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Cognitive load

In cognitive psychology , cognitive load is the effort being used in the working memory . According to work conducted in the field of instructional design and pedagogy , broadly...

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In cognitive psychology, cognitive load is the effort being used in the working memory. 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.
  • Germane cognitive load refers to the work put into creating a permanent store of knowledge (a schema).
  • Extraneous cognitive load refers to the way information or tasks are presented to a learner.

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] Much information, such as Declarative knowledge, may only be stored in long-term memory after first being attended to, and processed by, working memory. This was originally thought to be true for all long-term memory under the classic "gateway" model of the Atkinson–Shiffrin memory model. However, modern research has shown that some long-term memory can be encoded while bypassing, or working in parallel with, working memory.[6] Working memory, however, is extremely limited in both capacity and duration.[7] These limitations will, under some conditions, impede learning. Heavy cognitive load can have negative effects on task completion, and 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 the rise in digital technology and smartphones, students are more prone to experiencing high cognitive load, which can reduce academic success.[8]

Theory

In the late 1980s, educational psychologist 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".[9] 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,[10] Miller was perhaps the first to suggest that human 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.[11]

In 1973 Simon and Chase were the first to use the term chunk to describe how people might organize information in short-term memory.[12] This chunking of memory components has also been described as schema construction.[13]

In the late 1980s 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;[14]modality effect;[15][16]split-attention effect;[17]worked-example effect;[18][19] and expertise reversal effect.[20]

Categories

Cognitive load theory provides a general framework with broad implications for instructional design by focusing on the limitations of human working memory as a central constraint on learning. The primary aim of the theory is to guide the effective use of this limited cognitive resource by structuring learning conditions and instructional materials in ways that reduce extraneous cognitive load and optimize intrinsic cognitive load. By doing so, instructional designers can better direct learners' attention toward essential information and processes that support schema construction, thereby increasing germane cognitive load. Cognitive load theory distinguishes among three types of cognitive load: intrinsic, extraneous, and germane cognitive load.[9]

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.[21] 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.[22]

Germane load

La carga relevante se refiere a los recursos de memoria de trabajo que el estudiante dedica a gestionar la carga cognitiva intrínseca asociada a la información esencial para el aprendizaje. A diferencia de la carga intrínseca, que está directamente relacionada con la complejidad del material, la carga relevante no proviene de la información presentada, sino de las características del estudiante. No representa una fuente independiente de carga de memoria de trabajo, sino que está influenciada por la relación entre la carga intrínseca y la extrínseca. Si la carga intrínseca es alta y la extrínseca es baja, la carga relevante será alta, ya que el estudiante puede dedicar más recursos al procesamiento del material esencial. Sin embargo, si la carga extrínseca aumenta, la carga relevante disminuye y el aprendizaje se ve afectado porque el estudiante debe usar recursos de memoria de trabajo para lidiar con elementos externos en lugar del contenido esencial. Esto presupone un nivel constante de motivación, donde todos los recursos de memoria de trabajo disponibles se centran en gestionar tanto la carga cognitiva intrínseca como la extrínseca.

Extraño

La carga cognitiva extrínseca se genera por la forma en que se presenta la información a los estudiantes y está bajo el control de los diseñadores instruccionales. [ 21 ] Esta carga puede atribuirse al diseño de los materiales didácticos. Debido a que existe un único recurso cognitivo limitado que utiliza recursos para procesar la carga extrínseca, se reduce el número de recursos disponibles para procesar la carga intrínseca y la carga pertinente (es decir, el aprendizaje). Por lo tanto, especialmente cuando la carga intrínseca y/o pertinente es alta (es decir, cuando un problema es difícil), los materiales deben diseñarse de manera que reduzcan la carga extrínseca. [ 23 ]

Un ejemplo de carga cognitiva superflua se da cuando existen dos maneras posibles de describir un cuadrado a un estudiante. [ 24 ] Un cuadrado es una figura y debe describirse mediante un medio figurativo. Si bien un instructor puede describir un cuadrado verbalmente, resulta mucho más sencillo y rápido comprender a qué se refiere el instructor cuando se le muestra un cuadrado al estudiante, en lugar de escuchar una descripción verbal. En este caso, se prefiere la eficiencia del medio visual, ya que no sobrecarga al estudiante con información innecesaria. Esta carga cognitiva superflua se denomina carga superflua.

Chandler y Sweller introdujeron el concepto de carga cognitiva extrínseca. Este artículo se escribió para informar sobre los resultados de seis experimentos que realizaron para investigar esta carga de memoria de trabajo. Muchos de estos experimentos involucraron materiales que demostraban el efecto de atención dividida. Descubrieron que el formato de los materiales didácticos promovía o limitaba el aprendizaje. Propusieron que las diferencias en el rendimiento se debían a niveles más altos de carga cognitiva impuesta por el formato de instrucción. Carga cognitiva extrínseca es un término que se utiliza para referirse a esta carga cognitiva innecesaria (inducida artificialmente).

La carga cognitiva externa puede tener diferentes componentes, como la claridad de los textos o las exigencias interactivas del software educativo. [ 25 ]

Medición

En 1993, Paas y Van Merriënboer [ 3 ] desarrollaron un constructo conocido como eficiencia relativa de la condición, que ayuda a los investigadores a medir el esfuerzo mental percibido, un índice de carga cognitiva. Este constructo proporciona un medio relativamente sencillo para comparar condiciones de instrucción, teniendo en cuenta tanto las calificaciones de esfuerzo mental como las puntuaciones de rendimiento. La eficiencia relativa de la condición se calcula restando el esfuerzo mental estandarizado del rendimiento estandarizado y dividiendo el resultado por la raíz cuadrada de dos. [ 3 ]

Paas y Van Merriënboer utilizaron la eficiencia relativa de las condiciones para comparar tres condiciones de instrucción (ejemplos resueltos, problemas de resolución y práctica de descubrimiento). Descubrieron que los estudiantes que estudiaron ejemplos resueltos fueron los más eficientes, seguidos por aquellos que utilizaron la estrategia de resolución de problemas. Desde este estudio inicial, muchos otros investigadores han utilizado este y otros constructos para medir la carga cognitiva en relación con el aprendizaje y la instrucción. [ 26 ]

El enfoque ergonómico busca una expresión neurofisiológica cuantitativa de la carga cognitiva que se puede medir con instrumentos comunes, por ejemplo, utilizando el producto frecuencia cardíaca - presión arterial (PFR) como medida de la carga de trabajo ocupacional tanto cognitiva como física. [ 27 ] Consideran que es posible utilizar las mediciones de PFR para establecer límites a las cargas de trabajo y para determinar la carga de trabajo.

Existe un interés activo en la investigación sobre el uso de respuestas fisiológicas para estimar indirectamente la carga cognitiva, particularmente mediante el monitoreo del diámetro pupilar, la mirada, la frecuencia respiratoria, la frecuencia cardíaca u otros factores. [ 28 ] Si bien algunos estudios han encontrado correlaciones entre factores fisiológicos y carga cognitiva, los hallazgos no se han mantenido fuera de entornos de laboratorio controlados. La respuesta pupilar inducida por la tarea es una de esas respuestas fisiológicas de la carga cognitiva en la memoria de trabajo, y los estudios han encontrado que la dilatación pupilar ocurre con una alta carga cognitiva. [ 5 ]

Algunos investigadores han comparado diferentes medidas de carga cognitiva. [ 4 ] Por ejemplo, Deleeuw y Mayer (2008) compararon tres medidas de carga cognitiva de uso común y encontraron que respondían de manera diferente a la carga extrínseca, intrínseca y relevante. [ 29 ] Un estudio de 2020 mostró que puede haber varios componentes de demanda que juntos forman la carga cognitiva extrínseca, pero que puede ser necesario medirlos utilizando diferentes cuestionarios. [ 25 ] Algunas investigaciones sugieren que la teoría de la carga cognitiva tiene la "hipótesis de aditividad", que indica que los tres tipos separados de carga cognitiva pueden superponerse. Esto significaría que la teoría de la carga cognitiva necesitaría una distinción más clara entre los tipos de carga cognitiva. [ 30 ]

Efectos de una carga cognitiva elevada

A heavy cognitive load typically creates error or some kind of interference in the task at hand.[14][15][16][17][18][19][20] A heavy cognitive load can also increase stereotyping.[31] 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 to organize information into categories and identify the relationships between them.[32] Stereotypical associations may be automatically activated by the use of pattern recognition and schemas, producing an implicit stereotype effect.[33] Stereotyping is an extension of the fundamental attribution error, which also increases in frequency with heavier cognitive load.[34] 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).

Effects of the internet

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,[35] 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.

One prominent phenomenon illustrating this impact is the Google effect, also known as digital amnesia. This term describes the tendency to forget information readily available online, as individuals are less inclined to remember details they can easily access through search engines.[36] This reliance on external digital storage aligns with transactive memory theory, wherein people distribute knowledge within a group, focusing on who knows what rather than retaining all information individually. The internet extends this system, allowing vast data storage externally and emphasizing retrieval over internal recall.[36] While this can free up working memory for complex problem solving, it may also diminish long-term retention and comprehension. Studies have shown that when individuals expect information to be accessible online, they are less likely to deeply encode it, prioritizing access over understanding.[36]

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.[35] 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.[35] 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.[37] 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.[37] Additionally, the prevalence of hyperlinked texts, advertisements, and continuous updates contributes to fragmented attention, making sustained, deep learning more difficult.[37]

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.[37] 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.[37]

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.[38] This reliance on digital search engines can lead to a false sense of expertise, as users mistake access to information for actual comprehension.[38] 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.[38]

One domain where the effect of the internet may have adverse consequences for both cognitive performance and brain health is spatial navigation. As part of their training, London taxi-drivers need to memorize and navigate through the city's complex streets, leading to structural changes in the size of a key brain region associated with memory consolidation, the hippocampus.[39] More recent studies suggest such enlargement may provide protection against age-related dementia.[40][41] As a result, an over-reliance on GPS systems in apps like Google Maps, which dispense with the need for remembering routes and spatial reasoning, may also suppress brain plasticity for spatial navigation and its beneficial health effects.

While cognitive offloading[42] 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, attentional control, and even brain health. 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.[35] 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.[37]

Emerging effects of AI

A growing body of evidence also suggests that AI may have a uniquely harmful effect on our cognition.[43][44][45][46][47][48][49] Beyond search algorithms being relied on for spatial navigation, more recent machine learning applications outsource key mental functions, such as sensing the world (for example, face recognition), moving our bodies (robotics controllers), making choices (recommendation systems), and solving problems (chatbots). In the realm of education, productive struggle requiring the use of our working and long-term memory systems among others is considered "desirable difficulty" and facilitates improved cognitive performance.[47][48] Similarly, in healthcare, regularly applying skills ensures their retention and proficiency, as illustrated by a study showing poorer surgical outcomes among physicians, or deskilling, when their AI tool was discontinued.[44]

Sub-population studies

Individual differences

As of 1984 it was established, for example, that there are 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.[50] We also see structural differences in long-term memory as reflected in the London taxi driver studies cited above.[39]

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.[51] 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.[52] 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.[53] 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.[54] 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.[8][55]

Children

In 2004, British psychologists, Alan Baddeley and Graham Hitch proposed that the components of working memory are in place at six years of age.[56] They found a clear difference between adult and child knowledge. These differences were due to developmental increases in processing efficiency.[56] 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.[57] 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.[57] They also develop metacognition, which helps them to understand their own cognitive activities.[57] Lastly, they gain greater content knowledge through their experiences.[57] These elements help reduce cognitive load in children as they develop.

Gesturing is a technique children use to reduce cognitive load while speaking.[58] By gesturing, they can free up working memory for other tasks.[58] 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.[59] Additionally, gesturing about an object that is absent reduces the difficulty of having to picture it in their mind.[58]

Poverty

As of 2013 it has been theorized that an impoverished environment can contribute to cognitive load.[60] 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.[61]

Embodiment and interactivity

Bodily activity can both be advantageous and detrimental to learning depending on how this activity is implemented.[62] Cognitive load theorists have asked for updates that makes CLT more compatible with insights from embodied cognition research.[63] As a result, embodied cognitive load theory has been suggested as a means to predict the usefulness of interactive features in learning environments.[64] 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 the cockpit, cognitive load estimation has become an important problem for both automotive drivers and pilots. The issue has been addressed with various features such as drowsiness detection. For automotive drivers, researchers have explored various physiological parameters[65] like heart rate, facial expression,[66] and ocular parameters.[67] In aviation there are numerous simulation studies on analysing pilots' distraction and attention using various physiological parameters.[68] For military fast jet pilots, researchers have explored air-to-ground dive attacks and recorded cardiac, EEG[69] and ocular parameters.[70]

See also

References

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Further reading

  • Barrett, H. Clark; Frederick, David A.; Haselton, Martie G.; Kurzban, Robert (2006). "Can manipulations of cognitive load be used to test evolutionary hypotheses?". Journal of Personality and Social Psychology. 91 (3): 513–518. CiteSeerX 10.1.1.583.7931. doi:10.1037/0022-3514.91.3.513. PMID 16938033.
  • Cooper, Graham (1 December 1990). "Cognitive load theory as an aid for instructional design". Australasian Journal of Educational Technology. 6 (2). doi:10.14742/ajet.2322.
  • Cooper, Graham (1998). "Research into Cognitive Load Theory and Instructional Design at UNSW". Archived from the original on 30 August 2007.
  • Plass, J.L.; Moreno, R.; Brünken, R., eds. (2010). Cognitive Load Theory. New York: Cambridge University Press. ISBN 978-0-521-67758-5.
  • "UNSW Cognitive Load Theory Conference- Sydney Australia 24-26 March 2007". 31 October 2005. Archived from the original on 9 April 2007.
  • Khawaja, M. Asif; Chen, Fang; Marcus, Nadine (April 2014). "Measuring Cognitive Load Using Linguistic Features: Implications for Usability Evaluation and Adaptive Interaction Design". International Journal of Human-Computer Interaction. 30 (5): 343–368. doi:10.1080/10447318.2013.860579. S2CID 2374883.
  • Sweller, John (January 1994). "Cognitive load theory, learning difficulty, and instructional design". Learning and Instruction. 4 (4): 295–312. doi:10.1016/0959-4752(94)90003-5. S2CID 145058758.
  • Sweller, J. (1999). Instructional design in technical areas. Camberwell, Australia: Australian Council for Educational Research. ISBN 978-0-86431-312-6.

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