Within-Session Intra-Individual Variability

Our finding that PWA exhibited higher WS-IIV than healthy controls suggests that it is important to take moment-to-moment fluctuations into account when thinking about attention in PWA. Additionally, results of the current study showed that tasks with higher demands elicited higher degrees of WS-IIV across all participants, suggesting that when the complexity of a situation is increased, fluctuations in attention from moment to moment also increase. Specifically, Task 5 was found to elicit higher WS-COVs than either Task 1 or Task 2, a result that suggests that WS-IIV increases when true language processing demands are added.

The finding that WS-COVs were significantly higher for Task 3 than for Tasks 1 and 2 was unexpected. While Task 3 was not the main focus of the study, it is worth briefly discussing the occurrence of these higher WS-COVs, which was likely due to the specifics of the demands that Task 3 placed on the participant. The purpose of Task 3 was to create a “bridge” between the non- linguistic and linguistic tasks, one that added in multimodal integration and some degree of semantic processing, without adding any linguistic elements. It was determined that the best way to do this would be to use symbols that were easily

interpreted as representing emotions (happy and sad) and to teach participants two sounds that also represented those same emotions. However, it may be the case that this task was too unlike the other tasks, or that participants had

difficulty keeping the task instructions in mind, or that introducing the concept of emotions added another level of processing that was not intended, resulting in higher WS-COVs than had been anticipated.

Finally, we noted a slight (though non-significant) increase in WS-COV on Task 5 relative to Task 4 in PWA, but not in controls, hinting at the possibility that not only is WS-IIV higher in PWA than in controls across the board, but when true language processing demands are added to a task, the gap between the two groups widens even further. Also regarding WS-COVs in PWA, significant

negative associations were found between WS-COVs on Task 5 and months post onset, as well as CLQT-Attention score and TEA Map Search-1 minute score. The first of these associations suggest that as PWA progress through their recovery, WS-IIV in attention decreases. The other two associations suggest that individuals with higher levels of WS-IIV in linguistic attention achieve poorer scores on standardized measures requiring language and attention, a result that helps to validate our hypothesis that attention is intrinsically linked to time-based performance and that poor attention may therefore be associated with higher levels of WS-IIV.

As with BS-COV.adj, the results of our analyses investigating inter- individual differences in WS-COVs within the PWA group add some information

to our understanding of WS-IIV in attention in aphasia. The k-means cluster analysis on WS-COVs shows that different PWA exhibited different patterns of variability from task to task – i.e., that not all PWA reflected the pattern that was observed for the group overall. Cluster 3, for example, showed a roughly

increasing trend in WS-COVs from Task 1 through Task 5, whereas Cluster 1 did not show a noticeable increase in WS-COVs as task demands were added. These differences within the PWA group speak to the importance of evaluating WS-IIV separately for each individual: some PWA may be more impacted by task demands than others.

Similarly, the Crawford & Garthwaite analyses also investigated inter- individual differences in WS-IIV within the PWA group. This set of analyses revealed that despite the finding of significantly higher overall WS-COVs in the PWA group than in the control group, relatively few PWA exhibited a WS-COV that was significantly higher than the control group on any given task. This finding again points to the fact that not all PWA are alike when it comes to WS-IIV: some may show notably increased variability under certain conditions, whereas others may not. There was no evidence that the PWA participants who did show one or more high WS-COVs had anything in common with each other in terms of

standardized test scores. In terms of Task-based differences, it is worth noting that the majority of PWA WS-COVs that were flagged as “high” were WS-COVs for either Task 1 or Task 5, a result which aligns with the results of the t-tests comparing PWA vs. control WS-COVs on each of the five Tasks (only Task 1

and Task 5 show significant differences). The finding of PWA vs. controls difference for Task 5 again indicates that when true language processing demands are added to a task, PWA begin to exhibit substantially more within- session variability than controls. The finding of a group difference for Task 1 is more difficult to interpret, but it could speak to PWAs’ difficulty staying

consistently focused when a task is so simple that it becomes tedious, and/or is perceived to present no real challenge.

Finally, it is also worth noting that the clusters identified in the BS-COV.adj analysis and the clusters identified in the WS-COV analysis do not line up with one another, a point which, along with the relative lack of correlations between BS-COVs and WS-COVs, suggests that these two types of variability must be evaluated separately in PWA.