Anna

Hi Dominik, thank you very much for that hint, that is really interesting! Indeed, also in our data, even though high-pass filtering improves the overall model fit and leads to smaller RSS across particpants, the effect size of a control contrast (here: CS+ vs. CS- during fear conditioning) is considerably (!) greater for a model with default settings for PSR GLM and no additional high-pass filter. I found it surprising first, but guess it reflects that improved model fit does not necessarily imply...

Hi Ivan, my apologies, I somehow assumed that all filtering would be done during pre-processing of the pupil data, but it does make sense that this step is part of the GLM for PSR. I enabled the filter settings and it works flawlessly. Thanks for your help! Anna

Dear Ivan, many thanks for your cotninuous support and for looking into this! Changing the accepted range of min and max values, as you suggested, addresses the issue. When proceeding with the analysis I noticed that the data fluctuates slowly over the course of the experiment which naturally heavily affects the response estimates of the GLM (see GLM for that subject attached). For this reason I'd like to high-pass filter the data. If I understand the pupil pre-processing correctly (but I might be...

Hi Ivan, Thanks for the quick reply! Please find the entire error message below. Best, Anna Warning: Escaped character '\M' is not valid. See 'doc sprintf' for supported special characters. > In pspm_pupil_pp>preprocess (line 352) In pspm_pupil_pp (line 230) In pspm_cfg_run_pupil_preprocess (line 31) In cfg_run_cm (line 29) In cfg_util>local_runcj (line 1708) In cfg_util (line 968) In pspm_jobman (line 122) In PsPM_pupil_import_preprocess (line 29) Warning: Error using preprocess', 'PsPM_4.2.0\v4.2.0\pspm_pupil_pp.m',...

Hi, Thanks for the recent support on the SEBR analysis again! I now ran into problems trying to preprocess pupil data recorded with Eyelink. During interpolating/smoothing the data I receive the following Matlab error message: Warning: Escaped character '\M' is not valid. See 'doc sprintf' for supported special characters. In pspm_pupil_pp>preprocess (line 352) ... I hope you can replicate it with attached sample pupil data and script. Best regards, Anna

Dear Ivan, thanks a lot for looking into this! As you suggested, down-sampling indeed solves the file size problem and, as hoped, the flexible latency models capture the inter-trial/inter-individual onset latency differences really well. Thanks again! Best, Anna

Thank you very much for providing this fantastic toolbox! We are interested in trial-by-trial responses in fear conditioning studies and are using PsPM already for skin conductance data. For our type of paradigm (CS duration: 6.5 sec, ITI: mean 16.5 sec, range: 15-18 sec, startle probe delivery 6000 ms after CS onset, US delivery 6450 ms after CS onset) a single-trial GLM seems to provide the most robust results for SCR. In order to make the analysis coherent across different measures we would like...