7 SMIDSY – looked but not perceived; prevalence

SUMMARY – the way our brain scans our environment means that we can miss rare objects… only 3% of the vehicles on the road in Europe are PTWs so meeting one at a junction is actually a relatively rare event… even when drivers know there are particular vehicles around, they’ll miss the unusual ones… in experiments, drivers are even more likely to miss spotting buses than motorcycles…

So far we’ve looked at how a motorcycle’s physical characteristics – its relatively small size and visual conspicuity – as one factor in the ‘looked but failed to see’ problem, and we also seen how the way visual data flows between the eye and the brain plays a part. There is yet a third, even more complex, form of conspicuity. This time, the issues are the result of how the brain tries to make sense of the outside world – what’s known as ‘cognitive conspicuity’. In the next series of posts, we’ll look at what are sometimes referred to as:

  • prevalence
  • workload
  • inattentional blindness
  • semantic meaning
Prevalence cars

Prevalence refers to how common an object is. The understanding of this issue grew out of research into medical screening procedures. It’s been known for some time that highly trained staff looking through microscopes at tissue samples tend to miss the rare anomalies, even though they highly trained to see what they are looking for. Initially, it was thought to be poor training, tiredness or even carelessness, but studies showed that isn’t the case. It’s the way the brain scans the visual data which is the problem.

A few years back, I came across an study carried out by Lenné et al (2013) which looked at the behaviour of drivers primed to look for two different types of vehicle in a stream of vehicles. Lenne and his team noted that motorcycles make up a very small proportion of the overall flow of traffic – less than 1% on UK roads. They wondered if they are rare enough that even though drivers know they will encounter motorcycles, they would miss them and focus on other parts of the scene, just like missing medical anomalies.


To test their theory, the researchers suggested that the ability to detect motorcycles could be changed by making that observer more aware of motorcycles. They would do this by the simple expedient of showing the observers more motorcycles, but they also showed the participants a stream of traffic with a lot of buses. The drivers were split into two groups, placed in the simulator and sent on a 7.5 kilometre ‘exposure’ drive. All they had to do was drive following normal road rules, but the traffic stream was different. One group encountered an unusually high number of motorcycles with no buses appearing. The other drove with an unusually high number of buses but with no motorcycles appearing. The test drives were set in urban areas, with regular intersections and with a 60 kph / 37 mph speed limit. Apart from the target vehicles, everything else was four-wheeled. Vehicles appeared from right and left, as well as ahead. Traffic was moderate, with the target vehicles appearing at random.

The simulator was based on a vehicle cab constructed from genuine vehicle parts and standard controls together with an audio feed, to give an accurate ‘look and feel’, whilst the visual environment was provided by three 19″ screens providing a 120 degree view and what the researchers describe as ‘medium fidelity’. Two custom buttons on the steering wheel allowed the subjects to respond by pressing the appropriate button when they detected the targets. The participants were recorded as having missed a target if they failed to respond, or responded after the target had passed them. At the same time, their driving performance was monitored by a range of sensors including speed, lateral position, braking and acceleration.

Having completed that, both groups were sent on a second, longer 39 km drive, where they were asked to count the number of motorcycles or buses they saw, and they were told that their reaction time and accuracy were both being measured. In this longer drive, the ‘high motorcycle prevalence’ drive contained 120 motorcycles and 6 buses. In the ‘high bus prevalence’ drive, the numbers were reversed, with just 6 motorcycles and 120 buses.

You’ll probably not be surprised that the drivers told to look for buses missed seeing some motorcycles. You’ll remember that it’s commonly-held that motorcycles are hard to spot – ‘low salience’ in the jargon.

But I can almost guarantee you WILL be surprised that when tasked with looking for motorcycles, the drivers being assessed missed spotting EVEN MORE buses. That’s almost certainly not what we’d expect. If drivers aren’t spotting something as big as a bus, then it’s not a simple question of salience! I must make clear again that this isn’t ‘carelessness’, ‘not looking properly’, ‘bad training’ or any of the other blame-game explanations we adopt so readily. It’s an example of the hidden power (and weakness) of the brain, working below the level of our awareness. Neuroscientist David Eagleman explains in his TV series ‘The Brain’:

“This is not a failure of the brain. It doesn’t try to produce a perfect simulation of the world. The internal model is a hastily drawn approximation and more details are added on a need-to-know basis.”

The key point here is that “placing your eyes on an object is no guarantee of seeing”. Neither group of participants realised they hadn’t seen some of the motorcycles or some of the buses. As far as the brain’s concerned, ‘what we see is all there is’, and we see what we are used to seeing.

As motorcyclists, that should make us think twice. Firstly, what WE think we see around us is almost certainly not the whole visual story. And because what OTHER drivers believe they see is just as incomplete, and it might be our motorcycle that’s gone missing. And if drivers don’t see buses, that should really be a warning, although before you panic, most of the drivers successfully detected ALL vehicles. The successful detection rate was actually over 99%. But looking at the less than 1% of cases where drivers failed to see one or the other, 68% detected all buses and 78% detected all motorcycles.


Beanland et al (2014) then seem to have conducted a slightly different driving simulator experiment, again with the target vehicles being motorcycles and buses. Half of the subjects experienced a high prevalence of motorcycles with a low prevalence of buses, and half experienced a high prevalence of buses with a low prevalence of motorcycles. What they found was that drivers detected high-prevalence targets faster than low-prevalence targets for both vehicle types.

When motorcycles occurred more frequently, the subjects detected them an average of 51 metres further away than in the tests where they occurred less often. At a simulated driving speed of 60 kph, this would allow the drivers an extra three seconds to respond. Predictably, the higher salience buses were spotted even further off when subjects saw a lot of them, giving drivers an additional 4.4 seconds to react.

Their conclusions were that increasing the prevalence of visual search targets makes them more salient, and consequently easier to detect.

Motorcyclists only represent about 3% of road users in Europe so meeting one at a junction is actually a relatively rare event for a driver. As an aside, Lenné et al concluded that the detection rates for motorcycles would be improved by the ‘simple’ expedient of putting more motorcycles on the road. Not too surprisingly the research was picked up by interested parties such as the riders’ rights organisation FEMA and the UK’s industry body MCIA and used as an argument that motorcycles should be promoted as a means of transport to make them safer. In reality, we’re not about to see floods of powered two-wheelers on the roads, and so no ‘prevalence effect’ is about to influence drivers’ motorcycle detection rates overnight.

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Beanland, V., Lenné, M. G., Underwood, G. (2014) “Safety in numbers: Target prevalence affects the detection of vehicles during simulated driving” Attention, Perception, & Psychophysics. DOI 10.3758/s13414-013-0603-1

Lenné, M. G., Salmon, P. M.. Beanland, V., Walker, G. H., Underwood, G. and Filtness, A. (2013) “Interactions between Cars and Motorcycles: Testing Underlying Concepts through Integration of On-Road and Simulator Studies” In: Proceedings of the Seventh International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design

Last update:

Wednesday 1 May 2019 – typo fixed
Friday 23 November 2018 – minor edit for clarity


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