Matthieu Chidharom

2/ Preprint here: osf.io/preprints/ps...

Representation of goal activation in working memory as a function of participant motivation. According to the goal competition hypothesis, goal activation in working memory is influenced not only by cognitive control but also by the level of associated benefits. A goal with little or no benefit (neutral) will be weakly activated in working memory compared to conditions involving incentives (penalty avoidance, reward seeking, or the combination of both). It can be assumed that the combination of reward and penalty leads to stronger goal activation than penalty alone, potentially explaining the higher number of no-go errors observed in the penalty-only condition.

1/ To reduce distraction and boost focus, nothing works better than linking performance to rewards 💰 But what about focusing to avoid penalties? Turns out — it works too… just not as much as combining both!

Check out our new preprint 👇
w/ Ed Vogel and @monicarosenb.bsky.social

After distractions, rotating brain waves may help thought circle back to the task To get back on track after a distraction, the cortex appears to employ a rotating traveling wave, a new study by MIT neuroscientists finds.

To get back on track after a distraction, the brain appears to employ a rotating traveling wave, a new study by the lab of @earlkmiller.bsky.social finds. picower.mit.edu/news/after-d... @mitbcs.bsky.social #neuroscience #cognition

🏆 #CNS2026 Young Investigator Award Winners 🏆

Congratulations to Monica Rosenberg and Samuel D. McDougle, recipients of the 2026 Young Investigator Award! 🎉

We look forward to their award lectures at the CNS 2026 Annual Meeting in Vancouver, BC, Canada! 🇨🇦✨

@cogneuronews.bsky.social

Neurocomputational mechanisms underlying the distinct motivational influences of reward and punishment on cognitive control Human motivation is fundamentally shaped by one's expectations of the reward they could earn for good performance or the punishment they would avoid for poor performance. However, the extent to which ...

Thrilled to share our new preprint highlighting distinct neurocomputational mechanisms underlying how reward and punishment determine adaptive cognitive control - a massive fMRI study and collaborative team effort with the @shenhavlab.bsky.social 🧠

Link here:
www.biorxiv.org/content/10.1...

Check out our new preprint:
www.biorxiv.org/content/10.1...

And Paris' thread (@parboulakis.bsky.social):
bsky.app/profile/parb...

Where we explore the neural signature of mind blanking using EEG and fMRI combined!

Spatial attention and working memory are popularly thought to be tightly coupled. Yet, distinct neural activity tracks attentional breadth and WM load.

In a new paper @jocn.bsky.social, we show that pupil size independently tracks breadth and load.

doi.org/10.1162/JOCN...

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/ 🚀

Decoding Distraction From the Human Brain: A Unique Neural Signature Beyond Failures of Selective Attention and Control Distraction is a universal feature of human cognition, yet the reasons why it occurs remain poorly understood. Theories of sustained attention often point to failures of cognitive control in maintaini...

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.