Presuppositions are more persuasive than assertions if addressees accommodate them: Experimental evidence for philosophical reasoning

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Natural Language InferenceArgumentation and PersuasionNLP and Linguistics

Best practice and descriptive research claim that presuppositions, such as the “too” in “,” increase the persuasiveness of arguments. Surprisingly, there is hardly any causal evidence for this claim. Therefore, we tested experimentally if advertisements and political statements with presuppositions are more persuasive than equivalent assertions. In 1999, Sbisà already theorized that “persuasive presuppositions” incidentally urge addressees to extend their (ideological) knowledge to make true the unstated assumptions writers have about what their addressee knows, which leads to greater agreement. Following Sbisà, we hypothesized that the persuasiveness depends on the addressee’s need and willingness to accommodate the presupposed content. In three experiments, we manipulated (a) the presupposition trigger using either the German additive particle auch “too,” the iterative particle wieder “again,” or factive verbs compared to assertive equivalents and (b) the preceding discourse context which supported the presupposition or not. Results show that presuppositions are perceived as more persuasive if they convey discourse-new information, largely irrespective of addressees’ ideological involvement.

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Is language model reasoning authentic and what causes models to reason? How do false presuppositions and sycophancy drive persistent false beliefs in models? What factors drive AI persuasiveness and how can it be mitigated? What structural distinctions matter in reasoning and argumentation? How does dialogue structure affect linguistic grounding and shared meaning? How does evaluation scope and dimensionality affect what we measure? What enables genuine semantic understanding in language models? What happens to knowledge when intelligence becomes tokenized like a commodity? How should designers communicate what AI systems truly are and can do? How does AI-generated content undermine authentic engagement on social platforms? Why do some clarifying approaches produce understanding while others just satisfy? Should AI communication design follow human conversation norms or develop distinct machine-specific principles?