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在获得预测性关联的过程中,海马体的表征从错误切换到了预测。

Hippocampal representations switch from errors to predictions during acquisition of predictive associations.

机构信息

Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK.

School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK.

出版信息

Nat Commun. 2022 Jun 8;13(1):3294. doi: 10.1038/s41467-022-31040-w.

Abstract

We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete.

摘要

我们不断利用环境中的统计规律来帮助指导我们的感知。海马体被认为在学习环境统计数据以及利用这些数据生成感知预测方面发挥着关键作用。然而,目前尚不清楚海马体如何在编码新的预测关联和检索现有预测关联之间取得平衡。在这里,我们呈现了两项高分辨率人类 fMRI 研究的结果(两个实验的参与者均为 24 名),直接对此进行了研究。参与者接触到预测即将出现的视觉形状的听觉提示(有效率为 75%)。使用多体素解码分析,我们发现海马体最初优先表示意外的形状(即,违反提示规则的形状),但后来无论实际呈现的是哪种形状,都会切换到表示提示预测的形状。这些发现表明,海马体既参与获取又参与利用预测关联,并且根据学习是正在进行还是已经完成,由错误或预测主导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/af9b/9178037/09257ec3a029/41467_2022_31040_Fig1_HTML.jpg

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