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Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Predictive learning as a network mechanism for extracting low-dimensional latent space representations.

Stefano Recanatesi¹, Matthew Farrell², Guillaume Lajoie^3,4

¹University of Washington Center for Computational Neuroscience and Swartz Center for Theoretical Neuroscience, Seattle, WA, USA. stefanor@uw.edu.

Nature Communications

|March 4, 2021

Summary

Artificial neural networks learn useful representations by predicting world observations. This process reveals low-dimensional structures, aiding in understanding complex data and neural network function.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Computational neuroscience
Machine learning
Artificial intelligence

Background:

Artificial neural networks excel at sequential tasks, attributed to emergent low-dimensional latent structures in their activity.
Understanding how these representations form is crucial for interpreting neural network function and biological neural systems.

Purpose of the Study:

Investigate if learning to predict world observations generates representations with accessible low-dimensional latent structure.
Determine when sensory prediction mechanisms align with extracting underlying latent variables in recurrent neural networks.

Main Methods:

Trained a recurrent neural network model on predicting sequences of observations.
Quantified representation dimensionality using nonlinear intrinsic dimensionality measures.
Assessed latent variable extraction via linear decodability.

Main Results:

Network dynamics revealed low-dimensional, nonlinearly transformed representations of sensory inputs.
These representations successfully mapped the latent structure of the sensory environment.
Mathematical arguments provided insights into the emergence of these predictive representations.

Conclusions:

Learning to predict sensory data naturally yields representations that capture underlying environmental structure.
These findings offer a framework for analyzing and interpreting experimental data in neuroscience and AI.
Sensory prediction is a viable mechanism for generating semantically meaningful neural representations.