Discriminating tactile speed in absence of raised texture elements: Role of deformation and vibratory cues
Moscatelli, A.; Ryan, C. P.; Ciotti, S.; Cosentino, L.; Ernst, M. O.; Lacquaniti, F.
Show abstract
Motion encoding in touch relies on multiple cues, such as displacements of traceable texture elements, friction-induced vibrations, and gross fingertip deformations by shear force. We evaluated the role of deformation and vibration cues in tactile speed discrimination. To this end, we tested the discrimination of speed of a moving smooth glass plate, and compared the precision of the responses when the same task was performed with a plate having a fine texture. Participants performed the task with and without masking vibrations. Speed discrimination was nearly as precise among the two surface types, as assessed by the steep slope of the psychometric function. Consistent with our previous work, high-frequency vibrations impaired the ability of the participants in discriminating surface speed. Results of the current study showed that it is possible to discriminate motion speed even in absence of a raised texture. HighlightsO_LIOn a smooth surface, humans are able to discriminate the speed of a moving surface by frictional motion cues C_LIO_LIThe precision of speed discrimination is nearly the same with smooth and fine-textured surface types C_LIO_LIHigh frequency vibrations impair the ability to discriminate speed of moving surfaces C_LI
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Touch inhibits touch: sanshool-induced paradoxical tingling reveals perceptual interference between somatosensory submodalities 93%
- Can we tell where our bodies end and the world begins? Evidence for precise three dimensional internal body models 92%
- Influence of colour vision on attention to, and impression of, complex aesthetic images 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.