成年人在许多实验环境中都很难学习非母语语音类别(Goto,神经心理学,9(3)、317-3231971),但是在非母语语音具有功能意义的视频游戏范式中高效学习(Lim&Holt,认知科学,35(7)、1390-14052011)。来自此范式和其他范式的行为和神经证据指向强化学习机制在语音类别学习中的参与(Harmon,伊德玛,&Kapatsinski,认知,189、76-882019年;林,Fiez,&Holt,美国国家科学院院刊,116,2018119922019)。我们在计算上形式化了这个假设,并实现了一个深度强化学习网络,以在环境输入和动作之间进行映射。与监督学习模型相比,在两个实验中,我们证明了增强网络与人类行为的某些方面紧密匹配-合成听觉噪声标记的学习和语音辨别的改进。两种模型的性能都相当,并且每个模型输出的相似性使我们相信基于奖励的学习机制几乎没有内在的计算益处。我们建议,范式所涉及的特定神经回路以及纹状体与上颞区之间的联系在有效学习中起着至关重要的作用。
Adults struggle to learn non-native speech categories in many experimental settings (Goto, Neuropsychologia, 9(3), 317-323 1971), but learn efficiently in a video game paradigm where non-native speech sounds have functional significance (Lim & Holt, Cognitive Science, 35(7), 1390-1405 2011). Behavioral and neural evidence from this and other paradigms point toward the involvement of reinforcement learning mechanisms in speech category learning (Harmon, Idemaru, & Kapatsinski, Cognition, 189, 76-88 2019; Lim, Fiez, & Holt, Proceedings of the National Academy of Sciences, 116, 201811992 2019). We formalize this hypothesis computationally and implement a deep reinforcement learning network to map between environmental input and actions. Comparing to a supervised model of learning, we show that the reinforcement network closely matches aspects of human behavior in two experiments - learning of synthesized auditory noise tokens and improvement in speech sound discrimination. Both models perform comparably and the similarity in the output of each model leads us to believe that there is little inherent computational benefit to a reward-based learning mechanism. We suggest that the specific neural circuitry engaged by the paradigm and links between striatum and superior temporal areas play a critical role in effective learning.