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Record W2527937988

Do Older and Younger Adults Use and Benefit from Memory Aids

2012· dissertation· en· W2527937988 on OpenAlexfundno aff
Emily Schryer

Bibliographic record

VenueUWSpace (University of Waterloo) · 2012
Typedissertation
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsGerontologyPsychologyDevelopmental psychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

This research examines age differences in the use and value of memory compensation strategies for everyday memory tasks. Chapter 1 reviews the literature on memory compensation and aging. According to Selective Optimization with Compensation (SOC) model, older adults may be more likely than younger adults to take advantage of memory compensation strategies when they are available. Chapter 2 describes a diary study in which older and younger participants rated the extent to which they use compensation strategies in everyday life and reported everyday memory errors over the course of a week. Older adults reported fewer memory errors than younger adults and more use of memory aids. However, use of memory aids was unrelated to frequency of memory errors in either age group. Chapter 3 reports a laboratory experiment on the use of memory aids for recalling phone messages. Participants listened to phone messages while simultaneously completing a seating chart, and were asked to report the content of the messages to the experimenter. Participants were either allowed to use a memory aid for the phone message task, or not. Older participants reported using compensation strategies more frequently in everyday life, but they were no more likely than younger participants to search for or employ an aid in the phone message task. Using a memory aid was differentially beneficial, improving performance more for older than younger participants. In Chapter 4, participants completed two phone message recall and two seating plan tasks. Participants were encouraged to use whatever in the room that they might find helpful. On one round of tasks a pen was tied to a clipboard and participants could use it to write down the phone messages. On the other round no pen was available. The order of the trials was counterbalanced across participants. This design examined the calibration between participants’ use of memory aids and their performance on the recall task – whether participants’ performance on the first trial predicted their subsequent use of memory aids, and whether participants who chose to use a memory aid when it was available on the first trial performed particularly poorly on the second trial when no aid was present. As in Study 1, older adults reported using memory aids more frequently in everyday life but age was unassociated with whether or not participants used the pen when one was available. There was little evidence of calibration. Participants’ memory performance on an initial trial had little impact on their use of a memory aid on a subsequent trial. Participants who used a memory aid on the first trial actually recalled more phone message details on the second trial (without the aid) than those who did not. This was true for both age groups. Chapter 5 reflects on older and younger adults self-reported and observed uses of memory compensation strategies. Across all 3 studies older adults reported using external memory aids more frequently in everyday life. However, contrary to the SOC model, in Studies 2 and 3 there were no age differences in older and younger adults’ use of a pen to write down phone messages on the lab task. Nor was participants’ choice to use the memory aid associated with their unaided performance on the task. These results do not support the prediction derived from SOC that older adults use compensation strategies more frequently or more sensitively than younger adults. However, using the memory did improve performance on the task more for older than for younger adults. These results support the hypothesis that external memory aids are a particularly valuable strategy for older adults and suggest the need to better understand why some individuals engage in compensation use and others do not.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.223
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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