks as well as self-report measures of pubertal development and depressive symptoms. DDM parameters were derived using the EZ diffusion model, and moderation analyses tested whether DDM parameters interacted with pubertal timing to predict depressive symptoms. Earlier pubertal timing was associated with greater depressive symptoms across all models. Boundary separation, but not drift rate or non-decision time, significantly interacted with puberal timing in both the Stroop and Emotional Stroop tasks. Specifically, earlier development was associated with higher depressive symptoms only for youth with high boundary separation, potentially reflecting more cautious or effortful decision-making styles. These findings suggest that both cognitive and biological vulnerabilities both contribute to risk for depression during early adolescence. By identifying some of the specific latent decision-making processes associated with depressive symptoms, DDM may provide insight into cognitive features of depression like rumination, indecision, and increased error monitoring.\n                  <\/jats:p>","DOI":"10.1007\/s10802-026-01493-4","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:25:41Z","timestamp":1786699541000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Examining Pubertal and Cognitive Differences in Adolescent Depressive Symptoms Using Drift Diffusion Modeling"],"prefix":"10.1007","volume":"54","author":[{"given":"Kelly R.","family":"Barry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0944-6159","authenticated-orcid":false,"given":"Natasha","family":"Chaku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lindsay Till","family":"Hoyt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"issue":"5","key":"1493_CR1","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1177\/2167702616643008","volume":"4","author":"LB Alloy","year":"2016","unstructured":"Alloy, L. 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