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effect of lighting, because this tracking task relied on central vision. Also, practiced automatic operations (like this tracking task) do not demand a lot of attention, so they can be performed without experiencing interference from, or generating interference with, other ongoing information processes (Pashler, 1998). In contrast, math problems appear to have interfered with lighting conditions. Differences in reaction times between MH and HPS conditions became the largest (over 50 ms for most off-axis targets) when both tracking and distracting tasks were performed at the same time (i.e., the highest task load). Since combining all three tasks increased task load, their response selection stages likely shared attention resources and therefore interference occurred. In this experiment, the math performance was slightly improved by using MH lighting, but it was not yet significant. A similar and even larger enhanced effect of spectral power distributions (SPDs) on detection performance F I G U R E 2 Reaction time for metal halide and high-pressure lighting conditions (TT = tracking task; MP = math problem). appeared in another study using a more realistic, high-demand computer-simulated driving task. This study had subjects detect peripheral targets (detecting task) while virtually driving along a roadway projected on a large screen (tracking task) (Bullough and Rea, 2000). In this study, subjects missed targets 25 percent more frequently under HPS lighting than under MH lighting. Can results of such laboratory studies apply to real driving situations To answer this question, a field study (Akashi and Rea, 2002) had subjects detect targets located 15 deg and 23 deg off-axis (detecting task) while driving along a short, closed track and Effective lighting may reduce task load and therefore allow people to allocate more attention to target detection fixating on and reading aloud single digit numbers sequentially presented in the central visual field (tracking task).Reaction time was measured for each target presentation. The results suggested that average reaction time was around 720 ms for actual driving, compared to 250 ms for a single detection task in which subjects responded to a 15 deg off-axis target with the same luminance contrast in a laboratory (He et al.,1997).In the same study, when the car was static, average reaction time was around 670 ms.The use of MH lamps reduced reaction times by approximately 50 ms. This experiment did not control onsets of stimuli in the first and second tasks;thus,it does not allow for exploring detailed mechanisms of interaction between the two tasks. However, the results suggest interference between tasks and lighting conditions. This experiment used a target luminance of 0.3 (cd per sq m), a level at which the difference in reaction time between under the MH and HPS lamps should be very small, according to He et al.’s experiment (1997). However, it did show a 50-ms improvement in reaction time due to the use of MH lighting with a statistical significance. In this case, since both tracking and detecting tasks are visual tasks, the use of effective lighting can improve visibility for the tracking task and target conspicuity for the detecting task.The use of effective lighting delivers a twofold benefit to the detecting task: a shorter response selection time of the detection task itself, and a shorter slack time due to the shortened response selection time of the tracking task. This results in a much shorter reaction time than expected. This discussion leads to a conclusion that effective lighting may reduce task load and therefore allow people to allocate more attention to target detection. In a dual-task paradigm, when both tasks are visual or very difficult, task-lighting interference appears to enhance the effectiveness of lighting’s impact on performance. What Is Next We need more research to verify the validity of such enhanced effects of lighting. First, fine tuning experiments using the above mentioned dual-task technique could help us to better understand task-attentionlighting interference. This will determine how much each task type (e.g., changing lanes and using cellular phones) impairs detection performance and how much each lighting factor can regain the impaired performance. Second, to implement such December 2005 LD+A 11
RESEARCH MATTERS research results to roadway and automobile lighting practice, it is necessary to quantify a driver’s task load under different roadway characteristics (e.g., highways, interchanges, or curves) by using a standard detecting task such as one used by MacDonald and Ellis (1975).Finally,it is important to determine lighting requirements for each of the roadway characteristics while taking into account the influence of task load on detection performance. The evidence so far indicates that such understanding would help us to make our roadways safer. References Akashi Y, Rea MS, 2002. Peripheral detection while driving under a mesopic light level, Journal of the Illuminating Engineering Society, 31, 1, 85-94 Akashi Y, Neches J, 2003.The effect of foveal task load on peripheral target detection, Proceedings of CIE 25th Session in San Diego, D4, 44-47 Bullough J, Rea MS, 2000. Simulated driving performance and peripheral detection at mesopic light levels,Lighting Research and Technology, 32, 4, 194-198 He Y, Rea MS, Bierman A, Bullough J, 1997. Evaluating light source efficacy under mesopic conditions using reaction times, Journal of the Illuminating Engineering Society, 26, 1, 125-138 Harms L, 1986. Drivers’ attentional responses to environmental variation: a dual-task real traffic study, In Gale AG et al.Vision in Vehicles II,Amsterdam: Elsevier Science Publishers, 131-138 Johnson A, Proctor RW, 2004. Attention:Theory and Practice,Thousand Oaks,CA:Sage Publications,Inc.,30-37 MacDonald, Ellis, 1975. Driver workload for various turn radii and speeds, Proceedings of the annual Meeting of the Transportation Research Board,Washington, D.C. Navon D,Miller J,2002.Queuing or sharing A critical evaluation of the single-bottleneck notion. Cognitive Psychology, 44, 193-251 Pashler HE, 1998. The psychology of attention, Cambridge, MA: MIT press Teichner WH, Krebs MJ, 1972. Laws of the simple visual reaction time, Psychological Review, 79, 344-358 Treat JR,Tumbas NS, McDonald ST, Shinar D, Hume RD, Mayer RE, Stansifer RL, and Castellan NJ, 1977. Tri-level study of the causes of traffic accidents, Report No. DOT-HS034-3- 535-77. Verwey WB, 1993. How can we prevent overload of the driver Oarkes AM, Franzen S, Driving future vehicles,London:Taylor & Francis,235- 244. Vince M, 1949. Rapid response sequences and the psychological refractory period, British Journal of Psychology, 40, 23-40 Welford AT, 1952. The psychological refractory period and the timing of high speed performance-A review and a theory. British Journal of Psychology, 43, 2-19 RP-1 1/2 horiz 12 www.iesna.org
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