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. 2022 Oct 20;13:6205. doi: 10.1038/s41467-022-33418-2

Fig. 5. Overview of the main analyses for Experiment 3.

Fig. 5

In this experiment participants learned about real profiles on two Big-Five factors. Results indicate participants’ use of a fine correlation structure. a Similar to experiment 1, Model 5 [Fine Granularity & Population RP] is the best fitting model for experiment 3 (n = 59). This model uses the average population as a reference point and fine granularity for generalization. b Simulated data to find the best performing model (n = 59). In line with results from the participants, model 5 [Fine Granularity & Population RP] was the best performing model, demonstrating that participants used the best possible strategy. c Both plots display the average absolute PEs over time ± SEM. Top) Participants’ data shows a decrease in the PEs over time (ρ:−.42, least squares line (red)), which indicates that participants learned over time. Bottom) Simulated data from the best fitting model (Model 5) also shows a decrease in PEs over time (ρ:−0.764), showing that the models learned in a similar way to participants. d All three regressors (representing: 1 RW learning, 2 Coarse granularity, 3 Fine granularity), were significant (one-sided t-test), regressor 1: t(58) = −3.414, p < 0.001, regressor 2: t(58) = −3.6269, p < 0.001, regressor 3: t(58) = −9.4348, p < 0.001, showing participants (n = 59) learned over time but also made use of both coarse and fine granularity. Individual parameter estimates are indicated by the coloured dots, which are summarized by the adjacent boxplots (median (middle line), 25th, and 75th percentile (box), most extreme points not considered outliers (whiskers), outliers (1.5 times interquartile range) indicated with + signs). Due to high correlations between these regressors, conclusions regarding these regressors should be drawn with caution. [One-sided t-test; * indicates p < 0.05, ** indicates p < 0.001, no correction for multiple comparisons]. CG coarse granularity, FG fine granularity, RP reference point, # number of, PEs prediction errors, SEM standard error of the mean, LSLine least squares line.