CompCog: Computational Models of Plasticity and Learning in Speech Perception

当谈到言语感知时，听众是终身学习者。 虽然婴儿的感知在他们生命的第一年变得与他们的母语一致，但他们的语音类别在儿童和青少年时期继续发生变化。 成年人也继续表现出相当大的感知学习能力，特别是在涉及反馈或奖励的环境中。 这个项目使用计算模型来研究学习机制，使听众能够适应他们的语音感知特定的语言和环境。 通过建立听觉感知学习的理论，该项目将有助于我们理解成年人在学习另一种语言时所面临的困难。 它还可以为理解某些人群（如植入人工耳蜗的儿童）在学习第一语言时所面临的困难提供一个框架，并可能促进这些人群未来治疗或干预的发展。基于成人感知学习数据开发了两种类型的计算模型：概率线索加权模型，其被设计为捕捉听众对语音信号的不同部分的依赖的快速、逐个试验的变化，以及强化学习模型，其被设计为捕获响应于奖励（例如视频游戏中的点数）而发生的语音声音的长期、隐式感知学习。 在实验环境中测试模型捕捉成人感知学习行为的能力。 第二个系列的模拟，然后探讨这些模型，开发的成人数据是否以及如何可以预测方面的儿童的知觉学习的语音声音类别在实验室的歧视任务，涉及奖励，如令人兴奋的玩具，并在自然的设置中的讲话是更复杂的，有一个不太明显的奖励结构。 该项目的结果将有助于深入了解儿童和成人在不同发展阶段的言语表征类型，以及学习者在不同年龄和不同学习环境中所依赖的感知学习策略。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

When it comes to speech perception, listeners are lifelong learners. Although infants’ perception becomes tuned to their native language in their first year of life, their speech sound categories continue to change well into childhood and adolescence. Adults also continue to show substantial capacity for perceptual learning, particularly in settings that involve feedback or rewards. This project uses computational modeling to investigate the learning mechanisms that allow listeners to adapt their speech perception to particular languages and environments. By building theories of auditory perceptual learning, the project will contribute to our understanding of the difficulties that adults face when learning another language. It could also provide a framework for understanding the difficulties faced by certain populations, such as children with cochlear implants, when learning their first language and may facilitate future development of treatments or interventions for these populations.Two types of computational models are developed based on adult perceptual learning data: probabilistic cue weighting models, which are designed to capture fast, trial-by-trial changes in listeners’ reliance on different parts of the speech signal, and reinforcement learning models, which are designed to capture longer term, implicit perceptual learning of speech sounds that occurs in response to a reward, such as points in a video game. The models are tested on their ability to capture adults’ perceptual learning behavior in experimental settings. A second series of simulations then explores whether and how these models that were developed on adult data can predict aspects of children’s perceptual learning of speech sound categories in laboratory discrimination tasks that involve rewards, such as exciting toys, and in naturalistic settings where the speech is more complex and there is a less obvious reward structure. Results from the project are expected to provide insight into what types of speech representations children and adults have at different stages of development, as well as which perceptual learning strategies learners rely on at different ages and in different learning environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.