Integration of serial sensory information in haptic perception of softness

Redundant estimates of an environmental property derived simultaneously from different senses or cues are typically integrated  according to the Maximum Likelihood Estimation model (MLE): Sensory estimates are weighted according to their reliabilities,  maximizing the percept"s reliability. Mechanisms underlying the integration of sequentially derived estimates from one sense are less clear. Here we investigate the integration of seriallysampled redundant information in softness perception. We developed a  method to manipulate haptically perceived softness of silicone rubber stimuli during bare finger exploration. We then manipulated softness estimates derived from single movement segments (indentations) in a multi-segmented exploration to assess their  contributions to the overall percept. Participants explored two stimuli in sequence, using 2-5 indentations and reported which stimulus felt softer. Estimates of the first stimulus' softness contributed to the judgments similarly, whereas for the second stimulus  estimates from later as compared to earlier indentations contributed less. In line with unequal weighting, the percept"s reliability  increased with increasing exploration length less than predicted by the MLE model. This pattern of results is well explained by  assuming that the representation of the first stimulus fades when the second stimulus is explored, which fits with a   neurophysiological model of perceptual decisions (Deco et al., 2010).

There are zip files for every experiment (1 & 2a-d), which contain all data relative to the publication. The data of each participant is contained in a separate folder. This folder contains a *.raw file for each session of the experiment and a "data" folder, which contains movement trajectories (*.trj files) and the staircase reversals for each condition (*.pse files) in separate folders for each session. In every experiment folder there is a list of trials which were excluded from the analyses.

Variables of Experiment 1 are described in the file VARIABLE_CODES_EXP1.txt and the variables of Experiment 2a-d are described in the file VARIABLE_CODES_EXP2.txt.