Ever slam on the brakes after seeing a speed trap? Or better yet, slow down ahead in anticipation?

In our new paper w/ @anask07.bsky.social in @cp-iscience.bsky.social, we use #iEEG to study the neural basis of reactive and proactive control in medial and lateral PFC.
tinyurl.com/4bbwbffv

In any case, this “bath” does not really seem to affect the efficiency of the decoding for control and selective attention to the relevant stimulus. Maybe the bath affects (or is) the response selection, but I think we will need another task design to test this idea 🚀

Maybe what we are decoding is the noise induced by competition between alternative goals—we proposed this idea in another preprint using a task-switch paradigm : osf.io/preprints/ps...

The question is: what exactly is it bathing in, beyond an attentional state? Could it be the level of arousal? We have a preprint showing that it’s not really the case: osf.io/preprints/ps...

It’s likely that the goal (task-set) directs selective attention toward relevant stimuli and response selection, and that all of this “bathes” in an attentional state of low or high distraction.

That’s an excellent question, and modeling the effect could be really useful. If you look at the latency of decoding onset, it is earlier for the task-set than for selective attention.

In Figure 4, we found no difference between in- vs. out-task decoding. However, we think participants put more effort into maintaining the (abstract) task during out periods, which leads to more sustained task decoding.

We also love the controls in Fig. 3, thanks to the great Henry Jones!! We think that the zone decoding looks evoked probably because we are baselining the data…

We have a handgrip in the lab that allows us to measure response force, and I think it could be great to use it to address this question and see vigor difference in- vs out-of-the-zone

Thank you for your questions, I love this kind of feedback! It’s possible that we detect some low-level response feature, even if the motor response—pressing the space bar—remains quite simple, and differences in vigor are unlikely.

Thanks @benediktehinger.bsky.social for asking! I was initially planning to wait until the paper was accepted before making the code and data public, but why wait? Here’s the OSF link: osf.io/kw2fz/ 🚀

3/Preprint here: www.biorxiv.org/content/10.1...

2/Our RSA analysis also shows that this distractibility signature is unique — independent of failures in cognitive control (goal maintenance) or selective attention to relevant stimuli.

1/ Why are we so easily distracted? 🧠 In our new EEG preprint w/ Henry Jones, @monicarosenb.bsky.social and @edvogel.bsky.social we show that distractibility is associated w/ reduced neural connectivity — and can be predicted from EEG with ~80% accuracy using machine learning.