Front. Psychol. Frontiers in Psychology Front. Psychol. 1664-1078 Frontiers Media S.A. 10.3389/fpsyg.2025.1642513 Psychology Correction Correction: Forgetting phenomena in the Iowa Gambling Task: a new computational model among diverse participants Yang Tiancheng 1 † Xie Chenghan 2 † Wang Xuehe 1 * 1School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China 2College of Engineering, Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China

Edited and reviewed by: Lidia Ghosh, RCC Institute of Information Technology, India

 *Correspondence: Xuehe Wang wangxuehe@mail.sysu.edu.cn

†These authors share first authorship

 03 07 2025 2025 16 1642513 06 06 2025 17 06 2025 Copyright © 2025 Yang, Xie and Wang. 2025 Yang, Xie and Wang

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 A Correction on Forgetting phenomena in the Iowa Gambling Task: a new computational model among diverse participants by Yang, T., Xie, C., and Wang, X. (2025). Front. Psychol. 16:1510151. doi: 10.3389/fpsyg.2025.1510151 Forgetting Phenomena Iowa Gambling Task Exploitation and Exploration with Forgetting model Sequential Exploration Decay Forgetting Interval section-at-acceptance Decision Neuroscience

In the published article, there was an error. Two sentences in section 5.1. Extension of EEF model with loss aversion, Paragraphs 4 and 5, used outdated statistical values.

Corrections have been made to 5.1 Extension of EEF model with loss aversion, Paragraphs 4 and 5. These paragraphs previously stated:

“Figure 7 shows the comparison of SED and FI, and we can find that although the EEFLA model performed slightly worse than the EEF model (the SED of EEFLA is 1.598, with a difference of 0.106 from the original data, while the SED of EEF has a difference of only 0.008 from the original data; the FI of EEFLA is 4.02, with a difference of 2.22 from the original data, while the FI of EEF has a difference of 2.07), it still outperformed the other models. This indicates that the EEFLA model is still capable of effectively simulating human decision-making behaviors.

However, it can be seen from Figure 8 that the performance of the EEFLA model is not outstanding in terms of parameter recovery and fixed-effects statistical model analysis. The average correlation coefficient for EEFLA's parameter recovery is 0.77155 (range: 0.60 – 0.93), which is lower than the EEF model's 0.8299 (range: 0.64 – 0.96). Additionally, the EEFLA model did not perform well in the previously mentioned fixed-effects statistical model analysis.”

The corrected paragraphs appear below:

“Figure 7 shows the comparison of SED and FI, and we can find that although the EEFLA model performed slightly worse than the EEF model (the SED of EEFLA is 1.598, with a difference of 0.049 from the original data, while the SED of EEF has a difference of only 0.008 from the original data; the FI of EEFLA is 4.02, with a difference of 2.22 from the original data, while the FI of EEF has a difference of 2.11), it still outperformed most of the other models. This indicates that the EEFLA model is still capable of effectively simulating human decision-making behaviors.

However, it can be seen from Figure 8 that the performance of the EEFLA model is not outstanding in terms of parameter recovery and fixed-effects statistical model analysis. The composite correlation coefficient for EEFLA's parameter recovery is 0.785 (range: 0.58–0.93), which is lower than the EEF model's 0.849 (range: 0.69–0.96). Additionally, the EEFLA model did not perform well in the previously mentioned fixed-effects statistical model analysis.”

The original article has been updated.

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