Saurabh Bedi
@saurabhbedi.bsky.social
PhD student in Neuroeconomics, University of Zurich | https://saurabh9729.github.io/saurabhbedi/
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 🙂
These principles are likely to generalize to other contexts with bounded quantities 🔄
This work is with @GillesdeH and Christian Ruff 🙂
September 16, 2025 at 9:14 AM
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 🙂
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
➡️ Encoding → range-dependent distortions + variance patterns
➡️ Decoding → range-independent distortions + variance patterns
September 16, 2025 at 9:14 AM
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
➡️ 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.
This arises independently under both efficient encoding and Bayesian decoding of bounded quantities like probabilities.
September 16, 2025 at 9:14 AM
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.
This arises independently under both efficient encoding and Bayesian decoding of bounded quantities like probabilities.