Fig. 2. Behavior on Instagram is explained by reward learning (Study 1).

a Model comparison shows that the $\overline{\mathrm{R}}\mathrm{L}$ model explained behavior on Instagram (N = 2,039 independent individuals) 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 distribution of AIC_W is displayed in Supplementary Fig. 3. Source data are provided as a Source Data file. b The model-derived estimate of $\overline{\mathrm{R}}$, the average reward rate, predicted the latency between posts (N = 2,039 independent individuals). As implicated by reward learning theory, the latency between posts was shorter with high compared to low $\overline{\mathrm{R}}$. Points indicate the corresponding estimates from synthetic data, based on ten generative simulation runs of the $\overline{\mathrm{R}}\mathrm{L}$ model (see text for details). The colored line denotes the average effect in the simulated data. Results are presented as means (fixed effects regression estimates) +/− 99% CI from mixed-effects regression. c, d Model fit for an example individual. c The posting history of an individual user over 673 days was well approximated by the $\overline{\mathrm{R}}\mathrm{L}$ model. The model policy (or posting threshold) denotes the average response latency predicted by the model at a given time point. The faded purple lines show 100 simulations of τ_Post from the estimated model policy, which illustrate the expected degree of variability given that policy, and how the empirical τ_Post falls within this range. The yellow line indicates the model estimate of the net reward rate, $\overline{\mathrm{R}}$. Note that a higher estimated $\overline{\mathrm{R}}$ is associated with shorter response latencies (τ_Post). See Supplementary Fig. 4 for additional example individuals. Source data are provided as a Source Data file. d The distribution of τ_Post for the same individual. The faded purple line shows 100 simulations of τ_Post from the estimated model policy. Source data are provided as a Source Data file.