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THEORY-1242: In early-vision tasks, human performance varies across conditions as the information available at the retinal output does, while falling well below the retinal-stage ideal, so substantial losses lie in central factors

2 min read

Properties4
StatusProposed
Tagscognition, neuroscience, probabilistic-modeling
SourceLIT-1055 — Contributions of ideal observer theory to vision research
ExtendsTHEORY-336 — An ideal observer is optimal relative to the information it is given, so a constrained ideal is the optimal decoder of a degraded input, not a restricted decoder of the full one; comparing real observers with ideals taken at successive stages of the visual system places losses by stage, and in early-vision tasks human performance is reported often to parallel the retinal-stage ideal across conditions while falling well below it

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Source

Geisler (2011), LIT-1055, read in full in NOTE-870 from the NIH author manuscript. The review asserts the thesis as its account of the studies that added biological constraints to the ideal observer one at a time: “the variation in human performance across conditions is often predicted by the information available in the retinal responses”, and “the suboptimal performance of human observers implies substantial contributions of central factors.”

Where the evidence stands

What the review reports, and the logic of constrained ideals it rests on, is THEORY-336, Active, which this entry extends: that entry is the reading, this one the thesis. None of the primary stage-by-stage studies is read; the 2026-10-20 audit found Geisler (1989) and Banks, Geisler and Bennett (1987) closed. The promote_when above is THEORY-336’s condition before the status review of 2026-10-30 promoted it, carried here word for word, because it asks what would make the thesis true and not what the review says.

What this does not say

  • That the gap below the ideal identifies its cause. The review says the opposite: far-below-ideal performance leaves many models standing.
  • That every loss beyond the retina is in the input. The review names residual losses in the decision rule (uncertainty, nonlinearities, inefficient pooling).

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