Critically evaluate the extent to which expertise influences attention.

 

Critically evaluate the extent to which expertise influences attention.

 

Attention is a complex cognitive function that is essential for humans (Niu et al., 2021). It is a process that takes place in the brain when taking notice of someone or something (Lodge & Harrison, 2019). It is the first step in remembering a stimuli or environment (DeBettencourt et al., 2021). If an individual fails to pay adequate attention, they can fail to remember what they experience and encounter (DeBettencourt et al., 2021). Attention allows humans to choose information that is relevant to a specific task and filter out irrelevant information (Lodge & Harrison, 2019). This is done by selecting a certain event or object to concentrate on and remain focused on it (Angelopoulou & Drigas, 2021). Visual attention is a cognitive process used to help select important visual information from the environment which is then used to communicate this information to the brain (Orquin et al., 2021). It is the process used to limit the incoming stimuli from complex everyday encounters to decide what needs to be focused on and what can be inhibited to prevent overload of information (Orquin et al., 2021). Visual attention can influence the choices that are made and is an important component in decision processes (Orquin et al., 2021). This assignment will critically evaluate the extent to which expertise influences attention and look at how attention is measured. It will examine evidence on how experts and novices use top-down and bottom-up processes, the role of deliberate practice and memory, and ways in which attention is tracked. It will also examine evidence of how experts in athletics and radiography focus their attention.

Attention and memory are mainly used together (Turkileri et al., 2021). Exposure to stimuli or information can enhance memory (Turkileri et al., 2021). Information can be stored in the working memory but storing it in long term memory can enable complex tasks to be performed automatically without using a lot of effort, for example driving a car (Foerster et al., 2014). Working memory refers to the area of the brain which temporarily stores and manipulates information that is needed for complex cognitive tasks such as learning, reasoning, and language comprehension (Angelopoulou & Drigas, 2021). Working memory gives the individuals the ability to maintain and manipulate information which can then be used to complete tasks. Working memory is presented as a control system with limited storage and processing capabilities (Angelopoulou & Drigas, 2021). Typically, adults have a working memory capacity allowing them to hold a small amount of information and abstract ideas of roughly three or four objects/items (Angelopoulou & Drigas, 2021).

Visual attention plays a critical role in encoding information to visual working memory (Sasin et al., 2021). Visual working memory stores visual information for a short period of time which can also be stored in visual long-term memory (Nikolić & Singer, 2007). Visual long-term memory depends on the type of attention that is engaged, and the amount of attention engaged (Lodge & Harrison, 2019). A stimulus will more likely be remembered in the long-term memory if the task is repeated or processed deeply and the information is emotional or personal (Sasin et al., 2021). Although it is unclear exactly how much can be stored in the long-term memory, it has been established that it can hold a lot of information but could lack specific details (Brady et al., 2008). Data shows that long-term memory can form in many paths, but purposeful attention is the best way for information to be stored in the long-term memory (Sasin et al., 2021). Long term memory attention, however, it is unclear how it interacts with emotional arousal that guides attention, for example being an expert in a certain domain (Drigas & Mitsea, 2020). It is crucial to learn metacognitive procedures like monitoring, adaptation, and regulation to gain and improve high performance and functioning (Drigas & Mitsea, 2020). For an individual to be able to stay focused and alert for extended periods of time on a specific stimulus, they will have complex control procedures that have been learnt through metacognitive procedures (Drigas & Mitsea, 2020).

High levels of performance must be reached to become an expert (Ericsson, 2006). An expert is defined as someone who has a high level of knowledge or is skillful in a particular area, performing at a high standard (Bourne et al., 2014; Ericsson, 2006). Expertise rarely occurs naturally. It requires long periods of deliberate practice specially designed, consisting of highly structured activities with the explicit goal of gradually improving performance in a certain domain (Hambrick et al., 2020; Ericsson, 2021). Repetition of tasks and feedback are additionally needed so learners can correct errors along the way (Hambrick et al., 2020). As individuals gain expertise, they are able to selectively concentrate their attention on certain parts of information that is needed to improve their performance and ignore any irrelevant information (Niu et al., 2021). Many hours of deliberate practice are required to be spent on structured activities to gain expertise (Ericsson, 2021). For an individual to stay focused and alert for extended periods of time on specific stimuli, they will successfully gained expertise and have complex control procedures (Drigas & Mitsea, 2020).

The shape and breadth of attention is malleable and can change with experience (Hüttermann et al., 2014). Inexperienced or novice performers have shown to be less attentive than experts in attention tasks (Hüttermann et al., 2014). Studies have shown this can be down to experts having superior selective perceptual processing, allowing them to selectively process the relevant task and ignore irrelevant task information (Salmerón et al., 2020). Experts tend to focus their attention on the relevant tasks for longer and more frequently than novices (Salmerón et al., 2020). It is expected that professionals can also maintain attention greater than beginners (Hüttermann et al., 2014). For example, a soccer player must watch both the ball and defender to successfully play, therefore being able to use their attention broadly would be an advantage (Hüttermann et al., 2014). In terms of sport, Memmert et al. (2020) found that expert and novice athletes differ in their domain-specific and sport-specific abilities to perform specific tasks. There is no difference in their general visual abilities or basic attention when performing basic activities. However, athletes performed better on tasks of attention, working-memory-control, and working-memory-capacity as their level of expertise increases as these are key for a successful performance (Vaughan & Laborde, 2021).

Levels of attention are often measured using eye tracking as it is a reliable and non-intrusive method of investigating human visual behaviour (Kelberer et al., 2018). Monitoring what humans are focusing on provides insightful data and is a useful approach for examining problem solving processing (Joseph & Murugesh, 2020). A person’s eye gaze can indicate the thoughts in their cognitive processes and help to understand the areas they mostly pay attention too (Kelberer et al., 2018). Visual and gaze activity is measured using an eye tracker to monitor the direction of an individual’s gaze, movements of their eyes, blink frequency, fixations, and changes in the pupils. These gaze movements can provide insight of how humans process thought, their intentions and their cognitive processes (Joseph & Murugesh, 2020).

Eye movement research has revealed that cognitive activities can influence eye movements showing that cognitive processing affects visual attention allocation (Salmerón et al., 2020). A study by Hicken and Duke (2023) observed the gaze behaviour of flautists who had different levels of expertise. Each participant was shown nine brief video recordings of a flute, clarinet, and saxophone players; a juggler; a baseball batter; and a ballet dancer. Analysis of their gaze behaviour showed that the two most expert participants had longer fixation and higher prioritisation when observing the flute playing videos than the other videos (Hicken & Duke, 2023). This allocation of attention was not observed in the more novice participants. This is important as the findings align with the idea that experts allocate attention differently to novices in all domains of expertise. It demonstrates recording eye movements as a way to understand thinking, decision making, and attention (Hicken & Duke, 2023). Emhardt et al. (2020) investigated gaze behaviour, trying to understand how experts in professional programming adjusted their nonverbal behaviours in comparison to novices which were university students. This was measured by eye and mouse movements during a problem-solving task. They found that experts had shorter fixations than novices in the code area, indicating that they are faster, more efficient and put in less effort when processing information (Emhardt et al., 2020). Experts also used fewer transactions between the codes, which indicates they have a better working memory capacity because of the chunking mechanisms they use (Emhardt et al., 2020).

  Studies of eye movement analysis show that experts appear to adhere to top-down, goal-directed processing. Visual information enables experts to be quicker at perceiving new information and fixate on the required targets to complete a goal (Hicken & Duke, 2023). In contrast the gaze patterns of non-experts rely more on bottom-up, stimulus driven processing (Lockhofen & Mulert, 2021). Top-down attention refers to the voluntary guidance of attention towards attaining internal goals driven by cognition (Hobson et al., 2018). This is mainly used by experts as it is associated with broader narratives and interpretations (Hobson et al., 2018). Down-up attention is the involuntary capture of attention of the environment or by a stimulus (Hobson et al., 2018). They come together to modulate neural activity which then aids an individual’s perspectives of the environment (Lockhofen & Mulert, 2021).

Some techniques used by experts can lead to the development of probabilistic bias. If they selectively concentrate on specific information, they could accidentally miss relevant information (Niu et al., 2021). Experts focus their attention on a specific area and do not perceive changes (Niu et al., 2021). Radiography is a profession that requires a high-level of skill and critical thinking in the examination of medical images (Hardy, & Harvey, 2020). Radiologists learn through viewing thousands of medical images with clinically relevant features allowing them to reduce medical errors and optimize their performance in radiology (Alexander et al., 2020). Expert radiologists have been shown to identify lesions on lung x-rays more quickly and have fewer fixations than beginners (Hicken & Duke, 2023). However, research has found they can sometimes fixate only on locations that are likely to be informative, missing out vital information (Hicken & Duke, 2023). Carrigan et al. (2019) conducted a study on attentional bias in radiologists, who were asked to detect lung nodes through searching and interpreting images. In training, radiologists learn to conduct searches of chest radiographs following a sequential order (Carrigan et al, 2019). As they gain expertise radiologists develop higher sensitivity and can recognize potential masses more quickly and with greater accuracy than non-experts (Carrigan et al, 2019). This expertise can lead to the development of probabilistic bias, leading them to focus their attention on the upper right quadrant of the image as where lung nodules are more commonly located (Carrigan et al, 2019).

There has been a cause of concern regarding the role of artificial intelligence (AI) in relation to expertise (Agarwal et al., 2023). Growing literature suggests that they may replace expert practices like radiography (Hardy, & Harvey, 2020). AI is the development of computers that can perform tasks that require human intelligence, for example visual perception, decision-making, and prediction (Hardy, & Harvey, 2020). Studies suggest that AI can outperform humans on expert tasks (Agarwal et al., 2023). AI can complete computer vision tasks such as those used in medical imaging and diagnostic imaging used in radiography (Hardy, & Harvey, 2020). Increased digitization and automation can have a positive outcome through efficiency in processing the images which could reduce the potential for probabilistic attention bias (Hardy, & Harvey, 2020). This could leave radiographers with fewer jobs but allow them to use their expertise to confirm decisions developed by AI and cut waiting time for patients (Agarwal et al., 2023). However, some research argues that human radiologists should take advantage of this and use AI for assistance as it could substantially improve performance when working together (Agarwal et al., 2023).

Experts have superior selective perceptual processing, allowing them to selectively process relevant information and ignore irrelevant information. They can focus their attention on the relevant tasks for longer and more frequently than novices. Levels of attention can be measured using eye tracking, it is a reliable method for investigating visual behaviours. Cognitive processing affects visual attention allocation allowing eye movement methods to be used to examine cognitive activities. Experts have shorter fixations enabling them to be faster and more efficient. They also use top-down, goal-directed processing allowing less effort and better use their working memory capacity effectively. This allows them to perceive information quickly, however, expertise may lead to tasks being performed more quickly leading to probabilistic bias as limited attention can lead to relevant information being overlooked. Research suggests that AI may replace expert practices tasks that require human intelligence such as radiology. However, this can be beneficial for cutting down patient waiting time and having a lower workload for radiologists.

References

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