Modelling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time. Some initial works have attempted to utilize neural networks (and large language models) but often assume one common model for all humans and aim to emulate human behavior in aggregate. However, behavior of each human is distinct, heterogeneous and relies on specific past experiences in specific tasks. To that end, we build on a well known model of cognition, namely Instance Based Learning (IBL), that posits that decisions are made based on similar situations encountered in the past. We propose two new attention based neural network models to model 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 conduct extensive experiments with our two neural network models, IBL, and GPT3.5, and demonstrate that one of our neural network models achieves the best performance in representing human decision-making. We find an interesting trend that all models predict a human's decision better if that human is better at the task. We also explore explanation of human decisions based on what our model considers important in prediction. Overall, our work yields promising results for further use of neural networks in cognitive modelling of human decision making. Our code is available at https://github.com/shshnkreddy/NCM-HDM.

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。一些初步研究尝试利用神经网络（及大语言模型），但通常假设存在一个适用于所有人类的通用模型，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且异质的，并依赖于其在特定任务中的具体过往经验。为此，我们以一个著名的认知模型——基于实例的学习（Instance Based Learning, IBL）——为基础，该模型认为决策是基于过去遇到的相似情境做出的。我们提出了两种新的基于注意力的神经网络模型，用于在动态环境中对人类决策进行建模。我们利用从人类被试实验数据中收集的两个不同数据集进行实验：一个关注人类对钓鱼邮件的检测，另一个则模拟网络安全环境中人类作为攻击者选择攻击方案的情境。我们使用我们的两种神经网络模型、IBL模型以及GPT3.5进行了广泛的实验，结果表明我们的其中一种神经网络模型在表征人类决策方面取得了最佳性能。我们发现了一个有趣的趋势：所有模型对于任务表现更好的人类个体，其决策预测也更为准确。我们还基于模型在预测中认为重要的因素，探索了对人类决策的解释。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前景的结果。我们的代码可在 https://github.com/shshnkreddy/NCM-HDM 获取。