Validating the assumed relationship between task design, cognitive complexity, and second language task performance

Abstract

In research on Task-Based Language Teaching, it has traditionally been assumed that differing degrees of cognitive task complexity can be inferred through task design and/or observations of differing qualities in linguistic production elicited by L2 communication tasks. Without validating this assumption, however, it is unclear whether the designed or inferred difference in complexity, the key independent variable, is realized as intended. Furthermore, what exactly makes a task more or less complex has been under-investigated, leaving it unclear why task complexity manipulation worked (or did not) and what specifically it is about a task that positively or negatively affected L2 task performance.

Accordingly, this study adopted diverse methods from cognitive psychology for independently measuring cognitive complexity, including: dual-task methodology, time estimation, and self-ratings. In addition to 61 native speakers as a baseline, 120 English-L2 speakers in Japan, representing distinct proficiency levels, narrated four picture sequences, each containing different numbers of characters (hence, varying degrees of complexity by design). While performing the primary story-telling task, participants simultaneously completed a secondary task of reacting to a color change. After each task, they estimated their time-on-task, rated their perceptions of task difficulty and mental effort exerted, and provided written explanatory comments for their ratings.

Findings revealed a complicated relationship among task design, cognitive demands, and learner task performance. In general, the number of elements affected the level of cognitive task complexity as predicted. Critically, however, only large differences were detectable in terms of independent measures of cognitive load, underscoring the importance of validating the assumed relationship. Other factors identified as contributing to complexity included: conceptual input, code complexity, and performance factors. These factors were found to induce facilitative as well as extraneous cognitive demands; hence, they had both positive and negative impacts on learners’ task performances. Findings also indicated clear proficiency effects and interaction effects among learner proficiency, task design, and measures of cognitive load, as well as with performance variables.