When people retell stories, what guides their retelling? Most previous research on story retelling and story comprehension has focused on information accuracy as the key measure of stability in transmission. This paper suggests that there is a second, affective, dimension that provides stability for retellings, namely the audience affect of surprise. In a large-sample study with multiple iterations of retellings, we found evidence that people are quite accurate in preserving all degrees of surprisingness in serial reproduction – even when the event that produced the surprisingness in the original story is dropped or changed. Thus, we propose that the preservation of affect is an implicit goal of retelling: merely do retellers not recall highly surprising events better, but rather they register all levels of surprisingness precisely and aim to surprise their implied audience to same degree. This study used 2,389 participants.Significance Statement: Story retelling is a process whereby cultural information is transmitted horizontally across social networks and vertically down generations. For the most part, retelling research has focused on the relevance and stability of factual information, “who did what, where, when, and why”; comparatively little is known about the transmission of affective information. We suggest that affect can serve as a second axis of stability for retelling, partially independent from factual information. In serial reproduction tasks modeled after the telephone game, we find that surprisingness of stories is well preserved across retellings – even when the facts and events of the story are not. The findings are significant for the communication of information, and thereby also the stability and transformation of culture in general.

Demographic perception—the perception of social quantities of geopolitical scale and social significance—has been extensivelystudied in cognitive and political science (Citrin & Sides, 2008; Gilens, 2001; Herda, 2013). Regular patterns of over- and under-estimation emerge. Americans greatly overestimate, for instance, the proportion of citizens that identify as gay or Muslim, while underestimating those that are Christian. While these errors have been attributed to social factors such as fear of specific minorities (Gallagher, 2003; Wong, 2007), other work has suggested that these patterns result from the psychophysics of the perception of proportions (Landy, Guay & Marghetis 2018). A Bayesian formulation suggests that biases in the estimation of both social proportions and simple visual properties result from a common source: ‘hedging’ uncertain information toward a prior. Here we present a novel lab paradigm and two experiments that manipulate uncertainty in a simple (dot estimation) task, verifying the core assumptions of the Bayesian approach.