Computational modelling of social feedback prediction errors on
the neural level
Due to the consistent information carried by cue and outcome in the
neutral condition, the prediction error for each trial was only computed
for the social reward and social punishment conditions and employed as
trial-wise parametric modulator on the neural level (66). The prediction
error (PE) and expected value (EV) for each trial were estimated based
on the Rescorla-Wagner model (66-68) according to
\begin{equation}
PE_{t}=R_{t}-EV_{t}\nonumber \\
\end{equation}\begin{equation}
EV_{t+1}=EV_{t}+\alpha\times PE_{t}\nonumber \\
\end{equation}PE reflects the difference between the expectation and the actual
outcome, and EV reflects the expectation of receiving a certain feedback
on a given trail. R is the actual feedback received, t is the given
trial, and \(\alpha\) is the learning rate. The initial EV for social
reward anticipation and social punishment anticipation were set to 0.5
and -0.5 respectively. A learning rate α=0.7 was used across
participants (see 66, 69). The EV for the next trial is updated based on
the EV of the current trial and the prediction error of that trial
multiplied by the learning rate. Altough previous studies suggests that
a range of learning rates (0.2-0.7) does not affect computation (66, 67,
69-71), additional tests with learning rate = 0.2 were conducted and
confirmed the primary findings (see supplementary results ).
For determining the social feedback PE on the neural level EV and PE
were included on the first level as parametric modulators corresponding
to anticipation and outcome phase respectively. On the first level the
GLM models corresponded to the BOLD activation models and the EV and PE
were included as parametric modulators. Based on previous studies (66,
69) outcome stage was modeled without taking specific feedback into
account to avoid overfitting. Treatment effects on the second level were
examined by means of directly comparing the two treatment groups by
means of independent t tests.