Spatial hypertext is well suited for easily identifying and tracking user bias within representations of mental models. A basic memex interface implemented as rich hypertext affords the simple visualization of user bias in virtually any context due to the high signal-to-noise ratio afforded by the explicit outlining of atomic idea structures and their causal relationships made possible by such an interface. The presented system facilitates the nonlinear capture, review, and updating of mental models in personal or shared environments while tracking bias. General knowledge level bias and particular cognitive bias activity level, each conceivable as manifestations of anthropic bias, or selection effects, are mapped in the presented system as five bins heat mapped to node border color and as binary checkboxes, respectively. A vision for potential applications and initial insights into human computer interaction challenges are discussed.