The direct effect represents the predictor’s association with the outcome after accounting for the mediator. The indirect effect is estimated from the predictor-to-mediator and mediator-to-outcome paths, indicating how much of the relationship is statistically transmitted through the intermediate neural or biological measure. Examining both effects helps researchers determine whether a proposed pathway contributes to an observed behavioral association.
Indirect effects combine estimates from multiple paths and may not follow a normal sampling distribution. Bootstrapping repeatedly resamples the observed data to estimate the variability of that combined effect, producing a confidence interval without relying on normality. In a Mediation Model, this interval helps researchers evaluate whether the estimated pathway is sufficiently distinct from zero for their analysis.
The mediator should represent a plausible intermediate process linking the predictor with the outcome, such as brain activity, connectivity, or neurotransmitter level. Its selection determines which neural pathway the analysis evaluates. For example, using a connectivity measure tests a different mechanistic account than using a neurotransmitter measure, even when the predictor and behavioral outcome remain unchanged.
A significant indirect effect supports the statistical consistency of a proposed pathway between a predictor and an outcome. It can therefore refine explanations of how a stimulus, neural measure, or intervention relates to behavior. However, the result alone does not establish causation, so researchers should not treat the modeled pathway as definitive evidence that the mediator produces the outcome.
Researchers first specify a predictor, an outcome, and a theoretically justified mediator. They then use regression or path analysis to estimate the predictor-to-mediator path, the mediator-to-outcome path, and the remaining direct effect. Finally, they evaluate the indirect effect, commonly with a bootstrapped confidence interval, and interpret the pattern in relation to the proposed neural mechanism.
This approach is useful when researchers want to examine whether a neural process helps explain a behavioral or clinical relationship. Studies of cognition may assess brain activity as an intermediate process, while disease or treatment-response studies may examine connectivity or neurotransmitter levels. The resulting pathway estimates can refine theoretical models and identify mechanisms for further investigation without replacing causal research designs.