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Updated: Aug 4, 2026

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
Published on: September 1, 2016
The topography of tactile learning in humans
J A Harris1, I M Harris, M E Diamond
1Cognitive Neuroscience Sector, International School for Advanced Studies (SISSA), 34014 Trieste, Italy. jharris@sissa.it
Abstract:
The spatial distribution of learned information within a sensory system can shed light on the brain mechanisms of sensory-perceptual learning. It has been argued that tactile memories are stored within a somatotopic framework in monkeys and rats but within a widely distributed network in humans. We have performed experiments to reexamine the spread of tactile learning across the fingertips. In all experiments, subjects were trained to use one fingertip to discriminate between two stimuli. Experiment 1 required identification of vibration frequency, experiment 2 punctate pressure, and experiment 3 surface roughness. After learning to identify the stimuli reliably, subjects were tested with the trained fingertip, its first and second neighbors on the same hand, and the three corresponding fingertips on the opposite hand. As expected, for all stimulus types, subjects showed retention of learning with the trained fingertip. However, the transfer beyond the trained fingertip varied according to the stimulus type. For vibration, learning did not transfer to other fingertips. For both pressure and roughness stimuli, there was limited transfer, dictated by topographic distance; subjects performed well with the first neighbor of the trained finger and with the finger symmetrically opposite the trained one. These results indicate that tactile learning is organized within a somatotopic framework, reconciling the findings in humans with those in other species. The differential distribution of tactile memory according to stimulus type suggests that the information is stored in stimulus-specific somatosensory cortical fields, each characterized by a unique receptive field organization, feature selectivity, and callosal connectivity.
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