 According to Thagard, prima facie truths can cohere (fit together) or “incohere” (resist fitting together). The first type of relation includes relations of explanation and deduction, whereas the second type includes various types of incompatibility, such as logical inconsistency. If two propositions cohere, this gives rise to a positive constraint. If they incohere, the result is a negative constraint. A positive constraint between two propositions can be satisfied either by accepting both or by rejecting both. By contrast, satisfying a negative constraint means accepting one proposition while rejecting the other. A “coherence problem”, as Thagard sees it, is one of dividing the initial set of propositions into those that are accepted and those that are rejected in such a way that most constraints are satisfied. Thagard presents several different computational models for solving coherence problems, including a model based on neural networks.