TaskSense: Cognitive Chain Modeling and Difficulty Estimation for GUI Tasks
This addresses the need for better user behavior analysis and agent capability evaluation in GUI tasks, though it is incremental by extending difficulty estimation to cognitive aspects.
The paper tackled the problem of measuring GUI task difficulty by proposing Cognitive Chain, a framework that models cognitive demands beyond motor actions, and found that its estimated difficulty correlates with user completion time (step-level R-square=0.46).
Measuring GUI task difficulty is crucial for user behavior analysis and agent capability evaluation. Yet, existing benchmarks typically quantify difficulty based on motor actions (e.g., step counts), overlooking the cognitive demands underlying task completion. In this work, we propose Cognitive Chain, a novel framework that models task difficulty from a cognitive perspective. A cognitive chain decomposes the cognitive processes preceding a motor action into a sequence of cognitive steps (e.g., finding, deciding, computing), each with a difficulty index grounded in information theories. We develop an LLM-based method to automatically extract cognitive chains from task execution traces. Validation with linear regression shows that our estimated cognitive difficulty correlates well with user completion time (step-level R-square=0.46 after annotation). Assessment of state-of-the-art GUI agents shows reduced success on cognitively demanding tasks, revealing capability gaps and Human-AI consistency patterns. We conclude by discussing potential applications in agent training, capability assessment, and human-agent delegation optimization.