Urgent.News

What's breaking now, across thousands of outlets.

AI

Neural dynamics of confidence formation under uncertainty

Our perceptual decision-making can vary depending not only on changes in the surrounding environment but also on metacognitive processes. In particular, the balance between confidence and uncertainty may play a critical role in shaping behavior and the underlying neural dynamics over time, yet how this balance operates remains largely unclear, especially when uncertainty is high and confidence is…

Perceptual decision-making can be influenced by both environmental changes and internal metacognitive processes. The balance between confidence and uncertainty is crucial in determining behavior and neural dynamics over time, although its operation is still not fully understood, particularly when uncertainty is high and confidence is low, or vice versa, during perceptual decision-making.

To investigate this, researchers utilized the Multi-Attribute Attention Task, allowing participants to opt-out of perceptual judgments when desired. Participants with healthy brains were recorded using multichannel electroencephalography (EEG).

The study revealed that EEG-based functional connectivity throughout the brain increased in density as confidence formed during perceptual decision-making. This increase was exclusive to correct trials and became apparent prior to the decision itself. Consequently, correct decisions appear to be driven by the early activation of widely dispersed neural networks that facilitate the integration of sensory evidence and the establishment of reliable confidence.

The findings provide compelling evidence that confidence formation under uncertainty occurs through dynamic alterations in densely interconnected networks across various brain regions, enabling highly accurate and confident perceptual decision-making in humans.

Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at biorxiv.org →

More in AI

Why the New LLM Reasoning Leak Paper Matters for Your Team’s AI Workflow

A Quick Look at the Finding A group of researchers just released a paper titled Stealing Reasoning Traces from Proprietary LLM APIs (see the original site here ). In short, they show that when you call a commercial large‑language model (LLM) like Claude, GPT‑4, or Gemini, the service often returns encrypted “chain‑of‑thought” blocks .

  • Researchers can steal reasoning traces from proprietary LLM APIs.
  • Technique requires only two API calls to reverse-engineer internal reasoning.
  • Implications include privacy violations and erosion of trust in AI workflows.

More from Monday 14 September →