def test_population_fitting(task, agent, n_sessions=8, n_trials=1000, pop_params=None): """Simulate a set of sessions using parameters drawn from normal distributions specified by pop_params. Then fit the agent model to the simulated data and plot correspondence between true and fitted paramter values. """ sessions = simulate_sessions(task, agent, n_sessions, n_trials, pop_params) ML_fits, MAP_fits, pop_params = mf.fit_population(sessions, agent, max_iter=15) rp.plot_true_fitted_params(sessions, ML_fits, MAP_fits) return (sessions, ML_fits, MAP_fits, pop_params)