Visual–Haptic Adaptation Is Determined by Relative Reliability

TLDR: It is shown that if cue combination occurs according to relative reliability, then reliability-based calibration assures minimum-variance sensory estimates over time and the ratio of visual and haptic adaptation was quantitatively predicted by relative reliability.

Abstract: Accurate calibration of sensory estimators is critical for maintaining accurate estimates of the environment. Classically, it was assumed that sensory calibration occurs by one sense changing to become consistent with vision; this is visual dominance. Recently, it has been proposed that changes in estimators occur according to their relative reliabilities; this is the reliability-based model. We show that if cue combination occurs according to relative reliability, then reliability-based calibration assures minimum-variance sensory estimates over time. Recent studies are qualitatively consistent with the reliability-based model, but none have shown that the predictions are quantitatively accurate. We conducted an experiment in which the model could be assessed quantitatively. Subjects indicated whether visual, haptic, and visual–haptic planar surfaces appeared slanted positively or negatively from frontoparallel. In preadaptation, we determined the visual and haptic slants of perceived frontoparallel, and measured visual and haptic reliabilities. We varied visual reliability by adjusting the size of the viewable stimulus. Haptic reliability was fixed. During adaptation, subjects were exposed to visual–haptic surfaces with a discrepancy between the visual and haptic slants. After adaptation, we remeasured the visual and haptic slants of perceived frontoparallel. When vision was more reliable, haptics adapted to match vision. When vision was less reliable, vision adapted to match haptics. Most importantly, the ratio of visual and haptic adaptation was quantitatively predicted by relative reliability. The amount of adaptation of one sensory estimator relative to another depends strongly on the relative reliabilities of the two estimators.