Suppressenhance

Suppressenhance is a term used in cognitive science and related fields to describe a dual-action process in which a system simultaneously suppresses certain signals or representations while enhancing others. The term combines suppress and enhance to reflect the two complementary aims of the mechanism.

In neuroscience, suppressenhance refers to neural mechanisms where top-down control down-regulates processing of distractors while up-regulating task-relevant representations in sensory and prefrontal circuits, supporting focused attention and improved discrimination under challenging conditions.

In machine learning and artificial intelligence, suppressenhance describes training or model architectures that reduce the influence of noisy or irrelevant features while increasing the weight or salience of informative ones. Techniques include selective regularization, feature gating, and targeted data augmentation that emphasize signal over noise.

Applications span cognitive experiments, brain-computer interfaces, computer vision, and natural language processing, where maintaining signal integrity under interference is crucial. Suppressenhance can be used to explain how systems achieve robustness to distraction and noise.

Critics note that identifying which components to suppress or enhance can be difficult and may introduce bias. The approach often relies on assumptions about signal structure and may trade off sensitivity for specificity.

See also attention, inhibition, amplification, feature selection, and contrastive learning.