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Published on: May 7, 2021
Predicting Remembering: Judgments of Prospective Memory After Traumatic Brain Injury
Katy H O'Brien1, Mary R T Kennedy2
1Communication Sciences and Special Education, University of Georgia, Athens.
Purpose:
Adults with traumatic brain injuries (TBIs) often struggle with prospective memory (PM), the ability to remember to complete tasks in the future, such as taking medicines on a schedule. Metamemory judgments (or how well we think we will do at remembering) are linked to strategy use and are critical for managing demands of daily living. The current project used an Internet-based virtual reality tool to assess metamemory judgments of PM following TBI.
Method:
Eighteen adults with moderate to severe TBI and 20 healthy controls (HCs) played Tying the String, a virtual reality game with PM items embedded across the course of a virtual work week. Participants studied PM items and made two judgments of learning about the likelihood of recognizing the CUE, that is, when the task should be done, and of recalling the TASK, that is, what needed to be done.
Results:
Participants with TBI adjusted their metamemory expectations downward, but not enough to account for poorer recall performance. Absolute difference scores of metamemory accuracy showed that healthy adults were underconfident across PM components, whereas adults with TBI were markedly overconfident about their ability to recall TASKs.
Conclusions:
Adults with TBI appear to have a general knowledge that PM tasks will be difficult but are poor monitors of actual levels of success. Because metamemory monitoring is linked to strategy use, future work should examine using this link to direct PM intervention approaches.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

