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Related Experiment Video

Updated: Sep 24, 2025

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Integrating unsupervised and reinforcement learning in human categorical perception: A computational model.

Giovanni Granato^1,2, Emilio Cartoni¹, Federico Da Rold³

¹Laboratory of Computational Embodied Neuroscience, Institute of Cognitive Sciences and Technologies, National Research Council of Italy, Rome, Italy.

|May 10, 2022

Summary

A balanced mix of unsupervised learning and reinforcement learning is key for optimal categorical perception. Too much of either learning type hinders task performance and feature identification.

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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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Published on: November 2, 2012

Area of Science:

Cognitive Science
Computational Neuroscience
Machine Learning

Background:

Categorical perception involves perceptual system tuning during categorization tasks.
Existing models consider task-independent (unsupervised learning, UL) and task-dependent (reinforcement learning, RL) effects separately.
The interaction between UL and RL in categorical perception remains understudied.

Purpose of the Study:

To investigate the interaction between unsupervised learning and reinforcement learning in the emergence of categorical perception.
To propose and test a neuro-inspired computational architecture integrating UL and RL processes.

Main Methods:

Developed a system-level computational model with a perceptual component combining UL and RL.
Tested the model using a categorization task to evaluate performance under different UL/RL balances.

Main Results:

A balanced integration of UL and RL resulted in optimal categorical perception and task performance.
Excessive UL led to failure in identifying task-relevant features.
Excessive RL caused slow initial learning and sub-optimal performance.

Conclusions:

The interplay between unsupervised and reinforcement learning is crucial for effective categorical perception.
Model findings align with experimental evidence on extrastriate cortex activation.
Extreme model cases may explain sensory alterations observed in autistic individuals.