If perception is probabilistic, why does it not seem probabilistic?

Area of Science:

• Cognitive Science
• Neuroscience
• Philosophy of Mind

Background:

• The Bayesian perspective successfully explains many perceptual phenomena, leading to the view that perceptual representation is probabilistic.
• However, conscious perception does not fully reflect the probability functions suggested by this view (e.g., broadly tuned neurons in cortical area MT).
• Existing solutions, like sampling, are better suited for perceptual decision-making than for perception itself.

Purpose of the Study:

• To address the discrepancy between probabilistic perceptual representation and conscious perception.
• To propose an alternative Bayesian approach that does not rely on probabilistic representations.

Main Methods:

• Conceptual analysis of Bayesian models of perception.
• Critique of existing explanations for the apparent non-probabilistic nature of conscious perception.
• Argument for a revised Bayesian framework.

Main Results:

• The standard sampling account of perception is insufficient as it conflates perception with decision-making.
• A Bayesian approach to perception can be formulated without requiring probabilistic representations.

Conclusions:

• Conscious perception may not be inherently probabilistic, even within a Bayesian framework.
• The best Bayesian models of perception do not necessitate probabilistic representations for conscious experience.