chmathys
commented
9 months ago Hi Vae - just vey quickly, scanning your problem, I suspect that at some point you're taking the log (or square root) of a negative number, which gives you a complex result. This probably happens because you're using RT's instead of log-RT's. When you use a Gaussian model with RT's, negative values are possible according to that model, which doesn't make sense. The solution is to use either a log-Gaussian model with RT's or a Gaussian model with log-RT's
Heechberri
alexjhess
Hi HGF experts,
I am a newbie in coding and computational neuroscience, so please be patient with me ;D
I am using tapas_hgf_whatworld, tapas_logrt_linear_whatworld (edited, edits explained below), and tapas_quasinewton_optim to model the Bayesian inference parameters the reaction time from a pSRTT task (for experiment details see below, adapted from Marshall et al, 2016).
About half of the model outputs contain complex numbers. Here is a sample of the output:
Changes made to the response model logrt_linear_whatworld (attached).
1. I am using original RT (milliseconds/100) values instead of log RT. RT in milliseconds is divided by 100 to scale the values of the reaction time down closer to the scale of the Bayesian parameters which are around the scan of 10 to the power of 1 to -2
2. I changed the response variables to RT~ be0 + be1.prediction error at level 1 + be2.precision weights at level 2 + be3.precision weighted prediction error at level 3, and be4.mean of level 3 + be5.post error slowing + ze.
There are two things I couldn't understand about the original script,
1. Why were the trajectories not used directly in the response model instead of the infStates?
I assumed that Marshall et al, 2016 used the linear_logrt_whatworld scripts, however, the variables are different from what is reported in Figure 3 of the paper. From Figure, I assumed that the variable of interest is first-level PE, third-level precision weighted prediction errors, and third-level mean and post error slowing. These perceptual parameters can already be found in the traj variable. I tried to use actual traj variables (attached as whatworld3 family of scripts) and equations from mathys et al. 2011;14 to derive the variables I needed (attached as whatworld2 family of scripts) and both produced complex numbers.
From my understanding, the first equation in linear_logrt_whatworld is calculating Shannon's surprise, i.e. -log(probability seeing that stimulus), but I don't quite understand the other two equations. which brings me to my next question
2. Why were the calculated response variables "transformed to 1st level" and how are they transformed to the first level?
I am well aware that my edits could have caused the weird problems, and I have spent quite some time trying to troubleshoot myself to no avail... Thank you for all the patience and understanding and help!
Vae
tapas_rt_linear_whatworld2.txt tapas_rt_linear_whatworld3.txt tapas_hgf_whatworld2.txt tapas_hgf_whatworld3.txt tapas_hgf_whatworld3_config.txt tapas_rt_linear_whatworld3_config.txt tapas_rt_linear_whatworld2_config.txt tapas_hgf_whatworld2_config.txt
I also turned on verbose (in tapas_quasinewton_optim_config) for one of the subjects just to see what is going on and here is the output if it helps. After about 9 iterations the improvements were really very small (less than 1), I am not sure why the program is still optimizing.