Exploring language proficiency through Recurrence Quantification Analysis (RQA)

Recurrence Quantification Analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. By manipulating a writing task (writing in L1 or a foreign language), and using language proficiency and typing skill information, we aimed to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures in contrast to writing in a foreign language. Further, we expected typing patterns to reveal more regularities whenever conventional variables exploring pauses, bursts, or revisions indicate more fluent writing processes. 40 native German students (Mage = 23.8, SD = 3.04, 19 female) performed two standardized writing assignments, one in German and one in English in a counterbalanced within-subjects design. To reduce learning effects while maintaining the comparability of each task design, we developed two prompted writing assignments that featured the description of two different aspects: a known procedure, the preparation of a meal, and a known and visible object, the flat the participants currently lived in. Further, we assessed language proficiency levels with a standardized cloze test and typing skills with a standardized copy task. The data was derived from keystroke logging protocols, recorded with Inputlog 8 (www.inputlog8.net). The log files were individually time-filtered using the pre-processing time filter of Inputlog to remove noise at the beginning and the end of each writing process and analyze the actual text production from the first to the last character or revision only. The resulting data set was used for a pause, burst, and revision analysis (with a pause threshold > 2000ms), and RQA. RQA was performed using the crqa-package in R (Coco & Dale, 2014). The pause time between each keystroke served as the target event for RQA.The delay and the embedding parameters were estimated based on the AMI and FNN functions for each time series. Averaging the obtained values resulted in a delay parameter t = 1, and an embedding parameter m = 7. Furthermore, we chose a threshold parameter r = 300, yielding an average recurrence rate between 1 % and 5 % across time series. After, we computed the most common RQA measures: recurrence rate, determinism rate, average diagonal line length, maximum diagonal line length, entropy, laminarity, and trapping time.

递归量化分析（Recurrence Quantification Analysis，RQA）是一种利用打字数据自相关特性检测书写过程规律性的时间序列分析方法。本研究通过操控书写任务变量（要求受试者使用母语或外语书写），并结合语言熟练度与打字技能相关信息，旨在阐明RQA的各项结果指标可被解读为键盘打字表现所受的技能驱动型约束。我们假设，书写熟练度越高、过程越流畅，其打字时间序列模式的结构性就越强。据此，我们预期相较于使用外语书写，熟练掌握的母语书写对应的相关RQA指标数值会更高。此外，我们预期，当传统变量（如停顿、爆发式输入或修订行为）体现出书写过程更流畅时，打字模式也会呈现出更强的规律性。本研究招募了40名德语母语者作为受试者（平均年龄M=23.8，标准差SD=3.04，其中女性19名），采用被试内平衡设计，让每位受试者完成两项标准化书写任务：一项使用德语，另一项使用英语。为减少学习效应并保证两项任务设计的可比性，我们开发了两款带有提示的书写任务，分别要求描述两类熟悉事物：一类是已知流程（如备餐流程），另一类是熟悉且可直观观察的物品（如受试者当前居住的公寓）。此外，我们采用标准化完形填空测试评估受试者的语言熟练度，通过标准化抄写任务测评其打字技能。本研究的数据来源于按键日志记录协议，使用Inputlog 8软件（www.inputlog8.net）进行录制。我们使用Inputlog自带的预处理时间过滤工具，对每份日志文件进行单独的时间滤波处理，以去除每次书写过程首尾的噪声，仅保留从首个字符输入到末个字符或修订操作之间的实际文本生成阶段数据。所得数据集将用于停顿、爆发式输入与修订行为分析（停顿阈值设置为>2000ms）以及RQA分析。RQA分析通过R语言的crqa工具包完成（Coco & Dale, 2014）。将每两次按键之间的停顿时长作为RQA分析的目标事件。针对每条时间序列，基于平均互信息（Average Mutual Information, AMI）和虚假最近邻（False Nearest Neighbors, FNN）函数估计延迟参数与嵌入维度参数。对各序列得到的参数取平均后，最终确定延迟参数t=1，嵌入维度参数m=7。此外，我们将阈值参数r设置为300，使得所有时间序列的平均递归率处于1%至5%之间。随后，我们计算了最常用的RQA指标：递归率、确定性率、平均对角线长度、最大对角线长度、熵、层流率与滞留时间。