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

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, which includes the medial prefrontal gyrus, inferior parietal lobule, and middle temporal gyrus, exhibited higher activation when more prior information was available. This approach highlights how applying AI technology to neuroscience questions can enhance our understanding of neural mechanisms underlying scene recognition.