Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No: 27930 Page 1 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Abstract Young drivers have been studied by both cognitive and social psychology (e.g. Underwood, Crundall, Bowden, & Chapman, 2002; Ulleberg & Rundmo, 2003). The current study aimed to integrate these areas by measuring risk attitudes, and examining whether they influence hazard search in young drivers. A driving risk-­‐attitude questionnaire was administered online to potential subjects. Eligible participants (18-­‐25 years old with a full driving license) then completed an eye-­‐tracking experiment (n = 27), watching video clips of roads under the instruction to search for hazards. Eye movements were recorded and correlated with the questionnaire. Components of the riskiness questionnaire were negatively correlated with eye movements, so that drivers who were more risk-­‐inclined made less saccades and fixations than more cautious drivers. Future research should attempt to refine risk attitude measures and relate them to hazard search. Page 2 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Acknowledgements I am deeply grateful for the help of Dr. Graham Hole, who assisted in developing the project prior to and during conduction of the study. Interest in the psychological research of driving led me to background reading on both cognitive and social aspects of driving research. I devised the initial idea to combine the two areas by myself, at which point Dr. Graham Hole helped with planning and design issues. Dr. Graham Hole also suggested possible items for the risk attitude questionnaire as well as providing feedback for items that had been translated or reworded. He also provided the video clips used in the eye-­‐tracking experiment. My thanks also go to Dr. Sam Hutton, who helped in aspects of planning, design and execution of the eye-­‐tracking experiment. These included helping import and structure the videos within the Eyelink programme as a complete experiment, and producing eye movement data from Eyelink to use for analysis. I conducted the experiment and analysed the data independently, with some input from both Dr. Graham Hole and Dr. Sam Hutton. Page 3 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 1. Introduction The involvement of adolescent drivers in car accidents is disproportionate to the percentage of the driving population that they comprise. In 2009, UK drivers aged 20 and under were more likely to cause a fatal accident than be innocently involved (Clarke, Ward, Bartle & Truman, 2010). Furthermore, 26% of personal injury road accidents involved at least one driver aged 17-­‐24, despite the fact that the age group only comprises 12% of license holders (Department for Transport, 2009). Clearly, young drivers are over-­‐represented in the accident statistics. This has incentivized both government and research institutions to investigate the factors that put young people on the road at a higher risk of being involved in a serious accident. Psychologists studying the processes that put young drivers at risk often come from two different research traditions. Cognitive psychologists examine perception and attention in relation to the driving task, for example differences between experienced and inexperienced (i.e. adolescent) drivers; experienced drivers largely display superior hazard scanning compared to inexperienced drivers (e.g. Crundall & Underwood, 1998). Crundall, Underwood, Bowden and Chapman (2002) suggest that this is a result of inexperienced drivers having insufficient mental models. Whilst viewing videos of roads they were unfamiliar with (e.g. dual carriageways), inexperienced drivers showed less extensive searching compared to experienced drivers. Vehicle control or road markings did not need to be attended to in the study and so Underwood, Crundall, Bowden and Chapman (2002) argued that because inexperienced drivers still performed more poorly than the experienced drivers, the effect could be attributed to the formers’ insufficient mental model. Research in this area demonstrates that young and inexperienced drivers view the road differently to more experienced drivers. Social psychologists address the higher accident rate found in young drivers through psychometric means, measuring attitudes and personality traits and examining their relationship to risky driving behaviour and accident involvement. Positive attitudes towards traffic safety laws have been shown to negatively predict causal involvement in an accident (Lajunen, Parker & Stradling, 1998; West & Hall, 1997). This suggests that drivers’ attitudes, through their influence on driving behaviour, may predict potential accident. Young drivers in particular underestimate potential risks whilst at the same time overestimating their own ability (Brown & Groeger, 1988), a possible factor as to why adolescent drivers are over-­‐represented in accident statistics. Both areas of psychology suggest reasons why young and inexperienced drivers may be particularly at risk on the road. As of yet, there have been no attempts to cross these two disciplines, despite Page 4 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 their shared aims to explain accident risk in adolescent drivers. The current study attempts to address this by implementing a risk-­‐attitude questionnaire common to social psychological studies of young drivers, and exploring for a relationship between riskiness and hazard search. Just as there is a difference between experienced and inexperienced drivers, is there a difference in the hazard perception abilities of relatively risky drivers and cautious drivers? 1.1 Cognitive Factors in Driving A large body of driving research is guided by the notion of “schemas” or mental “scripts”: cognitive frameworks that allow a generalization of knowledge from one specific instance of an event or activity to other instances (e.g. Schank & Abelson, 1977). In a driving context, this allows related stimuli to inform the behaviour of those on the road; for example, seeing a “Children crossing” sign allows an individual to infer they are in a school area, and should limit their speed. The current goals of an individual can also determine what schemas are activated; a driver travelling along a rural road could act from a specific “rural driving” schema if they are particularly concerned with avoiding an accident (Groeger, 2000). The continued activation of an appropriate schema can aid performance of a task; by having past representations of the task and comparing them with current performance, schemas can create a feedback system (Groeger, 2000). For example, a driver observes another road user indicating that they will cross the driver’s path, which can be compared to previous experience. If the road user travels out of the expected path, this discrepancy may inform the driver that a potential hazard may occur. Visual information is of critical importance to the driving task (Sivak, 1996); in scenarios where cognitive demand is high the driver may not have the sufficient resources available to process all visible objects. Not all objects may be task-­‐relevant, and so drivers must be able to make decisions about which objects to attend to (Underwood, 2007). Schemas can aid such decisions if they contain knowledge of a previous encounter sufficiently similar to the current one; salient objects will be recognised and attended to. In this way, appropriate schemas may be advantageous to drivers by facilitating their interpretation of the visual scene. Studies measuring eye movement have found differences between experienced and inexperienced drivers, theorized to be due to incomplete schemas or “mental models”. In a study by Crundall, Underwood and Chapman (2002), experienced and novice drivers viewed video clips of different driving scenes. Participants were instructed to press a foot pedal if they saw a hazard. When hazards appeared, those sections were classified as “high demand”. A simultaneous secondary task involved pressing a button whenever a peripheral target light appeared in one of four locations on the screen. Page 5 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Experienced drivers demonstrated significantly higher target detection than inexperienced drivers, however in high demand sections there was significantly reduced target detection in both groups. Crundall and colleagues also found that experienced drivers were less affected by the “attentional capture” of a hazard (Crundall, Underwood & Chapman, 2002). They argued that this effect results from the developed schemas of experienced drivers; they are able to process information (i.e. the hazard) more quickly and so can reallocate their attention sooner. “Hazard search” tasks such are a common paradigm in driving research as asking participants to scan for hazards more realistically simulates where drivers will look in real life (Crundall, Underwood & Chapman, 2002), as well as the suggestion that hazard perception ability is a promising predictor of accident involvement (Elander, West & French, 1993; Chapman & Underwood ,1998). As a result, hazard search studies have been popular in the study of schemas and experience. A study by Chapman and Underwood (1998) used a hazard search paradigm to investigate the effects of perceived danger on visual search in experienced and inexperienced drivers. Participants watched videos filmed from the point of the view of the driver, and were instructed to press a response button as soon as they perceived a hazard. “Danger” periods saw a significant increase in fixation durations; however this was only significant for inexperienced drivers. Interestingly, there was no effect of experience on reaction times; both groups perceived hazards nearly simultaneously, but experienced drivers looked away after a briefer time. As in Crundall, Underwood and Chapman (2002), the researchers suggested that the more developed schemas of experienced drivers allowed them to process the hazard quicker and reallocate their attention (Chapman & Underwood, 1998). That novice drivers seem to remain inflexible in their hazard search, just when the road becomes more demanding, suggests that they are unaware of the possible dangers as a result of insufficient schemas (Underwood, 2007). In contrast to theories that ascribe this inflexibility to limited attentional resources (as a result of novices not having automatic vehicle control), Underwood, Crundall, Bowden and Chapman (2002) provided strong evidence for the schema theory of hazard search. They demonstrated that even when passively viewing clips of demanding dual carriageway, novice drivers made significantly less fixations and had a narrower horizontal spread of fixations than experienced drivers. Furthermore, they were poor at predicting what experienced drivers were looking at in the same clips (Underwood, Crundall, Bowden & Chapman, 2002). This quite clearly demonstrates that rather than a lack of attentional resources, inexperienced drivers appear to have insufficient schemas for certain road situations that result in less extensive hazard search. Page 6 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 As detailed above, schemas can act as a feedback loop for drivers, allow for quicker processing and reallocation of attention, and inform drivers when a more extensive hazard search is required. All of these processes appear to develop as a result of experience; however experience is not the sole quality that makes up an individual on the road. A driver who believes driving involves fewer risks, for example, may not have a sufficiently rich schema active to inform them of hazards. This may influence the efficacy of the schema as a feedback loop; a potential hazard goes unnoticed as it is not part of the schema, resulting in no expectation/performance discrepancy acting as a source of feedback. Previous research has indicated that inexperienced drivers demonstrate a level of hazard search that is not sufficient when more complex road situations are presented, so it is likely that a subsection of more risk-­‐inclined novice drivers are at a greater chance of being involved in an accident. 1.2 Social factors in driving Though the current study aims to investigate for a connection between risk attitudes and hazard search, it is worth examining previous research that has looked at social psychological constructs and their relation to driving behaviour and accident involvement. Developing an initial measure of driving-­‐related risk attitudes is important; risk-­‐taking is thought to be a domain specific phenomenon and so an individual who takes risks in other areas of life will not necessarily be a risky driver (Weber, Blais, & Betz, 2002). Negative attitudes towards traffic safety and the inclination to violate traffic rules also appear to be more greatly associated with the risk of accident involvement than simple errors (Parker, West, Stradling & Manstead, 1995), demonstrating that conscious attitudes are a possible predictor of risky driving behaviour. Other studies have attempted to link violations and risky driving behaviours to different attitudes. Ullerberg and Rundmo (2003) examined young drivers in Norway, in which risk-­‐taking attitudes were significantly correlated to self-­‐reported risky driving behaviour (e.g. violations) and accident frequency. Personality also appears to contribute to risk-­‐taking behaviour, mediated through attitudes; aggressive and sensation seeking drivers were more likely to have negative traffic safety attitudes and take more risks in traffic, whereas altruistic drivers were more likely to have positive traffic safety attitudes and take fewer risks (Ulleberg & Rundmo, 2003). This may partially explain the consistent but weak correlations found between personality traits and accident involvement (Elander, West & French, 1993; Ulleberg & Rundmo, 2003). The findings by Ulleberg and Rundmo also suggest that risk attitudes are a more promising and direct predictor of potential accident involvement. Page 7 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 A common problem within social psychological research on driving is that studies often have to link attitudes to self-­‐reported driving behaviour. By using retrospective data about behaviour, researchers cannot convincingly argue that risk-­‐taking attitudes predict risky driving behaviour. It may be that a past behaviour was consistent with the attitude held at the time, but then the attitude changes by the time it is measured (Iversen, 2004), reducing the apparent predictive power of attitudes. A longitudinal study by Iversen (2004) attempted to rectify this by collecting data on traffic safety attitudes, self-­‐reported violations and accident involvement at two points, separated by a 12 month gap. Three of the attitude dimensions collected at Time 1 were significant predictors of future risky driving behaviour at Time 2, accounting for 52% of the variance. In particular, attitudes towards violations and speeding were the most important predictors of future behaviour. Iversen’s study therefore contributes evidence of a predictive link between attitudes and behaviour to the literature. The current study aims to add to the literature concerning risk attitudes and accident involvement by measuring hazard search; by administering a risk attitude measure and then relating it to young drivers’ eye movements, the current study may potentially comment on risk attitudes as predictors of a behaviour that has not been obtained retrospectively (i.e. self reported driving behaviour) and is not a statistically rare occurrence at the individual level (i.e. accident involvement; Hole, 2007). 1.3 Hypotheses The current study aims to contribute in some small part to both social and cognitive research into young drivers’ accident risk. Social psychological studies struggle to infer predictive relationships between risk attitudes and driving behaviour as a result of utilizing retrospective self-­‐report measures. The current study addresses this by measuring risk attitudes before a behaviour (i.e. hazard search). The use of risk attitude as a potential predictor of hazard search may contribute to cognitive research by suggesting an overlooked factor that may influence young drivers’ mental models. If drivers who are more inclined to take risks and violate traffic rules have insufficient schemas , participants who score higher on a risk attitude scale should demonstrate fewer eye movements (saccades) and fixations during a “hazard” window than drivers who score lower on the scale. Therefore this should be the same difference observed by Underwood, Crundall, Bowden and Chapman (2002) between inexperienced and experienced drivers on the basis of experienced drivers having more sophisticated schemas. However, it may be that risk-­‐taking could influence schemas in another direction; by taking more risks, young drivers may come into contact with more hazards and so acquire a more developed schema than their cautious counterparts. This in turn may lead to more eye movements and fixations made by risky drivers than cautious drivers. Therefore the hypothesis Page 8 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 will be left two-­‐tailed to account for both possibilities; there will be a significant correlation between risk attitudes and number of saccades and fixations. 2. Method The experiment comprised of two stages; first, participants completed an online questionnaire to measure their attitude to driving-­‐related risk taking; a sample of these participants, who were in the desired age range (18-­‐25 years old) and who had completed a satisfactory amount of the questionnaire, were tested in the eye-­‐tracking phase of the experiment. Driving-­‐related risk attitude questionnaire 2.1. Scale Construction A forty-­‐five item Likert scale questionnaire was constructed to measure driving-­‐related risk attitudes, with possible responses ranging along a five-­‐point scale from “Strongly Disagree” to “Strongly Agree”. Seventeen of these items were adapted Ulleberg and Rundmo’s 2002 and 2003 studies. As Ulleberg and Rundmo’s questionnaires were conducted in Norwegian and then translated back to English, the items were reworded to improve comprehension. Four of the items from Ulleberg and Rundmo (2002) were also altered from a question to a statement so that they were compatible with the Likert scale. For example, “How often do you exceed the speed limit in built-­‐up areas by more than Page 9 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 5mph” was changed to “It is not risky to exceed the limit by 5mph in built up areas”. Reversed items were also created from the sixteen items to increase reliability, for example “If you are a skilled driver, speeding is an acceptable risk to take” was reversed to “You are taking a risk when speeding, even if you think you are a skilled driver”. For a full overview of the construction of the questionnaire items see Appendix 7. 2.2. Questionnaire Participants Voluntary participants were recruited through email advertisements sent via the University of Sussex Psychology Subjects Pool database and posters on the University of Sussex campus (see Appendix 8). Participants were informed that those eligible (i.e. 25 years old or under, and with a full driving license) for a second study (the eye-­‐tracking phase) would have the possibility to win a prize draw of £25. For the questionnaire study alone however, no incentive was given for participation. Seventy-­‐
seven participants completed the questionnaire with an age range of 18 to 68 years (M = 24.88 years, S.D. = 9.21 years). The sample consisted of mainly students. Fifty-­‐five were female (71.43%). All respondents had a full driving license. Twenty-­‐seven (35.06%) took part in the later eye-­‐tracking phase of the experiment. 2.3. Questionnaire Materials The questionnaire was presented to participants online at www.tinyurl.com/DrivingQuestionnaire (see Appendix 1). The questionnaire was preceded by a brief message detailing the questionnaire as a study measuring risk attitudes, and advising potentially eligible participants about a second study. The message also informed participants of the confidentiality of their data and their ability to have their data withdrawn by emailing the researcher. The complete questionnaire contained questions based around demographic information. These included asking participants their gender and age. Participants were also asked how long they had held a full driving license, with the selectable answers being “Under 1 Year”; “Between 1 and 2 Years”; “Between 2 and 3 Years”; “Between 3 and 5 Years”; and “More than 5 Years”. They were also asked to estimate in months how long they had driven on a regular day to day basis, and to estimate their average weekly mileage. Participants then completed the forty-­‐five item questionnaire; each item was presented in its own row with five possible answers (“Strongly Disagree”, “Disagree”, “Neither Agree or Disagree”, “Agree”, “Strongly Agree”). In each row only one answer was selectable, Page 10 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 which participants could indicate by mouse-­‐clicking a small option box beside each answer. Upon completion of the questionnaire, a message was displayed thanking participants for their time. Participants who did not proceed to the eye-­‐tracking experiment were debriefed via email when data collection for the study had concluded (see Appendix 11). Hazard Scanning Experiment 2.4 Apparatus and Materials Five video clips, each approximately a minute long, were chosen for presentation to participants. The clips were selected from a hazard perception test practice CD (Driving Test Success; Focus Multimedia), and imported into Experiment Builder (SR-­‐Research, Ontario) to produce the complete experiment. The clips were shot from the point of view of the driver, and were chosen on the basis of containing events characteristic of the road they depicted. Each clip acted as a trial, containing one event as the main hazard. The hazards are detailed below. City: The main hazard within this clip is a parked car emerging from a space, cutting across from the driver’s side of the road to the opposite side and causing the driver to brake. Country 1: The main hazard is the driver’s approach to the parked cars, where it can be seen that the car in front has had to stop in order to let other cars past first; the driver reacts to this by braking. Country 2: The main hazard is the presence of the two pedestrians walking on the left hand side of the road which only become visible as the driver rounds a corner: the driver reacts by overtaking to the right. Motorway: The main hazard is a car merging from a slip road into the lane in front, to which the driver responds by drifting right into the next lane. Town: The main hazard is two pedestrians appearing at a zebra crossing who were previously not visible as a result of a parked car. The driver of the car reacts by braking and waiting for the pedestrians to cross. The clips were shown on a computer monitor at a distance of 65cm. The size of the image on the screen was 14 x 19 cm, which subtended 16.29° horizontally and 12.15° vertically. The computer’s Page 11 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 keyboard was used as a dummy response unit by participants. Eye movements were recorded with an Eyelink II eye tracker (SR-­‐Research, Ontario). The experiment was prepared using Experiment Builder (SR-­‐Research, Ontario) to produce a complete trial that would display instructions, the necessary eye-­‐image and calibrating targets, and the video clips themselves. 2.5 Participants Twenty-­‐seven participants who had completed a satisfactory amount of the questionnaire (95% or more of the items completed) responded to a follow-­‐up email asking them to participate in a second driving study (see Appendix 9). Participants were informed in the email that completion of the second study would see them entered into a £25 prize draw. All participants were students from the University of Sussex, ranging in age from 18 to 25 years old (M= 21.07 years, S.D. = 1.54 years). Twenty-­‐two were female. All participants had normal or corrected to normal vision. 2.6 Procedure Upon arrival, participants were given a consent form which also served as the instructions for the experiment (see Appendix 10). The instruction sheet informed participants that they were to watch five video clips and that they should watch and behave as if they were the driver of the car, reacting to any events within the clips that would require the driver’s immediate attention. They were told to indicate any such reactions by pressing the space bar on the computer’s keyboard (a dummy response). Participants had the opportunity to ask any questions before signing the consent form. Once participation was agreed upon, the eye-­‐tracking equipment was put in place and calibrated before the experiment began. The clips were played in a randomized order to participants, and participants could rest between clips. Upon completion of the experiments, participants were given a debriefing sheet (see Appendix 11) and had the opportunity to ask questions. 2.7 Ethical implications An ethical evaluation of the study was submitted to the appropriate committee at the University of Sussex and was approved (see Appendix 5). The study was deemed to be unlikely to induce psychological distress any more than everyday driving which many people encounter. 3. Results Prior to the analysis proper it was decided that the attitude scales should comprise both an “overall” measure of risk, but also be tested as separate measures as in Ulleberg and Rundmo’s (2003) study Page 12 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 as one facet of risk attitude may be more predictive than another. Therefore the forty-­‐five items were combined to create the overall Riskiness scale. The five subscales that were derived from these items were (see Appendix 7 for item groupings): (a) Traffic Rules: Eleven items addressing attitudes to traffic rule violations. (b) Speeding: Fourteen items measuring attitudes towards the acceptability of speeding. (c) Joyriding: Four items addressing driving risk as a means of excitement (d) Peers: Six items on attitudes towards peer influences on risk taking (e) Other : Ten items addressing miscellaneous aspects of risk taking whilst driving, for example going through a red light on an empty road. To determine whether the measures were reliable, Cronbach’s alphas were derived for both the overall Riskiness scale and the five subscales. Both the overall Riskiness measure and the subscales were shown to have moderate to good reliability as measures of attitude as per Kline’s criteria (1999: as cited in Field, 2005) (see Table 1). Only the Joyriding and Peers scales were cause for concern with substantially lower alphas than the other scales (α = .64 and α = .64 respectively). Table 1. Mean scores, standard deviations and reliability measures for the Riskiness attitude scale and the five subscales. Table 1. Mean scores, standard deviations and Cronbach's alphas for the Riskiness attitude scale and subscales Number of Standard Scale Mean Cronbach's alpha (α) items Deviation Riskiness Subscale 45 108.59 19.99 .913 Traffic Rules 11 31.63 7.10 .849 Speeding 14 35.70 7.80 .819 Joyriding 4 7.85 3.00 .641 Peers 6 12.52 2.94 .644 Page 13 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Other 9 19.56 4.50 .701 For the purposes of analysis, eye movement data were taken from the “hazard” period (approximately eight to ten seconds) from each trial, i.e. from when the hazard was first visible to when it no longer required action from the driver. The data output from the eye tracking experiment included fixation count, average fixation duration, blink count, saccade count, average fixation duration and average saccade amplitude. Pearson’s r was used to correlate the attitude measures with the eye movement data, and simple or multiple regressions were used to assess whether the attitude scales were significant predictors of any one of the eye movement measures. Each regression contained at least one measure from the questionnaire as the predictor variable, and one type of eye movement data as the outcome variable. All regression models reported were found to meet parametric assumptions unless otherwise stated; the assumption of independent errors was confirmed through Durbin-­‐Watson tests as all d values were not lower than the appropriate dU value (Durbin & Watson, 1951). The assumption of no multicollinearity was also met for all models; tolerance values obtained were above .2 (Field, 2005). Kolomogorov-­‐Smirnov tests suggested that all measures were normally distributed unless otherwise stated. This suggests that the models can make accurate predictions about the sample population. City Several significant correlations emerged between the attitude scales and the eye movement data, as can be seen in Table 2. The number of saccades made during the hazard period correlated significantly with the Riskiness (r = -­‐.49, p < .01), Traffic Rules (r = -­‐.45, p < .05), Speeding (r = -­‐.48, p < .05), and Other (r = -­‐.39, p < .05) attitude scales. All other correlations were non-­‐significant. Table 2. Pearson’s correlations between the risk attitude measures and eye movement data obtained during the “City” hazard period. Measure Number of saccades Number of blinks Number of fixations Average fixation duration (ms) Average saccade amplitude Riskiness -­‐.495** .273 .067 -­‐.029 -­‐.131 Page 14 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Subscales Traffic Rules -­‐.448* .132 .016 -­‐.021 -­‐.193 Speeding -­‐.480* .372 .013 .066 -­‐.088 Joyriding -­‐.207 .076 .158 -­‐.173 -­‐.119 Peers -­‐.160 -­‐.110 .112 -­‐.104 -­‐.031 Other -­‐.395* .301 .046 -­‐.033 .021 Note: * = p <.05, ** = p < .01, *** = p < .001 To further explore risk attitudes as a predictor of the number of saccades made, a simple regression was conducted with the Riskiness scale. Riskiness was not a significant predictor of number of saccades (F (1, 24) = .08, ns). As the Riskiness scale comprised of subscales, some of which correlated and others that did not, a further regression was conducted with the scales that did correlate significantly with number of saccades (Traffic Rules, Speeding, and Other). The three subscales were also not a significant predictor of number of saccades, F (3, 22) = .04, ns (See Table 3). Table 3. Regressions for the number of saccades made during the “City” hazard period. F B SE B β t Constant -­‐ 16.621
7.773
-
2.138*
Riskiness .084
.020
.069
.059
.290
-­‐ 16.892
8.350
-
2.023
-­‐ -­‐ -­‐ -­‐ .029
.235
.031
.122
Constant Speeding/Joyriding/Other Speeding 8.245***
-­‐ Page 15 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Joyriding -­‐ .050
.262
.058
.193
Other -­‐ -.041
.367
-.029
-.111
Note: * = p <.05, ** = p < .01, *** = p < .001 Country 1 Significant correlations between the risk attitude scales and measures of eye movement were observed for the video clip Country 1 (See Table 4). There was a significant correlation between number of saccades made and the Riskiness (r = -­‐.45, p < .05), Speeding (r = -­‐.44, p < .05), Joyriding (r = -­‐.44, p < .05) and Other (r = -­‐.43, p < .05) attitude scales. The number of fixations made correlated significantly with the Riskiness (r = -­‐.46, p < .05), Speeding (r = -­‐.51, p < .01), and Joyriding (r = -­‐.43, p < .05) scales. All other correlations were non-­‐significant. Regressions were carried out to assess whether the attitude scales that significantly correlated with the eye movement data were predictors of number of saccades or fixations made. Riskiness was a significant predictor of the number of saccades made (F (1, 25) = 6.28, p < .05), accounting for 19.9% of the variance (R2 = .19). Although the complete Riskiness scale was a significant predictor, it also comprised of subscales which did not correlate significantly with number of saccades. To ascertain if the subscales that did correlate significantly produced a model with more predictive power than Riskiness, a multiple regression was conducted. However, though Speeding, Joyriding and Other subscales all correlated significantly with number of saccades, they did not significantly predict the outcome variable, only reaching near-­‐significance (F (3, 23) = 2.84, p = .06). Table 4. Pearson’s correlations between the risk attitude measures and eye movement data obtained during the “Country 1” hazard period. Measure Number of saccades Number of blinks Number of fixations Average fixation duration (ms) Average saccade amplitude Riskiness -­‐.446* .230 -­‐.461* .056 -­‐.353 Subscales -­‐.212 .059 -­‐.254 -­‐.072 -­‐.261 Traffic Rules Page 16 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Speeding -­‐.442* .344 -­‐.505** .123 -­‐.370 Joyriding -­‐.442* .032 -­‐.431* .165 -­‐.286 Peers -­‐.205 .013 -­‐.145 -­‐.122 -­‐.035 Other -­‐.428* .269 -­‐.375 .103 -­‐.287 Note: * = p <.05, ** = p < .01, *** = p < .001 Two potential outliers in the saccade data were identified through the observation that they had larger than expected standard residuals (see Appendix 11). A Kolmogorov-­‐Smirnov test also revealed the number of saccades measure was not normally distributed (D (26) = .173, p < .05) (see Appendix 11). With the outliers removed, the number of saccades was normally distributed (D (26) = .160, ns) (see Appendix 11 ). The predictive power of both the Riskiness and combined Speeding/Joyriding/Other models also improved; Riskiness (F (1, 23) = 17.88, p < .001) accounted for 43.7% of the variance in number of saccades (R2 = .43), and the model containing the Speeding, Joyriding and Other scales (F (3, 21) = 8.43, p < .001) accounted for 54.1% of the variance (R2 = .54) (see Table 5). Table 5. Regressions for the number of saccades made during the “Country 1” hazard period. F B SE B β t Constant -­‐ 43.183 4.000 -­‐ 10.797*** Riskiness 17.875*** -­‐.154 .036 -­‐.661 -­‐4.228*** Constant -­‐ 39.648 3.423 11.582* Speeding/Joyriding/Other 8.245*** -­‐ -­‐ -­‐ -­‐ Speeding -­‐ -­‐.139 .132 -­‐.233 -­‐1.052 Joyriding -­‐ -­‐.780 .291 -­‐.504 -­‐2.675* Other -­‐ -­‐.103 .205 -­‐.100 -­‐.501 Note: * = p <.05, ** = p < .01, *** = p < .001 Page 17 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Regressions were also undertaken to assess whether the attitude scales were significant predictors of the number of fixations; Riskiness was entered into a simple regression as a predictor, yielding a significant result (F (1, 25) = 6.75, p < .05), and accounting for 21.2% of the variance (R2 = .21). The subscales that significantly correlated (Speeding and Joyriding) with number of fixations were entered into a multiple regression to explore whether they offered more predictive power. The subsequent model was both significant (F (2, 24) = 4.70, p < .05) and accounted for 28.2% of the variance (R2 = .28), an improvement over the Riskiness model. Removal of two outliers with larger than expected standard residuals (see Appendix 11) improved the predictive power of both the Riskiness and combined Speeding/Joyriding models; Riskiness (F (1, 23) = 15.34, p < .001) accounted for 40% of the variance in number of fixations (R2 = .40), and the subscale model (F (2, 22) = 11.04, p < .001)accounted for 50.1% of the variance (R2 = .50)(see Table 6). Table 6. Regressions for the number of fixations made during the “Country 1” hazard period. F B SE B β t Constant -­‐ 42.508 5.223 -­‐ 8.139*** Riskiness 15.336*** -­‐.187 .048 -­‐.632 -­‐
3.916*** Constant -­‐ 39.350 4.119 9.554*** Speeding/Joyriding 11.037*** -­‐ -­‐ -­‐ -­‐ Speeding -­‐ -­‐.320 .143 -­‐.424 -­‐
2.228*** Joyriding -­‐ -­‐.712 .372 -­‐.364 -­‐1.91182 Note: * = p <.05, ** = p < .01, *** = p < .001 Country 2 Pearson’s correlations were conducted, of which only one was significant (See Table 7); there was a significant correlation between number of saccades made during the hazard period and the Other attitude scale (r = -­‐.40, p < .05). All other correlations were non-­‐significant. Page 18 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Table 7. Pearson’s correlations between the risk attitude measures and eye movement data obtained during the “Country 2” hazard period. Measure Number of saccades Number of blinks Number of fixations Average fixation duration (ms) Average saccade amplitude Riskiness -­‐.217 -­‐.131 -­‐.117 .011 -­‐.174 Subscales Traffic Rules .077 -­‐.069 .168 -­‐.102 -­‐.167 Speeding -­‐.240 -­‐.002 -­‐.147 .041 -­‐.115 Joyriding -­‐.258 -­‐.292 -­‐.206 -­‐.045 -­‐.106 Peers -­‐.139 -­‐.261 -­‐.039 .005 -­‐.124 Other -­‐.395* -­‐.109 -­‐.361 .152 -­‐.154 Note: * = p <.05, ** = p < .01, *** = p < .001 To ascertain whether the subscale was a significant predictor of the number of saccades, a simple regression was conducted. The model was significant, (F (1, 25) = 4.63, p < .05), accounting for 15.6% of the variance (R2 = .15) (see Table 8). Table 8. Regression for the number of saccades during the “Country 2” hazard period. F B SE B β t Constant -­‐ 27.928 4.274 -­‐ 6.535*** Other 4.625* -­‐.458 .213 -­‐.395 -­‐2.151* Note: * = p <.05, ** = p < .01, *** = p < .001 Motorway Pearson’s correlations revealed several significant correlations between the attitude subscales and measures of eye movement (See Table 9). Both the Speeding and Other subscales were correlated significantly with the number of saccades (r = -­‐.414, p < .05, and r = -­‐.416, p < .05, respectively) and the number of fixations made (r = -­‐.460, p < .05, and r = -­‐.439, p < .05, respectively). All other correlations were non-­‐significant. Page 19 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Table 9. Pearson’s correlations between the risk attitude measures and eye movement data obtained during the “Motorway” hazard period. Measure Number of saccades Number of blinks Number of fixations Average fixation duration (ms) Average saccade amplitude Riskiness -­‐.354 .144 -­‐.369 .134 -­‐.194 Subscales Traffic Rules -­‐.073 .231 -­‐.115 .143 -­‐.309 Speeding -­‐.414* .172 -­‐.460* .113 -­‐.196 Joyriding -­‐.292 -­‐.057 -­‐.244 .100 .018 Peers -­‐.105 -­‐.123 .010 -­‐.028 .041 Other -­‐.416* .066 -­‐.439* .131 -­‐.090 Note: * = p <.05, ** = p < .01, *** = p < .001 To ascertain whether the Speeding and Other subscales were significant predictors of number of saccade/fixations, multiple regressions were carried out. The subscales combined were not significant predictors of the number of saccades, F (2, 24) = 3.16, ns, however a stepwise method of entry yielded the Other subscale as a significant predictor of the number of saccades made (F (1, 25) = 5.25, p < .05), accounting for 17.3% of the variance (R2 = .17) (see Table 10). Table 10. Regression for number of saccades during the “Motorway” hazard period. F B SE B Constant -­‐ 35.173 4.053 Other 4.560* -­‐.429 .201 Note: * = p <.05, ** = p < .01, *** = p < .001 β t 8.679*** -­‐.400 -­‐2.135* The Speeding and Other subscales were however significant predictors of the number of fixations made (F (2, 24) = 3.89, p < .05), accounting for 24.5% of the variance (R2 = .24) (see Table 11). Page 20 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Table 11. Regression for the number of fixations made during the “Motorway” hazard period. F B SE B β t Constant -­‐ 38.665 5.888 -­‐ 6.567*** Speeding/Other 3.893* -­‐ -­‐ -­‐ -­‐ Speeding -­‐ -­‐.256 .199 -­‐.302 -­‐1.28726 Other -­‐ -­‐.356 .345 -­‐.242 -­‐1.03256 Note: * = p <.05, ** = p < .01, *** = p < .001 Town Pearson’s correlations were used to investigate for any correlations between the attitude scales and eye movements made during the Town clip, however all correlations were non-­‐significant (See Table 12). Table 12. Pearson’s correlations between the risk attitude measures and eye movement data obtained during the “Town” hazard period. Riskiness .007 .200 .003 Average fixation duration (ms) .155 Subscales Traffic Rules .118 .082 .102 .058 -­‐.264 Speeding -­‐.046 .206 -­‐.004 .138 -­‐.076 Joyriding -­‐.134 .205 -­‐.068 .381 -­‐.144 Peers -­‐.007 -­‐.114 .054 -­‐.032 -­‐.366 Other .041 .301 -­‐.096 .116 -­‐.065 Measure Number of saccades Number of blinks Number of fixations ** = p < .01, *** = p < .001 Note: * = p <.05, Average saccade amplitude -­‐.216 Experience Page 21 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 As most research relating to hazard search and schemas has focused on the experience of young drivers, it was decided that any effects that experience may have had on the data should be explored. A series of Pearson’s correlations between the experience measure obtained during the questionnaire (“How long have you held a full driving license?”) and the risk attitude scales yielded no significant correlations. However, when Experience was correlated with the eye movement data, some significant relationships were found; both the number of saccades and fixations made during the Motorway hazard period were significantly correlated with Experience (r = -­‐.39, p < .05, and r = -­‐
.43, p < .05) (See Table 13). All other correlations were non-­‐significant. Table 13. Pearson’s correlations of Experience (Length of time in possession of full driving license) and the eye movement measures from each trial. Measure Length of time in possession of full driving license (Years) Number of saccades made (City) -­‐.026 Number of blinks made (City) -­‐.105 Number of fixations made (City) -­‐.136 Number of saccades made (Country 1) -­‐.175 Number of blinks made (Country 1) -­‐.082 Number of fixations made (Country 1) -­‐.115 Number of saccades made (Country 2) -­‐.100 Number of blinks made (Country 2) -­‐.144 Number of fixations made (Country 2) -­‐.106 Number of saccades made (Motorway) -­‐.385* Number of blinks made (Motorway) -­‐.028 Number of fixations made (Motorway) -­‐.430* Number of saccades made (Town) -­‐.128 Number of blinks made (Town) -­‐.188 Number of fixations made (Town) -­‐.138 Note: * = p <.05, ** = p < .01, *** = p < .001 To ascertain whether Experience was a significant predictor of eye movement in the Motorway clip, multiple regressions were run with Experience as a forced entry predictor variable in addition to the Page 22 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 significant attitude subscales. The number of saccades made was significantly predicted by a model consisting of Experience and the Other subscales as predictor variables (F (2, 24) = 4.82, p < .05), accounting for 28.6% of the variance (R2 = .28) (see Table 14). This is an improvement of 11.3% over the Other subscale as the sole predictor. Table14. Regression for the number of saccades made during the “Motorway” hazard period with Experience included as a predictor variable. F B SE B Constant -­‐ 40.574 4.630 Experience/Other 4.817* -­‐ -­‐ -­‐ -­‐ Experience -­‐ -­‐1.779 .913 -­‐.339 -­‐1.949 Other -­‐ -­‐.475 .220 -­‐.375 -­‐2.157* Note: * = p <.05, ** = p < .01, *** = p < .001 β t 8.763*** Experience was also entered alongside the Speeding and Other subscales as a predictor of number of fixations; the subsequent model was a significant predictor (F (3, 23) = 4.94, p < .05), accounting for 36.4% of the variance (R2 = .36) (see Table 15). This represents an improvement of 12.1% over the model containing only the attitude subscale. Table 15. Regression for the number of fixations made during the “Motorway” hazard period with Experience included as a predictor variable. F B SE B β t Constant -­‐ 39.006 5.187 -­‐ 7.521*** Experience/Other 6.069** -­‐ -­‐ -­‐ -­‐ Experience -­‐ -­‐2.325 1.022 -­‐.381 -­‐2.274* Other -­‐ -­‐.577 .247 -­‐.392 -­‐2.339* Note: * = p <.05, ** = p < .01, *** = p < .001 Page 23 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 4. Discussion The current study aimed to examine whether risk attitudes are linked to young drivers’ hazard search by potentially influencing the schemas they use whilst observing the road. The study found some preliminary evidence to support this; the Riskiness attitude scale and the Speeding, Joyriding and Other subscales were found to significantly correlate with the number of saccades and fixations made during the hazard periods. These were negative correlations which suggest that a higher and therefore more risk inclined score on the risk attitude measure is associated with fewer saccades and fixations made during the hazard period. It could be argued that this is comparable to the effects of experience found by other researchers. For example, Chapman and Underwood (1998) suggested that experienced drivers were able to look away more quickly from a hazard than inexperienced drivers; the current study found the less risk inclined drivers to demonstrate similar behaviour by making more saccades and fixations during the hazard period. It is possible that the fewer fixations made by “riskier” drivers is a result of the “attentional capture” of a hazard as suggested by Chapman and Underwood (1998; see also Underwood, 2007); drivers with less developed schemas (whether risky or inexperienced) take longer to process the hazard, and so cannot reallocate their attention to other parts of the driving scene as quickly as experienced (or as it appears to be here, “cautious”) drivers. Regressions were then carried out to assess whether the attitude measures were significant predictors of the eye movement data. The risk attitude scales significantly predicted eye movement data obtained from the Country 1, Country 2 and Motorway video clips, sometimes favouring the complete Riskiness scale and other times producing a greater effect for selected subscales. While the subscale models were intended to improve on the predictive power of the complete Riskiness scales by disregarding those which did not correlate, the variance explanation seen in these models is unlikely to be tenable; often only one subscale made a significant contribution to a model, as indicated by the t values. The models also do not explain all of the variance; the largest amounts were around 50%. The same can be said for the correlations, which were moderate at best and were mostly weak. However this is not particularly troubling; the current study did not seek to suggest that risk attitudes solely accounted for variability in hazard search, just that there may be a relationship between the two. Further research that improves on the methodology and measures used in the current study will be able to confirm or reject the findings suggested here. As cognitive research on driving has largely focused on experience in relation to hazard search, analysis in the current study incorporated the measure of experience taken during the questionnaire Page 24 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 phase of the experiment. Experience (e.g. the number of years a full driving license was held) did not significantly correlate with any of the risk attitude scales; however it did correlate with participants’ eye movements on the Motorway hazard period, significantly predicting the number of saccades and number of fixations made when combined with the Other subscale. This would initially appear to agree with other research that experience influences how extensively drivers search for hazards (e.g. Underwood, Crundall, Bowden & Chapman, 2002; Underwood, 2007). However, on further inspection, the current study found that with more experience, less saccades and fixations were made. This finding contrasts with previous research that typically suggests experienced drivers make more saccades and fixations (Underwood, Crundall, Bowden & Chapman, 2002). There may be several reasons for these findings; inexperienced drivers in other research (e.g. Underwood, Crundall, Bowden & Chapman, 2002) have typically passed their driving test very recently, whereas in the current study only five participants had held their driving license for under a year. It may be that drivers in the current study have acquired risk inclined attitudes, before they have sufficient experience to produce a detailed schema. It is possible that the experience measure used in the current study was not sensitive or sufficient enough to comment on experience’s influence on hazard search; though the questionnaire took other measures (e.g. number of miles driven per week) that might have been used as better measures of experience, these were judged to be inaccurate and often ignored by participants. Therefore a better measure should be used in the future study of risk attitudes to distinguish whether experience is related to risk attitudes or mediates their effects. It must be said that although previous research often suggests drivers become safer as they gain more experience, this has not always been found to be the case; a study by Duncan, Williams and Brown (1991) found experienced drivers to be the worst out of three groups (the other two being novices and expert police drivers) for scanning and anticipation of hazards. This is theorized to be due to a lack of feedback, and so it is possible that the more experienced drivers in the current study have less sufficient scanning because of learnt “bad habits”. An interesting finding is that risk attitudes correlated with and predicted eye movements on a road type which has not produced significant results in other research; hazard search was significantly related to the risk attitude measures on both the Country 1 and Country 2 hazard periods. Previous research examining hazard search in drivers of differing experience has often only found differences between experienced and inexperienced drivers when the road featured is unfamiliar to the inexperienced drivers, for example a demanding section of dual carriageway (Underwood, Crundall, Bowden & Chapman, 2002). That risk attitudes were related to hazard search, on a road type common to many drivers, suggests that experience is not the sole determinant of hazard search. It Page 25 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 may be that in situations where experience does not necessarily confer an advantage to a driver, their risk attitudes may be the deciding factor in where they choose to look for danger. The current study has some methodological limitations which may be improved upon in future research. A useful measure would have been to have logged when participants indicated that they had seen a hazard. The use of a response button or key was used to good effect in Crundall, Underwood and Chapman (2002); by recording when participants indicated there was a hazard, the researchers were able to say when demand had increased for each driver and had prompted hazard search. Though the “dummy response” key-­‐press was originally intended to serve this function, unfortunately time constraints and the limitations of the experiment software used meant this was not possible. The risk attitude measures used also had their limitations. For example, the Other subscale was found to be both correlated and a significant predictor of eye movement in several clips, sometimes exclusively. Though it appears to be one of the more promising risk attitude measures taken, it is substantially less “pure” than some of the other subscales (e.g. Speeding). It contains items that address various aspects of driving from going through a red light to driving round corners too quickly. Therefore it is unclear which items are measuring risk attitudes that might predict hazard search, and which are irrelevant. Exploratory factor analysis is a possible option for refining the subscales; this might do away with the Other subscale by assigning the items to other subscales (or indeed suggesting a new one). This was considered; however the sample size obtained, even including eligible questionnaire respondents who did not proceed to the experiment (n = 57), fell significantly short of even a “poor” sample size for factor analysis (Field, 2005; Comrey & Lee, 1992). Thus a factor analysis could not be undertaken. Another limitation of the risk attitude measure was that it did not always directly address attitudes that might relate to schemas; items were often very general (e.g. “It is more important to keep traffic flowing than to always follow the traffic rules”). Some items did address situations that actually arose in the road clips used, for example “If you are a skilled driver, it is not risky to drive round corners quickly”, which may have been relevant to the Country 2 clip in which pedestrians appear on a blind corner. In determining the extent to which risk attitudes influence hazard search and subsequent driving behaviour, it is important to establish which attitudes may influence general driving behaviour and which may influence specific instances (e.g. whether it is acceptable to speed on the roads near a school). Page 26 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 A possible extension of the current study would be to compare drivers on two levels; their risk attitude and the level of experience. With a sample of both young and older drivers, an eye-­‐tracking study similar to the current study or Underwood, Crundall, Bowden and Chapman’s (2002) study might determine whether risk attitudes influence the hazard search of drivers in general, or if it is only inexperienced drivers that are in danger of scanning the road in an insufficient manner. The procedure of getting inexperienced drivers to predict where experienced drivers would look during a driving scene (as in Underwood, Crundall et al., 2002) could also be used as a test of whether risk-­‐
inclined drivers have schemas different to cautious drivers. 5. Conclusion The current study aimed to integrate two different traditions of driving research and their areas of interest; cognitive psychological research and hazard search, and social psychological research and risk attitudes. It was found that in young drivers, dimensions of a risk attitude scale predicted eye movements during hazard search; more risk inclined drivers made less eye movements and fixations than less risk inclined drivers. Future research may further investigate potential accident risk in young drivers by rectifying the methodological limitations present in the current study, and exploring the relationship of risk attitudes and experience to hazard search. Page 27 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 References Brown, I.D., & Groeger, J.A. (1988). Risk perception and decision taking during the transition between novice and experienced driver status. Ergonomics. 31 (4), pp 585-­‐597. Chapman, P., & Underwood, G. (1998). Visual search of driving situations: Danger and experience. Perception. 27, pp 951-­‐964. Clarke, D.D., Ward, P., Bartle, C., & Truman, W. (2010). Killer crashes: Fatal road traffic accidents in the UK. Accident Analysis and Prevention. 42, pp 764-­‐770. Comrey, A.L., & Lee, H.B. (1992). A First Course in Factor Analysis (2nd Ed.). New Jersey: Lawrence Erlbaum Associates. Crundall, D.E., & Underwood, G. (1998). Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics. 41 (4), pp 448-­‐458. Crundall, D., Underwood, G., & Chapman, P. (2002). Attending to the peripheral world while driving. Applied Cognitive Psychology. 16, pp 459-­‐475. Department for Transport. (2009). Reported road accidents involving young car drivers: Great Britain 2009. Road accident statistics factsheet no. 6. Retrieved April 11th, 2011, from http://www.dft.gov.uk/pgr/statistics/datatablespublications/accidents/casualtiesgbar/suppletablesf
actsheets/youngcardrivers.pdf Duncan, J., Williams, P., & Brown, I. (1991). Components of driving skill: experience does not mean expertise. Ergonomics. 34 (7), pp 919-­‐937. Durbin, J., & Watson, G.S. (1951). Testing for correlation in least squares regression, II. Biometrika. 38 (1), pp 159-­‐177. Elander, J., West, R., & French, D. (1993). Behavioral correlates of individual differences in road-­‐
traffic crash risk: An examination of methods and findings. Psychological Bulletin. 113 (2), pp 279-­‐
294. Field, A. (2005). Discovering Statistics Using SPSS (2nd Ed.) London: Sage Publications. Groeger, J.A. (2000). Understanding driving: Applying cognitive psychology to a complex everyday task. Hove: Psychology Press Ltd. Page 28 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Hole, G. (2007). The psychology of driving. New Jersey: Lawrence Erlbaum Associates. Iversen, H. (2004). Risk-­‐taking attitudes and risky driving behaviour. Transportation Research Part F. 7, pp 135-­‐150. Lajunen, T., Parker, D., & Stradling, S.G. (1998). Dimensions of driver anger, aggressive and highway code violations and their mediation by safety orientation in UK drivers. Transportation Research Part F. 1, pp 107-­‐121. Parker, D., West, R., Stradling, S., & Manstead, A.S. (1995). Behavioral characteristics and involvement in different types of traffic accident. Accident Analysis and Prevention. 27 (4), pp 571-­‐
581. Schank, R.C., & Abelson, R.P. (1977). Scripts, plans, goals and understanding: An inquiry into human knowledge structures. New Jersey: Lawrence Erlbaum Associates. Sivak, M. (1996). The information that drivers use: Is it indeed 90% visual? Perception. 25, pp 1081-­‐
1089. Ulleberg, P., & Rundmo, T. (2003). Personality, attitudes and risk perception as predictors of risky driving behaviour among young drivers. Safety Science. 41, pp 427-­‐443. Underwood, G., Chapman, P., Bowden, K., & Crundall, D. (2002). Visual search while driving: Skill and awareness during perception of the scene. Transportation Research Part F. 5, pp 87-­‐97. Underwood, G. (2007). Visual attention and the transition from novice to advanced driver. Ergonomics. 50 (8), pp 1235-­‐1249. Weber, E.U., Blais, A., & Betz, N.E. (2002). A domain-­‐specific risk-­‐attitude scale: Measuring risk perceptions and risk behaviors. Journal of Behavioral Decision Making. 15, pp 263-­‐290. West, R., & Hall, J. (1997). The role of personality and attitudes in traffic accident risk. Applied Psychology: An International Review. 46 (3), pp 253-­‐264. Page 29 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Appendix Contents 1. Page 1 of online attitude questionnaire Page 32 2. Page 2 of online attitude questionnaire Page 33 3. Page 3 of online attitude questionnaire Pages 34-­‐35 4. Ethical evaluation form Pages 36-­‐46 5. Ethical approval confirmation Page 47 6. Questionnaire items Page 48 7. Scale construction .............................................................. Construction of scales for use in project. Pages 49-­‐52 ............................................................................... “To English” editing of scales. Pages 53-­‐54 ...................................................................................... Item subscale groupings. Page 55-­‐56 8. Study advertisement poster and email 9. Eye-­‐tracking participation email Page 58 10. Eye-­‐tracking experiment information sheet/consent form Page 59 11. Debriefing sheet/email Page 57 Page 60 12. Eye-­‐tracking experiment screens Page 61 13. Outliers and normality statistics Page 62 Page 30 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 1 of online risk attitudes questionnaire Page 31 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 2 of online risk attitudes questionnaire Page 32 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 3 of online risk attitudes questionnaire Page 33 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 34 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Ethical approval application form Page 35 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 36 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 37 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 38 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 39 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 40 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 41 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 42 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 43 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 44 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 45 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Ethical approval confirmation email, dated 15/11/2010 Dear Anthony
re: 10018 - [no title given [driving risks]]
I have scrutinised this application and confirm that in my judgement the project is
low risk and that the ethical issues have been adequately addressed. I am pleased
to grant ethical approval on behalf of the School of Psychology.
Please include a copy of your application form and this approval email when you
submit your dissertation.
Good luck for your research.
R de V
Dr Richard de Visser
Ethics Coordinator
School of Psychology
University of Sussex
Falmer BN1 9QH
United Kingdom
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Sometimes it is necessary to bend the rules to keep traffic flowing It is more important to keep traffic flowing than to always follow the traffic rules Sometimes it is necessary to break the traffic rules in order for one to progress through traffic Sometimes it is necessary to take chances in traffic A person who takes chances is not necessarily a less safe driver A person who violates traffic rules is not necessarily a less safe driver. If you are a skilled driver, speeding is an acceptable risk to take Even if traffic conditions allow you to do so, speeding is not an acceptable risk to take Driving 5-­‐10 miles above the speed limit is acceptable because everyone does it If you are a safe driver, it is not risky to exceed the speed limit by 5mph in areas limited to 40mph It is an acceptable risk to drive over 60mph on country roads if there are no other vehicles on the road Speeding is the only way to feel excited whilst driving Driving is more than just a mode of transport – the risks involved are exciting It is not risky to exceed the limit by 5mph in built up areas It is not risky to exceed the speed limit on country roads by 5mph It is risky to go through a red light on an empty road It is not risky to drive through an amber light that is about to turn red It is risky to overtake the car in front when it is driving at the speed limit It is not risky to show your skills as a driver by driving fast It is risky to drive fast if your friends might distract you If you are a skilled driver, it is not risky to drive round corners quickly It is risky to drive too close to the car in front, regardless of their speedREMOVED Peer pressure is more likely to increase the risks we take whilst driving The traffic rules are inflexible with respect to the demands of the traffic. Getting through traffic is not as important as abiding by traffic rules. People don’t need to take risks to get through traffic. People who take chances are less safe whilst driving. Violating the traffic rules is a sign that someone is an unsafe driver. You are taking a risk when speeding, even if you think you are a skilled driver. If the traffic conditions are right, you can speed without taking any risks. Just because others do it doesn’t mean speeding is acceptable. Even if you think you are a safe driver, any speeding in a 40mph-­‐limit area is too risky. Speeding on a 60mph road is risky, regardless of whether there are other cars on the road. Speeding in order to feel excited is risky. Driving should only be a means of getting around, without any need for risk-­‐taking. You take a chance going over the speed limit in built-­‐up areas. Speeding on country roads involves a lot of risk. On an empty road, there’s no harm in going through a red light. When a light is amber and about to go red, it is safer to stop rather than go through it. It’s only risky to overtake a car that is going faster than the speed limit. Trying to show off whilst driving is always a bad idea. You can still focus on the road and drive fast even with other people in the car. Speeding around bends is risky. If you’re paying attention, following the car in front closely isn’t a risk. Friends do not affect the amount of risks we take. Page 47 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Construction of scales for use in empirical project Ulleberg & Rundmo 2003 •
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The study’s “risk-­‐attitudes to driving” all have Cronbach’s alphas of around 0.8 which indicates good internal reliability The items for this scale can be found in Fig 1. One task to be completed would be some work into correctly translating and clarifying the question, as some of the items can be appear a bit twisted or unclear in English. Some items not in the risk-­‐attitude scales used in this study could be implemented to measure risk-­‐attitudes with some adjustment, such as “Drive fast to show others that I am
tough enough”, “Drive fast to show others I can handle the car”, “ (I) Exceed the speed limit
in build-up areas (more than 10 km/h)”, “ (I) Exceed the speed limit on country roads (more
than 10 km/h)”, “ (I) Drive on a yellow light when it is about to turn red”, and “ (I) Disregard red
light on an empty road”.
All the items rated on a 5 point Likert-scale from 1 (“Strongly Disagree” or “Never”) to 5
(“Strongly Agree” or “Very often”)
The whole of those two scales (Attitude to Traffic safety and Risk taking behaviour in traffic)
were significantly correlated to a strong degree (-0.79). This could suggest they are
measuring similar things and so this could be a rationale for combining some of the items
together to create a more extensive scale of risk-attitudes.
Two other items measured self-perception of the risk of being involved in an accident, and of
being hurt in an accident.
Attitude scale 1:Traffic flow vs. rule obedience There are many traffic rules which cannot be
obeyed in order to keep up the traffic flow
Sometimes it is necessary to bend the rules to
keep traffic going
It is more important to keep up the traffic flow
rather than always follow the traffic rules
It is better to drive smooth than always follow the
traffic rules
Sometimes it is necessary to break the traffic
rules in order to get ahead
Sometimes it is necessary to ignore violations of
traffic rules
Sometimes it is necessary to take chances in the
traffic
Sometimes it is necessary to bend the traffic
rules to arrive in time
A person who take chances and violates some
traffic rules is not necessary a less safe driver
Attitude scale 2:Speeding If you have good skills, speeding is OK
Page 48 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 I think it is OK to speed if the traffic conditions
allow you to do so
Driving 5 or 10 miles above the speed limit is OK
because everyone does it
If your are a safe driver, it is acceptable to
exceed the speed limit by 10 km/h in areas
permitted to drive in 80 to 90 km/h
It is acceptable to drive in 100 km/h on a straight
road if there are no other vehicles in a miles
distance
Attitude scale 3:Funriding Adolescents have a need for fun and excitement
in traffic
Speeding and excitement belong together when
you are driving
Driving is more than transportation, it is also
speeding and fun
Fig. 1 Table of Risk-­‐attitude items from Ulleberg & Rundmo 2003 (Originals) Ulleberg & Rundmo 2002 Page 49 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 50 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Page 51 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 “To English” editing of scales from Ulleberg & Rundmo (2002; 2003) Yellow text indicates items that were included in the final questionnaire •
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“There are many traffic rules which cannot be obeyed in order to keep up the traffic flow” into “There are many traffic rules which can be ignored in order to keep up the flow of traffic” “Sometimes it is necessary to bend the rules to keep traffic going” into “ Sometimes it is necessary to bend the rules to keep traffic flowing” “It is more important to keep up the traffic flow rather than always follow the traffic rules” into “It is more important to keep traffic flowing than to always follow the traffic rules” “It is better to drive smooth than always follow the traffic rules” may need clarifying as to the intention, but could be translated simply as “It is better to drive smoothly than to always follow the traffic rules” “Sometimes it is necessary to break the traffic rules in order to get ahead” into “Sometimes it is necessary to break the traffic rules in order for one to progress through traffic” “Sometimes it is necessary to ignore violations of traffic rules” into “Sometimes it is necessary to ignore occasions where we violate the traffic rules” “Sometimes it is necessary to take chances in the traffic” into “Sometimes it is necessary to take chances in traffic” “Sometimes it is necessary to bend the traffic rules to arrive in time” into “Sometimes it is necessary to bend the rules whilst driving to arrive on time” “A person who take chances and violates some traffic rules is not necessary a less safe driver” into “A person who takes chances and violates some traffic rules is not necessarily a less safe driver” “If you have good skills, speeding is OK” into “If you are a skilled driver, speeding is an acceptable risk to take” or “If you are a skilled driver, speeding is acceptable (or OK)” “I think it is OK to speed if the traffic conditions allow you to do so” into “It is acceptable to speed if the traffic conditions allow you to do so” or “If the traffic conditions allow you to do so, speeding is an acceptable risk” Reversed wording: “Even if traffic conditions allow you to do so, speeding is not an acceptable risk to take” “Driving 5 or 10 miles above the speed limit is OK because everyone does it” into “Driving 5-­‐10 miles above the speed limit is acceptable because everyone does it” “If your are a safe driver, it is acceptable to exceed the speed limit by 10 km/h in areas permitted to drive in 80 to 90 km/h” into “If you are a safe driver, it is not risky to exceed the speed limit by 5mph in areas limited to 40mph” “It is acceptable to drive in 100 km/h on a straight road if there are no other vehicles in a miles distance” into “It is an acceptable risk to drive over 60mph on single-­‐lane roads if there are no other vehicles on the road” “Adolescents have a need for fun and excitement in traffic” into “Young people need fun and excitement whilst driving” “Speeding and excitement belong together when you are driving” into “Speeding is the only way to feel excited whilst driving” “Driving is more than transportation, it is also speeding and fun” into “Driving is more than just a mode of transport – the risks involved are exciting” Page 52 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Ulleberg & Rundmo 2002 • “(How often do you) Exceed the speed limit in built up areas (by more than 5mph)” into “It is not risky to exceed the limit in built up areas by 5mph” • “(How often do you) Exceed the speed limit on country roads (etc.)” into “It is not risky to exceed the speed limit on country road by 5mph” • “(How often do you) Disregard a red light on an empty road” into “It is risky to go through a red light on an empty road” • “(How often do you) Drive through an amber light when it is about to turn red” into “It is not risky to drive through an amber light that is about to turn red” Page 53 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Questionnaire Item Subscale Groupings Traffic Rules 1. Sometimes it is necessary to bend the rules to keep traffic flowing. 2. It is more important to keep traffic flowing than to always follow the traffic rules. 3. Sometimes it is necessary to break the traffic rules in order for one to progress through traffic. 4. Sometimes it is necessary to take chances in traffic. 5. A person who takes chances is not necessarily a less safe driver. 6. A person who violates traffic rules is not necessarily a less safe driver. 7. The traffic rules are inflexible with respect to the demands of the traffic (Reversed). 8. Getting through traffic is not as important as abiding by traffic rules (Reversed). 9. People don’t need to take risks to get through traffic (Reversed). 10. People who take chances are less safe whilst driving (Reversed). 11. Violating the traffic rules is a sign that someone is an unsafe driver (Reversed). Speeding 12. If you are a skilled driver, speeding is an acceptable risk to take. 13. Even if traffic conditions allow you to do so, speeding is not an acceptable risk to take (Reversed). 14. Driving 5-­‐10 miles above the speed limit is acceptable because everyone does it. 15. If you are a safe driver, it is not risky to exceed the speed limit by 5mph in areas limited to 40mph. 16. It is an acceptable risk to drive over 60mph on country roads if there are no other vehicles on the road. 17. It is not risky to exceed the limit by 5mph in built up areas. 18. It is not risky to exceed the speed limit on country roads by 5mph. 19. You are taking a risk when speeding, even if you think you are a skilled driver (Reversed). 20. If the traffic conditions are right, you can speed without taking any risks. 21. Just because others do it doesn’t mean speeding is acceptable (Reversed). 22. Even if you think you are a safe driver, any speeding in a 40mph-­‐limit area is too risky (Reversed). 23. Speeding on a 60mph road is risky, regardless of whether there are other cars on the road (Reversed). 24. You take a chance going over the speed limit in built-­‐up areas (Reversed). 25. Speeding on country roads involves a lot of risk (Reversed). Joyriding 26. Speeding is the only way to feel excited whilst driving. 27. Driving is more than just a mode of transport – the risks involved are exciting. 28. Speeding in order to feel excited is risky (Reversed). 29. Driving should only be a means of getting around, without any need for risk-­‐taking (Reversed). Peers 30. It is not risky to show your skills as a driver by driving fast. 31. It is risky to drive fast if your friends might distract you (Reversed). 32. Peer pressure is more likely to increase the risks we take whilst driving (Reversed). 33. Trying to show off whilst driving is always a bad idea (Reversed). 34. You can still focus on the road and drive fast even with other people in the car. 35. Friends do not affect the amount of risks we take. Page 54 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Other 36.
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It is risky to go through a red light on an empty road (Reversed). It is not risky to drive through an amber light that is about to turn red. It is risky to overtake the car in front when it is driving at the speed limit (Reversed). If you are a skilled driver, it is not risky to drive round corners quickly. It is risky to drive too close to the car in front, regardless of their speed (Reversed) On an empty road, there’s no harm in going through a red light. When a light is amber and about to go red, it is safer to stop rather than go through it (Reversed). It’s only risky to overtake a car that is going faster than the speed limit. Speeding around bends is risky (Reversed). If you’re paying attention, following the car in front closely isn’t a risk. Page 55 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Driving Study – Win £25! Study advertisement poster
Hi, I am doing a study of people’s attitudes towards driving for my third year empirical project and I would greatly appreciate your participation. All you need to do is go to the link below and complete the questionnaire, which should take no longer than 10 minutes. Once you have done this there may be the option of participating in another short study on driving (no longer than 15 minutes). Participating in that study will enter you into a prize draw to win £25! Unfortunately the study is only aimed towards people who are in possession of a full driving license, and the second optional study is open only 25 year olds and under. Thank you for your time and participation. The link for the study is http://tinyurl.com/DrivingQuestionnaire, or please feel free to take one of the address slips below (all the instructions above can be found at the link too) Thank you for your time, Anthony Matsell – [email protected] Study advertisement email Hi, I am doing a study of people’s attitudes towards driving for my third year empirical project and I would greatly appreciate your participation. All you need to do is go to the link below and complete the questionnaire, which should take less than 10 minutes. Once you have done this there may be the option of participating in another short study on driving (no longer than 15 minutes). Participating in that study will enter you into a prize draw to win £25! Unfortunately these studies are only aimed towards people who are in possession of a full driving license, and the second optional study is open only to people who are 25 years old or under. Thank you for your participation. The link for the study is http://tinyurl.com/DrivingQuestionnaire . Thank you for your time, Anthony Matsell – [email protected] Page 56 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Follow up email for participation in eye-­‐tracking experiment Hi, You recently completed my online questionnaire of driving behaviour. I am now about to begin conducting a second study for which you are eligible, and I was wondering if you would be interested in participating. By participating in the study you will be entered to a prize draw to win £25. Of course, if you are a psychology student doing a project I will be more than happy to do your study in exchange for doing mine. I’ll also have some chocolates and sweets for when you do the experiment. The experiment will involve wearing an eye-­‐tracking camera (similar to a helmet) whilst watching some video clips, and the experiment should take no more than 15 minutes, including setting the camera up and so on. So far there are slots available from Thursday 3rd February at 10:00am until around 12:30, and then many mores slot available starting from Monday 7th February. If you would like to take part, please email back a few times which you would be available for (perhaps 3 or 4, that are not clustered together), and then I will book whichever time is available. If you would prefer one time over another then you could possibly rank them in order of preference and I will do my best to accommodate your needs. If you would like to do the study but are not available in the next week or so due to other commitments, please let me know and I will make a note and email you again at a later date. Thank you for your time, I look forward to hearing from you. Anthony Matsell [email protected] Page 57 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Eye-­‐tracking experiment consent form/information sheet Thank you for participating in this study. The experiment you are involved in today is investigating eye movements drivers make whilst observing the road. In a moment the researcher will set up eye-­‐
tracking equipment, which will measure your eye-­‐movements as you watch five different video clips of various roads. These clips are all approximately 1 minute in length. As you are watching these clips, you should behave as if you were the driver of the car, responding to anything in the video clips that the driver would need to give immediate attention to. If you notice anything in the video clips that you as a driver would need to respond to immediately, you should indicate this by pressing the spacebar on the keyboard in front of you. You may press the spacebar key as many times as you feel there is something in the video clip you would respond to as a driver, but please avoid pressing it randomly as this will void the trial. If at any time you wish to cease your participation in this experiment, please alert the researcher and they will act accordingly. They will also remove your data and delete it permanently if you request them to do so. The data obtained from your participation in this study will be anonymously and confidentially held; only an unrelated identifier number will be used to link any of your details to the data you provide. If at any point after completion of this study, you wish to have your data removed from the experiment, please contact the experimenter by email and they will comply with your request and delete the data as above. Please sign below to indicate your consent to participate in this study. Signing will also indicate that you understand and accept the terms summarized in this statement: "I consent to the processing of my personal information for the purposes of this research study. I understand that such information will be treated as strictly confidential and handled in accordance with the Data Protection Act 1998". Sign here: ___________________________________ -­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐-­‐ Contact slip: I participated in a study run by Anthony Matsell on ______/________/________. Email: [email protected] Page 58 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Debriefing sheet and email Thank you for your time in completing my experiment. The purpose of this experiment is to investigate whether people’s attitudes to risk influence how well they look for hazards on the road. The questionnaire you completed before was designed to measure your attitude towards various aspects of risk whilst driving, which will then be analyzed with how extensively you scanned the five clips for hazards during this eye-­‐tracking experiment. Hopefully this experiment will highlight that attitudes to risk can be an important contributor to accident rates amongst young drivers. Once again thank you for your time. If you wish to remove your data or have any questions please feel free to contact the experimenter, Anthony Matsell, at [email protected] Page 59 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Experiment screens First slide: Thank you for participating in this experiment. You will view five video clips of various roads, from the point of view of a car travelling along the road. Please behave as if you were in the car, by paying attention to anything on the road that you would have to attend to if you were the actual driver. You should press the space bar whenever something occurs in the clips that would require your attention if you were actually driving. Please do not press the space bar at random or too often, as this will void the trial. There will be a pause in between trials. Last slide: The experiment is now complete. Thank you for your time. Page 60 of 61 Risk Attitudes and Their Relationship to Hazard Search in Driving Candidate No. 27930 Statistics tables Table 1. Outliers on the number of saccades measure of the “Country 1” hazard period. Case Number Participant Number Standardized Residual Fixation Count Predicted Value Residual 19 55 -2.766
10 25.694 -­‐15.694 26 73 2.766 40 24.306 15.693 Table 2. Outliers on the number of fixations measure of the “Country 1” hazard period. Case Number Participant Number Standardized Residual Fixation Count Predicted Value Residual 19 55 26 73 -2.471
2.610111 5 37 21.38293 19.69383 -­‐16.3829 -­‐16.3829 Table 3. Kolmogorov-­‐Smirnov statistics for the eye measurement data from each trial. Outliers have not been removed. Measure D Number of saccades made (City) Number of fixations made (City) Number of saccades made (Country 1) Number of fixations made (Country 1) Number of saccades made (Country 2) Number of fixations made (Country 2) Number of saccades made (Motorway) Number of fixations made (Motorway) Number of saccades made (Town) Number of fixations made (Town) .143
.156
.173*
.101
.163
.083
.116
.091
.169
.159
Note: * = p <.05, ** = p < .01, *** = p < .001 Table 4. Kolmogorov-­‐Smirnov statistic for the number of saccades from the “Country 1” hazard period, after the removal of two outliers. Measure D Number of saccades made (Country 1) .100 Page 61 of 61