# Cognitive Neural Architecture Search Reveals Hierarchical Entailment

arXiv: https://arxiv.org/abs/2502.11141 · HTML: https://mlsanity.com/paper/2502.11141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Hierarchical Representations for Efficient Architecture Search](https://mlsanity.com/paper/1711.00436/) (2017)
- [Genetic CNN](https://mlsanity.com/paper/1703.01513/) (2017)
- [Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs](https://mlsanity.com/paper/1909.06161/) (2019)
- [Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference](https://mlsanity.com/paper/2104.06788/) (2021)
- [Net2Brain: A Toolbox to compare artificial vision models with human brain responses](https://mlsanity.com/paper/2208.09677/) (2022)

## Abstract (arXiv)

Recent research has suggested that the brain is more shallow than previously thought, challenging the traditionally assumed hierarchical structure of the ventral visual pathway. Here, we demonstrate that optimizing convolutional network architectures for brain-alignment via evolutionary neural architecture search results in models with clear representational hierarchies. Despite having random weights, the identified models achieve brain-alignment scores surpassing even those of pretrained classification models - as measured by both regression and representational similarity analysis. Furthermore, through traditional supervised training, architectures optimized for alignment with late ventral regions become competitive classification models. These findings suggest that hierarchical structure is a fundamental mechanism of primate visual processing. Finally, this work demonstrates the potential of neural architecture search as a framework for computational cognitive neuroscience research that could reduce the field's reliance on manually designed convolutional networks.