Age group differences in learning-related activity reflect task stage, not learning stage

Merenstein, Jenna L.; Petok, Jessica R.; Bennett, Ilana J.

doi:10.1016/j.bbr.2021.113570

Cited by 5 publications

(13 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consistent with behavioral evidence of IAL, oldest-old adults had better learning performance during later (Stages 2 through 5) than earlier (Stage 1) task stages. Finding that learning performance increased across task stages, despite participants being unable to describe the regularity between the cues and targets, is in line with previous studies using the TLT in younger-old adults (Franco et al, 2021;Howard et al, 2008;Merenstein et al, 2021b;Seaman et al, 2013;Stillman et al, 2016bStillman et al, , 2016a. Our observation of preserved IAL in the oldest-old may have been facilitated by specific parameters of the TLT version used here, including the use of a deterministic regularity, fewer unique triplets, longer presentation times, and increased response time window.…”

Section: Discussionsupporting

confidence: 90%

“…Specifically, better learning performance (i.e., lower bin scores) was associated with better microstructure (i.e., decreased free and dispersed diffusion) of the posterior limb of the internal capsule. This collection of white matter fibers may be especially important to IAL performance because it relays neural signals between the cortex and basal ganglia regions (putamen, globus pallidum) that have previously been implicated in both earlier and later stages of IAL, including our own functional MRI studies using the TLT (Merenstein et al, 2021b;Simon et al, 2012). Similarly, the trending association between IAL performance and dispersed diffusion in the fornix body may reflect the role of this tract in transmitting neural signals between the prefrontal cortex and hippocampus, which have also been implicated in earlier stages of IAL tasks by functional MRI studies (Dennis and Cabeza, 2011;Merenstein et al, 2021b;Rieckmann et al, 2010;Simon et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

“…This collection of white matter fibers may be especially important to IAL performance because it relays neural signals between the cortex and basal ganglia regions (putamen, globus pallidum) that have previously been implicated in both earlier and later stages of IAL, including our own functional MRI studies using the TLT (Merenstein et al, 2021b;Simon et al, 2012). Similarly, the trending association between IAL performance and dispersed diffusion in the fornix body may reflect the role of this tract in transmitting neural signals between the prefrontal cortex and hippocampus, which have also been implicated in earlier stages of IAL tasks by functional MRI studies (Dennis and Cabeza, 2011;Merenstein et al, 2021b;Rieckmann et al, 2010;Simon et al, 2012). These results extend prior work using singletensor diffusion models in younger-old adults (Bennett et al, 2011) by identifying relationships between IAL performance and white matter microstructure in an advanced age group and using multicompartment diffusion modelling.…”

Section: Discussionmentioning

confidence: 99%

“…General procedure. Participants completed an abbreviated, deterministic version of an implicit associative learning task (triplet learning task, TLT; Franco et al, 2021;Merenstein et al, 2021b) at their personal residences on a Surface Pro tablet. Completion of the TLT occurred on average 22.3 ± 15.7 (range 8 -81 days) days after participants underwent MRI scanning.…”

Section: Triplet Learning Taskmentioning

confidence: 99%

“…Recognition task. A subset of participants (n = 12 cognitively normal, n = 5 CIND) also completed a subsequent computer-based recognition task to test for awareness of the frequently occurring cue-cue-target associations (Franco et al, 2021;Merenstein et al, 2021b). Participants indicated via keyboard responses whether a series of HF, LF, or never presented (no frequency, NF) triplets occurred "frequently", "infrequently", or "not at all" during the previously completed version of the TLT.…”

Section: Triplet Learning Taskmentioning

confidence: 99%

See 4 more Smart Citations

White matter microstructural correlates of associative learning in the oldest-old

Merenstein

Corrada

Kawas

et al. 2022

Cogn Affect Behav Neurosci

Self Cite

View full text Add to dashboard Cite

Younger-old adults (ages 65-85 years) exhibit declines in the ability to learn associations between events, which has been attributed to structural degradation of white matter tracts connecting the prefrontal cortex to the hippocampus (e.g., fornix) and striatum (e.g., internal capsule). However, deficits in associative learning abilities may increase in oldest-old adults (ages 90+ years) because advanced age is accompanied by (1) accelerated structural degradation of these white matter tracts and (2) increased prevalence of cognitive impairment. Here, we acquired multicompartment diffusion-weighted magnetic resonance imaging data from 22 oldestold adults with normal cognition (n = 15; 92.73 ± 1.67 years) or a diagnosis of cognitive impairment no dementia (CIND; n = 7; 93.29 ± 0.76 years) who also completed an associative learning task. Behavioral results revealed significantly better performance during later task stages, as expected if participants incidentally learned the cue-cue-target associations for frequently occurring event triplets. Moreover, better learning performance was significantly predicted by better microstructure of fronto-striatal white matter (internal capsule). There was no significant effect of cognitive status on learning performance or its relation to white matter microstructure. Thus, associative learning abilities are preserved into the 10 th decade of life and can be attributed to individual differences in the microstructure of white matter tracts connecting the prefrontal cortex to the striatum.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Triplet Learning Taskmentioning

confidence: 99%

Section: Triplet Learning Taskmentioning

confidence: 99%

See 3 more Smart Citations

White matter microstructural correlates of associative learning in the oldest-old

Merenstein

Corrada

Kawas

et al. 2022

Cogn Affect Behav Neurosci

Self Cite

View full text Add to dashboard Cite

show abstract

Implicit Learning in Healthy Aging

Howard¹,

Howard²

2022

The Cognitive Unconscious

View full text Add to dashboard Cite

The past fifty years have yielded substantial advances in understanding the aging mind and brain. This chapter focuses on three themes that have emerged. (1) Some cognitive abilities decline with adult age, but others do not. (2) Brain regions differ in the extent of age-related decline, and old brains often perform functions differently from younger ones, reflecting lifelong neural plasticity. (3) It is possible to modify some age-related declines. Much of this research has focused on explicit learning and memory. This chapter considers whether these themes apply to the aging of implicit, statistical sequence learning. It presents evidence, some limited, that all three themes apply. Future progress requires that measures of implicit learning be included in large-scale longitudinal correlational and intervention studies. Research on the aging of implicit learning and memory needs to be more integrated with the burgeoning research on promoting successful aging.

show abstract

Influence of age, training, intraoral scanner, and software version on the scan accuracy of inexperienced operators

Zarauz,

Pradíes,

Chebib

et al. 2023

Journal of Prosthodontics

View full text Add to dashboard Cite

PurposeTo evaluate the effect of operator age on the scan accuracy (trueness and precision) of inexperienced operators when compared with experienced operators, and how training, intraoral scanner (IOS), and software version affect scan accuracy.Material and methodsThirty‐four operators were sorted into groups: G1 (operators <25 years old, no experience), G2 (operators >40 years old, no experience), and G3 (experienced IOS operators). They conducted partial‐arch scans before and after a 4‐session training with two IOSs (Trios 3 and True Definition) and two software versions. These scans were compared with the reference scans obtained from conventional impressions and a laboratory scanner (IScan D103i) to evaluate trueness (mean root mean square values) and precision (standard deviation of root mean square values) with a software program (Geomagic Control X). Kruskal‐Wallis and post‐hoc Dunn's tests were used to evaluate the effect of age on the scan accuracy of inexperienced groups when compared with experienced operators, while the effect of training, IOS, and software version on scan accuracy was evaluated with Wilcoxon or Mann‐Whitney U tests (α = 0.05).ResultsBefore training, G1 and G2 scans had similar accuracy (p ≥ 0.065). After training, G1 scans had higher accuracy when IOS data was pooled and had higher precision with TD (p ≤ 0.004). Training increased the scan accuracy (p < 0.001), while newer software increased the trueness of inexperienced operator scans (p = 0.015).ConclusionsAge affected scan accuracy of inexperienced operators after training, indicating that extended training may be required for older operators. Training increased the scan accuracy and newer software increased the trueness of inexperienced operator scans.This article is protected by copyright. All rights reserved

show abstract

Age group differences in learning-related activity reflect task stage, not learning stage

Cited by 5 publications

References 53 publications

White matter microstructural correlates of associative learning in the oldest-old

White matter microstructural correlates of associative learning in the oldest-old

Implicit Learning in Healthy Aging

Influence of age, training, intraoral scanner, and software version on the scan accuracy of inexperienced operators

Contact Info

Product

Resources

About