I am trying to fit a brms model to data which shows a zero-inflated bimodal distribution, namely the participants’ eye fixation duration within a certain region of interest which can be anything from 0 to 4.6 seconds. My independent variable is the pitch which accompanies the trial (2 different pitch levels) and the random terms for study participant and object in the trial.

The following is the model I have been trying to fit, after I found that the gaussian()-model might not be the best fit:

```
model <- brm(fixation_duration ~ 1 + pitch_fac
+ (1 + pitch_fac | subject)
+ (1 | object),
family = hurdle_gamma(link = 'log'),
warmup = 1000,
iter = 2000,
data = total_fix_duration_eyes_per_trial %>% filter(group == 0),
cores = 2)
```

However, the ppcheck shows this is not an optimal fit, either:

I am not sure how to best improve the fit (i.e. which family and link to choose) and my modelling skills are limited. So, if any of you have encountered a similar situation or know how to best approach this, I would be glad to get some tipps.