ACC responses frequently correlate with stimulus
salience (18). However, their activation before near-threshold stimulus presentation predicts detection
(19). Caudate is engaged during audiovisual associative learning (20). Likewise, AIC and ACC are
engaged during multisensory integration (21).
There were no significant between-group differences in brain responses during conditioned hallucinations. However, hallucinators deactivated ACC
more [peak at (–16, 54, 14); cluster-extent thresholded, starting value 0.005, critical cluster extent
(ke) = 99] during correct rejections compared with
nonhallucinators (Fig. 2, E and F).
To further dissect conditioned hallucinations,
we modeled their underlying computational mech-
anisms (Fig. 3A) using the hierarchical Gaussian
filter (HGF) (11). We defined a perceptual model
consisting of low-level perceptual beliefs (X1), visual-
auditory associations (X2), and the volatility of
those associations (X3), as well as evolution
rates encoding the relationships between levels
(w, q). Critically, our perceptual model allowed
for variability in weighting between sensory evi-
dence and perceptual beliefs (n). For n = 1, prior
and observation have equal weight; for n > 1,
the prior has more weight than that of the ob-
servation (strong priors); and for n < 1, the ob-
servation has more weight than that of the prior
(weak priors). The resultant posterior proba-
bility of a tone is then fed to a separate response
Model parameters were fit to behavioral data,
and the model was optimized by using log mod-
el evidence and simulations of observed behavior
(figs. S3 and S4). Mean trajectories of perceptual
beliefs were compared across groups (Fig. 3, B
to D). Participants with hallucinations exhibited
stronger beliefs at levels 1 (X1: F11, 605 = 4.8, P =
3.89 × 10−7) (Fig. 3D) and 2 (X2: F11, 605 = 3.89, P =
1.84 × 10−5) (Fig. 3C). X3 beliefs evolved less in
those with psychosis, who failed to recognize the
increasing volatility in contingencies (F11, 605 =
2.11, P = 0.018) (Fig. 3A).
Consistent with strong-prior theory, n was significantly larger in those with hallucinations when
compared with their nonhallucinating counterparts
(Fig. 3E), regardless of diagnosis (F1,55 = 13.96, P =
4.45 × 10−4). Response model parameters did not
differ across the groups (Fig. 3F).
We regressed model parameters onto task-induced brain responses (Fig. 4A). The X1 trajectory
598 11 AUGUST 2017 • VOL 357 ISSUE 6351 sciencemag.org SCIENCE
Fig. 3. HGF analysis.
model, mapping from
experimental stimuli to
through perceptual and
response models. The
first level (X1) represents whether the subject believes a tone was
present or not on trial t.
The second level (X2) is
their belief that visual
cues are associated
with tones. The third
level (X3) is their belief
about the volatility of
the second level. The
HGF allows for individual variability in
sensory evidence and
(parameter n). (B) At
X3, there was a significant block-by-psychosis interaction.
*P < 0.05. (C and D)
Significant block-by-hallucination status
interactions were seen
at layers (D) X1 and
(C) X2. ***P < 0.001.
(E) n was significantly
higher in those with
compared with their
nonhallucinating counterparts. ***P < 0.001.
(F) No main effects of
group or interaction
effects were seen for
the decision noise
parameter within the
response model. Error
bars and line shadings
represent ±1 SEM. Purple, P+H+; blue, P–H+; red, P+H–; white, P–H–.