Background: Inconsistent observations of pupillary response and blink change in response to different specific tasks raise questions regarding the relationship between eye measures, task types and working memory (WM) models. On the one hand, studies have provided mixed evidence from eye measures about tasks: pupil size has mostly been reported to increase with increasing task demand while this expected change was not observed in some studies, and blink rate has exhibited different trends in different tasks. On the other hand, a WM model has been developed to integrate a component to reconcile recent findings that the human motor system plays an important role in cognition and learning. However, how different tasks correlate with WM components has not been experimentally examined using eye activity measurements. Aims: The current study uses a four-dimensional task load framework to bridge eye measures, task types and WM models. Sample: Twenty participants (10 males, 10 females; Age: M = 25.8, SD = 7.17) above 18 years old volunteered. All participants had normal or corrected to normal vision with contact lenses and had no eye diseases causing obvious excessive blinking. Methods: We examined the ability of pupil size and blink rate to index low and high levels of cognitive, perceptual, physical and communicative task load. A network of the four load types and WM components was built and analysed to verify the necessity of integrating a physical task-related component into the WM model. Results: Results demonstrate that pupil size can index cognitive load and communicative load but not perceptual or physical load. Blink rate can index the level of cognitive load but is best at discriminating perceptual tasks from other types of tasks. Furthermore, pupil size measurement of the four task types was explained better during structural and factor analysis by a WM model that integrates a movement-related component. Conclusions: This research provides new insights into the relationship between eye measures, task type and WM models and provides a comprehensive understanding from which to predict pupil size and blink behaviours in more complex and practical tasks.