Augmented input: The effect of visuographic supports on the auditory comprehension of people with chronic aphasia

Abstract

Background: Augmented input (AI), or the use of visuographic images and linguistic supports, is a strategy for facilitating the auditory comprehension of people with chronic aphasia. To date, researchers have not systematically evaluated the effects of various types of AI strategies on auditory comprehension.

Aims: The purpose of the study was to perform an initial evaluation of the changes in auditory comprehension accuracy experienced by people with aphasia when they received one type of AI. Specifically, the authors examined the effect four types of non-personalised visuographic image conditions on the comprehension of people with aphasia when listening to narratives.

Methods & Procedures: A total of 21 people with chronic aphasia listened to four stories, one in each of four conditions (i.e., no-context photographs, low-context drawings with embedded no-context photographs, high-context photographs, and no visuographic support). Auditory comprehension was measured by assessing participants' accuracy in responding to 15 multiple-choice sentence completion statements related to each story.

Outcomes & Results: Results showed no significant differences in response accuracy across the four visuographic conditions.

Conclusions: The type of visuographic image provided as AI in this study did not influence participants' response accuracy for sentence completion comprehension tasks. However, the authors only examined non-personalised visuographic images as a type of AI support. Future researchers should systematically examine the benefits provided to people with aphasia by other types of visuographic and linguistic AI supports.

Keywords:

• Aphasia
• Augmented input
• Auditory comprehension
• Resource allocation theory

Acknowledgments

All four authors contributed to the design of the study. The first two authors took equal responsibility for organising the findings of this project into this manuscript. Sarah Wallace took the lead for execution and data analysis. The authors would like to thank David R. Beukelman and Miechelle McKelvey for their assistance on this project. We would also like to thank our participants, without whom this research would not be possible.