  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.
