Dimension-wise Sequential Update for Learning a Multidimensional Environment in Human
Dimension-wise Sequential Update for Learning a Multidimensional Environment in Human
复制标题
用于学习人类多维环境的逐维顺序更新
DOI：
10.1162/jocn_a_01975
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发表时间：
2023
影响因子：
3.2
通讯作者：
Higashi Hiroshi
中科院分区：
文献类型：
--
作者：
Sunaga Masakazu;Takei Yuichi;Kato Yutaka;Tagawa Minami;Suto Tomohiro;Hironaga Naruhito;Ohki Takefumi;Takahashi Yumiko;Fujihara Kazuyuki;Sakurai Noriko;Ujita Koichi;Tsushima Yoshito;Fukuda Masato;Higashi Hiroshi
当面对多维环境问题时，人类可能需要联合更新跨各个维度的多个状态-动作-结果关联。人类行为和神经活动的计算建模表明，这种更新是基于贝叶斯更新原理实现的。然而，目前还不清楚人类是单独还是顺序地执行这些更新。如果更新是按顺序进行的，则关联的更新顺序很重要，并且可能会影响更新的结果。为了解决这个问题，我们使用人类行为和EEG数据测试了几个具有不同更新顺序的计算模型。我们的研究结果表明，进行维度顺序更新的模型最适合人类行为。在这个模型中，使用熵来决定维度的排序，熵索引了关联的不确定性。同时收集的EEG数据显示诱发电位与该模型的建议时间相关。这些发现提供了新的见解贝叶斯更新多维环境中的时间过程。
When confronted with multidimensional environment problems, humans may need to jointly update multiple state–action–outcome associations across various dimensions. Computational modeling of human behavior and neural activities suggests that such updates are implemented based upon Bayesian update principle. However, it is unclear whether humans perform these updates individually or sequentially. If the update occurs sequentially, the order in which the associations are updated matters and can influence the updated results. To address this question, we tested a few computational models with different update orders using both human behavior and EEG data. Our results indicated that a model undertaking dimension-wise sequential updates was the best fit to human behavior. In this model, ordering the dimensions was decided using entropy, which indexed the uncertainty of associations. Simultaneously collected EEG data revealed evoked potentials that were correlated to the proposed timing of this model. These findings provide new insights into the temporal processes underlying Bayesian update in multidimensional environments.