Local tactile coding in the human fingertip

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Humans are classically thought to use either spectral decomposition or averaging to identify vibrotactile signals. These are general purpose ‘global’ codes that require integration of the signal over long stretches of time. Natural vibrotactile signals, however, likely contain short signature events that can be detected and used for inference of textures, instantaneously, with minimal integration, suggesting a hitherto ignored ‘local code’. In this proposal we will firstly investigate whether such signatures exist in papillary ridges of glabrous skin in the human fingertip and whether they convey texture information that affects perceptual mechanisms using a local code. In the 1st funding period of this project we employed pulsatile stimuli and a passive (no finger movement) change detection psychophysical task to reveal that humans make use of local cues. Our results suggest that humans not only do use local cues but that local cues may even play a dominant role in perception, and that they can readily be stored in working memory. In parallel we have worked in biomechanics, neuronal coding and psychophysics in rats to be able to compare our results in humans with the prominent whisker-based tactile system in rodents. We have found evidence to suggest that quite similar coding strategies evolved in whisker and human fingertip-based tactile systems. In the proposed work program for a 2nd funding period we will build on these achievements to firstly investigate the role of egomovement for perception. We will systematically compare psychophysical performance using passive discrimination to that during palpation movements of the finger/arm. We will then investigate the biomechanical underpinnings of local coding. Local codes may be related to the dominant presence of frictional stick-slip movement of the integument, as shown to be the case for rat’s vibrissae. For fingertips this has hitherto not been shown on the level of single papillary ridges and with sufficient temporal resolution. We will firstly employ microscopic stimulation of single papillary ridges combined with measuring discrimination performance in humans. Further, using ultra-fast videography, we will monitor the fingerprint as it rubs across defined surfaces, at a spatio-temporal resolution needed to reveal the presence of frictional stick-slip movements of single papillary ridges.