Different routes to liking: how readers arrive at narrative evaluations

Abstract

When two people read the same story, they might both end up liking it very much. However, this does not necessarily mean that their reasons for liking it were identical. We therefore ask what factors contribute to "liking" a story, and-most importantly-how people vary in this respect. We found that readers like stories because they find them interesting, amusing, suspenseful and/or beautiful. However, the degree to which these components of appreciation were related to how much readers liked stories differed between individuals. Interestingly, the individual slopes of the relationships between many of the components and liking were (positively or negatively) correlated. This indicated, for instance, that individuals displaying a relatively strong relationship between interest and liking, generally display a relatively weak relationship between sadness and liking. The individual differences in the strengths of the relationships between the components and liking were not related to individual differences in expertize, a characteristic strongly associated with aesthetic appreciation of visual art. Our work illustrates that it is important to take into consideration the fact that individuals differ in how they arrive at their evaluation of literary stories, and that it is possible to quantify these differences in empirical experiments. Our work suggests that future research should be careful about "overfitting" theories of aesthetic appreciation to an "idealized reader," but rather take into consideration variations across individuals in the reason for liking a particular story.

Keywords: Appreciation; Bayesian multilevel modeling; Literature; Narratives; Reading.

Fig. 1

Visualization of the Analysis Pipeline. Note The arrows indicate the order of processing steps

Fig. 2

Posterior distributions of the population-level fixed effects of the relationships between the components and liking. Note The Intercept (A) represents the average liking score. The blue dashed lines indicate the limits of the 95% credible interval. If the credible interval of a parameter does not cross zero, this means that it is likely that the true value for that parameter is different from zero. Code for this figure is adapted from https://www.rensvandeschoot.com/tutorials/brms-started/

Fig. 3

Plot of the correlations between the slopes for the associations of the components and liking. Note Below the diagonal, scatterplots of the individual slopes are displayed. The diagonal represents density plots of the distributions of the slopes. Pearson correlation coefficients are given above the diagonal. *** indicates p < .001. Bonferroni correction for multiple comparisons was applied