Striatal and hippocampal entropy and recognition signals in category learning: simultaneous processes revealed by model-based fMRI.
Striatal and hippocampal entropy and recognition signals in category learning: simultaneous processes revealed by model-based fMRI.
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DOI：
10.1037/a0027865
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发表时间：
2012-07
影响因子：
2.6
通讯作者：
Preston, Alison R.
中科院分区：
文献类型：
--
作者：
Davis, Tyler;Love, Bradley C.;Preston, Alison R.
类别学习是一个复杂的现象，涉及多个认知过程，其中许多过程同时发生，并随着时间的推移动态地展开。例如，当人们遇到世界上的物体时，他们同时参与过程以确定其与当前知识结构的匹配，收集关于物体的新信息，并调整其表示以支持未来遇到的行为。许多用于理解类别学习的神经基础的技术都假设，在不同的实验中，有助于类别学习的多个过程可以被整齐地分开。基于模型的功能磁共振成像提供了一个有前途的工具，分离多个，同时发生的过程，使神经成像数据的分析更符合类别学习的动态和多方面的性质。我们使用基于模型的成像来探索识别的神经基础和参与者学习对新刺激进行分类时内侧颞叶和纹状体中的熵信号。与前海马和腹侧纹状体在动机性学习中对不确定性的反应中的作用的理论相一致，我们发现这两个区域的激活与基于模型的熵测量相关。同时，海马体和纹状体的单独子区域表现出与基于模型的识别强度测量相关的激活。我们的研究结果表明，基于模型的分析是非常有用的提取信息的认知过程中的神经影像数据。模型为识别影响行为的多个神经过程提供了基础，神经成像数据可以为约束和测试模型预测提供强大的测试平台。
Category learning is a complex phenomenon that engages multiple cognitive processes, many of which occur simultaneously and unfold dynamically over time. For example, as people encounter objects in the world, they simultaneously engage processes to determine their fit with current knowledge structures, gather new information about the objects, and adjust their representations to support behavior in future encounters. Many techniques that are available to understand the neural basis of category learning assume that the multiple processes that subserve it can be neatly separated between different trials of an experiment. Model-based functional magnetic resonance imaging offers a promising tool to separate multiple, simultaneously occurring processes and bring the analysis of neuroimaging data more in line with category learning’s dynamic and multifaceted nature. We use model-based imaging to explore the neural basis of recognition and entropy signals in the medial temporal lobe and striatum that are engaged while participants learn to categorize novel stimuli. Consistent with theories suggesting a role for the anterior hippocampus and ventral striatum in motivated learning in response to uncertainty, we find that activation in both regions correlates with a model-based measure of entropy. Simultaneously, separate subregions of the hippocampus and striatum exhibit activation correlated with a model-based recognition strength measure. Our results suggest that model-based analyses are exceptionally useful for extracting information about cognitive processes from neuroimaging data. Models provide a basis for identifying the multiple neural processes that contribute to behavior, and neuroimaging data can provide a powerful test bed for constraining and testing model predictions.
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