Cheng Yu-Ang, Rodriguez Ivan Felipe, Chen Sixuan, Kar Kohitij, Watanabe Takeo, Serre Thomas
Brown University.
York University.
ArXiv. 2024 Dec 26:arXiv:2411.03630v2.
Current neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs. The approach is extensively evaluated against various psychophysics experiments. We also show that the approximation can be used to optimize an "ideal-observer" RNN model to achieve an optimal tradeoff between speed and accuracy without human data. The resulting model is found to account well for human RT data. Finally, we use the approximation to train a deep learning implementation of the popular Wong-Wang decision-making model. The model is integrated with a convolutional neural network (CNN) model of visual processing and evaluated using both artificial and natural image stimuli. Overall, we present a novel framework that helps align current vision models with human behavior, bringing us closer to an integrated model of human vision.
当前灵长类动物视觉的神经网络模型专注于复制行为准确性的整体水平，常常忽略了感知决策丰富的动态本质。在此，我们引入了一个新颖的计算框架，通过学习将循环神经网络（RNN）的时间动态与人类反应时间（RT）对齐，来模拟人类行为选择的动态过程。我们描述了一种近似方法，它使我们能够用人类RT来限制RNN解决一项任务所需的时间步数。该方法针对各种心理物理学实验进行了广泛评估。我们还表明，这种近似方法可用于优化“理想观察者”RNN模型，以便在无需人类数据的情况下实现速度与准确性之间的最佳权衡。结果发现所得模型能很好地解释人类RT数据。最后，我们使用这种近似方法来训练流行的王 - 王决策模型的深度学习实现。该模型与视觉处理的卷积神经网络（CNN）模型集成，并使用人工和自然图像刺激进行评估。总体而言，我们提出了一个新颖的框架，有助于使当前的视觉模型与人类行为对齐，让我们更接近人类视觉的综合模型。
