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 🙂

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

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.

📢 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.