Deciphering the brain mechanisms for perceptual inference through bistable perception – brainference
01/06/2020-31/05/2023
Call: Retos de Investigación (ref. PID2019-111629GB-IOO)
Objective
The brain perceives its surroundings as an inference machine. It infers, for the unknown pieces of the environment, the most compatible situation with its current sensations and knowledge of the structure of the world. It is believed that, since the stimuli that the brain receives are noisy and ambiguous, the brain represents those states probabilistically and creates an approximate image of the world.
Despite the abundance of theoretical models, it is still unclear which approximate inference algorithm the brain is using. We propose to use bistable perception as an experimental framework to test the different models. Bistable perception is a perceptual phenomenon in which an observer, from the exposure of a visual, auditory or even tactile stimuli, can interpret it in two main ways which are exclusive one from another. Such is the case of the Necker cube.
In this project, we will study both inference in humans and inference models by modulating two parameters of the images shown to the observers: its bistability and its bias towards one interpretation. By playing with these parameters, we expect to find behavioral and neural signatures of the perceptual inference machinery in human experimental data.
Impact
The project will combine elements from mutliple disciplines, from probability theory, statistical physics to cognitive psychology and machine learning.
There are few existing theoretical work that have tried to relate bistable perception to the bare physical properties of bistable stimuli. In our research we will study what makes a stimulus bistable. Also, we propose the importance of studying bistable stimulus rather than standard (non-bistable) ones to understand the brain perception mechanisms.
Understanding perception in healthy humans is an important step towards understanding perceptual impairments in various cognitive pathologies, including schizophrenia.
Principal Investigator
Research group
Keywords
Bistable perception, inferential mechanisms