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, 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. This chunking of memory components has also been described as schema construction.
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 load, and extraneous cognitive load.
An example of extraneous cognitive load occurs when there are two possible ways to describe a square to a student. 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.
As of 1993 Paas and Van Merriënboer 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.
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.
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. They believe that it may be possible to use RPP measures to set limits on workloads and for establishing work allowance.
There is active research interest in using physiological responses to indirectly estimate cognitive load, particularly by monitoring pupil diameter, eye gaze, respiratory rate, heart rate, or other factors. While some studies have found correlations between physiological factors and cognitive load, the findings have not held outside controlled laboratory environments. 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.
Some researchers have compared different measures of cognitive load. 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. 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.
The internet has transformed how individuals process, store, and retrieve information, serving both as a cognitive aid and a potential burden on working memory. While digital tools can reduce cognitive strain by offloading memory demands onto external systems, they also introduce challenges such as information overload, decision fatigue, and attention fragmentation. These multifaceted effects necessitate a nuanced understanding of the internet’s impact on cognitive load.
Beyond memory offloading, digital tools enhance cognitive efficiency by simplifying complex tasks. Online learning platforms, for instance, offer interactive elements, real-time feedback, and adaptive technologies that structure information accessibly, aligning with the principle of reducing extraneous cognitive load—elements that consume mental resources without directly contributing to learning. Well-designed digital environments can enhance knowledge acquisition by minimizing unnecessary processing demands, allowing learners to focus on essential concepts. Features like auto-complete functions, digital calculators, and grammar-checking tools further streamline tasks, reducing the mental effort required for routine operations. These advantages demonstrate how, when effectively leveraged, the internet can optimize information processing and retrieval, thereby enhancing cognitive efficiency.
However, the internet also presents significant cognitive challenges. One major issue is information overload, where the vast amount of available content overwhelms cognitive capacity, leading to decision fatigue and reduced learning efficiency. The necessity of filtering through extensive information to assess credibility and relevance adds an extraneous cognitive burden, potentially diminishing focus on core learning objectives. Research indicates that excessive information can impair decision-making by increasing cognitive effort, resulting in less effective knowledge retention. Additionally, the prevalence of hyperlinked texts, advertisements, and continuous updates contributes to fragmented attention, making sustained, deep learning more difficult.
Another concern is the impact of media multitasking on cognitive function. Many individuals frequently switch between multiple online streams—checking emails, browsing social media, and engaging with various digital content sources simultaneously. While this behavior may seem productive, studies suggest that heavy media multitasking is associated with reduced working memory efficiency, diminished attentional control, and increased distractibility. The rapid alternation between tasks prevents sustained focus, leading to shallow information processing rather than deep comprehension. Neuroimaging research has shown that frequent multitaskers exhibit decreased activation in brain regions associated with sustained attention and impulse control, indicating that digital environments can fragment cognitive resources.
Furthermore, the internet may alter how individuals value and interact with knowledge. In traditional learning environments, effortful cognitive processing contributes to deeper retention and understanding. However, the instant accessibility of online information can create an illusion of knowledge, where individuals overestimate their understanding simply because they can quickly look up answers. This reliance on digital search engines can lead to a false sense of expertise, as users mistake access to information for actual comprehension. This shift in cognitive processing raises questions about how the internet may reshape intellectual engagement, particularly in academic and professional settings where deep learning and critical thinking are essential.
While cognitive offloading and digital tools offer clear advantages, the long-term consequences of internet reliance remain an active area of research. The challenge lies in balancing the use of digital aids to enhance cognitive efficiency with ensuring that such reliance does not compromise memory retention, critical thinking, and attentional control. As digital environments continue to evolve, researchers emphasize the need for strategies that optimize cognitive load management, such as designing educational interfaces that promote deep learning while minimizing distractions. Further investigation is needed to determine best practices for integrating digital tools into learning contexts without exacerbating the cognitive drawbacks associated with information overload and media multitasking.
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.
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. 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. A 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. Conversely, an increasing demand for balance can increase cognitive load.
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. 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.
As children grow older they develop superior basic processes and capacities. They also develop metacognition, which helps them to understand their own cognitive activities. Lastly, they gain greater content knowledge through their experiences. These elements help reduce cognitive load in children as they develop.
As of 2013 it has been theorized that an impoverished environment can contribute to cognitive load. 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.
Bodily activity can both be advantageous and detrimental to learning depending on how this activity is implemented. Cognitive load theorists have asked for updates that makes CLT more compatible with insights from embodied cognition research. As a result, Embodied Cognitive Load Theory has been suggested as a means to predict the usefulness of interactive features in learning environments. 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.
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 like heart rate, facial expression, ocular parameters and so on. In aviation there are numerous simulation studies on analysing pilots' distraction and attention using various physiological parameters. For military fast jet pilots, researchers explored air to ground dive attacks and recorded cardiac, EEG and ocular parameters.
For those wishing to learn more about cognitive load theory, please consider reading these journals and special issues of those journals:
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. 978-3-030-14272-8
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. /wiki/CiteSeerX_(identifier)
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. /wiki/Doi_(identifier)
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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539229
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. /wiki/Doi_(identifier)
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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604157
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. /wiki/Doi_(identifier)
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. /wiki/CiteSeerX_(identifier)
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. https://ris.utwente.nl/ws/files/288323612/Sweller1998cognitive.pdf
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. /wiki/CiteSeerX_(identifier)
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. /wiki/Doi_(identifier)
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. /wiki/CiteSeerX_(identifier)
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. https://research.utwente.nl/en/publications/3e475ae3-2b43-45c7-83e5-7d4da2ef88b8
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. /wiki/Roxana_Moreno
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. /wiki/CiteSeerX_(identifier)
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. /wiki/Doi_(identifier)
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. /wiki/Doi_(identifier)
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. /wiki/Doi_(identifier)
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. https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1141&context=edupapers
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. https://ris.utwente.nl/ws/files/288323612/Sweller1998cognitive.pdf
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. https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1133&context=edupapers
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. https://research.ou.nl/ws/files/1015152/Why%20minimal%20guidance%20during%20instruction%20does%20not%20work.pdf
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. https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1133&context=edupapers
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. /wiki/Doi_(identifier)
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] 978-0-7879-7728-3
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. https://doi.org/10.1002%2Fhbe2.184
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. /wiki/Doi_(identifier)
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. /wiki/Doi_(identifier)
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. /wiki/CiteSeerX_(identifier)
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. /wiki/Doi_(identifier)
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. https://doi.org/10.1109%2FTHMS.2017.2782483
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. /wiki/Doi_(identifier)
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. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539229
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. https://web.archive.org/web/20190222114604/http://pdfs.semanticscholar.org/3808/8da333aa85e0bb5c72d5eff3404b4b74edcf.pdf
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. https://doi.org/10.1002%2Fhbe2.184
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. https://research.utwente.nl/en/publications/3e475ae3-2b43-45c7-83e5-7d4da2ef88b8
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. /wiki/Roxana_Moreno
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. /wiki/CiteSeerX_(identifier)
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. /wiki/Doi_(identifier)
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. /wiki/Doi_(identifier)
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. /wiki/Doi_(identifier)
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. https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1141&context=edupapers
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Firth, Joseph; Torous, John; Stubbs, Brendon; Firth, Josh A.; Steiner, Genevieve Z.; Smith, Lee; Alvarez-Jimenez, Mario; Gleeson, John; Vancampfort, Davy; Armitage, Christopher J.; Sarris, Jerome (June 2019). "The "online brain": how the Internet may be changing our cognition". World Psychiatry. 18 (2): 119–129. doi:10.1002/wps.20617. ISSN 1723-8617. PMC 6502424. PMID 31059635. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502424
Firth, Joseph; Torous, John; Stubbs, Brendon; Firth, Josh A.; Steiner, Genevieve Z.; Smith, Lee; Alvarez-Jimenez, Mario; Gleeson, John; Vancampfort, Davy; Armitage, Christopher J.; Sarris, Jerome (June 2019). "The "online brain": how the Internet may be changing our cognition". World Psychiatry. 18 (2): 119–129. doi:10.1002/wps.20617. ISSN 1723-8617. PMC 6502424. PMID 31059635. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6502424
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