Individual differences in cognitive processing for roughness rating of fine and coarse textures
Makiko Natsume1, Yoshihiro Tanaka1,2, Astrid M L Kappers3
1Nagoya Institute of Technology, Department of Electrical and Mechanical Engineering, Nagoya, Japan.
Plos One
|January 31, 2019
Summary
Individual differences in roughness perception are significant. Both skin vibration and particle size influence how people feel texture, with varying importance across participants and materials.
Area of Science:
- Haptics and psychophysics
- Sensory perception research
- Material science and tactile feedback
Background:
- Skin vibration is key for fine texture roughness perception.
- Physical factors for coarse texture roughness are less understood and may be individual-dependent.
Purpose of the Study:
- To investigate roughness perception of coarse and fine textures across different materials.
- To determine the relationship between subjective roughness and physical parameters (skin vibration, friction, particle size).
- To analyze individual differences in weighting sensory information for roughness evaluation.
Main Methods:
- Subjective roughness ratings collected from 30 participants.
- Evaluation of glass particle surfaces and sandpapers.
- Correlation analysis between tactile perception and physical properties (vibration, friction, particle size).
Main Results:
- Both particle size (spatial information) and skin vibration (temporal information) strongly correlated with roughness perception for glass surfaces.
- Particle size showed a slightly higher correlation than skin vibration.
- Significant individual differences were observed in the weighting of spatial and temporal information, and in roughness ratings between materials.
Conclusions:
- Roughness perception is influenced by both spatial (particle size) and temporal (skin vibration) factors.
- Individual variability in weighting these factors and other mechanical properties is substantial.
- Understanding these individual differences is crucial for accurate tactile perception models.
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