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.