Aftereffects for face attributes with different natural variability: adapter position effects and neural models

Area of Science:

• Cognitive psychology
• Computational neuroscience
• Computer vision

Background:

• Facial adaptation is often explained by shifts in the perceived norm of facial features.
• Understanding how the brain adapts to distorted facial stimuli is crucial for visual perception research.

Purpose of the Study:

• To investigate how adaptation aftereffects vary with the distortion level of facial stimuli.
• To explore the underlying neural mechanisms of facial attribute coding using computational models.

Main Methods:

• Experimentally adapting participants to faces with varying levels of distortion in specific attributes (e.g., eye height).
• Analyzing the magnitude of perceptual aftereffects as a function of adapter distortion.
• Comparing empirical data with predictions from norm-based and exemplar-based neural models.

Main Results:

• The size of adaptation aftereffects varied with distortion level, differing between symmetric and asymmetric eye-height deviations.
• Symmetric deviations showed increasing aftereffects with distortion, while asymmetric deviations showed decreasing aftereffects.
• Perceptual shifts were generally towards the norm, supporting norm-based coding models over repulsion effects.

Conclusions:

• Facial attribute coding exhibits different ranges, influenced by natural variations in features like eye height.
• Norm-based models provide a better framework for understanding adaptation to facial distortions than exemplar-based models.
• The brain's adaptation mechanisms are sensitive to both the degree and type of facial distortion.