Inversion Effects in Humans and Deep Neural Networks

Samuel Sander1, Katharina Dobs2,3,4,
1 Department Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
2 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
3 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
4 Department of Psychology, Justus Liebig University Giessen, Giessen, Germany. Psychologie & Gehirn 2023
Poster

Unraveling the enigma of inversion effects, this work taps into the power of deep neural networks to challenge our understanding of object recognition, questioning how category characteristics impact our perceptions and if neural networks are good predictors for human behavior.

Abstract

Deep neural networks match or exceed human performance in various visual tasks and serve as effective models for human behavior and neural processes. However, like humans, these networks are affected by input data variations, including contrast, blur, or orientation. In humans, the face inversion effect disproportionately impacts face recognition compared to objects. With deep neural networks modeling human face and object recognition, we aim to examine if this orientation bias applies across different object categories. This work seeks to explore if deep neural networks display inversion effects and can predict such effects in humans. Initial analyses with varied network architectures ranked object categories based on their inversion effects, then examined these in humans. In a classification task, both subjects and networks categorized objects presented upright or inverted. The results suggest neural networks show significant behavioral inversion effects across categories, regardless of model architecture. In humans, similar response patterns to the networks were observed even without significant differences in the inversion effects of selected objects. This underscores the relevance of neural networks in modeling human visual processing.

Methods

Methodological Approach: The study's methodology entails using deep neural networks for behavioral response analysis. The Ecoset dataset is selected, containing over 1.5 million images across 565 object categories. The models utilized are AlexNet, VGG-16, and ResNet-50, and the performance of each is measured using the Ecoset test dataset to calculate inversion effects per category.

Participant Selection: 45 participants were chosen, including 28 females and 17 males aged 18-38 years. They engaged in a 10-Way-Classification-Task, with a focus on accuracy and latency of object recognition. Three participants were excluded due to abnormal response patterns and reaction times.

Experimental Stimuli: The experimental stimuli consisted of images from ten categories in the Ecoset test dataset, chosen to represent varying inversion effects. These categories ranged from objects like cogwheels to dolphins. The ten categories were chosen for their consistent inversion scores across all three models.

Experimental Paradigm: A 10-Way-Classification-Task was conducted to study inversion effects in humans and deep neural networks. Each participant/network was shown 100 images in both upright and inverted orientations and had to identify the category of each image. This was done under specific conditions, including a short presentation time of 100 ms, to ensure comparable feedforward processing to neural networks.

Results


Deep Neural Networks




Humans




Humans vs. Deep Neural Networks




Main Finding

This thesis investigated inversion effects (recognition accuracy for images presented upside down versus right side up) in both humans and deep neural networks (DNNs). The findings suggest that DNNs do exhibit significant inversion effects, which are also reflected across different object categories. However, the architecture of the neural network does not impact the magnitude of this effect. In humans, no significant differences were found across object categories, but the response patterns between humans and DNNs showed strong similarities. Future research could focus on identifying which object properties amplify these inversion effects in neural networks, and then confirm these properties in humans. This could help further understand the origins of the face inversion effect, which is the greater difficulty people have in recognizing faces presented upside down compared to other objects.


Poster

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