 Generalized truth values have a strong intuitive background, especially as a tool for the rational explication of incomplete and inconsistent information states. In particular, Belnap’s heuristic interpretation of truth values as information that “has been told to a computer” (see Belnap 1977a,b; also reproduced in Anderson, Belnap and Dunn 1992, §81) has been widely acknowledged. As Belnap points out, a computer may receive data from various (maybe independent) sources. Belnap’s computers have to take into account various kinds of information concerning a given sentence. Besides the standard (classical) cases, when a computer obtains information either that the sentence is (1) true or that it is (2) false, two other (non-standard) situations are possible: (3) nothing is told about the sentence or (4) the sources supply inconsistent information, information that the sentence is true and information that it is false. And the four truth values from \(\mathbf{B}_4\) naturally correspond to these four situations: there is no information that the sentence is false and no information that it is true \((\mathbf{N})\), there is merely information that the sentence is false \((\mathbf{F})\), there is merely information that the sentence is true \((\mathbf{T})\), and there is information that the sentence is false, but there is also information that it is true \((\mathbf{B})\).