Functional MRI and the study of human consciousness
Functional MRI and the study of human consciousness
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DOI：
10.1162/089892902760191027
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发表时间：
2002-08-01
影响因子：
3.2
通讯作者：
Lloyd, D
中科院分区：
文献类型：
--
作者：
Lloyd, D
功能性脑成像为研究最普遍的认知条件——人类意识——提供了新的机会。由于意识参与了人类认知生活的大部分，因此其研究需要对多个实验数据集进行二次分析。这里考虑了来自国家 fMRI 数据中心的四个预处理数据集：Hazeltine 等人，反应竞争期间的神经激活； Ishai 等人，人类枕叶和颞叶皮层中物体的表征； Mechelli 等人，单词和伪单词阅读过程中呈现速率的影响； Postle 等人，人类额叶皮层的活动与空间工作记忆和扫视行为相关。对意识的研究也来自多个学科。在本文中，现象学的哲学分支提供了意识分析概念上必需的现象结构的初步表征。这些结构包括现象意向性、现象叠加和经验时间性。这些结构产生的经验预测需要新的解释方法来证实。这些方法从单受试者（预处理）扫描系列开始，并将所有体素的模式视为现象信息的潜在多元编码。使用多元方法对四项研究中的 27 名受试者进行了分析，揭示了现象结构的类似物，特别是时间性结构。在第二种解释方法中，人工神经网络被用来从现象学中检测出更明确的预测，即当前的经验包含过去的意识状态和预期事件，并受到过去的意识状态和预期事件的影响。在本次分析的所有 21 名受试者中，网络都经过成功训练，能够提取相对过去和未来大脑状态的各个方面，并与统计上相似的对照进行比较。因此，这项探索性研究得出的结论是，所提出的“神经现象学”方法值得进一步应用，包括探索个体差异、认知任务条件之间的多元差异，以及探索可能有助于观察的特定大脑区域。然而，所有这些有吸引力的问题都必须保留给未来的研究。
Functional brain imaging offers new opportunities for the study of that most pervasive of cognitive conditions, human consciousness. Since consciousness is attendant to so much of human cognitive life, its study requires secondary analysis of multiple experimental datasets. Here, four preprocessed datasets from the National fMRI Data Center are considered: Hazeltine et al., Neural activation during response competition; Ishai et al., The representation of objects in the human occipital and temporal cortex; Mechelli et al., The effects of presentation rate during word and pseudoword reading; and Postle et al., Activity in human frontal cortex associated with spatial working memory and saccadic behavior. The study of consciousness also draws from multiple disciplines. In this article, the philosophical subdiscipline of phenomenology provides initial characterization of phenomenal structures conceptually necessary for an analysis of consciousness. These structures include phenomenal intentionality, phenomenal superposition, and experienced temporality. The empirical predictions arising from these structures require new interpretive methods for their confirmation. These methods begin with single-subject (preprocessed) scan series, and consider the patterns of all voxels as potential multivariate encodings of phenomenal information. Twenty-seven subjects from the four studies were analyzed with multivariate methods, revealing analogues of phenomenal structures, particularly the structures of temporality. In a second interpretive approach, artificial neural networks were used to detect a more explicit prediction from phenomenology, namely, that present experience contains and is inflected by past states of awareness and anticipated events. In all of 21 subjects in this analysis, nets were successfully trained to extract aspects of relative past and future brain states, in comparison with statistically similar controls. This exploratory study thus concludes that the proposed methods for "neurophenomenology'' warrant further application, including the exploration of individual differences, multivariate differences between cognitive task conditions, and exploration of specific brain regions possibly contributing to the observations. All of these attractive questions, however, must be reserved for future research.