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Related Experiment Video

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Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Meta-learned models of cognition.

Marcel Binz^1,2, Ishita Dasgupta³, Akshay K Jagadish^1,2

¹Max Planck Institute for Biological Cybernetics, Tübingen, Germany akshay.jagadish@tue.mpg.de eric.schulz@tue.mpg.de.

The Behavioral and Brain Sciences

|November 23, 2023

Summary

Meta-learning models offer a novel approach to understanding the human mind by acquiring knowledge from experience. This framework provides advantages over traditional cognitive models and establishes a new research program for computational cognition.

Keywords:

Bayesian inference cognitive modeling meta-learning neural networks rational analysis

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Area of Science:

Cognitive Science
Computational Neuroscience
Artificial Intelligence

Background:

Computational models are crucial for psychology and neuroscience.
Traditional models like cognitive architectures and Bayesian models are hand-designed.
Meta-learning offers a data-driven alternative for building cognitive models.

Purpose of the Study:

To synthesize existing research on meta-learned models of cognition.
To establish a coherent research program for meta-learning in cognitive science.
To highlight the advantages of meta-learning over traditional modeling approaches.

Main Methods:

Review and synthesis of existing literature on meta-learning in cognitive science.
Demonstration of meta-learning's ability to construct Bayes-optimal learning algorithms.
Analysis of meta-learning's connection to the rational analysis of cognition.

Main Results:

Meta-learning models acquire inductive biases from environmental interactions.
Meta-learning can be used to create Bayes-optimal learning algorithms.
Meta-learning offers advantages over hand-designed computational models.

Conclusions:

Meta-learning presents a promising framework for developing computational models of cognition.
This approach strengthens the link between computational models and the rational analysis of cognition.
A unified research program for meta-learned cognitive models is proposed.