MindSet: Vision. A toolbox for testing DNNs on key psychological experiments

Valerio Biscione, Dong Yin, Gaurav Malhotra, Marin Dujmovic, Milton L. Montero, Guillermo Puebla, Federico Adolfi, Rachel F Heaton, John E. Hummel, Benjamin D. Evans, Karim G. Habashy, Jeffrey Bowers

Keywords: Datasets, Visual Perception, Psychology, Human Vision, Deep Neural Networks, Visual Illusion

TL;DR: MindSet: Vision is a toolbox consisting of datasets and code designed to facilitate the testing of DNNs against controlled experiments in psychology.

Abstract: Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox MindSet: Vision, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-of-distribution classification, and decoder method), accessible via GitHub. We test ResNet-152 on each of these methods as an example of how the toolbox can be used.

Primary Area: datasets and benchmarks

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Submission Number: 10121