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Abstract

When sliding a finger across a bumpy surface, the finger follows the surface geometry (position signal). At the same time the finger is exposed to forces related to the slope of the surface (force signal) [1]. For haptic shape perception the brain uses both signals integrating them by weighted averaging [2]. This is consistent with the Maximum-Likelihood-Estimate (MLE) model on signal integration, previously only applied to passive perception.
The model further predicts that signal weight is proportional to signal reliability. Here, we tested this prediction for the integration of force and position signals to perceived curvature by manipulating material properties of the curve. Low as compared to high compliance decreased the reliability and so the weight of the sensorily transduced position signal. High as compared to low friction decreased the reliability and so the weight of the transduced force signal. These results demonstrat that the MLE model extends to situations involving active touch.