Explaining eye movement differences: A role for attentional load

A second plausible reason for differences in eye movement behaviour is the idea that the

attentional demands of driving limits the efficient distribution of eye movements. Before this

idea is explained in detail, it is necessary at this point to explain several concepts related to

‘attention’. Given its nature, the term attention is often difficult to fully explain. Yet, broadly speaking, attention can be defined as the selective processing of information (Pashler, 1998;

Pashler & Sutherland, 1998; Posner, 1994). Whereas visual attention has been described in the

context of this thesis as the processing of information entering the eyes, attention does not have

to be this specific and can refer to information processing from any source. What is important

in this section is specifically the idea that this attentional processing has limits and this in turn

may influence eye movements.

Cognitive load is the term often used to infer the attentional demands of a task (Wickens,

2002, 2008a), i.e. the mental difficulty of a task. It has been suggested that attentional

processing during a task is largely affected by the level of cognitive load (Tomasi, Chang,

Caparelli, & Ernst, 2007; Wickens & Hollands, 2000) where a higher cognitive load may limit

the speed at which items are processed or limit the amount of information able to be processed.

This idea is explained in a ‘multiple attentional resources’ type concept (e.g. Lavie, 2010;

Wickens, 2002). In any given task, there is a finite capacity of attentional resources that can be

allocated. Thus, if a task is cognitively demanding (high cognitive load), more attentional

resources are required, which in-turn may limit the amount of resources that can be given to a

secondary task (Lavie, 2010; Lavie, Hirst, De Fockert, & Viding, 2004).

These concepts relate back to the observed differences in eye movements in novice and

experienced drivers. A novice driver may experience driving as more cognitively demanding

as more attention may be required for vehicle control (e.g. dual task demands of steering and

changing gears). Fewer resources are then available to move the eyes around and the scene and

28 Furthermore, we know that through practice and experience, task performance improves when

actions become more automated. (Ackerman, 1988; Moors & De Houwer, 2006). By

automatic, what is meant is idea that a process may be unconscious, fast, and importantly,

requires few attentional resources (Moors & De Houwer, 2006). With driving, it may be the

case that through experience, controlling the vehicle also becomes more automatic and this

frees up resources to search to other parts of the scene. These ideas may help to explain some

of the differences we see across novice and experienced drivers.

Regarding this idea of cognitive load influencing eye movements, research has consistently

shown how increased cognitive load influences visual scanning. Recarte and Nunes (2000) and

Recarte and Nunes (2003) demonstrated that when drivers had to perform several mental tasks

while driving (e.g. simultaneous auditory, verbal or object detection type tasks), horizontal

scanning behaviour was reduced compared to when driving as a standalone task. Similarly,

Engström, Johansson, and Östlund (2005) also found increased gaze concentration towards the

centre of the scene when a higher cognitive load was induced, both during real and simulated

driving. In addition, Savage, Potter, and Tatler (2013) found increasing cognitive load (using a

simultaneous riddle solving task) reduced horizontal scanning on video-based hazard

perception tasks. Some research has suggested that increasing cognitive load reduces mirror

inspection also. Harbluk, Noy, Trbovich, and Eizenman (2007) found that when performing

complex mathematical problems whilst driving, the time spent inspecting the vehicle mirrors

was less compared to when completing simple mathematical problems and driving. These

results suggest that cognitive load may be a source for individual differences in drivers' eye

movements, and possibly the differences between novices and experienced drivers’ eye

movements. Importantly, this reduction in the distribution of eye movements due to cognitive

load has been found to correlate with poorer hazard detection (e.g. Lee, Lee, & Boyle, 2007;

29 These findings prompt an interesting question. If high cognitive load limits effective eye

movement behaviour when driving, does this mean that those who have better attentional

function exhibit more efficient eye movement behaviour? In other words, do those who can

better handle the cognitive demands of the driving task, also look around the scene more? It is

an interesting question that has not been tackled as of yet. The experiment described in Chapter

4 investigates this possibility.

Before continuing, it is important to briefly define the term ‘attentional function’ (Mackie,

Van Dam, & Fan, 2013). It is used typically to broadly describe an individual’s cognitive

control ability i.e. an ability to perform a number of attention tasks. It incorporates not only

executive function abilities (e.g. the ability to resolve cognitive conflict (Bush, Luu, & Posner,

2000)) but also aspects of attention alerting and attention orienting. Respectively, these

describe one’s level of attentional vigilance to impending stimuli and ability to select necessary information from various sensory inputs (Fan et al., 2009; Mackie et al., 2013; Posner & Fan,

2008). Perhaps the best source of research to help answer the question of how attentional

function ability relates to eye movement behaviour comes from the findings that better

attentional function is related to better driver performance.

One related example of this is the Useful Field of View (UFOV) test (Ball, Roenker, & Bruni,

1990). This test measures one’s ability to attend and process rapidly presented information. In

general, it measures how much relevant information one can attend to without moving the eyes

whilst ignoring distractor stimuli. It thus targets aspects of object identification, divided

attention and selective attention (attending to briefly presented targets) (Clay et al., 2005). Ball,

Owsley, and Beard (1990) found that those with poorer attentional ability, as measured by the

UFOV, also report more problems with driving. Ball, Owsley, Sloane, Roenker, and Bruni

(1993) found that poorer performance in the UFOV task correlates with more reported road

30 in the UFOV task is associated with poorer driving performance – suggesting a direct link

between attentional function and driving ability.

Since the UFOV was developed, there have been other successful attempts to demonstrate the

relationship between attentional function and driving performance, many of which use

variations of the visual attention tasks used in the original UFOV assessment (e.g. Aksan,

Anderson, Dawson, Uc, & Rizzo, 2015; Anstey, Horswill, Wood, & Hatherly, 2012; Casutt,

Martin, Keller, & Jäncke, 2014; Keay et al., 2009; Schuhfried, 2005). One recent assessment

test used is the Attention Network Test (ANT) (Fan, McCandliss, Sommer, Raz, & Posner,

2002). The ANT assessment tool is closely based on a known neurocognitive model of human

attention which separately assesses the three components of attentional functioning mentioned

above: executive control, attentional orienting and alerting networks. The executive control

networks involve mechanisms to deal with cognitive conflict and ignoring irrelevant stimuli.

The attentional orienting mechanisms are involved in selecting and guiding attention to

potentially relevant areas of the scene. And the alerting networks are sensitive to changes in

incoming stimuli, over both short and long periods of time (see Fan et al., 2002; Petersen &

Posner, 2012; Posner, 2008). This is important as these attentional components are likely

involved in successful driving. For example, one must be able to successfully attend to relevant

hazardous areas whilst ignoring other stimuli (executive control), orient attention to potential

hazardous cues (attentional orienting) and increase readiness to respond and sustain attention

to the driving environment (alerting network). Importantly it has been found that better

attentional function, as measured by the ANT test predicts better driving performance (Roca,

Crundall, Moreno-Rios, Castro, & Lupianez, 2013; Weaver, Bédard, McAuliffe, & Parkkari,

2009).

Therefore, since we know that better attentional function relates to better driving behaviour,

the next step is to determine if this holds true for eye movement behaviour when driving. Does

31 background, an experiment designed to answer this question is described in Chapter 4 of this

thesis.