The key theme running through the last few presentations is that the accuracy of eyewitnesses'
memory can be unreliable. So – is there any way in which we can differentiate
between an accurate memory and an inaccurate one?
Well – one possible indicator of an eyewitness’s accuracy is their level of confidence.
So – if an eyewitness says that they are absolutely definitely sure that a suspect
is the criminal – then presumably this judgement is likely to be more accurate than another
eyewitness who says that the suspect could be the criminal but they’re not completely
certain. In fact, some have argued that the confidence
an eyewitness displays is the most important factor in whether jurors and criminal justice
professionals believe their testimony or not. This is backed up by behavioural data, showing
a very strong relationship between the confidence shown by a witness and whether they’re considered
to be reliable by both laypeople and legal professionals.
In fact, in several Australian jurisdictions, judges will actually cite witness confidence
as an example of an indicator of witness credibility in their instructions to the jury.
So – is this all true? To what extent does an eyewitness’s confidence actually predict
their accuracy? Psychologists have investigated this question
and while it’s not true that confidence tells you literally nothing about accuracy
– the relationship has turned out to be much more complicated than previously thought.
To understand this, I’m going to need to explain what a correlation coefficient is.
A correlation coefficient is a statistic that measures the strength of a relationship between
two variables. It’s easiest to explain using a graph.
On this scatterplot, the range on the vertical axis shows all possible levels of an eyewitness’s
confidence – no confidence at the bottom to complete confidence at the top.
The horizontal axis shows two categories of accuracy – inaccurate – they got the identification
wrong – and accurate – they got the identification correct.
So first of all, let’s show the situation where confidence is a perfect predictor of accuracy.
Each dot I'm going to draw here is a different eyewitness.
The first eyewitness is 100% confident – and made a correct identification.
The second eyewitness is completely lacking in confidence – and turns out to be giving
us an inaccurate identification. And so on with other eyewitnesses – who
stand right on top of either one of the first two dots.
So – in this first example – we have no overlap between the accurate and inaccurate
eyewitnesses – all of the accurate eyewitnesses have the same level of confidence which was
100%, and all of the inaccurate eyewitnesses also have the same level of confidence but
for them it was 0%.
Here, confidence is a perfect predictor of accuracy – and in this situation the correlation
coefficient will be at its maximum of one: denoting a perfect relationship between the
two variables – where knowing an eyewitness’s confidence allows you to know without any
doubt whether their identification is accurate or not.
If this happened in real life – you would always be able to know whether to believe
an eyewitness or not based entirely on whether they were high or low in confidence.
Now let’s see what other, rather more plausible outcomes might look like.
Now we have some variation in confidence levels, where this scatter means that the correlation
won’t quite be, technically, a perfect one. However, there’s still no overlap between
the confidence levels of the accurate and inaccurate eyewitnesses. And so, in principle,
as long as we knew in advance the point at which the confidence level flipped from yielding
inaccurate answers to accurate ones, we could still tell with a hundred percent certainty
whether an eyewitness could be believed or not based entirely on their level of confidence.
The correlation coefficient for this data would be about point nine.
Now in this example we have overlap, where some of our reliable eyewitnesses are actually less
confident than some of our unreliable eyewitnesses. So, in this particular situation, the correlation
coefficient would be about point seven – rather less than perfect.
So, eyewitness confidence gives us some indication of their accuracy. But, it’s
also going to be misleading some of the time. Now the overlap is much more severe.
It’s still the case that, on average, the accurate eyewitnesses are more confident than
the inaccurate eyewitnesses. But if we rely on confidence to tell whether we believe
an individual eyewitness or not, then we’re going to be misled an awful lot of the time.
In this scenario, confidence is an unreliable indicator of whether an individual eyewitness
is accurate or not.
And, finally, here we have a zero correlation
between confidence and accuracy – the case where confidence tells us absolutely nothing
whatsoever about whether to believe our eyewitnesses or not.
The two variables are completely independent of one another.
So, that’s the range of relationships we could get. In the next presentation we’ll
find out which of these relationships between accuracy and confidence
is the reality.