Abstract
Statistically optimal integration of multimodal signals is known to take place in direct interactions with environmental objects. In the present study we tested whether the same mechanism is responsible for perceptual biases observed in a task, in which participants enclose visual objects by manually controlled visual cursors. We manipulated the relative reliability of visual object information and measured the impact of body-related information on object perception as well as the perceptual variability. The results were qualitatively consistent with statistically optimal sensory integration. However, quantitatively, the observed bias and variability measures systematically differed from the model predictions. This outcome indicates a compensatory mechanism similar to the reliability-based weighting of multisensory signals which could underlie action’s effects in visual perception reported in diverse context conditions.
Highlights
Using a task in which participants were to enclose a visual target object with manually controlled cursors we demonstrated that actions’ effects in visual perception are accompanied by the effects of visual signals on body perception as predicted by the multisensory approach
According to the maximum likelihood estimation (MLE) rule optimal multisensory integration corresponds to a weighted average of unimodal signals with weights being proportional to signal reliability[1]
We tested whether a previously observed impact of a body-related variable on the visual perception of a distant object can be explained by the MLE model
Summary
Using a task in which participants were to enclose a visual target object with manually controlled cursors we demonstrated that actions’ effects in visual perception are accompanied by the effects of visual signals on body perception as predicted by the multisensory approach. Such indications of multisensory integration of body-related signals and visual information relating to a distant object being a goal of body’s movement are not entirely new. The precision of discrimination performance should not deviate from the model prediction and should be increased as compared to both unimodal conditions[19]
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