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Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Deep learning and cognitive science.

Pietro Perconti¹, Alessio Plebe¹

¹University of Messina, Department of Cognitive Science, v. Concezione 8, 98121 Messina, Italy.

|June 21, 2020

Summary

Deep learning algorithms, initially engineered, offer valuable insights into cognitive science. These models, despite lacking biological grounding, surprisingly mirror brain activity, prompting new research questions.

Keywords:

Artificial neural networks Deep learning Embodied cognition Enactive vision Mental representations

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Area of Science:

Cognitive Science
Artificial Intelligence
Neuroscience

Background:

Deep learning has revolutionized AI, achieving human-like performance in complex tasks.
While engineered for applied goals, deep learning models show potential for cognitive research.
This potential is likened to biological exaptation, where structures serve new functions.

Purpose of the Study:

To argue for the integration of deep learning into cognitive science.
To trace the evolution of deep learning from its connectionist roots.
To explore the utility of deep learning models for cognitive topics like perception and language.

Main Methods:

Historical analysis of deep learning's evolution from connectionism.
Examination of deep learning model performance in cognitive tasks.
Identification of key questions deep learning poses for cognitive science.

Main Results:

Deep learning demonstrates remarkable continuity and divergence from earlier connectionist models.
Deep learning models achieve human-comparable performance in areas such as perception and language processing.
Deep convolutional models, despite their abstract nature, closely align with brain visual system activation patterns.

Conclusions:

Deep learning presents a significant opportunity for advancing cognitive science.
The surprising parallels between deep learning and brain function warrant further investigation.
Cognitive science should actively engage with deep learning to address fundamental questions about intelligence and cognition.