{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T05:53:07Z","timestamp":1790401987807,"version":"4.1.0"},"reference-count":87,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T00:00:00Z","timestamp":1661904000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Int Neuropsychol Soc"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S1355617722000431_as1\">\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1355617722000431_as2\">\n                    <jats:title>Method:<\/jats:title>\n                    <jats:p>This study utilized trial-level data from the stop signal task from 8916 children (9\u201310 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1355617722000431_as3\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls\u2019 wide boundary separation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S1355617722000431_as4\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/s1355617722000431","type":"journal-article","created":{"date-parts":[[2022,8,31]],"date-time":"2022-08-31T03:22:40Z","timestamp":1661916160000},"page":"492-502","source":"Crossref","is-referenced-by-count":24,"title":["Examining reaction time variability on the stop-signal task in the ABCD study"],"prefix":"10.1017","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3464-5256","authenticated-orcid":false,"given":"Jeffery N.","family":"Epstein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah 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