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Related Experiment Video

Updated: Jun 16, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Statistically optimal perception and learning: from behavior to neural representations.

József Fiser¹, Pietro Berkes, Gergo Orbán

¹National Volen Center for Complex Systems, Brandeis University, Volen 208/MS 013, Waltham, MA 02454, USA. fiser@brandeis.edu

Trends in Cognitive Sciences

|February 16, 2010

Summary

This summary is machine-generated.

Human perception relies on statistical inference from sensory data. This review proposes integrating perception and learning by modeling the sensory environment, suggesting spontaneous neural activity aids in representing information and uncertainty.

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Area of Science:

Neuroscience
Cognitive Science
Computational Neuroscience

Background:

Human perception is understood as statistical inference from noisy sensory inputs.
Neural representations of uncertainty are known to support probabilistic computations.

Purpose of the Study:

To propose a joint framework for perception and learning.
To re-evaluate neural representations of uncertainty for statistically optimal learning.

Main Methods:

Review of existing evidence for statistically optimal learning in humans and animals.
Re-evaluation of neural representations of uncertainty.

Main Results:

Learning an internal model of the sensory environment is crucial for statistical inference in perception.
Spontaneous neural activity may play a functional role in representing information and uncertainty.

Conclusions:

Perception and learning should be treated as a unified statistical inference procedure.
A novel, sampling-based framework for cortical information and uncertainty representation is proposed.