Getting in Touch with the Lost Self: Vicarious and Affective Touch in Depersonalisation

We conducted an online study featuring two experiments in order to examine the relationship between depersonalization experiences (DP) (i.e. feelings of being detached from one’s self and body) and vicarious affective touch and self-touch. Experiment 1 examined to what extent DP traits modulate the perceived pleasantness and/or vividness of tactile experiences as imagined being received by the self and other. In experiment 2 we designed a new affective self-touch intervention in order to explore the effect of CT-optimal self-touch stroking on one’s dorsal forearm on the perceived pleasantness and vividness of tactile experiences as being received by the self and other. We found that low DP individuals reported higher perceived pleasantness and vividness rating for touch. By contrast, the high DP cohort rated all touch experiences as significantly less pleasant. No significant interaction effects for vividness ratings of touch experiences across low and high DP. In addition, our results suggest that people with low DP rate the perceived pleasantness of the imagined social touch experiences as received by the self higher than if received by the other. Interestingly, in high DP individuals, there is no difference in the perceived pleasantness of affective touch imagined as being received by the self vs the other. Finally, we found that both low and high DP participants, following our tailored CT-optimal affective self-touch intervention on one’s own body, report significantly higher ratings of vividness of tactile perception.

Action Enhances Predicted Touch

It is widely believed that predicted tactile action outcomes are perceptually attenuated. The present experiments determined whether predictive mechanisms necessarily generate attenuation or, instead, can enhance perception—as typically observed in sensory cognition domains outside of action. We manipulated probabilistic expectations in a paradigm often used to demonstrate tactile attenuation. Adult participants produced actions and subsequently rated the intensity of forces on a static finger. Experiment 1 confirmed previous findings that action outcomes are perceived less intensely than passive stimulation but demonstrated more intense perception when active finger stimulation was removed. Experiments 2 and 3 manipulated prediction explicitly and found that expected touch during action is perceived more intensely than unexpected touch. Computational modeling suggested that expectations increase the gain afforded to expected tactile signals. These findings challenge a central tenet of prominent motor control theories and demonstrate that sensorimotor predictions do not exhibit a qualitatively distinct influence on tactile perception.

Target Classification Method of Tactile Perception Data with Deep Learning

In order to improve the accuracy of manipulator operation, it is necessary to install a tactile sensor on the manipulator to obtain tactile information and accurately classify a target. However, with the increase in the uncertainty and complexity of tactile sensing data characteristics, and the continuous development of tactile sensors, typical machine-learning algorithms often cannot solve the problem of target classification of pure tactile data. Here, we propose a new model by combining a convolutional neural network and a residual network, named ResNet10-v1. We optimized the convolutional kernel, hyperparameters, and loss function of the model, and further improved the accuracy of target classification through the K-means clustering method. We verified the feasibility and effectiveness of the proposed method through a large number of experiments. We expect to further improve the generalization ability of this method and provide an important reference for the research in the field of tactile perception classification.