cognitive load

 

  • 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).

  • [25] A 2020 study showed that there may be various demand components that together form extraneous cognitive load, but that may need to be measured using different questionnaires.

  • Much later, other researchers developed a way to measure perceived mental effort which is indicative of cognitive load.

  • Since this early study many other researchers have used this and other constructs to measure cognitive load as it relates to learning and instruction.

  • [41] Regardless of the task at hand, or the processes used in solving the task, people who experience poverty also experience higher cognitive load.

  • When it comes to learning, their lack of experience with numbers, words, and concepts increases their cognitive load.

  • When many cognitive resources are available, the probability of switching from one task to another is high and does not lead to optimal switching behavior.

  • [21] Effects of heavy cognitive load See also: Audience effect and Drive theory A heavy cognitive load typically creates error or some kind of interference in the task at
    hand.

  • [6] Theory In the late 1980s, John Sweller developed cognitive load theory out of a study of problem solving,[2] in order “to provide guidelines intended to assist in the
    presentation of information in a manner that encourages learner activities that optimize intellectual performance”.

  • 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.

  • [4] For example, Deleeuw and Mayer (2008) compared three commonly used measures of cognitive load and found that they responded in different ways to extraneous, intrinsic,
    and germane load.

  • [19] An example of extraneous cognitive load occurs when there are two possible ways to describe a square to a student.

  • [26] This is because a heavy cognitive load pushes excess information into subconscious processing, which involves the use of schemas, the patterns of thought and behavior
    that help us to organize information into categories and identify the relationships between them.

  • 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.

  • He suggests problem solving by means-ends analysis requires a relatively large amount of cognitive processing capacity, which may not be devoted to schema construction.

  • [39] Pointing allows a child to use the object they are pointing at as the best representation of it, which means they do not have to hold this representation in their working
    memory, thereby reducing their cognitive load.

  • 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.

  • [16] Types Cognitive load theory provides a general framework and has broad implications for instructional design, by allowing instructional designers to control the conditions
    of learning within an environment or, more generally, within most instructional materials.

  • Experts have more knowledge or experience with regard to a specific task which reduces the cognitive load associated with the task.

  • In cognitive psychology, cognitive load refers to the amount of working memory resources used.

  • With increased distractions, particularly from cell phone use, students are more prone to experiencing high cognitive load which can reduce academic success.

  • [44] As a result, Embodied Cognitive Load Theory has been suggested as a means to predict the usefulness of interactive features in learning environments.

  • Specifically, it provides empirically-based guidelines that help instructional designers decrease extraneous cognitive load during learning and thus refocus the learner’s
    attention toward germane materials, thereby increasing germane (schema related) cognitive load.

  • [37] Children lack general knowledge, and this is what creates increased cognitive load in children.

  • [22] The ergonomic approach seeks a quantitative neurophysiological expression of cognitive load which can be measured using common instruments, for example using the heart
    rate-blood pressure product (RPP) as a measure of both cognitive and physical occupational workload.

  • [7] Measurement As of 1993 Paas and Van Merriënboer[3] had developed a construct known as relative condition efficiency, which helps researchers measure perceived mental effort,
    an index of cognitive load.

  • Because there is a single limited cognitive resource using resources to process the extraneous load, the number of resources available to process the intrinsic load and germane
    load (i.e., learning) is reduced.

  • [45] In this framework, the benefits of an interactive feature (such as easier cognitive processing) need to exceed its cognitive costs (such as motor coordination) in order
    for an embodied mode of interaction to increase learning outcomes.

  • Task-invoked pupillary response is one such physiological response of cognitive load on working memory, with studies finding that pupil dilation occurs with high cognitive
    load.

  • This construct provides a relatively simple means of comparing instructional conditions, taking into account both mental effort ratings and performance scores.

  • [5] Some researchers have compared different measures of cognitive load.

  • The elderly, students, and children experience different, and more often higher, amounts of cognitive load.

  • With this article, cognitive load researchers began to seek ways of redesigning instruction to redirect what would be extraneous load, to now be focused toward schema construction
    (germane load).

  • [3][4] Task-invoked pupillary response is a reliable and sensitive measurement of cognitive load that is directly related to working memory.

  • [38] These elements help reduce cognitive load in children as they develop.

 

Works Cited

[‘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.
1. ^ Jump up to:a b c 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.
2. ^ Jump up to:a b c 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.
3. ^ Jump up to:a b 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.
4. ^ Jump up to:a b 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.
5. ^ 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.
6. ^ Jump up to:a b c d Sweller, John; van Merrienboer, Jeroen J. G.; Paas, Fred G. W. C. (1998). “Cognitive Architecture
and Instructional Design” (PDF). Educational Psychology Review. 10 (3): 251–296. doi:10.1023/A:1022193728205. S2CID 127506.
7. ^ Miller, George A. (1956). “The magical number seven, plus or minus two: some limits on our capacity for processing information”.
Psychological Review. 63 (2): 81–97. CiteSeerX 10.1.1.308.8071. doi:10.1037/h0043158. PMID 13310704. S2CID 15654531.
8. ^ Chase, William G.; Simon, Herbert A. (January 1973). “Perception in chess”. Cognitive Psychology. 4 (1): 55–81. doi:10.1016/0010-0285(73)90004-2.
9. ^
Jump up to:a b Paas, Fred G. (1992). “Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach”. Journal of Educational Psychology. 84 (4): 429–434. doi:10.1037/0022-0663.84.4.429.
10. ^ Jump up
to:a b Moreno, Roxana; Mayer, Richard E. (1999). “Cognitive principles of multimedia learning: The role of modality and contiguity”. Journal of Educational Psychology. 91 (2): 358–368. CiteSeerX 10.1.1.458.4719. doi:10.1037/0022-0663.91.2.358.
11. ^
Jump up to:a b Mousavi, Seyed Yaghoub; Low, Renae; Sweller, John (1995). “Reducing cognitive load by mixing auditory and visual presentation modes”. Journal of Educational Psychology. 87 (2): 319–334. CiteSeerX 10.1.1.471.2089. doi:10.1037/0022-0663.87.2.319.
12. ^
Jump up to:a b Chandler, Paul; Sweller, John (June 1992). “The split-attention effect as a factor in the design of instruction”. British Journal of Educational Psychology. 62 (2): 233–246. doi:10.1111/j.2044-8279.1992.tb01017.x. S2CID 40723362.
13. ^
Jump up to:a b Cooper, Graham; Sweller, John (1987). “Effects of schema acquisition and rule automation on mathematical problem-solving transfer”. Journal of Educational Psychology. 79 (4): 347–362. doi:10.1037/0022-0663.79.4.347.
14. ^ Jump up
to:a b Sweller, John; Cooper, Graham A. (14 December 2009). “The Use of Worked Examples as a Substitute for Problem Solving in Learning Algebra”. Cognition and Instruction. 2 (1): 59–89. doi:10.1207/s1532690xci0201_3.
15. ^ Jump up to:a b Kalyuga,
Slava; Ayres, Paul; Chandler, Paul; Sweller, John (March 2003). “The Expertise Reversal Effect”. Educational Psychologist. 38 (1): 23–31. doi:10.1207/S15326985EP3801_4. S2CID 10519654.
16. ^ Jump up to:a b Chandler, Paul; Sweller, John (December
1991). “Cognitive Load Theory and the Format of Instruction”. Cognition and Instruction. 8 (4): 293–332. doi:10.1207/s1532690xci0804_2. S2CID 35905547.
17. ^ Kirschner, Paul A.; Sweller, John; Clark, Richard E. (June 2006). “Why Minimal Guidance
During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching” (PDF). Educational Psychologist. 41 (2): 75–86. doi:10.1207/s15326985ep4102_1. hdl:1874/16899. S2CID
17067829.
18. ^ Ginns, Paul (December 2006). “Integrating information: A meta-analysis of the spatial contiguity and temporal contiguity effects”. Learning and Instruction. 16 (6): 511–525. doi:10.1016/j.learninstruc.2006.10.001.
19. ^ Clark,
Ruth C.; Nguyen, Frank; Sweller, John (2005). Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load. Wiley. ISBN 978-0-7879-7728-3.[page needed]
20. ^ Jump up to:a b Skulmowski, Alexander; Rey, Günter Daniel (2020). “Subjective
cognitive load surveys lead to divergent results for interactive learning media”. Human Behavior and Emerging Technologies. 2 (2): 149–157. doi:10.1002/hbe2.184.
21. ^ Paas, Fred; Tuovinen, Juhani E.; Tabbers, Huib; Van Gerven, Pascal W. M. (March
2003). “Cognitive Load Measurement as a Means to Advance Cognitive Load Theory”. Educational Psychologist. 38 (1): 63–71. CiteSeerX 10.1.1.670.1047. doi:10.1207/S15326985EP3801_8. S2CID 16587887.
22. ^ Fredericks, Tycho K.; Choi, Sang D.; Hart,
Jason; Butt, Steven E.; Mital, Anil (December 2005). “An investigation of myocardial aerobic capacity as a measure of both physical and cognitive workloads”. International Journal of Industrial Ergonomics. 35 (12): 1097–1107. doi:10.1016/j.ergon.2005.06.002.
23. ^
Heard, Jamison; Harriet, Caroline E.; Adams, Julie A. (2018). “A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load”. IEEE Transactions on Human-Machine Systems. 48 (5): 434–451.
doi:10.1109/THMS.2017.2782483.
24. ^ DeLeeuw, Krista E.; Mayer, Richard E. (2008). “A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load” (PDF). Journal of Educational Psychology.
100 (1): 223–234. doi:10.1037/0022-0663.100.1.223. S2CID 4984926. Archived from the original (PDF) on 2019-02-22.
25. ^ Biernat, Monica; Kobrynowicz, Diane; Weber, Dara L. (October 2003). “Stereotypes and Shifting Standards: Some Paradoxical Effects
of Cognitive Load”. Journal of Applied Social Psychology. 33 (10): 2060–2079. doi:10.1111/j.1559-1816.2003.tb01875.x.
26. ^ “Cognitive Load Theory”. www.cs.virginia.edu. Retrieved 2023-04-20.
27. ^ Hinton, Perry (2017-09-01). “Implicit stereotypes
and the predictive brain: cognition and culture in “biased” person perception”. Palgrave Communications. 3 (1): 1–9. doi:10.1057/palcomms.2017.86. ISSN 2055-1045. S2CID 54036730.
28. ^ Gilbert, D. T. (1989). Thinking lightly about others: Automatic
components of the social inference process. In J. S. Uleman & J. A. Bargh (Eds.), Unintended thought (pp. 189–211). New York, Guilford Press.
29. ^ Murphy, Gregory L.; Wright, Jack C. (1984). “Changes in conceptual structure with expertise: Differences
between real-world experts and novices”. Journal of Experimental Psychology: Learning, Memory, and Cognition. 10 (1): 144–155. doi:10.1037/0278-7393.10.1.144.
30. ^ Wingfield, Arthur; Stine, Elizabeth A.L.; Lahar, Cindy J.; Aberdeen, John S. (27
September 2007). “Does the capacity of working memory change with age?”. Experimental Aging Research. 14 (2): 103–107. doi:10.1080/03610738808259731. PMID 3234452.
31. ^ Andersson, Gerhard; Hagman, Jenni; Talianzadeh, Roya; Svedberg, Alf; Larsen,
Hans Christian (May 2002). “Effect of cognitive load on postural control”. Brain Research Bulletin. 58 (1): 135–139. doi:10.1016/s0361-9230(02)00770-0. PMID 12121823. S2CID 22614522.
32. ^ Faulkner, Kimberly A.; Redfern, Mark S.; Cauley, Jane A.;
Landsittel, Douglas P.; Studenski, Stephanie A.; Rosano, Caterina; Simonsick, Eleanor M.; Harris, Tamara B.; Shorr, Ronald I.; Ayonayon, Hilsa N.; Newman, Anne B.; Health, Aging, and Body Composition, Study. (April 2007). “Multitasking: Association
Between Poorer Performance and a History of Recurrent Falls”. Journal of the American Geriatrics Society. 55 (4): 570–576. doi:10.1111/j.1532-5415.2007.01147.x. PMID 17397436. S2CID 32223760.
33. ^ Calderwood, Charles; Ackerman, Phillip L.; Conklin,
Erin Marie (June 2014). “What else do college students ‘do’ while studying? An investigation of multitasking”. Computers & Education. 75: 19–29. doi:10.1016/j.compedu.2014.02.004.
34. ^ 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.
35. ^ Sana, Faria; Weston, Tina; Cepeda, Nicholas J. (March 2013). “Laptop
multitasking hinders classroom learning for both users and nearby peers”. Computers & Education. 62: 24–31. doi:10.1016/j.compedu.2012.10.003.
36. ^ Jump up to:a b Gathercole, Susan E.; Pickering, Susan J.; Ambridge, Benjamin; Wearing, Hannah (2004).
“The Structure of Working Memory From 4 to 15 Years of Age”. Developmental Psychology. 40 (2): 177–190. CiteSeerX 10.1.1.529.2727. doi:10.1037/0012-1649.40.2.177. PMID 14979759.
37. ^ Jump up to:a b c d Siegler, Robert S.; Alibali, Martha Wagner
(2005). Children’s Thinking. Pearson Education/Prentice Hall. ISBN 978-0-13-111384-8.[page needed]
38. ^ Jump up to:a b c Ping, Raedy; Goldin-Meadow, Susan (May 2010). “Gesturing Saves Cognitive Resources When Talking About Nonpresent Objects”.
Cognitive Science. 34 (4): 602–619. doi:10.1111/j.1551-6709.2010.01102.x. PMC 3733275. PMID 21564226.
39. ^ Ballard, Dana H.; Hayhoe, Mary M.; Pook, Polly K.; Rao, Rajesh P. N. (1 December 1997). “Deictic codes for the embodiment of cognition”.
Behavioral and Brain Sciences. 20 (4): 723–742. CiteSeerX 10.1.1.49.3813. doi:10.1017/s0140525x97001611. PMID 10097009. S2CID 1961389.
40. ^ Mani, A.; Mullainathan, S.; Shafir, E.; Zhao, J. (29 August 2013). “Poverty Impedes Cognitive Function”.
Science. 341 (6149): 976–980. Bibcode:2013Sci…341..976M. CiteSeerX 10.1.1.398.6303. doi:10.1126/science.1238041. PMID 23990553. S2CID 1684186.
41. ^ Hackman, Daniel A.; Farah, Martha J. (February 2009). “Socioeconomic status and the developing
brain”. Trends in Cognitive Sciences. 13 (2): 65–73. doi:10.1016/j.tics.2008.11.003. PMC 3575682. PMID 19135405.
42. ^ Skulmowski, Alexander; Rey, Günter Daniel (7 March 2018). “Embodied learning: introducing a taxonomy based on bodily engagement
and task integration”. Cognitive Research: Principles and Implications. 3 (1): 6. doi:10.1186/s41235-018-0092-9. PMC 5840215. PMID 29541685.
43. ^ Paas, Fred; Sweller, John (6 September 2011). “An Evolutionary Upgrade of Cognitive Load Theory: Using
the Human Motor System and Collaboration to Support the Learning of Complex Cognitive Tasks”. Educational Psychology Review. 24 (1): 27–45. doi:10.1007/s10648-011-9179-2. hdl:1765/31101.
44. ^ Skulmowski, Alexander; Pradel, Simon; Kühnert, Tom;
Brunnett, Guido; Rey, Günter Daniel (January 2016). “Embodied learning using a tangible user interface: The effects of haptic perception and selective pointing on a spatial learning task”. Computers & Education. 92–93: 64–75. doi:10.1016/j.compedu.2015.10.011.
S2CID 10493691.
45. ^ Healey, J.A.; Picard, R.W. (June 2005). “Detecting stress during real-world driving tasks using physiological sensors”. IEEE Transactions on Intelligent Transportation Systems. 6 (2): 156–166. CiteSeerX 10.1.1.73.4200. doi:10.1109/TITS.2005.848368.
S2CID 1409560.
46. ^ Sezgin, Tevfik Metin; Davies, Ian; Robinson, Peter (2009). Multimodal inference for driver-vehicle interaction. Proceedings of the 2009 international conference on Multimodal interfaces (ICMI-MLMI ’09). New York, NY, USA: Association
for Computing Machinery. pp. 193–198. CiteSeerX 10.1.1.219.4733. doi:10.1145/1647314.1647348.
47. ^ Prabhakar, Gowdham; Mukhopadhyay, Abhishek; Murthy, Lrd; Modiksha, Madan; Sachin, Deshmukh; Biswas, Pradipta (1 December 2020). “Cognitive load estimation
using ocular parameters in automotive”. Transportation Engineering. 2: 100008. doi:10.1016/j.treng.2020.100008.
48. ^ Kramer, Arthur F. (2020). “Physiological metrics of mental workload: A review of recent progress”. In Damos, D. (ed.). Multiple
Task Performance. CRC Press. pp. 279–328. doi:10.1201/9781003069447-14. ISBN 978-1-003-06944-7. S2CID 241713101.
49. ^ Wilson, GF; Fullenkamp, P; Davis, I (February 1994). “Evoked potential, cardiac, blink, and respiration measures of pilot workload
in air-to-ground missions”. Aviation, Space, and Environmental Medicine. 65 (2): 100–5. PMID 8161318.
50. ^ Babu, Mohan Dilli; JeevithaShree, D. V.; Prabhakar, Gowdham; Saluja, Kamal Preet Singh; Pashilkar, Abhay; Biswas, Pradipta (30 July 2019).
“Estimating pilots’ cognitive load from ocular parameters through simulation and in-flight studies”. Journal of Eye Movement Research. 12 (3). doi:10.16910/jemr.12.3.3. PMC 7880144. PMID 33828735.
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