A comparison of processing load during non-verbal decision-making in two individuals with aphasia

Abstract

Event Abstract Back to Event A comparison of processing load during non-verbal decision-making in two individuals with aphasia Salima Suleman1*, Esther S. Kim1 and Tammy Hopper1 1 University of Alberta, Canada INTRODUCTION A growing body of evidence suggests people with aphasia (PWA) can have impairments to cognitive functions such as attention, working memory and executive functions.(1-5) Such cognitive impairments have been shown to negatively affect the decision-making (DM) abilities adults with neurological damage. (6,7) However, little is known about DM abilities of PWA.(8) Pupillometry is “the measurement of changes in pupil diameter”.(9;p.1) Researchers have reported a positive relationship between processing load and phasic pupil size (i.e., as processing load increases, pupil size increases).(10) Thus pupillometry has the potential to be a useful tool for investigating processing load during DM in PWA. AIMS The primary aim of this study was to establish the feasibility of using pupillometry during a non-verbal DM task with PWA. The secondary aim was to explore non-verbal DM performance in PWA and determine the relationship between DM performance and processing load using pupillometry. METHOD DESIGN. A single-subject case-study design with two participants was used in this study. PARTICIPANTS. Two adult males with anomic aphasia participated in this study. Participants were matched for age and education. Both participants were independent, able to drive, and had legal autonomy. MEASURES. PERFORMANCE ON A DM TASK. We used a computerized risk-taking card game called the Iowa Gambling Task (IGT) as our non-verbal DM task.(11) In the IGT, participants made 100 selections (via eye gaze) from four decks of cards presented on the computer screen with the goal of maximizing their overall hypothetical monetary gain. PROCESSING LOAD. The EyeLink 1000+ eye tracking system was used to collect pupil size measures while participants deliberated before each deck selection during the IGT. For this analysis, we calculated change in pupil size as a measure of processing load. RESULTS P1. P1 made increasingly advantageous decisions as the task progressed (Fig.1). When asked to rank order the decks, P1 consistently identified the advantageous decks as the best decks after block two. We found a significant negative non-parametric correlation between trial and change in pupil size (rs = - 0.481, n = 100, p < 0.0001). P2. P2 made increasingly disadvantageous decisions as the task progressed (Fig. 1). When asked to rank the decks, P2 was unable to accurately and consistently identify advantageous decks at the end of the task. At the end of block five, P2 stated “I didn’t have the sense of ‘that was going to be the good one’”. We found a significant negative correlation between change in pupil size and trial number (rs = - 0.379, n = 100, p < 0.0001). DISCUSSION Two participants with similar aphasia demonstrated disparate DM performance. P1 quickly determined the way to maximize his gain, while P2 was unable to discern the best way to maximize his gain and made more disadvantageous decisions as the task progressed. Both participants showed decreasing changes in pupil size related to processing load. These results demonstrate the feasibility of using pupillometry in a computerized DM task with PWA. Further, we know that even when language profiles are similar, DM abilities may be differentially affected in aphasia. These preliminary data will be used to inform a subsequent, larger study of DM in PWA. Figure 1 References