Table 3.

Robustness checks in the regions of interest showing a significant effect of shifting latent state (from peak voxel of clusters reported in Table 1)

Region/model	Mean beta	t-value	p-value (uncorrected)
Inferior temporal gyrus (−61, −24, −24)
Prewhitened	0.0375	3.68	8.78e-4
Minimal model	0.0693	4.57	7.37e-5
Time-shifted	0.0729	4.19	2.14e-4
Occipitoparietal cortex (21, −69, 67)
Prewhitened	0.0624	3.16	0.00347
Minimal model	0.0372	1.13	0.265
Time-shifted	0.0859	4.09	2.81e-4
Left orbitofrontal cortex (−16, 49, −15)
Prewhitened	0.0256	2.27	0.0304
Minimal model	0.0517	3.43	0.00172
Time-shifted	0.0720	3.98	3.91e-4
Superior parietal lobule (−28, −48, 52)
Prewhitened	0.0175	1.82	0.0792
Minimal model	0.0116	0.547	0.588
Time-shifted	0.0656	4.22	2.00e-4
Right orbitofrontal cortex (27, 43, −18)
Prewhitened	0.0271	2.18	0.0367
Minimal model	0.0586	3.93	4.45e-4
Time-shifted	0.0640	4.06	3.11e-4
Occipital pole (−13, −93, −9)
Prewhitened	0.0243	2.68	0.0116
Minimal model	0.0539	3.47	0.00153
Time-shifted	0.0426	3.08	0.00435

Peak-centered spheres were re-analyzed in three ways. The prewhitened analysis used unsmoothed voxels that were spatially prewhitened (Walther et al., 2016). The minimal model analysis used a regression model that only contained an intercept, the latent state predictor, and 15 off-diagonal autocorrelation terms. The time-shifted analysis used a time-shifted “shifting latent state” regressor in which representations at the time of outcome on a given trial were modeled as reflecting the beliefs that would guide behavior on the subsequent trial. This was offset by one trial from our original analysis, which assumed that representations upon viewing an outcome would reflect the beliefs that were formed in anticipation of that outcome rather than the updated ones that incorporated it.