Integrating complementary learning principles in aphasia rehabilitation via adaptive modeling

失语症是一种语言障碍，通常由中风和其他后天脑损伤引起，影响超过 在美国有两百万人，对生活质量有很大的负面影响。Anomia(即，单词查找 困难)是失语症患者的主要挫败感，对失调症的命名治疗也很普遍 研究并广泛应用于临床。用于命名治疗方法，以对 对于失语症患者的生活，他们必须在单词查找方面产生持久的收益，这种收益超出了 治疗环境。然而，大多数以理论为动机的命名治疗研究未能解决长期的- 训练单词的术语保留及其对连接语音的泛化，限制了它们的临床影响。 流行的学习理论表明，“理想的困难”改善了治疗的保持性和 泛化。因此，目前的提案力求改进《公约》的持久性和上下文概括性。 通过结合基于模型的算法进行基于计算机的命名处理，以自适应地保持所需的 困难重重。我们将在平行的临床试验中测试两种不同的模型。我们的中心前提是这些模型将 促进在历史上被认为是不同的学习方法之间的平衡：无误 学习与费力提取(研究1)和集中练习与分散练习(研究2)。相反，我们的模特将 通过将极端静态对比替换为连续任务组件来集成这些方法 根据持续的患者表现进行适应性修改。研究1将适应性地平衡努力和 使用基于我们开发的模型的加速命名截止日期的准确性 随着时间的推移，个体在图片命名中的速度和准确性之间的权衡。研究2将使用以下方法操纵试验间隔 广泛使用的开源闪存卡软件中内置的自适应调度和内存衰减模型。 在这两项研究中，我们预测当与匹配的传统非适应性治疗相比时 条件，我们的适应条件将产生更成功地保持训练的单词3个月和6个月 对命名探测的后处理(目标1a、2a)，并在以下情况下更好地对连接语音进行上下文泛化 对包含未经训练的训练词样本的复杂场景描述进行测试(目标1b、2b)。我们也 预测研究2中的自适应试验间隔将成功训练比当前可能的单词多得多的单词 标准护理。此外，研究1和研究2中产生的数据将用于开发下一代 自适应计时模型(目标1c和2c)，刺激个性化医疗的未来创新。 成功的临床试验结果将证明，基于计算机的适应性命名治疗提供了一种 产生大的、持久的和可推广的治疗收益的新方法，以及积极的研究2发现可能是 使用免费开源软件立即在临床实践中大规模实施。成功的建模 结果将导致更有效的干预措施，并为变革性研究奠定基础 这一议程最终可能导致失语症康复的全面适应性学习系统。

Aphasia is a language disorder commonly caused by stroke and other acquired brain injuries that affects over two million people in the US and has a large negative effect on quality of life. Anomia (i.e., word-finding difficulty) is a primary frustration for people with aphasia, and naming treatments for anomia are both widely researched and commonly used in clinical practice. For naming treatments to make a meaningful impact on the lives of people with aphasia, they must produce durable gains in word-finding which generalize beyond the treatment context. However, most theoretically-motivated naming treatment research fails to address the long- term retention of trained words and their generalization to connected speech, limiting their clinical impact. Prevailing learning theory suggests that “desirable difficulty” improves treatment retention and generalization. The current proposal therefore seeks to improve the durability and context generalization of computer-based naming treatment by incorporating model-based algorithms to adaptively maintain desirable difficulty. We will test two distinct models in parallel clinical trials. Our central premise is that these models will facilitate a balance between what have historically been framed as contrasting learning approaches: errorless learning vs. effortful retrieval (Study 1) and massed vs. distributed practice (Study 2). Instead, our models will integrate these approaches by replacing extreme static contrasts with continuous task components which can be adaptively modified based on ongoing patient performance. Study 1 will adaptively balance effort and accuracy using speeded naming deadlines based on a model we have developed which characterizes individuals’ speed-accuracy tradeoffs in picture naming over time. Study 2 will manipulate trial spacing using an adaptive scheduling and memory decay model built into widely available, open-source flashcard software. In both studies, we predict that when compared to matched traditional non-adaptive treatment conditions, our adaptive conditions will produce more successful retention of trained words 3 and 6 months post-treatment on naming probes (Aims 1a, 2a), and better context generalization to connected speech when tested on complex scene descriptions containing untrained exemplars of trained words (Aims 1b, 2b). We also predict that adaptive trial spacing in Study 2 will successfully train many more words than is possible in current standard care. In addition, data generated in Studies 1 and 2 will be used to develop the next generation of adaptive timing models (Aims 1c and 2c), spurring future innovations in personalized medicine. Successful clinical trial outcomes will demonstrate that adaptive computer-based naming treatments provide a novel way to produce large, durable, and generalizable treatment gains, and positive Study 2 findings could be immediately implemented in clinical practice at scale using free open-source software. Successful modeling outcomes will lead to even more effective interventions and lay the groundwork for a transformative research agenda that could ultimately lead to comprehensive adaptive learning systems for aphasia rehabilitation.