University of New South Wales, Sydney, New South Wales, Australia.
Erasmus University Rotterdam, Rotterdam, The Netherlands.
Br J Educ Psychol. 2023 Aug;93 Suppl 2:318-338. doi: 10.1111/bjep.12577. Epub 2022 Dec 26.
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.
The current study uses a four-dimensional task load framework to bridge eye measures, task types and WM models.
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.
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 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.
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.
不同特定任务引起的瞳孔反应和眨眼变化的不一致观察结果引发了关于眼部测量、任务类型和工作记忆 (WM) 模型之间关系的问题。一方面,从眼部测量结果来看,研究提供了混合证据:瞳孔大小大多随着任务需求的增加而增加,而在一些研究中没有观察到这种预期的变化,眨眼频率在不同任务中表现出不同的趋势。另一方面,已经开发了一个 WM 模型来整合一个组件,以调和最近的发现,即人类运动系统在认知和学习中起着重要作用。然而,使用眼部活动测量来检查不同任务与 WM 组件的相关性尚未得到实验检验。
本研究使用四维任务负荷框架来连接眼部测量、任务类型和 WM 模型。
20 名年龄在 18 岁以上的参与者(10 名男性,10 名女性;年龄:M=25.8,SD=7.17)自愿参加。所有参与者均有正常或矫正视力,可佩戴隐形眼镜,无引起明显过度眨眼的眼部疾病。
我们检查了瞳孔大小和眨眼率指数高低认知、感知、身体和交流任务负荷的能力。建立并分析了四个负荷类型和 WM 组件的网络,以验证将身体相关任务组件整合到 WM 模型中的必要性。
结果表明,瞳孔大小可以指数认知负荷和交流负荷,但不能指数知觉或身体负荷。眨眼率可以指数认知负荷水平,但最能区分知觉任务和其他类型的任务。此外,在结构和因子分析中,通过整合运动相关组件的 WM 模型,更好地解释了四种任务类型的瞳孔大小测量结果。
这项研究为眼部测量、任务类型和 WM 模型之间的关系提供了新的见解,并提供了全面的理解,以便在更复杂和实际的任务中预测瞳孔大小和眨眼行为。