Dataset Information

Short-term visual deprivation, tactile acuity, and haptic solid shape discrimination.

ABSTRACT: Previous psychophysical studies have reported conflicting results concerning the effects of short-term visual deprivation upon tactile acuity. Some studies have found that 45 to 90 minutes of total light deprivation produce significant improvements in participants' tactile acuity as measured with a grating orientation discrimination task. In contrast, a single 2011 study found no such improvement while attempting to replicate these earlier findings. A primary goal of the current experiment was to resolve this discrepancy in the literature by evaluating the effects of a 90-minute period of total light deprivation upon tactile grating orientation discrimination. We also evaluated the potential effect of short-term deprivation upon haptic 3-D shape discrimination using a set of naturally-shaped solid objects. According to previous research, short-term deprivation enhances performance in a tactile 2-D shape discrimination task - perhaps a similar improvement also occurs for haptic 3-D shape discrimination. The results of the current investigation demonstrate that not only does short-term visual deprivation not enhance tactile acuity, it additionally has no effect upon haptic 3-D shape discrimination. While visual deprivation had no effect in our study, there was a significant effect of experience and learning for the grating orientation task - the participants' tactile acuity improved over time, independent of whether they had, or had not, experienced visual deprivation.

SUBMITTER: Crabtree CE

PROVIDER: S-EPMC4232490 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Short-term visual deprivation, tactile acuity, and haptic solid shape discrimination.

Crabtree Charles E CE Norman J Farley JF

PloS one 20141114 11

Previous psychophysical studies have reported conflicting results concerning the effects of short-term visual deprivation upon tactile acuity. Some studies have found that 45 to 90 minutes of total light deprivation produce significant improvements in participants' tactile acuity as measured with a grating orientation discrimination task. In contrast, a single 2011 study found no such improvement while attempting to replicate these earlier findings. A primary goal of the current experiment was t ...[more]

PMID: 25397327

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Project description:Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabilities for active exploratory touch and directed manual exploration that associates surfaces and object properties with their spatial locations. This is in stark contrast to robotics. In this field, the relative lack of good real-world interaction models-along with very restricted sensors and a scarcity of suitable training data to leverage machine learning methods-has so far rendered haptic exploration a largely underdeveloped skill. In robot vision however, deep learning approaches and an abundance of available training data have triggered huge advances. In the present work, we connect recent advances in recurrent models of visual attention with previous insights about the organisation of human haptic search behavior, exploratory procedures and haptic glances for a novel architecture that learns a generative model of haptic exploration in a simulated three-dimensional environment. This environment contains a set of rigid static objects representing a selection of one-dimensional local shape features embedded in a 3D space: an edge, a flat and a convex surface. The proposed algorithm simultaneously optimizes main perception-action loop components: feature extraction, integration of features over time, and the control strategy, while continuously acquiring data online. Inspired by the Recurrent Attention Model, we formalize the target task of haptic object identification in a reinforcement learning framework and reward the learner in the case of success only. We perform a multi-module neural network training, including a feature extractor and a recurrent neural network module aiding pose control for storing and combining sequential sensory data. The resulting haptic meta-controller for the rigid 16 × 16 tactile sensor array moving in a physics-driven simulation environment, called the Haptic Attention Model, performs a sequence of haptic glances, and outputs corresponding force measurements. The resulting method has been successfully tested with four different objects. It achieved results close to 100% while performing object contour exploration that has been optimized for its own sensor morphology.

Dataset Information

Short-term visual deprivation, tactile acuity, and haptic solid shape discrimination.

Publications

Short-term visual deprivation, tactile acuity, and haptic solid shape discrimination.

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