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Related Experiment Video

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

How much to trust the senses: likelihood learning.

Yoshiyuki Sato¹, Konrad P Kording²

¹Graduate School of Information Systems, University of Electro-Communications, Japan.

Journal of Vision

|November 16, 2014

Summary

Humans can learn new visual information, called likelihood, similar to how they learn prior knowledge. This learning is statistically optimal and generalizes to new situations, showing we actively learn likelihood functions.

Keywords:

Bayesian models context-dependent learning likelihood learning sensorimotor integration

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Bayesian Brain Hypothesis

Background:

Bayesian models explain perception and action by integrating prior knowledge and sensory likelihood.
Prior knowledge is known to be learned, but how likelihoods are learned remains less understood.
Dynamic environments necessitate learning and updating likelihood functions over time.

Purpose of the Study:

To investigate whether human subjects can learn likelihood functions from sensory data.
To determine if this learning aligns with principles of statistically optimal learning.
To assess if learned likelihoods generalize to new prior distributions.

Main Methods:

Experimental paradigm involving visual cues with context-dependent likelihood functions.
Behavioral experiments measuring human subjects' estimations based on learned visual distributions.
Modeling of learning processes using Bayesian inference and optimal learning principles.

Main Results:

Human subjects demonstrated the ability to learn the statistical distribution of visual cues (likelihood function).
Learning of the likelihood function was predictable by models of statistically optimal learning.
A learned likelihood function generalized effectively to novel prior distributions.

Conclusions:

Subjects actively learn likelihood functions from sensory information, not just assume them.
This learning process is consistent with statistically optimal inference.
The brain's ability to learn and generalize likelihoods is crucial for adapting perception and action in changing environments.