Re-learning and remembering in the lesioned brain

Abstract

It is well known that re-learning language skills after a brain lesion can be very difficult. However, while learning and memory challenges have been extensively researched in amnesic individuals, very little research attention has been directed at understanding the characteristics of learning and memory that are relevant to recovery and rehabilitation of acquired language impairments. Even in the absence of damage to the medial temporal lobe regions classically associated with learning and memory, these individuals often suffer damage to frontal and other subcortical areas associated with learning and memory that may contribute to the learning challenges they face. Therefore, an understanding of the learning and memory profiles of post-stroke language impairments is important for the development and optimization of rehabilitation approaches. In two studies, we examine the degree to which certain basic characteristics of learning and memory, identified in neurotypical individuals, are intact in individuals with post-stroke language impairment. We specifically consider fundamental principles regarding the optimal spacing of learning trials that have been shown to reliably operate in neurotypical adults, across a wide range of language domains. We report on two studies that examine whether or not these principles also apply in language re-learning and retention for individuals with acquired deficits in written language production. Study 1 compared distributed vs. clustered training schedules, while Study 2 examined-for the first time in the context of re-learning-the relationship between the spacing of training trials and the retention period. This investigation revealed that, despite significant cognitive deficits and brain lesions, remarkably similar principles govern re-learning and retention in the lesioned brain as have been found to apply in neurologically healthy individuals. These results allow us to begin to integrate our understanding of recovery with the broader literature on learning and memory and have implications for the optimal organization of rehabilitation. Specifically, the findings raise questions regarding the traditional compression of rehabilitation within relatively short time windows.

Keywords: Optimal spacing; Post-stroke learning; Re-learning and retention.

Copyright © 2019 Elsevier Ltd. All rights reserved.

Figures

Figure B1.

Effect of schedules on rate of improvement, plotting raw data (mean accuracy) in bins of 5 sessions. Error bars reflect standard error of the mean (N = 11). For a depiction of the LMEM analysis results, which control for covariates, refer to Figure 5 in the text.

Figure B2.

Effect of schedules across Pre, Post, and Follow-up, plotting raw data (mean accuracy). Error bars reflect standard error of the mean (N = 11). For a depiction of the LMEM analysis results, which control for covariates, refer to Figure 6 in the text.

Figure B3.

Spacing effect, plotting raw data (mean accuracy) in bins of short (21 days) duration. Error bars reflect standard error of the mean (N = 23). For a depiction of the LMEM analysis results, which control for covariates, refer to Figure 7 in the text.

Figure 1.

The figure (adapted from Cepeda et al., 2006) depicts the finding that shorter interstudy intervals (ISI’s) are more beneficial when material must be retained for shorter time periods, while the reverse is true for longer retention periods.

Figure 2.

A. Hypothetical learning schedules for a word trained on a Clustered schedule (CAT) and a word trained on a Distributed schedule (PEN). Training for both words is dosage matched (12 training trials). In the Clustered schedule, words are trained in massed bursts within sessions, whereas for the Distributed schedule learning trials are distributed across sessions. B. An example of a Distributed schedule of learning. For Study 2, the figure illustrates that the testing accuracy for PEN on day 7 is associated with an ISI = 2 days and a RI = 4 days, while testing accuracy for PEN on day 10 is associated with an ISI = 4 days and an RI = 3 days. In this way, the four-day interval between day 3 and day 7 serves as either an ISI or an RI.

Figure 3.

Lesion overlap for the 23 participants, with warm colors indicating areas of greater overlap. Highest lesion density occurs in left hemisphere posterior frontal, anterior parietal and superior temporal lobe areas.

Figure 4.

The distribution of intervals (number of days) between training trials across the 23 participants and all training trails. Because items were tested and trained on the same trials, these values served both as ISI’s and RI’s.

Figure 5.

The effect of training schedules on learning rates. The figure depicts the model-predicted improvement in spelling (y-axis) across training sessions (x-axis), for the Distributed (blue) and Clustered schedules (orange). The shaded errors reflect the 95% confidence interval around the fixed effects, as returned by the R package effects (Fox & Weisberg, 2018).

Figure 6.

The effect of training Schedules on spelling accuracy for trained words at Pre- and Post-treatment and at a 3-month Follow-up. Depicted are the model-predicted accuracy improvements; error bars reflect 95% confidence intervals around the fixed effects estimates (Fox & Weisberg, 2018).

Figure 7.

Depiction of the results of the LMEM analysis of spacing of study, showing model-predicted spelling accuracy (y-axis) for 3 levels of RI (x-axis; 2, 50, and 100 days) across 3 levels of ISI (1, 20, and 40 days in green, blue, and yellow respectively).