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Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Human-like individual differences emerge from random weight initializations in neural networks.

Herrick Fung¹, N Apurva Ratan Murty¹, Dobromir Rahnev¹

¹School of Psychology, Georgia Institute of Technology, Atlanta, GA 30332.

Biorxiv : the Preprint Server for Biology

|November 24, 2025

Summary

Artificial neural networks (ANNs) show individual differences similar to humans. These ANNs can capture and predict specific human behaviors, moving beyond modeling the average person.

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Area of Science:

Cognitive Science
Artificial Intelligence
Computational Neuroscience

Background:

Current AI research often models the average human, following Turing's imitation game.
Human behavior exhibits significant individual variability, which is not captured by average models.

Purpose of the Study:

To investigate if artificial neural networks (ANNs) exhibit individual differences comparable to humans.
To determine if ANNs can align with and predict specific human behavioral patterns.

Main Methods:

Trained multiple instances of three ANNs on a digit recognition task using human response data (N=60).
Collected human behavioral metrics: accuracy, confidence, and response time.
Analyzed inter-instance ANN variability and alignment with individual human subjects across metrics.

Main Results:

ANN instances displayed significant variations from each other, mirroring human individual differences.
Consistent alignment was observed between specific ANN instances and individual human subjects across multiple behavioral metrics.
Utilizing these alignments improved the prediction accuracy of individual human responses.

Conclusions:

ANNs trained with different initializations can capture human behavioral variability at an individual level.
This approach enables the development of AI models that align with specific individuals, not just the average.
Findings open new avenues for personalized AI and understanding human cognition through computational modeling.