Research School of Behavioural and Cognitive Neuroscience, University of Groningen, Hanzeplein, Groningen 9713 GZ, the Netherlands; Department of Experimental Psychology, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK; Department of Epileptology, University Hospital Bonn, Venusberg Campus, Bonn 53127, Germany.
Research School of Behavioural and Cognitive Neuroscience, University of Groningen, Hanzeplein, Groningen 9713 GZ, the Netherlands; Department of Experimental Psychology, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK.
Curr Biol. 2024 Nov 4;34(21):5054-5061.e4. doi: 10.1016/j.cub.2024.09.063. Epub 2024 Oct 21.
Learning never stops. As we navigate life, we continuously acquire and update knowledge to optimize memory-guided action, with a gradual shift from the former to the latter as we master our environment. How are these learning dynamics expressed in the brain and in behavioral patterns? Here, we devised a spatiotemporal image learning task ("Memory Arena") in which participants learn a set of 50 items to criterion across repeated exposure blocks. Critically, brief task-free periods between successive image presentations allowed us to assess multivariate electroencephalogram (EEG) patterns representing the previous and/or upcoming image identity, as well as anticipatory eye movements toward the upcoming image location. As expected, participants eventually met the performance criterion, albeit with different learning rates. During task-free periods, we were able to readily decode representations of both previous and upcoming image identities. Importantly though, decoding strength followed opposing slopes for previous vs. upcoming images across time, with a gradual decline of evidence for the previous image and a gradual increase of evidence for the upcoming image. Moreover, the ratio of upcoming vs. previous image evidence directly followed behavioral learning rates. Finally, eye movement data revealed that participants increasingly used the task-free period to anticipate upcoming image locations, with target-precision slopes paralleling both behavioral performance measures as well as EEG decodability of the upcoming image across time. Together, these results unveil the neural and behavioral dynamics underlying the gradual transition from learning to memory-guided action.
学习永不止步。在我们的生活中,我们不断地获取和更新知识,以优化记忆引导的行动,随着我们对环境的掌握,这种学习动态逐渐从前者转变为后者。这些学习动态是如何在大脑和行为模式中表现出来的?在这里,我们设计了一个时空图像学习任务(“记忆竞技场”),参与者在重复的暴露块中学习一组 50 个项目,直到达到标准。关键的是,在连续呈现图像之间短暂的无任务期允许我们评估代表先前和/或即将出现的图像身份的多变量脑电图(EEG)模式,以及对即将出现的图像位置的预期眼动。不出所料,参与者最终达到了性能标准,尽管学习速度不同。在无任务期间,我们能够轻松解码先前和即将出现的图像身份的表示。然而,重要的是,解码强度随时间对先前图像和即将出现的图像呈相反的斜率,先前图像的证据逐渐减少,即将出现的图像的证据逐渐增加。此外,即将出现的图像与先前图像的证据之比直接跟随行为学习速度。最后,眼动数据显示,参与者越来越多地利用无任务期来预测即将出现的图像位置,目标精度斜率与行为表现以及随时间推移对即将出现的图像的 EEG 可解码性平行。总之,这些结果揭示了从学习到记忆引导行动的逐渐转变的神经和行为动态。
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