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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:
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.
Cognitive load theory was developed in the late 1980s out of a study of problem solving by John Sweller. 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. Task-invoked pupillary response is a reliable and sensitive measurement of cognitive load that is directly related to working memory. 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 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.
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.
In the late 1980s John Sweller developed cognitive load theory (CLT) while studying problem solving. 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; modality effect; split-attention effect; worked-example effect; and expertise reversal effect.
An example of extraneous cognitive load occurs when there are two possible ways to describe a square to a student.
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.
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.
One prominent phenomenon illustrating this impact is the Google effect, also known as Google effect. 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. 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. 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.
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 children grow older they develop superior basic processes and capacities.
Gesture is a technique children use to reduce cognitive load while speaking. By gesturing, they can free up working memory for other tasks. 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. Additionally, gesturing about an object that is absent reduces the difficulty of having to picture it in their mind.
For ergonomics standards see:
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