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@ARTICLE{OberwellandWeiss:890479,
author = {Oberwelland Weiss, E. and Kruppa, Jana and Fink, Gereon R.
and Herpertz-Dahlmann, Beate and Konrad, Kerstin and
Schulte-Rüther, Martin},
title = {{D}evelopmental {D}ifferences in {P}robabilistic {R}eversal
{L}earning: {A} {C}omputational {M}odeling {A}pproach},
journal = {Frontiers in neuroscience},
volume = {14},
issn = {1662-453X},
address = {Lausanne},
publisher = {Frontiers Research Foundation},
reportid = {FZJ-2021-00989},
pages = {536596},
year = {2021},
abstract = {Cognitive flexibility helps us to navigate through our
ever-changing environment and has often been examined by
reversal learning paradigms. Performance in reversal
learning can be modeled using computational modeling which
allows for the specification of biologically plausible
models to infer psychological mechanisms. Although such
models are increasingly used in cognitive neuroscience,
developmental approaches are still scarce. Additionally,
though most reversal learning paradigms have a comparable
design regarding timing and feedback contingencies, the type
of feedback differs substantially between studies. The
present study used hierarchical Gaussian filter modeling to
investigate cognitive flexibility in reversal learning in
children and adolescents and the effect of various feedback
types. The results demonstrate that children make more
overall errors and regressive errors (when a previously
learned response rule is chosen instead of the new correct
response after the initial shift to the new correct target),
but less perseverative errors (when a previously learned
response set continues to be used despite a reversal)
adolescents. Analyses of the extracted model parameters of
the winning model revealed that children seem to use new and
conflicting information less readily than adolescents to
update their stimulus-reward associations. Furthermore, more
subclinical rigidity in everyday life (parent-ratings) is
related to less explorative choice behavior during the
probabilistic reversal learning task. Taken together, this
study provides first-time data on the development of the
underlying processes of cognitive flexibility using
computational modeling.},
cin = {INM-3 / INM-11},
ddc = {610},
cid = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-11-20170113},
pnm = {525 - Decoding Brain Organization and Dysfunction
(POF4-525) / 5251 - Multilevel Brain Organization and
Variability (POF4-525)},
pid = {G:(DE-HGF)POF4-525 / G:(DE-HGF)POF4-5251},
typ = {PUB:(DE-HGF)16},
pubmed = {33536865},
UT = {WOS:000613265600001},
doi = {10.3389/fnins.2020.536596},
url = {https://juser.fz-juelich.de/record/890479},
}