Related Experiment Video
Updated: Feb 12, 2026

Visualizing Visual Adaptation
Published on: April 24, 2017
Short and long-term visual deprivation leads to adapted use of audiovisual information for face-voice recognition
Stefania S Moro1, Adria E N Hoover2, Jennifer K E Steeves1
1Department of Psychology and Centre for Vision Research, York University, Toronto, Canada; The Hospital for Sick Children, Toronto, Canada.
Abstract:
Person identification is essential for everyday social interactions. We quickly identify people from cues such as a person's face or the sound of their voice. A change in sensory input, such as losing one's vision, can alter how one uses sensory information. We asked how people with only one eye, who have had reduced visual input during postnatal maturation of the visual system, use faces and voices for person identity recognition. We used an old/new paradigm to investigate unimodal (visual or auditory) and bimodal (audiovisual) identity recognition of people (face, voice and face-voice) and a control category, objects (car, horn and car-horn). Participants learned the identity of 10 pairs of faces and voices (Experiment 1) and 10 cars and horns (Experiment 2) and were asked to identify the learned face/voice or car/horn among 20 distractors. People with one eye were more sensitive to voice identification compared to controls viewing binocularly or with an eye-patch. However, both people with one eye and eye-patched viewing controls use combined audiovisual information for person identification more equally than binocular viewing controls, who favour vision. People with one eye were no different from controls at object identification. The observed visual dominance for binocular controls is larger for person compared to object identification, indicating that faces (vision) play a larger role in person identification and that person identity processing is unique from that for objects. People with long-term visual deprivation from the loss of one eye may have adaptive strategies, such as placing less reliance on vision to achieve intact performance, particularly for face processing.
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