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@@ -184,11 +184,11 @@ Sampled VEDB frames were first processed using a deterministic common pipeline:
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  For the **Periph-NF condition**, the following condition-specific transformation was then applied before the shared SimCLR augmentations:
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  1. Obtain the participant's synchronized per-frame gaze location. When a reliable gaze location was unavailable, use the image center as the fallback fixation location.
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- 2. Apply [NeuroFovea](https://github.com/ArturoDeza/NeuroFovea_PyTorch) to the frame, with the transformation centered on the gaze-defined fixation location.
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- 3. Use the NeuroFovea scale parameter **`s = 0.4`**, producing an eccentricity-dependent, metamer-like transformation in which precise spatial structure is progressively replaced by texture-like information farther from fixation.
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- 4. Resize the NeuroFovea output to the network's **`224 × 224`** input resolution, if necessary.
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- 5. Occlude the gaze-centered central region of the NeuroFovea-transformed image with a uniform gray circular scotoma (`128`).
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- 6. Gaussian-blur the scotoma boundary (`kernel = 15`) to produce a feathered transition between the masked central region and the retained peripheral image.
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  This condition-specific construction occurred **before** the shared SimCLR augmentation pipeline.
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  For the **Periph-NF condition**, the following condition-specific transformation was then applied before the shared SimCLR augmentations:
185
 
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  1. Obtain the participant's synchronized per-frame gaze location. When a reliable gaze location was unavailable, use the image center as the fallback fixation location.
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+ 2. Center a circular scotoma on the gaze-defined fixation location and fill the masked central region with uniform gray (`128`).
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+ 3. Gaussian-blur the scotoma boundary (`kernel = 15`) to produce a feathered transition between the masked central region and the retained peripheral image.
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+ 4. Apply [NeuroFovea](https://github.com/ArturoDeza/NeuroFovea_PyTorch) to the scotoma-masked frame, with the transformation centered on the gaze-defined fixation location.
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+ 5. Use the NeuroFovea scale parameter **`s = 0.4`**, producing an eccentricity-dependent, metamer-like transformation in which precise spatial structure is progressively replaced by texture-like information farther from fixation.
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+ 6. Resize the resulting NeuroFovea-transformed image to the network's **`224 × 224`** input resolution, if necessary.
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  This condition-specific construction occurred **before** the shared SimCLR augmentation pipeline.
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