Perception, illusions and Bayesian inference.

Area of Science:

• Neurobiology
• Cognitive Science
• Psychopathology

Background:

• Descriptive psychopathology differentiates between veridical (accurate) and illusory (false) perceptions tied to sensory stimuli.
• Illusory perception involves perceiving a non-existent object despite a sensory stimulus.

Purpose of the Study:

• To re-examine the distinction between veridical and illusory perception.
• To integrate new theoretical and computational neurobiology findings into the understanding of perception.

Main Methods:

• Review of theoretical and computational neurobiology research.
• Application of Bayesian statistical inference models to perception.
• Analysis of how sensory signals and prior expectations interact.

Main Results:

• All perception, including veridical and illusory, can be modeled as Bayesian statistical inference.
• Bayesian inference successfully addresses the 'inverse optics' problem for veridical perception.
• This framework offers a biologically plausible explanation for various illusory phenomena.

Conclusions:

• Veridical and illusory perceptions are generated by the same underlying inferential mechanisms in the brain.
• Bayesian perceptual inference provides a unified account of accurate and inaccurate perceptions.
• This neurobiological approach challenges traditional distinctions in psychopathology.