Task-dependent fMRI decoder with the power to extend Gabor patch results to Natural images.

Area of Science:

• Neuroscience
• Vision Science

Background:

• Vision science often relies on simplified stimuli like Gabor patches.
• Generalizability of findings from simple stimuli to complex natural images remains a key question.

Purpose of the Study:

• To test if findings from Gabor patch experiments apply to natural images using fMRI decoding.
• To evaluate the efficacy of fMRI decoding in bridging laboratory findings and real-world visual perception.

Main Methods:

• Functional magnetic resonance imaging (fMRI) experiments were conducted.
• Participants performed depth and resolution tasks using both Gabor patches and natural images.
• An fMRI decoder was trained on Gabor patch data to classify brain activity patterns.

Main Results:

• The fMRI decoder, trained on Gabor patches, successfully predicted task engagement (depth vs. resolution) with natural images.
• Decoding accuracy was notable in the V3 and middle temporal (MT+) brain regions.
• Results align with prior research on depth perception over display resolution processing.

Conclusions:

• fMRI decoding can validate the application of laboratory findings (Gabor patches) to naturalistic stimuli.
• This approach offers a novel method for investigating the mechanisms of visual perception.
• Findings support the relevance of simplified stimuli research for understanding complex visual processing.