An ideal observer for passive tactile spatial perception

Abstract

Event Abstract Back to Event An ideal observer for passive tactile spatial perception How does the sense of touch resolve the spatial details of stimuli pressed against the skin? Two popular tests of passive (finger stationary) tactile spatial acuity are the classic two-point discrimination task, in which the subject reports the number of caliper points (one or two) in contact with the skin, and the newer grating orientation task, in which the subject reports the orientation (horizontal or vertical) of an impressed square wave grating. Experiments suggest that the firing rates of slowly adapting type-1 (SA1) mechanoreceptive afferents encode the structure of these and other static tactile stimuli, but how the brain decodes these inputs is unknown. We developed an ideal Bayesian observer that decodes simulated somatosensory neuronal firing patterns evoked by spatially structured stimuli. The generative model incorporates the known receptive field structures and density of SA1 afferents, and firing rate noise that is either low (Fano factor < 1), characteristic of SA1 afferents, or high (Fano factor = 1, Poisson), characteristic of primary somatosensory cortical neurons. The Bayesian observer uses model selection to perform two-alternative forced-choice tasks (two-point discrimination, grating orientation), and parameter estimation for graded perceptual judgments (point stimulus localization, skin indentation estimation). Qualitatively, the Bayesian observer performs similarly to human subjects. For instance, the Bayesian observer yields psychometric functions that increase monotonically with point separation (two-point discrimination task) and spatial period (grating orientation task). Quantitatively, the Bayesian observer outperforms humans. When the Bayesian observer operates on high-fidelity (low-noise) inputs, its psychometric functions lie well to the left of humans'. The introduction of Poisson input noise degrades performance, but even in this condition the tactile acuity of the Bayesian observer exceeds that of humans. The results suggest that the brain lacks access to the low-noise signal conveyed by individual peripheral afferents, and instead relies upon noisier, presumably somatosensory cortical activity to infer stimulus structure. Since human performance falls short of the Bayesian observer's, even when the observer operates on noisy inputs, human performance is presumably compromised by more than just cortical noise. Several peripheral inputs may converge to generate a somatosensory cortical neuronal receptive field, with consequent loss of spatial resolution. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). An ideal observer for passive tactile spatial perception. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.058 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Jan 2009; Published Online: 30 Jan 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.