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BIVIP: a competitive neural network for bilateral visuo-haptic processing
Eleonore Federica Di Rosa1, Aaisha Sheth2, Jeffrey Min-In Yau2
1Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi," University of Bologna, Bologna, Italy; Department of Computer Control and Management Engineering, Sapienza University of Rome, Rome, Italy.
Summary
This study explores how vision and touch compete during bilateral stimulus detection. A neural network model reveals cross-sensory inhibition, impacting tactile perception, especially with simultaneous visual and tactile cues.
Area of Science:
- Cognitive Neuroscience
- Human-Computer Interaction
- Robotics
Background:
- Visuo-haptic interactions are crucial for robotics and rehabilitation.
- Understanding temporal dynamics of cross-sensory competition is limited.
- Previous research documented unilateral intersensory switching benefits.
Purpose of the Study:
- Investigate if bilateral stimulus detection involves cross-sensory competition.
- Model sensorineural dynamics of visuo-tactile interactions.
- Explain switch costs in unisensory and multisensory processing.
Main Methods:
- Developed a multi-level neural network model.
- Conducted speeded reaction time tasks with unilateral/bilateral stimuli.
- Analyzed behavioral data and network predictions for various trial types and modalities.
Main Results:
- Network accurately predicted higher switch costs for same-hand, different-modality stimuli.
- Model captured effects of inter-stimulus interval, trial type, and modality.
- Behavioral data showed response slowdown for repeated simultaneous visuo-tactile targets.
Conclusions:
- Cross-sensory competition and inhibition influence bilateral perception.
- Vision can exert offline influences on tactile perception.
- Neural network modeling provides insights into sensorineural dynamics.
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