Computational models of spatial updating in peri-saccadic perception

Area of Science:

• Neuroscience
• Computational Vision
• Perception

Background:

• Perceptual phenomena around saccades, such as peri-saccadic mislocalization and saccadic suppression of displacement, are crucial for understanding spatial stability.
• These phenomena are often viewed as errors in trans-saccadic spatial transformations, offering insights into visual processing mechanisms.

Purpose of the Study:

• To review computational models of peri-saccadic spatial perception.
• To link diverse perceptual phenomena and connect them to the functional requirements of spatial stability.
• To bridge the gap between perceptual effects and underlying neuronal mechanisms for vision across saccades.

Main Methods:

• Review and comparative analysis of existing computational models of peri-saccadic spatial perception.
• Examination of how different models address specific perceptual phenomena.
• Identification of generalizations and interconnections between models.

Main Results:

• Computational models offer a quantitative approach to understanding vision across saccades.
• Analysis reveals potential for generalizing and interconnecting models focused on specific peri-saccadic phenomena.
• Different perceptual effects can be coherently linked to neuronal mechanisms.

Conclusions:

• A quantitative, model-based approach is essential for a deeper understanding of brain processes during saccades.
• Linking various perceptual phenomena provides a more unified explanation for vision across saccades.
• The reviewed models contribute to explaining how neuronal mechanisms ensure spatial stability during eye movements.