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Cognitive load information


In cognitive psychology, cognitive load refers to the amount of working memory resources used. However, it is essential to distinguish it from the actual construct of Cognitive Load (CL) or Mental Workload (MWL), which is studied widely in many disciplines. According to work conducted in the field of instructional design and pedagogy, broadly, there are three types of cognitive load: intrinsic cognitive load is the effort associated with a specific topic; extraneous cognitive load refers to the way information or tasks are presented to a learner; and germane cognitive load refers to the work put into creating a permanent store of knowledge (a schema). However, over the years, the additivity of these types of cognitive load has been investigated and questioned. Now it is believed that they circularly influence each other.[1]

Cognitive load theory was developed in the late 1980s out of a study of problem solving by John Sweller.[2] Sweller argued that instructional design can be used to reduce cognitive load in learners. Much later, other researchers developed a way to measure perceived mental effort which is indicative of cognitive load.[3][4] Task-invoked pupillary response is a reliable and sensitive measurement of cognitive load that is directly related to working memory.[5] Information may only be stored in long term memory after first being attended to, and processed by, working memory.[citation needed] Working memory, however, is extremely limited in both capacity and duration.[6] These limitations will, under some conditions, impede learning.[citation needed] Heavy cognitive load can have negative effects on task completion, and it is important to note that the experience of cognitive load is not the same in everyone.[citation needed] The elderly, students, and children experience different, and more often higher, amounts of cognitive load.[citation needed]

The fundamental tenet of cognitive load theory is that the quality of instructional design will be raised if greater consideration is given to the role and limitations of working memory. With increased distractions, particularly from cell phone use, students are more prone to experiencing high cognitive load which can reduce academic success.[7]

  1. ^ Orru G., Longo L. (2019). "The Evolution of Cognitive Load Theory and the Measurement of Its Intrinsic, Extraneous and Germane Loads: A Review". Human Mental Workload: Models and Applications. Communications in Computer and Information Science. Vol. 1012. pp. 23–48. doi:10.1007/978-3-030-14273-5_3. ISBN 978-3-030-14272-8. S2CID 86587936. {{cite book}}: |journal= ignored (help)
  2. ^ Sweller, John (April 1988). "Cognitive Load During Problem Solving: Effects on Learning". Cognitive Science. 12 (2): 257–285. CiteSeerX 10.1.1.459.9126. doi:10.1207/s15516709cog1202_4. S2CID 9585835.
  3. ^ Paas, Fred G. W. C.; Van Merriënboer, Jeroen J. G. (23 November 2016). "The Efficiency of Instructional Conditions: An Approach to Combine Mental Effort and Performance Measures". Human Factors: The Journal of the Human Factors and Ergonomics Society. 35 (4): 737–743. doi:10.1177/001872089303500412. S2CID 67201799.
  4. ^ Skulmowski, Alexander; Rey, Günter Daniel (2 August 2017). "Measuring Cognitive Load in Embodied Learning Settings". Frontiers in Psychology. 8: 1191. doi:10.3389/fpsyg.2017.01191. PMC 5539229. PMID 28824473.
  5. ^ Granholm, Eric; Asarnow, Robert F.; Sarkin, Andrew J.; Dykes, Karen L. (July 1996). "Pupillary responses index cognitive resource limitations". Psychophysiology. 33 (4): 457–461. doi:10.1111/j.1469-8986.1996.tb01071.x. PMID 8753946.
  6. ^ Xu, Chaoer; Qian, Yingzhu; Chen, Hui; Shen, Mowei; Zhou, Jifan (October 2023). "Remembering Sets: Capacity Limit and Time Limit of Ensemble Representations in Working Memory". Behavioral Sciences. 13 (10): 856. doi:10.3390/bs13100856. ISSN 2076-328X. PMC 10604157. PMID 37887506.
  7. ^ Frein, Scott T.; Jones, Samantha L.; Gerow, Jennifer E. (November 2013). "When it comes to Facebook there may be more to bad memory than just multitasking". Computers in Human Behavior. 29 (6): 2179–2182. doi:10.1016/j.chb.2013.04.031.

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