Negation without symbols: the importance of recurrence and context in linguistic negation

A simple recurrent network with a perceptual simulation layer was trained on a corpus of affirmative and negated sentences. Linguistic negation can be encoded by the network via the inclusion (or absence) of features and categories associated with the senses, in one step, without the need for an explicit logical operation or for treating the negating word any differently than any other words. Visualizing negation as a trajectory in perceptual simulation space is explored in detail, and the implications for artificial intelligence, embodied computational models, and more practical implications of everyday use of negations are discussed.