Literature DB >> 15065940

A quantitative model-based approach to examining aging effects on information-integration category learning.

J Vincent Filoteo1, W Todd Maddox.   

Abstract

Information-integration category learning was examined in older and younger adults. Accuracy results indicated that older participants learned less well than younger participants in both linear and nonlinear conditions. Model-based analyses indicated that both groups in the linear condition tended to use information integration but that later in training younger participants were more likely to do so. In contrast, the 2 groups in the nonlinear condition were equally likely to use information integration. Further analysis indicated that younger adults were more accurate than older adults when an information-integration approach was adopted, whereas fewer age-related differences were observed when a rule-based approach was used, suggesting that age can have a negative impact on information-integration category learning processes but less impact on rule-based learning.

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Year:  2004        PMID: 15065940     DOI: 10.1037/0882-7974.19.1.171

Source DB:  PubMed          Journal:  Psychol Aging        ISSN: 0882-7974


  20 in total

1.  Computational Models Inform Clinical Science and Assessment: An Application to Category Learning in Striatal-Damaged Patients.

Authors:  W Todd Maddox; J Vincent Filoteo; Dagmar Zeithamova
Journal:  J Math Psychol       Date:  2010-02-01       Impact factor: 2.223

2.  Age differences in implicit learning of probabilistic unstructured sequences.

Authors:  Jessica R Simon; James H Howard; Darlene V Howard
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2010-10-25       Impact factor: 4.077

Review 3.  Models in search of a brain.

Authors:  Bradley C Love; Todd M Gureckis
Journal:  Cogn Affect Behav Neurosci       Date:  2007-06       Impact factor: 3.282

4.  Age-related declines in the fidelity of newly acquired category representations.

Authors:  Tyler Davis; Bradley C Love; W Todd Maddox
Journal:  Learn Mem       Date:  2012-07-18       Impact factor: 2.460

5.  Aging and a genetic KIBRA polymorphism interactively affect feedback- and observation-based probabilistic classification learning.

Authors:  Nicolas W Schuck; Jessica R Petok; Martijn Meeter; Brit-Maren M Schjeide; Julia Schröder; Lars Bertram; Mark A Gluck; Shu-Chen Li
Journal:  Neurobiol Aging       Date:  2017-09-05       Impact factor: 4.673

6.  The effects of aging on the neural basis of implicit associative learning in a probabilistic triplets learning task.

Authors:  Jessica R Simon; Chandan J Vaidya; James H Howard; Darlene V Howard
Journal:  J Cogn Neurosci       Date:  2011-08-23       Impact factor: 3.225

7.  Rule-based and information-integration category learning in normal aging.

Authors:  W Todd Maddox; Jennifer Pacheco; Maia Reeves; Bo Zhu; David M Schnyer
Journal:  Neuropsychologia       Date:  2010-06-12       Impact factor: 3.139

8.  Adult age differences in subjective and objective measures of strategy use on a sequentially cued prediction task.

Authors:  Kendra L Seaman; Darlene V Howard; James H Howard
Journal:  Neuropsychol Dev Cogn B Aging Neuropsychol Cogn       Date:  2014-03-27

9.  Category learning strategies in younger and older adults: Rule abstraction and memorization.

Authors:  Christopher N Wahlheim; Mark A McDaniel; Jeri L Little
Journal:  Psychol Aging       Date:  2016-03-07

10.  Dual systems of speech category learning across the lifespan.

Authors:  W Todd Maddox; Bharath Chandrasekaran; Kirsten Smayda; Han-Gyol Yi
Journal:  Psychol Aging       Date:  2013-12
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