Neural Systems in Auditory and Speech Categorization

使用互补的多模式神经成像方法(功能磁共振成像(FMRI)和 脑电地形图(ECoG)结合严格的行为方法，我们将研究 多个皮质-纹状体和感觉皮质网络在获取和自动化新的非 语言和成熟成人大脑中的语言类别。我们检验了双重学习的科学前提 反馈依赖过程中用fMRI或ECoG探测神经功能的系统(DLS)模型 分类学习。与流行的单一学习系统(SLS)方法不同，DLS假设两个神经- 可分离的皮质-纹状体系统对言语学习至关重要：一个明确的、声音规则的皮质-纹状体 系统，将声音映射到规则，以及隐含的、声音到奖励的皮质纹状体系统，隐含地 将声音与能立即获得回报的行动联系起来。根据DLS，这两个系统有助于 学习者的新兴专业知识。通过闭合环路，高度可塑性的皮质-纹状体系统的关键更少 通过有效的规则或奖励对信息进行分类的不稳定的颞叶网络。一旦类别被 学习到了自动点，皮质-纹状体网络不再需要来调节行为。 取而代之的是，颞叶皮质内的抽象分类信息推动了高度准确的语音分类。 在Aim 1.1中，我们使用功能磁共振成像来检查两个皮质-纹状体网络在学习中的相对优势 多维非语音类别结构，被实验者限制为依赖于规则(规则- 基于，RB)，或基于多维线索的隐含整合(信息整合，II)。我们预测这一关键 声音-规则网络、前额叶皮质(PFC)、海马区和尾状核显示 在RB期间，相对于II学习，更大的激活；相比之下，声音-奖励网络中的关键区域， 相对于RB学习，壳核和腹侧纹状体在II阶段表现出更大的激活。在AIMS 1.2和 1.3，我们利用来自高密度网格的ECoG测量的时间精度，在时间、PFC和 海马区检查在RB学习过程中颞叶表征变化的程度 是前额叶和海马体内错误监测过程的结果。在目标2中，我们探索神经 使用fMRI或ECoG评估网络和代表性变化的功能 本地超细分和细分类别到本地一样的性能水平。我们预测早些时候 “新手”语音习得涉及声音到规则的映射；后来的“有经验的”涉及声音到奖励的映射 映射。相反，只有大脑皮层网络在分类中处于“天然的自律性”状态。 使用创新的单次试验分类和网络级解码分析ECoG数据，我们检查 学习诱导的颞叶内语音表征的变化。此外，我们还考察了 PFC和海马区内的哪种错误监测过程驱动着出现的颞叶 新的语音类别的表示。

Using complementary multi-modal neuroimaging methods (functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG)) in conjunction with rigorous behavioral approaches, we will examine the role of multiple cortico-striatal and sensory cortical networks in the acquisition and automatization of novel non- speech and speech categories in the mature adult brain. We test the scientific premise of a dual-learning systems (DLS) model by probing neural function using fMRI or ECoG during the process of feedback-dependent category learning. In contrast to popular single-learning system (SLS) approaches, DLS posits that two neurally- dissociable cortico-striatal systems are critical to speech learning: an explicit, sound-to-rule cortico-striatal system, that maps sounds onto rules, and an implicit, sound-to-reward cortico-striatal system that implicitly associates sounds with actions that lead to immediate reward. Per DLS, the two systems contribute to the emerging expertise of the learner. Via closed loops, the highly plastic cortico-striatal systems ‘train’ key less labile temporal lobe networks to categorize information by validated rules or rewards. Once categories are learned to the point of automaticity, cortico-striatal networks are no longer required to mediate behavior. Instead, abstract categorical information within the temporal cortex drives highly accurate speech categorization. In Aim 1.1, we use fMRI to examine the relative dominance of the two cortico-striatal networks in learning multidimensional non-speech category structures that are experimenter-constrained to either rely on rules (rule- based, RB), or on implicit integration of multidimensional cues (information-integration, II). We predict that key regions of the sound-to-rule network, the prefrontal cortex (PFC), hippocampus, and caudate nucleus show greater activation during RB, relative to II learning; in contrast, key regions within the sound-to-reward network, the putamen and the ventral striatum show greater activation during II, relative to RB learning. In Aims 1.2 and 1.3, we leverage the temporal precision of ECoG measurements from high-density grids in temporal, PFC, and Hippocampal regions to examine the extent to which temporal lobe representational changes during RB learning are an outcome of error-monitoring processes within the PFC and hippocampus. In Aim 2, we probe neural function using fMRI or ECoG to assess network and representational changes during the acquisition of non- native supra-segmental and segmental categories to native-like performance levels. We predict that early ‘novice’ speech acquisition involves sound-to-rule mapping; later ‘experienced’ involves sound-to-reward mapping. In contrast, only cortical networks are active at the point of ‘native-like automaticity’ in categorization. Using innovative single-trial classification and network-level decoding analyses on ECoG data, we examine learning-induced changes in speech representation within the temporal lobe. Further, we examine the extent to which error monitoring processes within the PFC and the hippocampus drive emergent temporal lobe representations of novel speech categories.