 Moreover, logical semantics shows how certain syntactic manipulations lead from truths to truths regardless of the specific meanings of the symbols involved in these manipulations (and these notions can be extended to uncertain inference, though this remains only very partially understood). Thus, logical semantics provides a basis for assessing the soundness (or otherwise) of inference rules. While human reasoning as well as reasoning in practical AI systems often needs to resort to unsound methods (abduction, default reasoning, Bayesian inference, analogy, etc.), logical semantics nevertheless provides an essential perspective from which to classify and study the properties of such methods. A strong indication that cognitively motivated conceptual representations of language are reconcilable with logically motivated ones is the fact that all proposed conceptual representations have either borrowed deliberately from logic in the first place (in their use of predication, connectives, set-theoretic notions, and sometimes quantifiers) or can be transformed to logical representations without much difficulty, despite being cognitively motivated.