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Reposted by Saurabh Bedi

Niklas Buergi @niklasbuergi.bsky.social · 09/03/2026

How does the brain decide which mental strategy to use when inferring others' beliefs? Excited to (finally!) see my first first-author paper out @natneuro.nature.com Summary below 🧵 #CogSci #CogNeuro www.nature.com/articles/s41...

nature.com

A neural signature of adaptive mentalization | Nature Neuroscience

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Reposted by Saurabh Bedi

Arkady Konovalov @arkadykonovalov.bsky.social · 19/11/2025

📢 New preprint! 📢 Very excited to be a part of the project led by @saurabhbedi.bsky.social on how the brain learns from multimodal inputs (e.g. audiovisual): Separable neurocomputational mechanisms underlying multisensory learning biorxiv.org/content/10.1...

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Saurabh Bedi @saurabhbedi.bsky.social · 16/09/2025

When we induce new boundaries, the distortions are largely range-independent, pointing to the decoding stage as the main source. These principles are likely to generalize to other contexts with bounded quantities 🔄 This work is with @GillesdeH and Christian Ruff 🙂

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Saurabh Bedi @saurabhbedi.bsky.social · 16/09/2025

Efficient encoding & Bayesian decoding origins of distortions and variance (via cognitive boundary repulsions) come with dissociable signatures: ➡️ Encoding → range-dependent distortions + variance patterns ➡️ Decoding → range-independent distortions + variance patterns

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Saurabh Bedi @saurabhbedi.bsky.social · 16/09/2025

The mechanism is cognitive boundary repulsion: during inference, the posterior is pushed away from boundaries, generating distortions and variance patterns. This arises independently under both efficient encoding and Bayesian decoding of bounded quantities like probabilities.

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Saurabh Bedi @saurabhbedi.bsky.social · 16/09/2025

📢 Preprint out! biorxiv.org/content/10.1... What gives rise to probability weighting, a cornerstone of Prospect Theory? We show it comes from the natural boundedness of probabilities + cognitive noise. Adding boundaries adds multiple distortions, across risky choice & perception.

biorxiv.org

Probability weighting arises from boundary repulsions of cognitive noise

In both risky choice and perception, people overweight small and underweight large probabilities. While prospect theory models this with a probability weighting function, and Bayesian noisy coding mod...

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