activationcontributes

Activationcontributes is a term used in cognitive neuroscience and machine learning to describe the extent to which the activation of a neural unit, brain region, or model feature contributes to a behavioral output, decision, or prediction. The concept emphasizes the incremental influence of activation on outcomes, beyond baseline effects or other predictors, and it is used to analyze how different components shape behavior or model performance.

Definition and scope include both causal and correlational interpretations. Operationally, activationcontributes refers to the change in predictive performance attributable to including a given activation feature in a model. It can be quantified as the increment in explained variance, an increase in mutual information, or a delta in likelihood when a unit’s activation is added to a model while controlling for other variables. The measure is often compared across units, regions, or layers to map where the most informative activations arise.

Computation typically involves a baseline model and one or more augmented models. A common workflow is to collect data, fit the baseline model, add activation predictors, and evaluate the change in performance on held-out data. Cross-validation is used to estimate out-of-sample contributions, and attribution scores can be computed to produce a contribution map across units, regions, or layers.

Applications span interpreting neural representations, comparing regional contributions in the brain, and assessing feature importance in neural networks. In neuroscience, activationcontributes helps link neural activity to decisions; in AI, it informs explainability and model diagnostics. Limitations include dependence on the chosen model, potential confounding by correlated predictors, and the need for careful interpretation of interactions.

See also: attribution, feature importance, incremental information, ablation studies, explainable AI, neural coding.