@inproceedings{83edf30b02254ead9988c96bbcf05f8b,
title = "ARIMA and RNN for selection sequences prediction in Iowa gambling task",
abstract = "The Iowa Gambling Task (IGT) has become the classical experiment with many studies of cognitive decision models. In this work, we explore whether Autoregressive Integrated Moving Average (ARIMA) models and Recurrent Neural Networks (RNN) in time series analysis can be applied to extract the decision features of IGT participants. The simulation results of IGT show that both models can capture the selection characteristics of participants and make subsequent selection prediction accordingly. Furthermore, the RNN containing selection features with different preferences can represent the corresponding participants to participate in the IGT experiment.",
keywords = "Autoregressive integrated moving average, Forecasting, Recurrent neural network, decision-making",
author = "Yuemeng Guo and Sensen Song and Hanbo Xie and Xiaoxue Gao and Jianlei Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022 ; Conference date: 12-02-2022 Through 14-02-2022",
year = "2022",
doi = "10.1109/AISP53593.2022.9760558",
language = "英语",
series = "2022 2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022",
address = "美国",
}