iller identiication. This is exactly what we found.
At the lowest level of conidence, most identiications were of
illers – as we would expect if witnesses were simply guessing.
However, identiications at the medium level of conidence were
evenly divided between suspect and illers. At the highest level of
conidence more than 80% of identiications were of suspects.
This shows that while eyewitnesses do make mistakes, and
falsely identify innocent people, they usually have little coni-
dence in such decisions. On the other hand, when their coni-
dence is high they make many fewer mistakes.
We also found that the number of identiications of suspects
with corroborating evidence increased systematically from low
to high conidence, reaching more than 90% in the latter cate-
gory. This result further supports the idea that when eyewitnesses
have high conidence they have a high probability of identi-
fying the culprit.
Although our indings provide strong indirect evidence that
eyewitness accuracy is related to conidence, we were unable
to measure accuracy directly because we didn’t know the propor-
tion of lineups in which the suspect was guilty of the crime.
However, the mathematics of choosing from a lineup can
provide useful insights.
If the suspect is not the culprit and the witness identiies a
photo, there is a one-in-six chance that the identiied individual
will be the suspect. On the other hand, if the suspect is the
culprit and the witness identiies a photo, the probability that
they choose the suspect is a measure of their accuracy. If accu-
racy is zero (the witness is guessing) then this probability will
be one-in-six. On the other hand, if accuracy is 100% then they
will always identify the suspect.
However, to measure this probability we have to know the
number of lineups conducted at the Robbery Division of the
Houston Police Department in which the police suspect was
indeed the culprit. To solve this problem we turned to a math-
ematical model of memory used by other members of our team
(John Wixted, Laura Mickes, John Dunn and Steven Clark) to
understand the relationship between conidence and accuracy.
According to the model, the different levels of low, medium
and high conidence simply relect whether memory strength
exceeds a low, medium or high criterion. This is analogous to
the bar of a high jump: strong, medium and weak jumpers can
all get over a low bar, but only strong and medium jumpers can
get over a medium bar, and only strong jumpers can get over the
high bar. Similarly, weak memories that are likely to be inaccurate
may be still be strong enough to exceed a low criterion, but
only strong memories that are likely to be accurate can exceed
the high criterion.
As well as helping us to understand our results, a remark-
able feature of the model is that we could use it to estimate the
proportion of lineups in which the suspect was also the culprit.
However, before we applied it to our data, we irst tested the
model against a large-scale simulation conducted by a different
group of researchers in Australia.
Because it was a simulation, the researchers arranged the
test so that the suspect was the culprit in half of the lineups. We
used the results from this study to construct eyewitness iden-
tiication rates for lineups in which the culprit was present or
absent. When we applied the model to these different combi-
nations it determined the proportion of culprit-present lineups
with a very high level of accuracy.
Armed with this knowledge, we then applied the model to
our real-world data and estimated the proportion of culprit-
present lineups. Unlike almost all laboratory-based studies,
where usually 50% of lineups are culprit-present, our results
suggested that only 35% of lineups at the Robbery Division of
the Houston Police Department contained the culprit. In many
ways this is not surprising, as it is likely that the police would
want to know if a person responsible for one crime was also
responsible for similar crimes.MAY 2016|| 19