Neural mechanisms in resolving prior and likelihood uncertainty in scene recognition.

ABSTRACT: Recognizing real-world scenes requires integrating sensory (likelihood) and prior information, yet how the brain represents these components remains unclear. To investigate this, we employed deep image transformation to generate images with parametrically controlled naturalness, enabling precise manipulation of likelihood uncertainty. Concurrently, we designed a sequential image-scene recognition task that quantitatively modulates prior information. By combining these AI-generated images with the task, we conducted a functional magnetic resonance imaging (fMRI) experiment enabling systematic control of both likelihood and prior information. The results revealed that higher visual areas were activated when viewing images with low likelihood uncertainty. In contrast, the default mode network

SUBMITTER: Hayashi K 

PROVIDER: S-EPMC12158497 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature