Fig. 3.

Model comparison based on single- and whole-report data. (A) Mean difference in log likelihood of each model from the stochastic sampling model (with independence between items), for a benchmark dataset of single-report experiments. More positive values indicate better fits to data. Error bars indicate $\pm$1 SE across participants. (B) The same comparison for a set of whole-report experiments. (C) Total difference in log likelihood between models across single- and whole report experiments. (D) Fano factor (ratio of variance to mean) of precision distribution. A constant Fano factor is characteristic of the stochastic model and contrasts with the varying Fano factor (dependent on set size and number of samples) in fixed sampling. (E) Mean difference in log likelihood for differing levels of discretization in the generalized stochastic model (Top), and number of participants best fit with each discretization level (Bottom). Differences in log likelihood are plotted relative to the maximum discretization ($p=1$; Left) corresponding to the standard stochastic model with Poisson-distributed precision. Lower discretization ($p<1$) corresponds to more samples each of lower precision, converging to a continuous Gamma distribution over precision as $p$ approaches zero (Right). All models have the same number of free parameters and include a fixed per-item probability of swap errors (SI Appendix).