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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.

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Summary
This summary is machine-generated.

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.

Keywords:
Bilateral touchCross-sensory competitionNeural network modelSwitch costVisuo-tactile

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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.