Cognitive load

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. Working memory, however, is extremely limited in both capacity and duration. These limitations will, under some conditions, impede learning. 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. The elderly, students, and children experience different, and more often higher, amounts of cognitive load.

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.[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".[7] Sweller's theory employs aspects of information processing theory to emphasize the inherent limitations of concurrent working memory load on learning during instruction. It makes use of the schema as primary unit of analysis for the design of instructional materials.

History

The history of cognitive load theory can be traced to the beginning of cognitive science in the 1950s and the work of G.A. Miller. In his classic paper,[8] Miller was perhaps the first to suggest our working memory capacity has inherent limits. His experimental results suggested that humans are generally able to hold only seven plus or minus two units of information in short-term memory.

In 1973 Simon and Chase were the first to use the term "chunk" to describe how people might organize information in short-term memory.[9] This chunking of memory components has also been described as schema construction.

In the late 1980s John Sweller developed cognitive load theory (CLT) while studying problem solving.[2] Studying learners as they solved problems, he and his associates found that learners often use a problem solving strategy called means-ends analysis. 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. Sweller suggested that instructional designers should prevent this unnecessary cognitive load by designing instructional materials which do not involve problem solving. Examples of alternative instructional materials include what are known as worked-examples and goal-free problems.

In the 1990s, cognitive load theory was applied in several contexts. The empirical results from these studies led to the demonstration of several learning effects: the completion-problem effect;[10] modality effect;[11][12] split-attention effect;[13] worked-example effect;[14][15] and expertise reversal effect.[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. 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. This theory differentiates between three types of cognitive load: intrinsic cognitive load, germane cognitive load, and extraneous cognitive load.[7]

Intrinsic

Intrinsic cognitive load is the inherent level of difficulty associated with a specific instructional topic. The term was first used in the early 1990s by Chandler and Sweller.[17] According to them, all instructions have an inherent difficulty associated with them (e.g., the calculation of 2 + 2, versus solving a differential equation). This inherent difficulty may not be altered by an instructor. However, many schemas may be broken into individual "subschemas" and taught in isolation, to be later brought back together and described as a combined whole.[18]

Extraneous

Extraneous cognitive load is generated by the manner in which information is presented to learners and is under the control of instructional designers.[17] This load can be attributed to the design of the instructional materials. 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. Thus, especially when intrinsic and/or germane load is high (i.e., when a problem is difficult), materials should be designed so as to reduce the extraneous load.[19]

An example of extraneous cognitive load occurs when there are two possible ways to describe a square to a student.[20] A square is a figure and should be described using a figural medium. Certainly an instructor can describe a square in a verbal medium, but it takes just a second and far less effort to see what the instructor is talking about when a learner is shown a square, rather than having one described verbally. In this instance, the efficiency of the visual medium is preferred. This is because it does not unduly load the learner with unnecessary information. This unnecessary cognitive load is described as extraneous.

Chandler and Sweller introduced the concept of extraneous cognitive load. This article was written to report the results of six experiments that they conducted to investigate this working memory load. Many of these experiments involved materials demonstrating the split attention effect. They found that the format of instructional materials either promoted or limited learning. They proposed that differences in performance were due to higher levels of the cognitive load imposed by the format of instruction. "Extraneous cognitive load" is a term for this unnecessary (artificially induced) cognitive load.

Extraneous cognitive load may have different components, such as the clarity of texts or interactive demands of educational software.[21]

Germane

Germane cognitive load is the processing, construction and automation of schemas. It was first described by Sweller, Van Merriënboer and Paas in 1998. While intrinsic cognitive load is generally thought to be immutable (although techniques can be applied to manage complexity by segmenting and sequencing complex material), instructional designers can manipulate extraneous and germane load. It is suggested that they limit extraneous load and promote germane load.[7]

Until the 1998 article by Sweller, Van Merriënboer & Paas, cognitive load theory primarily concentrated on the reduction of extraneous 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). Thus it is very important for instructional designers to "reduce extraneous cognitive load and redirect learners' attention to cognitive processes that are directly relevant to the construction of schemas".[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. This construct provides a relatively simple means of comparing instructional conditions, taking into account both mental effort ratings and performance scores. Relative condition efficiency is calculated by subtracting standardized mental effort from standardized performance and dividing by the square root of two.[3]

Paas and Van Merriënboer used relative condition efficiency to compare three instructional conditions (worked examples, completion problems, and discovery practice). They found learners who studied worked examples were the most efficient, followed by those who used the problem completion strategy. Since this early study many other researchers have used this and other constructs to measure cognitive load as it relates to learning and instruction.[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.[23] They believe that it may be possible to use RPP measures to set limits on workloads and for establishing work allowance.

Task-invoked pupillary response is a form of measurement that directly reflects the cognitive load on working memory. Greater pupil dilation is found to be associated with high cognitive load.[5] Pupil constriction occurs when there is low cognitive load.[5] Task-invoked pupillary response shows a direct correlation with working memory, making it an effective measurement of cognitive load explicitly unrelated to learning.

Some researchers have compared different measures of cognitive load.[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.[24] 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.[21]

Established eye movement and pupillary response indicators of cognitive load are:[25]

  • pupillary diameter mean
  • pupillary diameter deviation
  • number of gaze fixations > 500 milliseconds
  • saccade speed
  • pupillary hippus[26]

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.[10][11][12][13][14][15][16] A heavy cognitive load can also increase stereotyping.[27] 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.[28] Stereotypical associations may be automatically activated by the use of pattern recognition and schemas, producing an implicit stereotype effect. [29] Stereotyping is an extension of the Fundamental Attribution Error which also increases in frequency with heavier cognitive load.[30] The notions of cognitive load and arousal contribute to the "Overload Hypothesis" explanation of social facilitation: in the presence of an audience, subjects tend to perform worse in subjectively complex tasks (whereas they tend to excel in subjectively easy tasks).

Sub-population studies

Individual differences

As of 1971, some evidence had been found that individuals systematically differed in their processing capacity.[31][32] As of 1984 it was established for example, that there were individual differences in processing capacities between novices and experts. Experts have more knowledge or experience with regard to a specific task which reduces the cognitive load associated with the task. Novices do not have this experience or knowledge and thus have heavier cognitive load.[33]

Elderly

The danger of heavy cognitive load is seen in the elderly population. Aging can cause declines in the efficiency of working memory which can contribute to higher cognitive load.[34] Heavy cognitive load can disturb balance in elderly people. The relationship between heavy cognitive load and control of center of mass are heavily correlated in the elderly population. As cognitive load increases, the sway in center of mass in elderly individuals increases.[35] An 2007 study examined the relationship between body sway and cognitive function and their relationship during multitasking and found disturbances in balance led to a decrease in performance on the cognitive task.[36] Conversely, an increasing demand for balance can increase cognitive load.

College students

As of 2014, an increasing cognitive load for students using a laptop in school has become a concern. With the use of Facebook and other social forms of communication, adding multiple tasks jeopardizes students performance in the classroom. 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.[37] In a study from 2013, both students who were heavy Facebook users and students who sat nearby those who were heavy Facebook users performed poorly and resulted in lower GPA.[38][39]

Children

In 2004, British psychologists, Alan Baddeley and Graham Hitch proposed that the components of working memory are in place at 6 years of age.[40] They found a clear difference between adult and child knowledge. These differences were due to developmental increases in processing efficiency.[40] Children lack general knowledge, and this is what creates increased cognitive load in children. Children in impoverished families often experience even higher cognitive load in learning environments than those in middle-class families.[41] These children do not hear, talk, or learn about schooling concepts because their parents often do not have formal education. When it comes to learning, their lack of experience with numbers, words, and concepts increases their cognitive load.

As children grow older they develop superior basic processes and capacities.[41] They also develop metacognition, which helps them to understand their own cognitive activities.[41] Lastly, they gain greater content knowledge through their experiences.[41] These elements help reduce cognitive load in children as they develop.

Gesturing is a technique children use to reduce cognitive load while speaking.[42] By gesturing, they can free up working memory for other tasks.[42] 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.[43] Additionally, gesturing about an object that is absent reduces the difficulty of having to picture it in their mind.[42]

Poverty

As of 2013 it has been theorized that an impoverished environment can contribute to cognitive load.[44] Regardless of the task at hand, or the processes used in solving the task, people who experience poverty also experience higher cognitive load. A number of factors contribute to the cognitive load in people with lower socioeconomic status that are not present in middle and upper-class people.[45]

Embodiment and interactivity

Bodily activity can both be advantageous and detrimental to learning depending on how this activity is implemented.[46] Cognitive load theorists have asked for updates that makes CLT more compatible with insights from embodied cognition research.[47] As a result, Embodied Cognitive Load Theory has been suggested as a means to predict the usefulness of interactive features in learning environments.[48] 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.

Application in driving and piloting

With increase in secondary tasks inside cockpit, cognitive load estimation became an important problem for both automotive drivers and pilots. The research problem is investigated in various names like drowsiness detection, distraction detection and so on. For automotive drivers, researchers explored various physiological parameters[49] like heart rate, facial expression,[50] ocular parameters[51] and so on. In aviation there are numerous simulation studies on analysing pilots’ distraction and attention using various physiological parameters.[52] For military fast jet pilots, researchers explored air to ground dive attacks and recorded cardiac, EEG[53] and ocular parameters.[54]

See also

References
  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. H-WORKLOAD 2018. Communications in Computer and Information Science, Springer, Cham. Communications in Computer and Information Science. 1012: 23–48. doi:10.1007/978-3-030-14273-5_3. ISBN 978-3-030-14272-8. S2CID 86587936.
  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. 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.
  7. Sweller, John; van Merrienboer, Jeroen J. G.; Paas, Fred G. W. C. (1998). "Cognitive Architecture and Instructional Design". Educational Psychology Review. 10 (3): 251–296. doi:10.1023/A:1022193728205. S2CID 127506.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  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. Buettner, Ricardo (2013). Cognitive Workload of Humans Using Artificial Intelligence Systems: Towards Objective Measurement Applying Eye-Tracking Technology. KI 2013: 36th German Conference on Artificial Intelligence, September 16-20, 2013, Vol. 8077 of Lecture Notes in Artificial Intelligence (LNAI). Koblenz, Germany: Springer. pp. 37–48. doi:10.1007/978-3-642-40942-4_4.
  26. Buettner, Ricardo (2014). Analyzing Mental Workload States on the Basis of the Pupillary Hippus (PDF). NeuroIS 2014 Proceedings: Gmunden Retreat on NeuroIS 2014. Gmunden, Austria. p. 52.
  27. 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.
  28. "Cognitive Load Theory". www.cs.virginia.edu. Retrieved 2023-04-20.
  29. 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.
  30. 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.
  31. Scandura, Joseph M. (1971). "Deterministic Theorizing in Structural Learning: Three Levels of Empiricism". Journal of Structural Learning. 3 (1): 21–53. CiteSeerX 10.1.1.532.3585. ERIC EJ085112.
  32. Voorhies, D.; Scandura, J. M. (1977). "Determination of memory load in information processing". In Scandura, J. M. (ed.). Problem Solving: A Structural Process Approach with Instructional Implications. New York: Academic Press. pp. 299–316]. ISBN 978-0-12-620650-0.
  33. 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.
  34. 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.
  35. 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.
  36. 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.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. Siegler, Robert S.; Alibali, Martha Wagner (2005). Children's Thinking. Pearson Education/Prentice Hall. ISBN 978-0-13-111384-8.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. 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.
  54. 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.

Further reading

Journal special issues

For those wishing to learn more about cognitive load theory, please consider reading these journals and special issues of those journals:

  • Educational Psychologist, vol. 43 (4) ISSN 0046-1520
  • Applied Cognitive Psychology vol. 20(3) (2006)
  • Applied Cognitive Psychology vol. 21(6) (2007)
  • ETR&D vol. 53 (2005)
  • Instructional Science vol. 32(1) (2004)
  • Educational Psychologist vol. 38(1) (2003)
  • Learning and Instruction vol. 12 (2002)
  • Computers in Human Behavior vol. 25 (2) (2009)

For ergonomics standards see:

  • ISO 10075-1:1991 Ergonomic Principles Related to Mental Workload – Part 1: General Terms and Definitions
  • ISO 10075-2:1996 Ergonomic Principles Related To Mental Workload – Part 2: Design Principles
  • ISO 10075-3:2004 Ergonomic Principles Related To Mental Workload – Part 3: Principles And Requirements Concerning Methods For Measuring And Assessing Mental Workload
  • ISO 9241 Ergonomics of Human System Interaction
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