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Iranian Journal of Environmental Health, Science and Engineering
Iranian Association of Environmental Health (IAEH)
ISSN: p-ISSN: 1735-1979
Vol. 4, Num. 2, 2007, pp. 133-138

Untitled Document

Iranian Journal of Environmental Health Science & Engineering,Vol. 4, No. 2, 2007, pp. 133-138


1T. Allahyari, *1G. Nasl Saraji, 1J. Adl, 2M. Hosseini, 3M. Younesian, 4M. Iravani

1Department of Occupational Health, School of Public Health, Medical Sciences/ University of Tehran, Tehran, Iran
2Department of Epidemiology and Biostatistics, Medical Sciences/ University of Tehran, Tehran, Iran
3Department of Environmental Health, Medical Sciences/ University of Tehran, Tehran, Iran
4Department of Psychology, Faculty of Psychology and Educational Sciences, University of Tehran, Iran

*Corresponding author-Email: Telefax: +98 21 8895 1390

Received 15 January 2007; revised 20 February 2007; accepted 30 March 2007

Code Number: se07020


Inattention plays an important role in the traffic accidents which are due to human error. Attention is defined as the ability of individuals to process information from the environment or capability of receiving and processing stimuli. In different driving situations, drivers encounters with different types of stimuli, visual or auditory, from different sources, and for safe performance should have an accurate perception of them. Driving context also provide a complex information processing situation from view point of direction, continuous, quantity and ambiguity of stimulus. So, drivers' safety and performance are influenced significantly by attention skills of drivers. Previous studies revealed that, failure of attention and deficiency of information processing is one of the major causes of accidents (Shinar, 1993). From ergonomics prospective, any incompatibility between cognitive resources and job demands results in deterioration of performance and occurrence of errors. In driving tasks, fails of cognitive abilities in each phase of information processing system, i.e. sensing, perception, attention, and decision making could threaten traffic safety. Practical research demonstrated that, individual differences in attention can be measured and used as a predictor for ranges of real world tasks (Arthur and Doverspike, 1992). One of the visual attention measures or tests is useful field Of view (UFOV). UFOV is defined as the region of the visual field, from which, information can be acquired without any movement of the eyes or the head (Ball et al., 1988).The size of UFOV is very important for rapid extracting and identifying of information details in the scene of driving. Recent studies concluded that, if there is any deterioration in UFOV performance, drivers may be act slowly in extracting information details and risk of accident would be increased.

The concept of the UFOV was originally described by Sanders (1970) who used the term "functional visual field" to define the visual field area, over which, information can be obtained in a brief glance without eye or head movements. Subsequently, Verriest et al., (1985) described UFOV as an "Occupational Visual Field". They distinguished it from the clinical visual sensory field, typically evaluated by perimetry in ophthalmologic settings. The term "useful field of view" was first used by Ball et al., and has subsequently come to be most widely associated with a specific computer-based test. UFOV was used to assess visual processing speed, divided attention, and selective attention.

UFOV can be measured by instructing the subject to perform a dual task: a central task and a peripheral task. The size of the UFOV is smaller than peripheral visual field (Ball and Owsley, 1993). Some investigators assess the UFOV by simply instructing the subjects to detect the presence of a peripheral signal and identify it (Williams, 1982; 1995; Ball et al., 1993), whereas, others demand localization (Ball and Owsley, 1993; Sekuler et al., 2000). Ball et al., (1993) proposed that the limit of the visual field depends on the subject's ability to locate peripheral signals.

In the present study, the size of the useful visual field was measured through a computerized task, including detection followed by localization of the peripheral stimulus. Authors such as Ball and Owsley, (1994); Ball, (1993) attempted to examine the relationship between the reduction of the useful visual field and the number of accidents in real situations, using retrospective design, while the mentioned author described prospective design, in cooperation with Owsley, McGwin, 1999. A recent Meta-analysis revealed that, UFOV is a valid and reliable index of driving performance (Clay et al., 2005). However, some researchers take a different approach. For instances, Myers et al., (2000) revealed that poor performance on the UFOV test was associated with a high number of driving errors (failing to stop at a stop sign, missing important road signs, making errors of judgment or taking a wrong position on the road) in older drivers. Roge et al., suggested that, ability of processing peripheral stimulus and driving performance decreased with age. The reduction in target localization task of UFOV negatively correlated with managing of challenging scenario in simulated car driving and reaction time. Only speed, in their study showed a negative correlation with target detection tasks. Authors concluded that collision risk should be estimated only based on target localization task (Roge et al., 2004). Besides numerous studies on UFOV, effects of UFOV reduction on simulator driving performance are insufficiently investigated.

The present study examines the relationship between UFOV and driving performance and effect of UFOV reduction on driver's response to challenging scenario in driving simulator. The proposed hypothesis is people who have a poor performance on UFOV test because of delay and error in detecting of peripheral stimulus may be fail in successfully managing challenging scenario (suddenly entrance of pedestrian onto road) and may be experienced a collision. In addition, general driving performance in simulator and performance elements including reaction time and speed may be influenced. Finally determine which subtests of UFOV suggest a significant relationship with driving performance or collision at simulator.


A sample consisting of 90 professional male drivers from government sectors, aged 22 to 62 (Mean =42.5, SD=9.9), voluntarily participated in this study. With coordination and justification of study objectives for transportation department managers of these organizations, they requested to provide possibility of drivers to participate at the current study as a part of traffic safety promotion program. Based on age, subjects were divided into two groups, young group with ages £42.5 (M=33.5, SD=6.1, n=47) and older group aged >42.5 (M=50, SD=5, n=56). All participants had normal or corrected-to-normal vision. The research adhered to the tenets of the ethic committee of the Tehran University of Medical Sciences, all subjects gave informed consent before participating in the research after explanation of the nature and possible consequences of the study.

Devices and instruments

A computerized task was developed same as Sekuler et al., making some changes for measuring of UFOV (Sekuler et al., 2000). The central stimulus included four geometric figures presented in the center of a grey background. From one trial to the next, the shape was selected randomly from the figures. The peripheral target was a white spot that could appear in one of 24 positions, each marked by a white circle, slightly larger than the target spot. The 24 locations were arranged into eight evenly spaced radial spokes, and each spoke contained three locations at eccentricities of 6, 12, and 18 degrees. Both central and peripheral stimuli were presented for 90 ms. In the divided and selective attention subtests, the central and peripheral tasks were presented simultaneously.


Before driving in simulator, participants performed the UFOV test. Test consisted of four parts: central task, peripheral task, divided attention, and selective attention. Before each stage some practice trials were included. Total test completed for approximately 15 minutes. Participants used a mouse to start the test and indicated their responses. If a subject had difficulty to use the mouse, they were responded by pointing to the appropriate target position and a technician made the mouse responses for the subject's choice. Viewing was binocular from a distance of approximately 40 cm. There were three attention conditions: focused, divided, and selective. In the focused condition, participant performed the central and peripheral task in separate stages of tests. In divided and selective attention condition, central and peripheral stimuli presented simultaneously and selective condition is similar to the divided attention task, but, there were some distractors. Tasks were presented as following order: focused-central, focused peripheral, divided and selective (Fig. 1). Scores of all subtests calculated based on the proportions of errors that a transformation was used by the inverse sine of their squareroot to normalize the variance (Sekuler and et al., 2000). For peripheral task, error scores was based on the proportion of times a subject misidentified the radial and/or eccentric position of the stimuli.

Simulated driving task

After measuring of UFOV, subjects performed a simulated car-driving task on the driving simulator (Fater Technology Co., Iran). The simulator used in the study consisted of an open cabin with real car parts (steering wheel, gear shifter, clutch, accelerator, brake pedals, handbrake, light button and safety belt mounted on a solid base). Road scenes were presented on three seventeen inch LCD monitors giving a 120 degree field of view. Before driving, there was a familiarization with simulator elements. Then participants completed a practice trial for 10 minutes on simulator. Then, all participants experienced the same simulator scenario for comparison purposes. The road included highway and City Street as direct and curved. The simulator task completed approximately for 20 minutes. Drivers encountered with challenging scenario approximately 5 minutes after starting driving session. Our defined event was "suddenly entrance of pedestrian to road". This was a situation that could result in accident if driver has a delay on acquiring visual information about peripheral target stimuli (pedestrian). The point of entrance and speed of pedestrian for all samples was the same. Four indices about driver's performance were recorded: collision, braking reaction time, speed, and general driving performance in simulator.

After driving on simulator, examiner completed a scale consisted of 13 items that assessed driving behaviors and skills. Driving related components monitored were speed, using indicator and correct stop before junction and so on. All items rated on a 1- 3 Likert scale (corresponding to Not At All, Sometimes and Often, respectively). Total score calculated from sum of all item scores. The higher score indicated a better performance. The reduction of UFOV based on subject's error scores on all UFOV subtests between young and old age group was statistically analyzed. Pearson correlation coefficients between simulator driving performance parameters as a dependent variables and UFOV subtests as an independent were calculated. Student's t-test was used to examine significant differences between subtests error scores between accident-involved and non-accident groups. A univariate logistic regression analysis was used to determine the extent to which reduction of UFOV predicts accident in simulator as a dependent variable. In a second step, age was used as covariate in a multivariate logistic regression analysis. Regression analysis was used to examine the relationship between UFOV subtests and reaction time. All correlation coefficients and statistical analysis were considered to be significantly different when the probability of error was 0.05.


Table 1 presents descriptive data for UFOV subtests. The correlation analysis between UFOV and age revealed a high and significant relationship except for focused attention condition (Table 2a, 2b ). Student's t-test was used for comparing the mean of UFOV subscales between young and old groups. The analysis suggested significant differences between two groups on UFOV performance in central divide attention t (87)= -5.4, P<0.001), peripheral divided attention t (87)= -4.3, P<0.001), central selective attention t (87)= -3.0, P<0.01) and peripheral selective attention t (87)= -2.5, P<0.01).

The correlation coefficients between target detection (central task) and target localization (peripheral) error scores suggested a significant correlation in central attention condition (r=0.29, P=0.004), divided attention (r=0.553, P=0.000) and selective attention(r=0.442, P=0.000). The more error in detection task, the more limited the localization task.

Analysis of simulator driving data

Correlation analysis was used to examine the relationship between simulator performance and UFOV subscales. A negative significant correlation was found between the divided attention (peripheral) score and driving performance (r=-0.281, p<0.01). In other words, subject who have more error on divided attention subtest show a poor performance in simulator driving. Of 85 participants that completed all of study tests, 45 people having no accidents in simulator driving session, 38 people had one accident and 14 people had two accidents. As an exploratory analysis the sample divided into an accident-involved and non-involved. Student's t-test revealed significant differences in divided attention (central and peripheral) and selective peripheral scores between two groups (Table 2a, 2b ).

Assuming a 40% reduction in UFOV as the pass-or-fail cutoff score, it was that, we defined 40% or more reduction in UFOV if any subtests of UFOV had a 40% or more errors. Then, a logistic regression was conducted to determine whether UFOV could be used to predict whether a driver was involved in crashes and or not. The result revealed that 40% reduction of UFOV, regardless of age, increased risk of accident involvement (OR=12.1, 95% CI, 2.6-56.3). The resulting logistic regression coefficients and relevant statistics are shown in Table 3.

Regression analysis was used to examine the effect of UFOV reduction on braking reaction time. Divided attention (peripheral) task and selective attention (peripheral) showed a significant prediction on braking reaction time, F (1, 78) = 4.7, P<0.05, r=0.241) and F (1, 78) = 4.2, P<0.05, r=0.22), respectively. In other words, subjects with more error in these subtests have a long reaction time.


Age showed significant correlation with UFOV subtests except for focused attention conditions. There was also a correlation between central (target detection) and peripheral (target localization) tasks. These result confirmed the result of Roge et al., 2005.

The relationship between simulator driving performance and UFOV subtests indicated that, only peripheral task score in divided attention subtest had a negative correlation with diving performance. On the other hand, the analysis of UFOV subtest's means between accident involved and non-involved subjects in simulator driving session revealed that only peripheral tasks scores in divided and selective conditions have significant differences between two groups. These findings emphasized on the important role of peripheral vision on safety and performance of driving. Also confirmed the finding of Roge et al., that showed risk of accident only could be estimated by localization task (Roge et al., 2004). When a noticeable reduction in UFOV considered (as defined) and entered to the logistic regression model, risk of being involved in accident increased

(OR=12.1). These results are the same as the study of Ball et al., that, revealed a strong association between UFOV performance and retrospective crashes (Ball et al., 1993) and prospective crash involvement (Owsley et al., 1998). They reported that UFOV was a significant predictor of crash rate, and individuals with UFOV reduction of 40% or more were 2.2 times more likely to be involved in a crash than those with less than 40%. Ball et al., in their retrospective study found that older drivers with serious-more than 40%- loss in the UFOV were 6 times more likely than those with minimal or no UFOV reduction to have been at least partially responsible for a crash within the last five years. However, none of these studies specifically reviewed risk of accident in a simulated car driving experiment. Between UFOV subtests only peripheral tasks scores in divided and selective conditions have significant differences between accident involved and non involved groups. Also, only peripheral condition scores showed a negative correlation with driving performance. In other analysis on braking reaction time it was found that, subjects with high error in peripheral subtest of UFOV had a long reaction time.

It could be concluded that driving safety and performance most affected by peripheral task in UFOV and effect of all subtests were not the same. This confirm finding of Roge et al., study (Roge et al., 2004).

In conclusion, the result of our study demonstrated that, UFOV could be used to predict driving performance and risk of accident. The result can help to identify high risk drivers which may be useful to licensing authorities. Although license examiners more involved with screening of drivers, occupational physicians and occupational health professionals should assess the UFOV and other cognitive abilities of drivers for determining fitness to drive.


The research has been supported Center for Environmental Research (CER), Tehran University of Medical Sciences, grant #132.5959.


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© 2007 Tehran University of Medical Sciences Publications

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