RI: Small: CompCog: Leveraging Deep Neural Networks for Understanding Human Cognition

在过去的几年里，人工智能和机器学习取得了重大突破，导致系统在解释图片和文字方面接近甚至超过人类的表现。这个项目探讨了这些突破对理解人类思维如何工作的影响。该项目专注于人工神经网络，这是最近许多突破背后的关键技术，能够发现复杂刺激的新表示，该项目有两个目标。首先，通过检查在神经网络模型发现的表示中相似的图片或单词是否也被人判断为相似，来评估人类和机器学习之间的对应程度。第二，开发增加这种对应性的方法，目标是能够使用神经网络表示来生成关于人们如何使用真实的图像或文本学习和形成类别的良好预测。这个研究项目将回答关于当代神经网络发现的表示如何与人类认知相关的基本科学问题。然后，它将探讨什么架构和培训制度产生与这些属性的表示。此外，该项目将解决如何修改这些表示以更好地与人类认知保持一致的方法问题。解决这个问题将带来强大的新工具，用于在自然环境中建立人类行为模型，利用机器学习的最新成果来扩大认知科学实验研究的范围。通过在人类和机器学习之间建立更强的联系，该项目将对这两个领域产生影响。即使目前的神经网络系统与人类学习有很大的不同，它们也提供了最先进的图像和文本表示，可以作为开发更好的人类表示的起点。通过发现人工神经网络学习的表示与人类不同的方式，人们可以识别新的算法和训练方法，从而实现更紧密的对齐。由于人类仍然是可以解决某些问题的系统的最佳例子，这种对齐提供了一条扩展当前人工智能系统的能力并使其更易于被人解释的道路，这在需要人机交互的环境中至关重要。

The last few years have seen significant breakthroughs in artificial intelligence and machine learning, resulting in systems that approach or even exceed human performance in interpreting pictures and words. This project explores the implications of these breakthroughs for understanding how the human mind works. Focusing on artificial neural networks, a key technology behind many recent breakthroughs that is capable of discovering novel representations for complex stimuli, the project has two goals. First, assessing the degree of correspondence between human and machine learning by examining whether the pictures or words that are similar in the representations discovered by neural network models are also judged to be similar by people. Second, developing methods for increasing this correspondence, with the goal of being able to use neural network representations to generate good predictions about how people learn and form categories using real images or text.This research project will answer basic scientific questions about how the representations discovered by contemporary neural networks relate to human cognition. It will then explore what architectures and training regimes produce representations with these properties. In addition, the project will address the methodological question of how one can modify these representations to produce better alignment with human cognition. Answering this question will lead to powerful new tools for making models of human behavior in naturalistic contexts, leveraging the latest results in machine learning to broaden the scope of experimental research in cognitive science. By building stronger links between human and machine learning, this project will have implications for both fields. Even if current neural network systems turn out to differ significantly from human learning, they provide state-of-the-art representations for images and text that can be used as a starting point for developing better accounts of human representations. By discovering the ways in which the representations learned by artificial neural networks differ from those of humans, one can identify new algorithms and training methods that will result in a closer alignment. Since human beings remain the best examples available of systems that can solve certain problems, such an alignment offers a path toward expanding the capacities of current artificial intelligence systems and making them more interpretable by people, which is critical in settings that require human-machine interaction.