Discourse expectations: explaining the implicit causality biases of verbs

This article presents a linguistic account explaining particular mechanisms underlying the generation of expectations at the discourse level. We further develop a linguistic theory - the Empty Slot Theory - explaining the phenomenon of implicit verb causality. According to our proposal, implicit causality (IC) verbs introduce lexically determined slots for causal content of specific types. If therequired information is not derivable from the current or preceding context, IC verbs generate the expectation that these slots will be filled in the upcoming discourse. The cognitive mechanism underlying the bias is grounded in the general processing strategy of avoiding accommodation. Empirical evidence for the proposed theory is provided in three continuation experiments in German with comprehensive semantic annotation of the continuations provided by the participants. The reported experiments consistently show that IC bias can be manipulated in systematic ways. Experiment 1 demonstrates important ontological constraints on causal content crucial for our theory. Experiments 2 and 3 show how IC biases can be manipulated in predictable ways by filling the hypothesized slots in the prompt. Experiment 2 illustrates that stimulus-experiencer (experiencer-object) verbs in contrast to causative agent-patient verbs can be manipulated with respect to coherence and coreference by employing adverbial modification. Filling the lexically determined slot of psychological verbs resulted in predictable changes in coherence relations and types of explanations, resulting in the predicted effects on coreference. Experiment 3 extends the empirical investigations to so-called "agent-evocator" verbs. Again, filling the semantic slot as part of the prompt resulted in predictable shifts in coherence relations and explanation types with transparent effects on coreference. The reported experiments shed further light on the close correspondence between coherence and coreference as a hallmark of natural language discourse.