Cognitive Load Theory and Working Memory

cognitive processes influences the problem-solving capabilities of the individ- ual. Anderson et al. (2011) define cognition as the process involved with the acquisition and understanding of knowledge. The human brain uses working memory to execute these cognitive processes (Engle,2002). Paas and Van Mer- riënboer (1994) described cognitive load as a multidimensional concept that describes the load that is imposed on a cognitive system of an individual when performing a certain task. Sweller, Van Merriënboer and Paas (1998) state that cognitive load theory aims to provide suggestions which could aid in the

presentation of information to optimise intellectual performance.

In order to explain the concept of working memory, Baddeley (1992) uses the analogy of computer architecture. The Central Processing Unit (CPU) of a computer represents working memory while short-term memory is repre- sented by temporary data buffers or Random-Access Memory (RAM). Finally, the sensory perceptions and reactions resulting from it are represented by the input/output communications. If the cognitive processes that are required in problem-solving are greater than an individual’s cognitive processing capacity, performance will be negatively affected. In the computer architecture analogy, this negative effect on the performance results in a computer freezing or lag- ging. Of course, this analogy is only intended to aid in the understanding of working memory and is by no means comprehensive enough to represent the complex construct of working memory fully.

Chandler and Sweller (1991) distinguished between three different types of cognitive load, namely: germane, intrinsic, and extraneous. Each of these loads have a distinct effect on the decision making process. Seufert, Jänen and Brünken (2007) explained that the sum of the three types of cognitive loads determines the overall load that is placed on the person’s working memory. Anderson et al. (2011) summarised these findings in Figure 2.7. Notice how the Task Performance curve is inversely affected by the Cognitive Load curve. The load-sub types remain constant in the decision making process. A de- scription of the three types of cognitive loads and their impact on information presentation is given below.

Figure 2.7: Combination of germane, intrinsic and extraneous load to form working memory capacity.

Germane cognitive load is concerned with the way in which knowledge is acquired (Sweller, 2010). It is described by Anderson et al. (2011) as the working memory that is used to learn a new cognitive schema. This schema can be described as the internal representations which are constructed when a person learns how to interpret a new presentation format. Once a new schema is developed, it can be used multiple times in tasks which are similar. As a result of this, the contribution of the germane cognitive load to the overall cog- nitive load is high until the schema is established in which case the germane load becomes minimal.

In terms of information presentation, this can be described by the con- struction of mental representations of information by using, for example, a pie graph. Pie graphs can be used to illustrate different proportions. The germane load is described as the effort needed to create mental representations of the proportions displayed by the pie graph which can then be used in problem solving tasks and decision making. The first time a person is tasked with us- ing a pie graph, the germane load is high because of the person’s unfamiliarity with pie graphs. However, once the person has become comfortable with using a pie graph, the germane load involved in decoding information conveyed in pie graphs is minimal.

Intrinsic cognitive load is referred to by Anderson et al. (2011) as the demands that are placed on a person’s working memory by the underlying complexity of the information that is being transferred. Sweller (2010) ex- plains that the intrinsic load of a task is determined by the task itself and the knowledge of the person. The intrinsic load of a task can thus only be altered by changing either the task or the knowledge of the person and is not affected by the presentation of the information. There is an intrinsic cognitive load involved in adding two numbers or learning the meaning of a chemical symbol. In information presentations, there is an intrinsic cognitive load involved in finding the highest number in a set, regardless of whether the information is presented in a graph, a table or text.

Extraneous cognitive load is a measure of the cognitive load that is placed on a user as a result of a task’s design (Paas, Renkl and Sweller,2003). Seufert et al.(2007) have shown that the way in which information is presented affects this type of load. Anderson et al. (2011) used Figure 2.8 to illustrate two ways of describing data. The numerical description is on the left while the right is a visual description. It is clear that the visual summary provided by the box plot requires less working memory to interpret than the numerical display. However, it is important to notice that the box plot on the right only requires less working memory if the schema of interpreting a box plot has been

established. In other words, the box plot is only easier to read if a person has already learnt how to interpret a box plot.

3, 5, 28, 78, 72, 40, 52, 37 76, 6, 26, 68, 96, 70, 66, 75 34, 33, 20, 74, 36, 85, 99, 51 99, 33, 18, 38, 14, 18, 37, 53 25, 8, 69, 85, 25, 65, 30, 28 12, 87, 59, 54, 6, 30, 16, 59 97, 66, 23, 84, 87, 76, 36, 15 97, 87, 93, 12, 70, 56, 94, 97 Box Plot 3 47 99 Table of Data

Figure 2.8: Two ways of describing data.

Adopted fromAnderson et al.(2011).

Ginns(2006) explained that tasks with high intrinsic and/or germane loads are generally perceived to be difficult by the user. Designers of information should ensure that difficult tasks are presented in a format which places a low extraneous load on the user to promote understanding. Recall that the over- all cognitive load placed on the working memory is the sum of the germane, intrinsic and extraneous loads. Figure 2.7 shows that the task performance effectively becomes zero or insignificant if the overall cognitive load exceeds the capacity of the working memory. Since the intrinsic load is not influenced by the presentation of information, a high extraneous load results in little working memory being available for the germane load. If the working memory that is available for the germane load is insufficient, there might be a reduc- tion in the deeper meaningful cognitive processes of the person interpreting the information. This, in turn, could result in the person not obtaining a deep understanding of the information.

Huang, Eades and Hong (2009) discovered that the overall cognitive load is influenced by certain factors. However, instead of assigning the factors to the three load types ofChandler and Sweller (1991), they were labelled causal factors and categorised into one of six different types of complexities. These causal factors are believed to influence the overall cognitive load which, in turn, influences the assessment factors such as response time and response accuracy. This concept is illustrated in Figure 2.9.

The six causal factors and short descriptions of each are given below. 1. Domain complexity: The data formats, contents, tasks and visualisation

DomainEcomplexity DataEcomplexity TaskEcomplexity MentalEeffort Response time Cognitive cost DemographicEcomplexity VisualEcomplexity Response

accuracy Cognitive_gain TimeEcomplexity C O G N I T I V EE L O A D Between-factorsEcomplexity CausalEfactors AssessmentEfactors

Figure 2.9: The construct of cognitive load for visualisation understanding.

Adopted fromHuang et al.(2009).

and used. Kashihara, Oppermann, Rashev, Simm et al. (2000) found that, although exploring concepts of an unknown domain might be an effective way of learning, it can also result in cognitive overload. Huang et al. (2009) state that this factor serves as the basis of the overall com- plexity as it is responsible for the specific visualisation constraints. For example, the visualisation requirements for sociologists and biologists will differ as the information required by each is unique to their domain. In a PAM environment, data related to certain asset performance and maintenance metrics might be difficult for the other departments (such as the legal and accounting departments) to interpret. On the other hand, these metrics should be common in the engineering department. 2. Data complexity: This refers to the characteristics of the data such as,

but not limited to, the number of observations, dimensions present, re- lationships between variables and attributes of the objects. Lin et al. (2006) and Koronios and Lin (2006) summarise the differences in data characteristics between an Engineering Asset Management (EAM) en- vironment and other typical business environments. These differences are shown in Table 2.3. Aggarwal (2013) pointed out that advances in hardware technology have enabled the collection of data by using Global Positioning Systems (GPS) and other sensory equipment. These new data types which are being recorded require novel interpretation meth- ods. The large diversity of data types in the AM environment means that the cognitive load associated with the presentation of data is normally higher than that of other typical business environments. This makes the task of the choice of presentation all that more important in AM.

Table 2.3: Comparison of data characteristics between AM and other busi- ness environments.

Typical Business

Environment EAM Environment

Self-descriptive Non self-descriptive

Static Dynamic

Intrinsic quality Intrinsic/extrinsic quality Discrete value with fewer or

no constraints Continuous value with constraints (e.g.within a range), precision value

Current Temporal

Transactional data Time-series streaming data Often structured Often unstructured

Easy to audit Difficult to be audited Can be cleansed using

existing tools Difficult to be cleansed using existingtools Similar data types Diversity of data types

Adapted fromKoronios and Lin(2006).

3. Task complexity: Huang et al.(2009) reason that tasks have certain de- mands that impose a load on the person(s) executing the task. These demands include the number of objects and how the objects interact. Kalyuga (2011) described the task complexity as a measure of the load that has to be processed by the working memory as a result of the inter- connectedness of the information related to the essential elements in a task. Task complexity should not be confused with task difficulty. Task complexity refers to an objective measure which is independent of the subject completing the task. Difficulty refers to a viewer’s perception of the task based on other factors in addition to task complexity.

Tuovinen and Sweller(1999) explained that learning the order of words is an example of this. Word order can only be learned when all of the words are considered simultaneously. In AM, the same could be said about de- ciding between projects. The task complexity of deciding between three projects will be higher than deciding between two projects regardless of the person(s) responsible for the decision or the presentation of the information of each alternative.

4. Visual complexity: Visual elements as well as their spatial distributions influence visual complexity. The visual representations of the elements, how well the spatial relationships and intrinsic structural links match,

the extent of conformity between spatial relationships and human visual perception conventions and the required cognitive processes required by the task all determine the visual complexity (Huang et al.,2009). Conse- quently, visualisations that contain fewer elements or that are based on general aesthetics are not necessarily of lower visual complexity. How- ever,Heylighen(1999) andHarper, Michailidou and Stevens(2009) men- tion that the selection of objects, colours, patterns, range of objects and surface styles all contribute to the overall cognitive load.

Bacim, Ragan, Scerbo, Polys, Setareh and Jones (2013) investigated the relationship among several factors such as visual complexity, task scope, display fidelity and the spatial ability of the user. The study found that visual complexity and task scope had a direct impact on user response speeds with higher levels of either factor resulting in slower user performance. Three different complexity levels were used in the study and they are illustrated in Figure 2.10.

Low Complexity Medium Complexity

High Complexity

Figure 2.10: Examples of the three levels of visual complexity.

In a PAM environment, the choice of visualisation used to convey infor- mation could have a significant impact on the visual complexity. Even if the right choice is made with respect to the type of visualisation to be used, colours and patterns used to distinguish between different data sets or elements could increase the visual complexity. As employees in PAM environments are generally exposed to large amounts of data which they have to process in short amounts of times, illustrations which are too visually complex might be disregarded.

5. Demographic complexity: Demographic complexity refers to the char- acteristics of the individual interpreting the information and includes a wide variety of factors such as motivation, age, gender, cognitive capa- bilities, knowledge of the domain as well as mental status. These factors contribute to the cognitive load experienced by the subject when com- pleting a task. For example, a user with sufficient domain knowledge will require less effort to understand social networks. Recall that task complexity was described as an objective measure of the size of the load placed on the user and that the difficulty of the task is dependent on the user. This perceived difficulty of the task, as experienced by the user, is determined by the user’s demographic or personal characteristics. Engineers with years of maintenance experience and a clear understand- ing of the inner workings of the specific AM system will interpret con- dition monitoring and failure statistics with relative ease compared to a young accountant with no experience or natural aptitude for the infor- mation.

Moreover, as Oblinger (2003) notes, younger professionals do not con- sider computers and other pieces of electronic hardware as new technol- ogy as they are accustomed to computers. Consequently, the cognitive load associated with using advanced electronic condition monitoring sys- tems might not be as large for younger employees as for older employees who have always used the same traditional systems.

6. Time complexity: The amount of time required to understand visual- isations depends on the situation. Time complexity is also influenced by the user’s perception of the time available. A study by Kerick and Allender(2004) investigated the effect that various task demands had on the shooting performances of soldiers. The study concluded that time stress had the most significant effect on the soldiers’ perceptions of the workload and subsequent shooting performances. If the time that is available for the interpretation of information is short, lengthy reports (even if they are insightful) will add to the unnecessary cognitive load experienced by the user.

Huang et al. (2009) underline that cognitive load is not only impacted by the factors individually, but also by the interaction between the factors.

The overall cognitive load affects performance in three ways, namely: men- tal effort, response accuracy and response time (see Figure 2.9). Mental effort and response time are considered to be cognitive costs. Generally, cognitive costs should be as low as possible. Response accuracy, on the other hand, is considered as the cognitive gain and a higher amount is better. As with the causal factors, there is also interaction between the assessment factors. An example of this is when more time is used (higher cognitive cost) to achieve better performance (higher cognitive gain). The only way to improve perfor- mance in a set task while not increasing the cognitive costs is by exerting more mental effort.

Huang et al. (2009) proposed another model which aims to explain the interaction between cognitive load and the factors used to assess it under the limits of memory. Figure 2.11 shows the theoretical model of task perfor- mance, mental effort and cognitive load. The model shows that performance is dependent on both the memory that is being used and the memory that is demanded by the task. There are five regions of varying cognitive loads in the model and each of the regions are subsequently discussed.

Maximum memory capacity

Effort (memory in use) Increasing cognitive load (memory demand of task)

E1 A1 A2 B E2 Performance Ef fo rt b y u ser ( m em o ry in u se) P er fo rm an ce

Figure 2.11: Mental effort, task performance and cognitive load.

1. In Region E1, boredom and disregard affect performance negatively as there is a consistently low cognitive load. The increased memory that is being used is as a result of the viewer attempting to remain focused. In PAM, this can happen when irrelevant or self-evident information is being emphasised in data reporting. Managers and other employees alike lose interest in the information being conveyed as they do not find it intellectually stimulating.

2. In Region A1, there is an increased demand on memory, but the demand is met by the viewer without devoting any extra effort. The resulting performance remains at an optimum level and the memory used is well within the viewer’s memory capacity.

3. In Region A2, the performance remains at an optimum high, but it can only be maintained if the viewer exerts more effort than in Region A1 (though still well within the viewer’s memory capacity). If the ef- fort which is required by the viewer’s memory approaches the maximum memory capacity, a shift from Region A2 to Region B occurs. All re- porting in a PAM environment should aim to take place in regions A1 or A2 as this is where optimal performance takes place.

4. In Region B, the memory effort needed by the viewer approaches the viewer’s maximum capacity which leads to a decrease in task perfor- mance. Any increase in effort in this region severely decreases perfor- mance. Once the effort needed reaches the maximum capacity, Region E2 is entered.

5. In Region E2, the demands placed on the viewer exceeds the capabilities of the viewer’s memory to such an extent that performance becomes insignificantly small. If the overall cognitive load of the message exceeds capabilities of user, no information can be transferred.

It should be noted that the maximum memory capacity is dependent on the individual performing the task. Figure 2.11 draws attention to the fact that a low cognitive load does not necessarily lead to better performance. As a result of little cognitive stimulation, a viewer might become bored and thus disengaged. On the other hand, if complex information produces a low cog- nitive load it implies that a visualisation is not being understood effectively. As the cognitive load induced is made up of different factors, it is difficult to isolate and measure the effect of each individual factor (and the combined effect). Huang et al.(2009) state that any cognitive load that is not related to the task that needs to be accomplished (such as the cognitive load that results