Hello, and thanks for your interest in our work!

I accidentally shared the wrong link earlier — here is the correct one to read the article:
www.biorxiv.org/content/10.1...

🙏 Thanks to the amazing collaborators

Alexandre Salvador, Sabrine Hamroun, @mael-lebreton.bsky.social and @stepalminteri.bsky.social

📄 Want all the details? Read the full preprint here: submit.biorxiv.org/submission/p...

So, what did we learn?

🧩 The parahippocampal cortex supports forward planning

🧩 The parietal cortex helps build an internal model of the task’s structure

A dissociation of roles in multi-step reinforcement learning!

🧠 At outcome time, brain activity highlighted a different network — involving the bilateral parietal cortex.

→ Its activity correlated with the structure learning signal.

🧠 Results showed that first-stage decisions — the forward-looking ones — recruited a network centered on the parahippocampal cortex.

→ This region appears crucial for model-based planning.

🎯 Participants (N=28) completed this task inside an fMRI scanner.
We analyzed both their behavior and brain activity to understand the neural basis of:
– Forward planning
– Structure learning (i.e., learning transition probabilities)

We used a two-step reinforcement learning task

At each first-stage choice, participants had to think ahead:
– Should I go for the immediate reward?
– Or choose an option that brings me to a better future state?

Real-life decisions often involve a trade-off between short- and long-term outcomes.

Choosing a small reward now vs. a bigger one later? That’s forward thinking.

But how does the brain manage that?