Reimagining the Semantics of Narratives and Counterfactuals

We propose a semantics for narratives and counterfactuals that explicitly connects them to cognitive models of temporal / causal inferences in narrative discourse comprehension. Both narrative discourses and counterfactuals are understood by (i) constructing temporally-indexed sequences of situation models, which are analog vector-space representations of meaning grounded in our experience perceiving and understanding the world (our learned model of the world), and then (ii) enriching these representations with temporal inferences through a temporal coherence-seeking process. The account, which we term Distributed Situation-model Semantics (DSS), builds on the Distributed Situation Space model of narrative discourse comprehension in Frank et al. 2003, showing how we can use it to move beyond theoretically primitive notions of counterfactual similarity by computing / constructing a historically-structured similarity relation through this coherence-seeking interpretation process. We formalize a semantics that integrates linguistic input with rich temporal world knowledge, interprets narratives and counterfactuals in a graded and context-sensitive manner, and inherently links probabilistic representations of truth-conditions to processing time. In doing so, DSS challenges the traditional semantics-pragmatics and competence-performance distinctions, treating learning world models (acquiring world knowledge), processing time, and continuous, differentiable, probabilistic truth-conditional representations as intrinsic components of semantic theory. Semantic interpretation is conceptualized as content-addressable memory (a continuous Hopfield network, Hopfield 1984), the counterfactual antecedent and the actual context as a memory cue, and counterfactual similarity is understood as timeline retrieval / pattern completion of this cue.

Location: Fowler Museum A139