Investigating
the influence of priors in human fear generalization using a Bayesian model
Kampermann L, Büchel C
& Onat S
When organisms
form associations through learning, responses often generalize to stimuli that
bare resemblance to the initially reinforced stimulus (CS+). During
generalization, shifts in maximal responses away from the actual reinforced
stimulus are commonly observed when tested with multiple stimuli along a similarity
continuum, a phenomenon known as peak shift. We have developed a Bayesian
framework that can account for peak shifts observed in behavioral ratings by postulating
a group-level prior that is common to all participants over the range of
stimuli forming the similarity continuum. The interaction of the prior distribution
with a generalization component centered on the CS+ can act as a “magnet”,
resulting in shifts of maximal responses depending on which stimuli was used as
CS+. This formalism allows us to reverse-engineer subjects’ latent prior
distributions by evaluating to what extent a specific hypothesis on priors can
explain the observed shifts.
We employed a
fear conditioning procedure using 8 faces organized along a circular similarity
continuum varying in gender and identity dimensions. Two opposite faces were
randomly selected as the CS+ and CS– for each participant (n = 141). Following aversive
conditioning, we obtained fear generalization gradients by asking participants for
explicit UCS expectancy ratings, thus obtaining a two-sided generalization
gradient ranging from CS+ to CS–. We compared the performance of the Bayesian
model to fits carried out on a single-subject basis using two versions of Gaussian
functions and subsequently tested different hypotheses on priors for the
Bayesian model.
As expected a flexible
Gaussian model with 2 parameters (width and location parameters) fitted the
data significantly better than a simple Gaussian model (only width parameter) centered
on the CS+ face (r = .91 vs. r = .74) underlining the presence of peak shifts
in behavioral ratings. On the other hand, the Bayesian model performed
significantly better than the simple Gaussian model, despite being based on
only two more free parameters (flexible Gaussian: 2n parameters; simple Gaussian: n
parameters; Bayesian model: n+2
parameters). Furthermore the Bayesian model explained behavioral gradients
best, when a bimodal prior distribution peaking at both gender prototypes was
used (r = .84). Testing different gender categories individually, a unimodal
prior centered on the male category explained as much variance as one centered
on female category. Overall, the predominance of a bimodal prior indicates that
peak shifts can result from “magnet” effects of categorical face
representatives instead of adversity attributions to a specific gender.
