Literature DB >> 9640581

Predictors of eyeblink classical conditioning over the adult age span.

D S Woodruff-Pak1, M E Jaeger.   

Abstract

The major aim was to identify predictors of the large age differences that exist in eyeblink classical conditioning. Eyeblink conditioning was assessed in 190 participants over the age range of 20-89 years, with 150 trained in the paired condition and 40 trained in the explicitly unpaired control condition. Timed-interval tapping was used to assess cerebellar function. Blink reaction time and explicit learning and memory were also assessed. Stepwise multiple regression indicated that the effect of age accounted for the largest proportion of the variance, but the cerebellar measure also predicted eyeblink conditioning at a significant level. Reaction time and explicit memory measures did not account for a significant amount of the variance in eyeblink conditioning. Age-related effects in the cerebellum apparently affect timing and learning in normal adults.

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Year:  1998        PMID: 9640581     DOI: 10.1037//0882-7974.13.2.193

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


  13 in total

Review 1.  Eyeblink classical conditioning differentiates normal aging from Alzheimer's disease.

Authors:  D S Woodruff-Pak
Journal:  Integr Physiol Behav Sci       Date:  2001 Apr-Jun

2.  Effects of paradigm and inter-stimulus interval on age differences in eyeblink classical conditioning in rabbits.

Authors:  Diana S Woodruff-Pak; Susan E Seta; LaToya A Roker; Melissa A Lehr
Journal:  Learn Mem       Date:  2007-04-06       Impact factor: 2.460

3.  Consensus paper: Decoding the Contributions of the Cerebellum as a Time Machine. From Neurons to Clinical Applications.

Authors:  Martin Bareš; Richard Apps; Laura Avanzino; Assaf Breska; Egidio D'Angelo; Pavel Filip; Marcus Gerwig; Richard B Ivry; Charlotte L Lawrenson; Elan D Louis; Nicholas A Lusk; Mario Manto; Warren H Meck; Hiroshi Mitoma; Elijah A Petter
Journal:  Cerebellum       Date:  2019-04       Impact factor: 3.847

4.  Keep up the pace: declines in simple repetitive timing differentiate healthy aging from the earliest stages of Alzheimer's disease.

Authors:  Ashley S Bangert; David A Balota
Journal:  J Int Neuropsychol Soc       Date:  2012-08-29       Impact factor: 2.892

5.  Aging in the cerebellum and hippocampus and associated behaviors over the adult life span of CB6F1 mice.

Authors:  J A Kennard; K L Brown; D S Woodruff-Pak
Journal:  Neuroscience       Date:  2013-06-11       Impact factor: 3.590

6.  Implicit sequence learning: effects of level of structure, adult age, and extended practice.

Authors:  Darlene V Howard; James H Howard; Karin Japikse; Cara DiYanni; Amanda Thompson; Rachel Somberg
Journal:  Psychol Aging       Date:  2004-03

7.  Age-dependent impairment of eyeblink conditioning in prion protein-deficient mice.

Authors:  Yasushi Kishimoto; Moritoshi Hirono; Ryuichiro Atarashi; Suehiro Sakaguchi; Tohru Yoshioka; Shigeru Katamine; Yutaka Kirino
Journal:  PLoS One       Date:  2013-04-10       Impact factor: 3.240

8.  Age sensitivity of behavioral tests and brain substrates of normal aging in mice.

Authors:  John A Kennard; Diana S Woodruff-Pak
Journal:  Front Aging Neurosci       Date:  2011-05-25       Impact factor: 5.750

9.  Characterizing cognitive aging of associative memory in animal models.

Authors:  James R Engle; Carol A Barnes
Journal:  Front Aging Neurosci       Date:  2012-09-12       Impact factor: 5.750

Review 10.  The involvement of the human cerebellum in eyeblink conditioning.

Authors:  M Gerwig; F P Kolb; D Timmann
Journal:  Cerebellum       Date:  2007       Impact factor: 3.648

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