Bridging the Gap Between Deep Neural Networks and Human Visual Perception: A Toolbox for Testing Psychological Phenomena

Core Concepts

MindSet: Vision is a toolbox that provides a large, configurable set of image datasets and scripts to test deep neural networks on a wide range of well-replicated visual experiments and phenomena reported in psychology. This enables systematic evaluation of how well DNNs capture key aspects of human visual perception.

Abstract

MindSet: Vision is a toolbox aimed at facilitating the testing of deep neural networks (DNNs) on visual psychological phenomena. It provides a large, easily accessible, and highly configurable set of 30 image datasets covering a wide array of well-replicated visual experiments and phenomena reported in psychology.
The datasets span low-level vision (e.g., Weber's law), mid-level vision (e.g., Gestalt effects), visual illusions, and object recognition tasks. Each dataset can be easily regenerated with different configurations (image size, background color, stroke color, number of samples, etc.), offering great versatility for different research contexts.
To enable experimentation, the toolbox provides scripts for three testing methods: Similarity Judgment Analysis, Decoder Approach, and Out-of-Distribution classification. These methods allow researchers to systematically evaluate how well DNNs capture key aspects of human visual perception, going beyond the typical observational benchmarks.
The authors provide examples illustrating the use of these methods with a classic feed-forward CNN (ResNet-152), and the code is extensively documented to facilitate adoption and extension by the research community.
By bridging the gap between computational modeling and psychological research, MindSet: Vision aims to drive further interest in testing DNN models against key experiments reported in psychology, in order to better characterize DNN-human alignment and build better DNN models of human vision.

Stats

Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision.
Deep neural networks (DNNs) provide the best solution for visual identification of objects short of biological vision, and many researchers claim that DNNs are the best current models of human vision and object recognition.
Key evidence in support of this claim comes from the finding that DNNs perform the best on various behavioural and brain benchmarks.

Quotes

"Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision."
"Deep neural networks (DNNs) provide the best solution for visual identification of objects short of biological vision, and many researchers claim that DNNs are the best current models of human vision and object recognition."

Key Insights Distilled From

MindSet

by Valerio Bisc... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05290.pdf

Deeper Inquiries

How can the MindSet: Vision toolbox be extended to test deep learning models on other domains of human psychology beyond vision, such as memory, language, and speech perception?

The MindSet: Vision toolbox can be extended to test deep learning models on other domains of human psychology by creating new datasets and testing methodologies tailored to these specific areas. For memory-related experiments, datasets could be designed to assess pattern recognition, recall accuracy, and memory encoding processes. Language-related experiments could involve datasets with linguistic stimuli, such as word associations, sentence comprehension, and syntactic processing tasks. Speech perception experiments could include datasets with phonetic variations, speech recognition challenges, and auditory processing tasks.
To expand the toolbox beyond vision, researchers can collaborate with experts in memory, language, and speech perception to identify key psychological phenomena and design experiments that can be translated into datasets for testing DNNs. By incorporating a diverse range of stimuli and tasks from these domains, the toolbox can provide a comprehensive platform for evaluating the alignment between DNNs and human cognitive processes beyond vision.

To what extent can the insights gained from testing DNNs on the psychological phenomena in MindSet: Vision inform the development of more biologically plausible neural network architectures?

The insights gained from testing DNNs on the psychological phenomena in MindSet: Vision can significantly inform the development of more biologically plausible neural network architectures. By evaluating DNNs on tasks that mimic human cognitive processes, researchers can identify areas where current models fall short in capturing the complexities of human perception and cognition. This can lead to the refinement of neural network architectures to better emulate the mechanisms underlying human vision, memory, language, and speech perception.
Specifically, the findings from testing DNNs on psychological experiments can highlight the need for models that exhibit characteristics such as hierarchical processing, attention mechanisms, memory storage, and context-dependent processing. By incorporating these insights into the design of neural network architectures, researchers can move towards developing models that not only perform well on benchmark tasks but also demonstrate a deeper understanding of how humans perceive and interact with the world.

What are the potential limitations of the current testing methods provided in the MindSet: Vision toolbox, and how could they be further improved to provide a more comprehensive evaluation of DNN-human alignment?

The current testing methods in the MindSet: Vision toolbox have some limitations that could be addressed for a more comprehensive evaluation of DNN-human alignment. One limitation is the reliance on pre-trained DNNs, which may not capture the full range of human cognitive processes. To improve this, researchers could consider training DNNs from scratch on the provided datasets to assess their ability to learn human-like representations.
Another limitation is the focus on specific tasks and stimuli, which may not cover the full spectrum of human cognitive abilities. To enhance the toolbox, researchers could incorporate a wider variety of tasks, including more complex cognitive processes like reasoning, problem-solving, and decision-making. Additionally, incorporating feedback mechanisms and recurrent connections in the neural network models could better simulate the iterative nature of human cognition.
Furthermore, the toolbox could benefit from including datasets that involve multimodal stimuli, combining visual, auditory, and linguistic inputs to mimic real-world cognitive challenges. By expanding the range of tasks, stimuli, and model architectures, the MindSet: Vision toolbox can offer a more holistic evaluation of DNN-human alignment across various domains of human psychology.