In addition to setting more informative priors, as @spinkney suggests, I would also suggest trying a simpler model specification.
The first line of your formula corresponds to effects on the mean of the ex-Gaussian distribution, \mu, which is given by the mean of the Gaussian component \xi and the mean (inverse rate) of the exponential component \beta.
Then, you additionally model effects on the mean of the exponential component \beta. I suspect that those effects on \mu and \beta would be quite strongly correlated with each other. Also, there’s some work suggesting that the ex-Gaussian parameters (i.e., Gaussian mean \xi and SD \sigma, and exponential mean \beta) do not have clear psychological interpretations.
So taken together, you might want to consider dropping the line in your model formula with effects on \beta.
In terms of speeding up, you could also look into within-chain parallelization, assuming you have more CPU cores available than what you’re currently specifying in the cores input argument.
Side note: You’re specifying thin = 2, but thinning is generally discouraged in Stan, unless you’re running into memory problems.
Hope that helps.