The Relative Importance of Joke and Audience Characteristics in Eliciting Amusement

Abstract

The current work estimated the relative importance of joke and audience characteristics for the occurrence of amusement. Much psychological research has focused on stimulus characteristics when searching for sources of funniness. Some researchers have instead highlighted the importance of perceiver characteristics, such as dispositional cheerfulness. Across five preregistered studies ( Ns = 118–54,905) with varied stimuli and perceiver samples (website visitors, students, Mechanical Turk and Prolific users), variance-decomposition analyses found that perceiver characteristics account for more variance in funniness ratings than stimulus characteristics. Thus, psychological theories focusing on between-persons differences have a relatively high potential for explaining and predicting humor appreciation (here, funniness ratings). Crucially, perceiver-by-stimulus interactions explained the largest amount of variance, highlighting the importance of fit between joke and audience characteristics when predicting amusement. Implications for humor-appreciation theories and applications are discussed.

The Relative Importance of Target and Judge Characteristics in Shaping the Moral Circle

“…We are not aware of any method that allows one to estimate how many participants are required to estimate variance components with a given level of precision. We aimed to collect at least 200 participants as prior work suggests that relatively precise estimates can be obtained with this sample size (e.g., Rosenbusch et al., 2022, Study 3; Xie et al., 2019, Study 2). The survey remained open until no additional participant completed the study within a span of 3 days, which resulted in a final sample of 258 participants.…”

“…These insights have already spurred research on levels of analysis that had been largely ignored thus far to gain a better understanding of the determinants of first impressions (Cook et al., 2022; Jaeger, Jones, Satchell, Schild, & Van Leeuwen, 2022; Stolier et al., 2018). The variance decomposition approach has also been applied to other areas of research to better understand the relative importance of target and judge characteristics, for example, in predicting humor appreciation (Rosenbusch et al., 2022) and attractiveness perception (Hönekopp, 2006).…”

Humor appreciation can be predicted with machine learning techniques

“…The prediction target, humor appreciation, was measured for ten different humor stimuli with two items per stimulus. These stimuli were selected from a previously published corpus of 105 diverse humor stimuli 27 . The selected humor stimuli were categorized by the original authors as falling into five categories (there were more in the corpus): affiliative, self-defeating, aggressive, self-enhancing, and sexual 58 , 59 .…”

“…Note that the assignment of the stimuli into one of the five categories is not objective. The original authors write “Note that most stimuli fell into multiple categories […] because everyday humor usually combines different dimensions” ( 27 , p. 1390). Table 1 shows an example stimulus for each of the five categories.…”

“…In a variance-decomposition study, ICC values ascribed higher importance to raters than to stimuli when it came to funniness ratings, and even more variance by pooling variance for specific rater X stimulus pairs 27 . This suggested that the totality of interaction effects harbors the highest predictive power, but it remains to be determined which specific effects account for this finding, and how accurate resulting prediction models are, especially when applied outside the training sample.…”