Much of our knowledge is transmitted socially rather thanthrough firsthand experience. Even our memories depend onrecollections of those around us. Surprisingly, when people re-call memories with others, they do not reach the potential num-ber of items they could have recalled alone. This phenomenonis called collaborative inhibition. Recently, Luhmann and Ra-jaram (2015) analyzed the dynamics of collaborative inhibitionat scale with an agent-based model, extrapolating from previ-ous small-scale laboratory experiments. We tested their modelagainst human data collected in a large-scale experiment andfound that participants demonstrate non-monotonicities not ev-ident in these predictions. We next analyzed memory transmis-sion beyond directly interacting agents by placing agents intonetworks. Contrary to model predictions, we observed highsimilarity only within directly interacting pairs. By comparingbehavior to model predictions in large-scale experiments, wereveal unexpected results that motivate future work in eluci-dating the algorithms underlying collaborative memory.

Language that describes people in a concise manner may con-flict with social norms (e.g., referring to people by their race),presenting a conflict between transferring information effi-ciently and avoiding offensive language. When a speakeris describing others, we propose that listeners consider thespeaker’s use or absence of potentially offensive language toreason about the speaker’s goals. We formalize this hypothe-sis in a probabilistic model of polite pragmatic language un-derstanding, and use it to generate predictions about interpre-tations of utterances in ambiguous contexts, which we testempirically. We find that participants are sensitive to poten-tially offensive language when resolving ambiguity in refer-ence. These results support the idea that listeners representconflicts in speakers’ goals and use that uncertainty to inter-pret otherwise underspecified utterances.