# Assessing the Alignment of Popular CNNs to the Brain for Valence Appraisal

arXiv: https://arxiv.org/abs/2509.21384 · HTML: https://mlsanity.com/paper/2509.21384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs](https://mlsanity.com/paper/1909.06161/) (2019)
- [Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition](https://mlsanity.com/paper/1406.3284/) (2014)
- [FindingEmo: An Image Dataset for Emotion Recognition in the Wild](https://mlsanity.com/paper/2402.01355/) (2024)
- [Pixels to Voxels: Modeling Visual Representation in the Human Brain](https://mlsanity.com/paper/1407.5104/) (2014)
- [EmoCAM: Toward Understanding What Drives CNN-based Emotion Recognition](https://mlsanity.com/paper/2407.14314/) (2024)

## Abstract (arXiv)

Convolutional Neural Networks (CNNs) are a popular type of computer model that have proven their worth in many computer vision tasks. Moreover, they form an interesting study object for the field of psychology, with shown correspondences between the workings of CNNs and the human brain. However, these correspondences have so far mostly been studied in the context of general visual perception. In contrast, this paper explores to what extent this correspondence also holds for a more complex brain process, namely social cognition. To this end, we assess the alignment between popular CNN architectures and both human behavioral and fMRI data for image valence appraisal through a correlation analysis. We show that for this task CNNs struggle to go beyond simple visual processing, and do not seem to reflect higher-order brain processing. Furthermore, we present Object2Brain, a novel framework that combines GradCAM and object detection at the CNN-filter level with the aforementioned correlation analysis to study the influence of different object classes on the CNN-to-human correlations. Despite similar correlation trends, different CNN architectures are shown to display different object class sensitivities.