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Compositional Learning of Functions in Humans and Machines

Core Concepts
Humans and machines can efficiently learn and generalize compositional functions, demonstrating sensitivity to contextual changes.
Abstract: Humans and neural network models can learn and reason with compositional functions. Human participants can generalize novel visual function compositions across different interaction conditions. Introduction: Humans excel at composing functions together, showing early mastery of these skills. Linguistic studies formalize different context-shifting phenomena into four types of function ordering. Past Research: Neural networks struggle with systematic generalizations despite AI advances. Models have difficulty converging to compositional solutions in computing composite lookup tables. Behavioral Experiment: Participants were trained on individual functions and tested on their compositions under various interaction conditions. Humans demonstrated high accuracy in generalizing from single functions to compositions, contrary to previous theories. Modeling Results: A standard Transformer model mimicked human behavior in composing novel function pairs through meta-learning. Fine-tuning the model on human-generated data improved its performance and error patterns alignment with humans.
Our results suggested that humans are sensitive to contextual changes during function composition, generating different outputs based on the order of operations.
"We observed slightly lower overall accuracy from the MLC model with only base training." "Our results did not find any participant preferences for a particular set of function interactions in accordance with either linguistic theories."

Key Insights Distilled From

by Yanli Zhou,B... at 03-20-2024
Compositional learning of functions in humans and machines

Deeper Inquiries

How can the findings of this study be applied practically in educational settings or artificial intelligence development?

The findings of this study shed light on how humans and machines learn and reason with compositional functions. In educational settings, understanding how individuals grasp complex function compositions can inform teaching strategies. Educators can design curriculum and learning materials that emphasize contextual changes during function composition, helping students develop better problem-solving skills. Additionally, insights from this study can guide the development of AI systems that mimic human-like compositional reasoning. By incorporating principles observed in human behavior, AI algorithms can be enhanced to perform tasks requiring flexible generalizations and context-sensitive operations.

What potential limitations or biases could impact the conclusions drawn from this research?

Several limitations and biases could affect the conclusions drawn from this research. One limitation is the use of a specific experimental paradigm focused on visual function compositions, which may not fully capture all aspects of compositional learning across different domains. Biases such as participant selection bias (e.g., using Amazon Mechanical Turk workers) or task-specific biases (e.g., simplification of functions for experimental purposes) could also influence the results obtained. Furthermore, there might be inherent cognitive biases in how participants interpret and respond to function composition tasks, potentially affecting the generalizability of the findings.

How might understanding compositional learning in humans contribute to advancements in machine learning algorithms?

Understanding how humans comprehend and manipulate compositional functions provides valuable insights for advancing machine learning algorithms. By studying human behavior in function composition tasks, researchers can identify effective strategies for training neural networks to exhibit similar capabilities. This knowledge can lead to improved algorithmic performance in areas requiring sequential reasoning or context-dependent operations. Incorporating principles of compositional learning observed in humans into AI models may enhance their ability to generalize effectively across diverse datasets and tasks, ultimately leading to more robust and adaptable machine learning systems.