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EnglishPerception of external events often depends on integrating different sensory information. Many studies show strong evidence for visual–tactile integrations. Understanding how visual and tactile information are merged together is still a challenging problem. Here, a neural network model was used to investigate the mechanisms underlying visual–tactile interactions. It includes two unimodal areas (visual and tactile, respectively), sending feedforward connections into a downstream bimodal area. The unimodal areas influence each other via two synaptic mechanisms: feedback synapses from the bimodal area and direct reciprocal connections. The network reproduces a variety of visual–tactile interactions: 1) detection of faint tactile stimuli is facilitated by concomitant visual input; 2) tactile spatial resolution is improved by visual information; 3) cross-modal advantages are maximum when poor unisensory information is available (inverse effectiveness); and 4) conflict situations are resolved based on the more reliable sensory cue. The model identifies distinct roles for the feedback and direct synapses: the first are fundamental to improve detection of low intensity tactile stimuli in cross-modal stimulation, and the second are mostly implicated in visual enhancement of tactile spatial localization and resolution. A better comprehension of how vision and touch interact in the neural system may contribute to physiological knowledge, clinical practice, and technological applications.
| Titolo: | Integrating Information From Vision and Touch: A Neural Network Modeling Study |
| Autore/i: | MAGOSSO, ELISA |
| Autore/i Unibo: | |
| Anno: | 2010 |
| Rivista: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/TITB.2010.2040750 |
| Abstract: | Perception of external events often depends on integrating different sensory information. Many studies show strong evidence for visual–tactile integrations. Understanding how visual and tactile information are merged together is still a challenging problem. Here, a neural network model was used to investigate the mechanisms underlying visual–tactile interactions. It includes two unimodal areas (visual and tactile, respectively), sending feedforward connections into a downstream bimodal area. The unimodal areas influence each other via two synaptic mechanisms: feedback synapses from the bimodal area and direct reciprocal connections. The network reproduces a variety of visual–tactile interactions: 1) detection of faint tactile stimuli is facilitated by concomitant visual input; 2) tactile spatial resolution is improved by visual information; 3) cross-modal advantages are maximum when poor unisensory information is available (inverse effectiveness); and 4) conflict situations are resolved based on the more reliable sensory cue. The model identifies distinct roles for the feedback and direct synapses: the first are fundamental to improve detection of low intensity tactile stimuli in cross-modal stimulation, and the second are mostly implicated in visual enhancement of tactile spatial localization and resolution. A better comprehension of how vision and touch interact in the neural system may contribute to physiological knowledge, clinical practice, and technological applications. |
| Data prodotto definitivo in UGOV: | 2010-12-11 17:36:25 |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/11585/94076
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