  Logics with analogous semantics, such as Fuzzy Logic, have a number of
explanatory and application advantages, the most well-known being the ability
to help experts develop control systems. From a cognitive systems perspective,
such languages also have the advantage of being grounded in perception. For
social decision making in humans, it is vital that logical conclusions about
others (cognitive empathy) are grounded in empathic emotion (affective
empathy). Classical Fuzzy Logic, however, has several disadvantages: it is not
obvious how complex formulae, e.g., the description of events in a text, can be
(a) formed, (b) grounded, and (c) used in logical reasoning. The two-layered
Context Logic (CL) was designed to address these issue. Formally based on a
lattice semantics, like classical Fuzzy Logic, CL also features an analogous
semantics for complex fomulae. With the Activation Bit Vector Machine (ABVM),
it has a simple and classical logical reasoning mechanism with an inherent
imagery process based on the Vector Symbolic Architecture (VSA) model of
distributed neuronal processing. This paper adds to the existing theory how
scales, as necessary for adjective and verb semantics can be handled by the
system.
