Curious about the claim regarding bistable perception as the brain “settling” differently on two distinct but roughly equally plausible generative model parameters behind an observation. In standard statistical terms, should I think of it as: two parameters having similarly high Bayesian posterior probability, but the brain not explicitly representing this posterior, instead using something like local hill climbing to find a local MAP solution—bistable perception corresponding to the two different solutions this process converges to?
If correct, to what extent should I interpret the brain as finding a single solution (MLE/MAP) versus representing a superposition or distribution over multiple solutions (fully Bayesian)? Specifically, in which context should I interpret the phrase “the brain settling on two different generative models”?
should I think of it as: two parameters having similarly high Bayesian posterior probability, but the brain not explicitly representing this posterior, instead using something like local hill climbing to find a local MAP solution—bistable perception corresponding to the two different solutions this process converges to?
Yup, sounds right.
to what extent should I interpret the brain as finding a single solution (MLE/MAP) versus representing a superposition or distribution over multiple solutions (fully Bayesian)?
I think it can represent multiple possibilities to a nonzero but quite limited extent; I think the superposition can only be kinda local to a particular subregion of the cortex and a fraction of a second. I talk about that a bit in §2.3.
in which context should I interpret the phrase “the brain settling on two different generative models”
I wrote “your brain can wind up settling on either of [the two generative models]”, not both at once.
I wrote “your brain can wind up settling on either of [the two generative models]”, not both at once.
Ah that makes sense. So the picture I should have is: whatever local algorithm oscillates between multiple local MAP solutions over time that correspond to qualitatively different high-level information (e.g., clockwise vs counterclockwise). Concretely, something like the metastable states of a Hopfield network, or the update steps of predictive coding (literally gradient update to find MAP solution for perception!!) oscillating between multiple local minima?
Curious about the claim regarding bistable perception as the brain “settling” differently on two distinct but roughly equally plausible generative model parameters behind an observation. In standard statistical terms, should I think of it as: two parameters having similarly high Bayesian posterior probability, but the brain not explicitly representing this posterior, instead using something like local hill climbing to find a local MAP solution—bistable perception corresponding to the two different solutions this process converges to?
If correct, to what extent should I interpret the brain as finding a single solution (MLE/MAP) versus representing a superposition or distribution over multiple solutions (fully Bayesian)? Specifically, in which context should I interpret the phrase “the brain settling on two different generative models”?
Yup, sounds right.
I think it can represent multiple possibilities to a nonzero but quite limited extent; I think the superposition can only be kinda local to a particular subregion of the cortex and a fraction of a second. I talk about that a bit in §2.3.
I wrote “your brain can wind up settling on either of [the two generative models]”, not both at once.
…Not sure if I answered your question.
Ah that makes sense. So the picture I should have is: whatever local algorithm oscillates between multiple local MAP solutions over time that correspond to qualitatively different high-level information (e.g., clockwise vs counterclockwise). Concretely, something like the metastable states of a Hopfield network, or the update steps of predictive coding (literally gradient update to find MAP solution for perception!!) oscillating between multiple local minima?