New paper in Nature Communications 🎉 Led by @romanececchi.bsky.social, with @sgluth.bsky.social, we show that attention shapes value normalization in human reinforcement learning. Eye-tracking models explain nonlinear reward scaling better than an ad hoc parameter. www.nature.com/articles/s41...

My lab will be present en masse at #sbdm2026 Paris. Here is a first sample of the posters, presented by @romanececchi.bsky.social "Dynamic range adaptation in vast decision spaces" and @fabiencerrotti.bsky.social "Correcting mis-conceptions and shaping preferences about energy sources with RL"

Very happy that @PNASNews agreed to publish our (w/ @romanececchi.bsky.social) response to Prakhar's thought-provoking study! You can find the final version at the link below. See the following tweet for Prakhar's response to our response. Happy to hear your thoughts! www.pnas.org/doi/10.1073/...

Genuine learning biases persist after accounting for temporally decreasing learning rates: Insight from fitting six datasets | PNAS

Genuine learning biases persist after accounting for temporally decreasing learning rates: Insight from fitting six datasets

pnas.org

@prakhargodara.bsky.social · 8mo ago

Here is the continuation of the conversation for those who are interested. www.pnas.org/doi/10.1073/...

🧵 New preprint out! 📄 "Elucidating attentional mechanisms underlying value normalization in human reinforcement learning" 👁️ We show that visual attention during learning causally shapes how values are encoded w/ @sgluth.bsky.social & @stepalminteri.bsky.social 🔗 doi.org/10.31234/osf...

Epistemic biases in human reinforcement learning: behavioral evidence, computational characterization, normative status and possible applications. A quite self-centered review, but with a broad introduction and conclusions and very cool figures. Few main takes will follow osf.io/preprints/ps...

New preprint! 🚨 Performance of standard reinforcement learning (RL) algorithms depends on the scale of the rewards they aim to maximize. Inspired by human cognitive processes, we leverage a cognitive bias to develop scale-invariant RL algorithms: reward range normalization. Curious? Have a read!👇

🚨New preprint alert!🚨 Achieving Scale-Invariant Reinforcement Learning Performance with Reward Range Normalization. Where we show that things we discover in psychology can be useful for machine learning. By the amazing @maevalhotellier.bsky.social and Jeremy Perez. doi.org/10.31234/osf...