Fig. 3. Signatures of reward learning on three social media sites (Study 2).

a–c Model comparison shows that the $\overline{\mathrm{R}}\mathrm{L}$ model explained behavior on the three social media sites (total N = 2,127 independent individuals. a N = 543, b N = 773, c N = 813) better than a model without learning. The AIC_W expresses the relative likelihood for each model, and are presented as means +/− 99% CI. The horizontal line at 0.5 represents the chance level of no difference between models. The exceedance probability for the $\overline{\mathrm{R}}\mathrm{L}$ model was 1 in all three datasets. The distribution of AIC_W is displayed in Supplementary Fig. 3. Source data are provided as a Source Data file. d–f The model derived estimate of $\overline{\mathrm{R}}$, the average reward rate, predicted the latency between posts on each social media platform (d N = 543, e N = 773, f N = 813 independent individuals). In line with reward learning theory, the latency between posts was shorter with high compared to low $\overline{\mathrm{R}}$. The colored points indicate the corresponding estimates from simulated data, based on ten generative simulation runs of the $\overline{\mathrm{R}}\mathrm{L}$ model (see text for details). The colored lines show the average effect in the simulated data. Results are presented as means (fixed effects regression estimates) ± 99% CI from mixed-effects regressions.