Modeling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time because such models can help make AI systems more intuitive, personalized, mitigate any human biases, and enhance training in simulation. Some initial work has attempted to utilize neural networks (and large language models) but often assumes one common model for all humans and aims to emulate human behavior in aggregate. However, the behavior of each human is distinct, heterogeneous, and relies on specific past experiences in certain tasks. For instance, consider two individuals responding to a phishing email: one who has previously encountered and identified similar threats may recognize it quickly, while another without such experience might fall for the scam. In this work, we build on Instance Based Learning (IBL) that posits that human decisions are based on similar situations encountered in the past. However, IBL relies on simple fixed form functions to capture the mapping from past situations to current decisions. To that end, we propose two new attention-based neural network models to have open form non-linear functions to model distinct and heterogeneous human decision-making in dynamic settings. We experiment with two distinct datasets gathered from human subject experiment data, one focusing on detection of phishing email by humans and another where humans act as attackers in a cybersecurity setting and decide on an attack option. We conducted extensive experiments with our two neural network models, IBL, and GPT3.5, and demonstrate that the neural network models outperform IBL significantly in representing human decision-making, while providing similar interpretability of human decisions as IBL. Overall, our work yields promising results for further use of neural networks in cognitive modeling of human decision making.

翻译：长期以来，在动态决策任务中对人类认知过程进行建模一直是人工智能领域的一项努力，因为此类模型有助于使人工智能系统更加直观、个性化，减轻人类偏见，并增强模拟训练。一些初步工作尝试利用神经网络（以及大型语言模型），但通常假设一个适用于所有人的通用模型，并旨在从整体上模拟人类行为。然而，每个个体的行为都是独特、异质的，并且依赖于在特定任务中的具体过往经验。例如，考虑两个对网络钓鱼邮件做出反应的个体：一个先前遇到过并识别过类似威胁的人可能很快识别出来，而另一个没有此类经验的人则可能上当受骗。在这项工作中，我们基于实例学习（IBL）理论展开研究，该理论假设人类的决策基于过去遇到的类似情境。然而，IBL依赖于简单的固定形式函数来捕捉从过去情境到当前决策的映射。为此，我们提出了两种新的基于注意力的神经网络模型，它们具有开放形式的非线性函数，用于对动态环境中独特且异质的人类决策进行建模。我们使用从人类受试者实验数据收集的两个不同数据集进行实验，一个侧重于人类对网络钓鱼邮件的检测，另一个则是在网络安全环境中人类作为攻击者并决定攻击选项。我们使用我们的两种神经网络模型、IBL以及GPT3.5进行了广泛的实验，结果表明，在表征人类决策方面，神经网络模型显著优于IBL，同时提供了与IBL类似的人类决策可解释性。总体而言，我们的工作为在人类决策的认知建模中进一步使用神经网络提供了有希望的结果。