I am running a classical conditioning experiment with three possible outcomes: reward, punishment, neutral condition. I am probing the conditioning effect using skin conductance as well as pupillometry. As for the latter, I am wondering whether I should use the model for pupil size changes elicited by (il)luminance changes or the model for pupil size changes elicited by fear conditioning.
On the one hand, my aim is to evaluate the presence of conditioning (so the fear conditioning model may sound appropriate). On the other hand, I am not targeting fear learning. I am simply using money as US (gain, loss, no change) and three colours as CS. I want to check for changes of pupil size at the onset of the colours.
How would you proceed?
Many thanks and best,
Ambra
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the illuminance model is unlikely to be of much help here (unless you are interested in deconvolving the time course of neural input into the pupillary system, something that is not implemented in PsPM yet).
So the question is, does the fear conditioning model fit appetitive learning experiments, too.
Since we haven't explicitly tested this (and have no data to test it either), I'd suggest a quick fix. Plot the CS+ and CS- responses and their difference. If the difference curve looks 'similar' to the ones depicted in Korn et al. 2017 then the model is likely to fit. Note that there is some heterogeneity between fear conditioning studies as well. The common responses to the CS themselves may well differ but this is not relevant for the model.
Hope this helps. You are welcome to send (by email if you don't want to post this publicly) the averaged pupil timecourses if you need guidance.
Best
Dominik
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I have got a very dummy question: how do I get the CS+ and CS- responses?
Also, I have preprocessed the data as follows, I don't know if it sounds reasonable:
- reject samples associated to saccades or blinks (according to Eyelink events)
- trim timecourse based on start and end of each block
- interpolate missing data
At the moment I sistematically get negative betas, but I don't know where the problem is.
I have taken care of specifying all the experimental events in the condition file for the GLM.
Thanks for your help.
Best, Ambra
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many thanks for the reply.
I have a question regarding extract segments. In line with MRI GLM specification, in the GLM condition file I specify durations = 0, since my stimuli are almost istantanueos (~17 ms).
However, as a result of this, extract segments returns empty outputs. In particular, if you open pspm_extract_segments and follow what happens, you realise that "segment_length" = 0, therefore "start" equals "stop", segments{c}.data is empty, segments{c}.mean = nan, segments{c}.std = nan, segments{c}.sem = nan.
How shall I deal with durations?
Many thanks and best,
Ambra
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going back to the extract segments function, what does "segment length" refer to?
Is it the time window across which I would compute the mean responses per condition?
You suggested to set it to 10 seconds, is it because we assume that it's back to baseline at 10 seconds?
Thanks and best,
Ambra
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yes, segment length is simply the time interval after the event onset that you want to extract, and yes, pupil size usually is back to 0 after around 10 s. Of course you can use shorter or longer segment length, depending on your data.
Hope this helps
Dominik
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hello,
I am running a classical conditioning experiment with three possible outcomes: reward, punishment, neutral condition. I am probing the conditioning effect using skin conductance as well as pupillometry. As for the latter, I am wondering whether I should use the model for pupil size changes elicited by (il)luminance changes or the model for pupil size changes elicited by fear conditioning.
On the one hand, my aim is to evaluate the presence of conditioning (so the fear conditioning model may sound appropriate). On the other hand, I am not targeting fear learning. I am simply using money as US (gain, loss, no change) and three colours as CS. I want to check for changes of pupil size at the onset of the colours.
How would you proceed?
Many thanks and best,
Ambra
Hi Ambra
the illuminance model is unlikely to be of much help here (unless you are interested in deconvolving the time course of neural input into the pupillary system, something that is not implemented in PsPM yet).
So the question is, does the fear conditioning model fit appetitive learning experiments, too.
Since we haven't explicitly tested this (and have no data to test it either), I'd suggest a quick fix. Plot the CS+ and CS- responses and their difference. If the difference curve looks 'similar' to the ones depicted in Korn et al. 2017 then the model is likely to fit. Note that there is some heterogeneity between fear conditioning studies as well. The common responses to the CS themselves may well differ but this is not relevant for the model.
Hope this helps. You are welcome to send (by email if you don't want to post this publicly) the averaged pupil timecourses if you need guidance.
Best
Dominik
Hello Dominik,
I have got a very dummy question: how do I get the CS+ and CS- responses?
Also, I have preprocessed the data as follows, I don't know if it sounds reasonable:
- reject samples associated to saccades or blinks (according to Eyelink events)
- trim timecourse based on start and end of each block
- interpolate missing data
At the moment I sistematically get negative betas, but I don't know where the problem is.
I have taken care of specifying all the experimental events in the condition file for the GLM.
Thanks for your help.
Best, Ambra
GUI -> tools -> extract segments
Hello Dominik,
many thanks for the reply.
I have a question regarding extract segments. In line with MRI GLM specification, in the GLM condition file I specify durations = 0, since my stimuli are almost istantanueos (~17 ms).
However, as a result of this, extract segments returns empty outputs. In particular, if you open pspm_extract_segments and follow what happens, you realise that "segment_length" = 0, therefore "start" equals "stop", segments{c}.data is empty, segments{c}.mean = nan, segments{c}.std = nan, segments{c}.sem = nan.
How shall I deal with durations?
Many thanks and best,
Ambra
Specify duration = 10
Hello Dominik,
going back to the extract segments function, what does "segment length" refer to?
Is it the time window across which I would compute the mean responses per condition?
You suggested to set it to 10 seconds, is it because we assume that it's back to baseline at 10 seconds?
Thanks and best,
Ambra
Hi Ambra
yes, segment length is simply the time interval after the event onset that you want to extract, and yes, pupil size usually is back to 0 after around 10 s. Of course you can use shorter or longer segment length, depending on your data.
Hope this helps
Dominik