1. Introduction: Theoretical Developments; From Unconscious Inference to Intrinsic Image:

1.1. Basic ambiguity: luminance vs. lightness; lightness constancy

1.2. Inferring the illuminance: Helmholtz

1.3. Hering's paradox

1.4. Gestalt and the relational approach

1.5. Contrast theories versus relational theories

1.6. Intrinsic image theories

1.6.1. Edge integration.

1.6.2. Edge classification.

1.6.3. Parsing into layers

1.8. Two weaknesses of the intrinsic image models.

1.8.1. The problem of errors.

1.8.2. The missing anchor.

2. The Anchoring Problem

2.1. A concrete example

2.2. Mapping luminance onto lightness

2.3. Wallach on anchoring: Highest Luminance Rule

2.4. Helson on anchoring: Average Luminance Rule

2.5. Anchoring in intrinsic image models

2.5.1. Anchoring applied to reflectance layer.

2.6. Absolute lightness versus absolute luminance.

2.7. Anchoring versus scaling

3. The Rules of Anchoring in Simple Displays

3.1. What are minimal conditions?

3.2. Highest Luminance versus Average Luminance

3.3. Luminosity Problem: Direct Contradiction to HLR

3.3.1. Wallach on increments: HLR does not apply

3.3.2. Common factor for decrements and increments: Geometric, not photometric

4. Area effects

4.1. Surround rule fails

4.2. Highest luminance plus area

4.3. The Area Rule

4.3.1. Other empirical findings

4.4. Luminosity and the Area Rule

4.4.1. Area Rule implies luminosity:

4.4.2. Luminosity induction versus grayness induction.

4.4.3. Phenomena related to the Area Rule

4.4.4. Luminosity threshold and area: Bonato and Gilchrist

4.4.5. Maximum luminance is not the same as the anchor

4.5. Anchoring rules for simple images: A summary

5. Anchoring in Complex Images: A New Theory

5.1. What are the relevant components of a complex image?

5.2. Framework

5.3. Local and global frameworks

5.4. Weighting

5.5. Belongingness and grouping factors

5.5.1. Importance of T-junctions

5.5.1. Role of luminance gradients

5.6. Theoretical value of belongingness.

5.7. The scale normalization effect.

6. Testing the model: The staircase Gelb effect

6.1. Applying the anchoring model

6.2. Compromise

6.3. Weighting factors in the staircase Gelb effect

6.3.1. Configuration.

6.3.2. Articulation.

6.3.3. Field Size.

6.3.4. Perceived size is crucial, not retinal size

6.3.5. Insulation

7. Anchoring and the Pattern of Lightness Errors

7.1. Gilchrist (1988): Failures due to classification problem

7.1.2. Staircase Gelb data inconsistent

7.1.3. Anchoring model consistent with Gilchrist (1988) data.

7.2. Source of Errors

8. The Model Applied to Illumination-Dependent Errors

8.1. Errors associated with level of illumination

8.2. Errors associated with reflectance of target.

8.3. Errors associated with reflectance of backgrounds

8.3.1. The key: Increments versus decrements

8.4. Errors associated with articulation level

8.4.1. Burzlaff experiments.

8.4.2. Arend & Goldstein experiments.

8.4.3. Schirillo experiments.

8.4.4. Application of model to depth and lightness.

8.5. Errors associated with field size

9. The Model Applied to Background-Dependent Errors (Simultaneous Lightness Contrast)

9.1. Anchoring component

9.2. The scale normalization component

9.3. Predictions of the model

9.3.1. The locus of the error.

9.3.2. The size of the error.

9.3.3. No contrast effect with double increments

9.3.4. Segregating effect of luminance ramps

9.3.5. Belongingness creates the illusion

9.4. The Benary effect

9.5. White's illusion.

9.6. Checkerboard contrast.

9.7. Agostini and Proffitt: Common fate.

9.8. Laurinen and Olzak.

9.9. Wolff illusion

9.10. Depth manipulations:

9.11. Adelson's corrugated Mondrian

9.11.1. Wishart experiments.

10. Brightness Induction: Contrast or Anchoring?

10.1. Induction

10.2. Area effects

10.3. Separation

10.4. Depth separation: Gogel and Mershon

10.5. Gelb effect: Contrast versus anchoring

10.6. Summary: application of model to reduction conditions.

10.6.1. Lightness versus brightness

11. Tests of scaling normalization effect

11.1. Crispening effect

12. Problems for the Intrinsic Image Models

12.1. Problem of the staircase Gelb effect data.

12.2. Problem of area effects.

12.3. Problem of articulation effects.

12.4. Problem of errors.

12.5. A response paradox.

13. New Model vs. Intrinsic Image Models

13.1. Classified edge integration

13.2. A Critical Test

13.2.1. Method and results: New model wins.

13.2.2. Low articulation replication.

13.2.3. Review of 1983 data.

14. Evaluating The Model

14.1. Range of application.

14.2. Prediction of errors.

14.3. Unification of constancy failures.

14.4. Rigor and concreteness.

14.5. The role of perceived illumination.

14.6. Choice of scaling rule for the global framework.

15. Debt to the Early Literature

15.1. Koffka

15.2. Compromise and intelligence

15.3. The aperture problem for lightness