Several studies have recently investigated the effects of increased perceptual load on driving performance measures in driving simulators (e.g. Redondo & Lee, 2009; Marciano & Yeshurun, 2012; 2015). For example, Marciano and Yeshurun (2015) manipulated perceptual load for subjects in control of a driving simulator, on the road itself by varying the density of vehicles in the immediate vicinity of the ego-car (i.e. the subject's car) and the density of pedestrians on the side of the roadway (see Figure 1-8).
35 Several measures of driving performance were collected during simulated
driving under each of the four conditions, including average driving speed and reaction time to respond to pre-planned critical peripheral and central events such as a pedestrian suddenly crossing the road or the leading vehicle braking in front of the ego-car. Results showed that when central perceptual load was low, subjects' average speed increased, indicating an assumption that they could maintain safe driving at higher speeds. However, this was also
accompanied by an unintuitive finding that drivers were less likely to respond to critical peripheral events in good time, seemingly due to the increased speed, perhaps highlighting a general shortcoming in the validity of simulator studies, where safe driving is not actually critical to the driver. The effect of central load on response to central critical events, and the effect of peripheral load on both types of critical event, was more normative however, with subjects being more likely to respond correctly under conditions of low perceptual load.
Figure 1-8. Screenshots of driving simulator in the experimental conditions employed by Marciano and Yeshurun (2015). Central load relates to the number of vehicles occupying the road surrounding the ego-car, while peripheral load was manipulated with the number of nearby pedestrians
36 In another recent simulator study, Murphy and Greene (2016) investigated the effects of perceptual load on drivers' awareness for a pedestrian or large animal situated at the side of the road. Perceptual load was modulated by a task
requiring the participant to indicate whether their vehicle would fit between two rows of vehicles lined up at the side of the road. In the low load condition, the space between the vehicles was obviously too large or too small for the ego-car to pass through, while in the high load condition the gap was only slightly too narrow or wide.
On a random low and high load section of driving the pedestrian or large animal was presented to the side of the road; immediately after the participant had completed the critical trial (i.e. by driving through or around the two rows of parked vehicles) a prompt asked the participant whether they had noticed anything unusual in that section of driving. In line with previous laboratory manipulations of perceptual load, subjects' awareness for task-irrelevant stimuli was reduced when the perceptual load of the task-at-hand was high.
While these simulator studies mark an important research venture into the application of perceptual load theory to real world situations, the current state is Figure 1-9. Example of low perceptual load in Murphy and Greene's (2016) simulator experiment. This represents a critical trial, as a pedestrian is placed at the side of the road.
37 analogous to that in traditional laboratory research. Perceptual load has hitherto been modulated a priori by the experimenters through recourse to the original operational definitions of perceptual load, and the field is lacking a measure of the perceptual load of a driving situation without invoking the effects of
perceptual load. Furthermore, while studies involving simulators claim a level of ecological validity, the visual quality of the simulation is often deprived – with highly planar, simplistic graphics (see above). In Chapter 5, I therefore attempt to address both of these shortcomings, producing a model which operates on real-world driving footage to estimate the perceptual load induced by the driving situation.
In adopting a data-driven model-fitting approach to this problem we are
necessarily faced with a challenge with regard to obtaining data which captures visual features of driving along with an associated, assumed ground-truth measurement of perceptual load. In modelling a simple task such as the response-competition paradigm, for example, the properties of the task itself can be manipulated precisely and numerically (e.g. increasing the number of search distractors), and a direct objective dependent variable can be obtained (i.e. the reaction time interference effect of congruent vs. incongruent flanker stimuli), thus enabling the construction of a mapping between task features and the dependent variable (as in Roper et al., 2013). The greater inherent
complexity and variability present in real-world driving however, precludes us from implementing such precisely defined conditions and manipulations. It is therefore apt to employ a semi-observational study design, in which natural driving footage is collected as experimental stimuli along general heuristics rather than within a tight factorial design, and associate with each driving situation a value of perceptual load in the driving task. Using this footage, labelled with perceptual load values, a regression model can be fit between fundamental spatio-temporal features of the dynamic scene and perceptual
38 load, resulting in a system able to estimate the level of perceptual load induced by a given driving scenario.
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2 Methodological background
This chapter outlines and gives historical context to the methods used
throughout the rest of the thesis. Chapters 3 and 4 employ functional magnetic resonance imaging (fMRI) techniques to investigate the effect of perceptual load on fundamental feature representation in visual cortex. A brief summary of basic fMRI technologies through to the modern data analysis approaches used in this work is therefore covered in the next section. Following this, the methods which underpin the modelling work of Chapter 5 are described, such as the extraction of semantics from natural imagery, nonlinear kernel regression, and the method of pairwise comparisons.