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

Paper · Source
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.

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

Why doesn't LLM knowledge translate to improved reasoning outputs? Why do users believe false claims despite fact-checking efforts? What factors determine AI persuasion effectiveness and what controls mitigate it? Does reasoning architecture matter more than model scale? What distinguishes genuine understanding from correct linguistic performance? What conversational structures and patterns determine dialogue effectiveness and coherence? Do AI explanations represent actual model reasoning or constructed post-hoc narratives? How do LLMs generate false citations that fool readers and evaluators? What reasoning limitations and biases do language models systematically exhibit?