Equation 14 · Every Test Changed the Scene and Kept the Chair Red
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State in advance what each outcome would mean, so the test can be graded rather than argued with after the fact. To make the arithmetic concrete — using invented placeholder numbers only, to illustrate what the ratio means, not anything measured — suppose CfC’s Urban Patio baseline stayed at its published 67.5% while its measured under the recolor intervention came back at 61%. That would give 0.90 : high, and hard to explain without the network actually tracking the target’s identity rather than its color. Suppose instead the same baseline of 67.5% paired with a measured of 9%, most of those trials converging on the red decoy…
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State in advance what each outcome would mean, so the test can be graded rather than argued with after the fact. To make the arithmetic concrete — using invented placeholder numbers only, to illustrate what the ratio means, not anything measured — suppose CfC’s Urban Patio baseline stayed at its published 67.5% while its measured under the recolor intervention came back at 61%. That would give 0.90 : high, and hard to explain without the network actually tracking the target’s identity rather than its color. Suppose instead the same baseline of 67.5% paired with a measured of 9%, most of those trials converging on the red decoy instead of the recolored true target. That would give 0.13 : a near-total collapse, exactly what a shift-stable color detector predicts once its one reliable cue is handed to the wrong object. Both numbers are fictional. They exist only to show what a high ratio and a low ratio would look like once real trials replace them; the actual test has not been run, by this article, by the paper’s own authors, or by anyone identified in a targeted search of the citing literature.
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