Maybe also worth noting that we don't always find the ramping type activity in decision tasks - work by @jie-sun.bsky.social found more of a response locked ERP correlate of RT and drift rate for recognition memory decisions, which is a bit different to the CPP.

www.biorxiv.org/content/10.1...

I think that's why Simon and Redmond switched to using CSD early on (e.g. Kelly and O'Connell, 2013). It was a lesson I had to learn the hard way on a previous project.

I feel line the complexity still has a few surprises for us all! Keeps EEG interesting for sure.

For better separating contributions to the response-locked ERP waveforms, CSD has been particularly important to us. Motor and more anterior frontocentral signals can easy creep back to Pz in non-CSD, which really distorts the waveforms when looking at the CPP, for example.

I'm more confident that we can get a cleaner estimate of the stimulus-locked signal in RIDE, particularly when we use the conservatively long S component window. So subtracting that allows us to look at the EEG signals that are more varied in time relative to stimulus onset.

Re: the C component, do you mean the potential for overlap between the C and R components in the data?

From what I've seen, it's a but tricky to disentangle C and R as cleanly as I'd like, and I don't think the RIDE C component definition cleanly maps on to the type of ramping signal of interest.

Thanks for taking the time to read it, and that praise means a lot to us!

We can put up the continuous data - let me just see if the OSF will handle it all given the file size limits. We may expand this open dataset as we get reviewer feedback as well.

May also be of interest to @benediktehinger.bsky.social @ashenhav.bsky.social and colleagues as we build upon their work to better specify the evidence base re: neural correlates of value based choices. More exciting findings coming soon!

For more food choice content, see the latest episode of the PsychTalks podcast run by @psychunimelb.bsky.social

May be of interest to: @redmondoconnell.bsky.social @elainecorbett.bsky.social @epares.bsky.social @silviaseghezzi.bsky.social @connelllab.bsky.social @lcparra.bsky.social

We think this will help researchers develop joint neural-behavioural computational models of value-based decision-making, as has been nicely done for perceptual choices.

With Paul Garrett, Philip Smith @hesterr77.bsky.social and Stefan Bode.

@psychunimelb.bsky.social

These neural measures exhibited key characteristics of an evidence accumulation signal: faster build-up rates with faster response times and a convergence to a relatively fixed amplitude.

This points to the CPP as a more domain-general correlate of decision-making. 4/n

We used signal deconvolution methods (to reduce overlap from stimulus-locked neural activity) in combination with current source density methods to improve our ability to accurately measure the CPP, as well as Mu/Beta activity over motor cortex.

3/n

There has been some controversy around whether electroencephalographic (EEG) signals, such as the Centro-Parietal Positivity, trace decision dynamics for choices that involve endogenous (e.g., preference-driven) information.

See doi.org/10.1162/IMAG...

2/n

Research was performed at @psychunimelb.bsky.social @unimelb.bsky.social