This post will outline some of the basic methods vision scientists use in experiments, in plain everyday language. I’m anticipating that future posts will refer back to this as a sort of glossary, so pay attention, this will be on the test…
Aims of vision research
Although it seems effortless, vision is pretty complicated. Your brain processes a huge amount of information very, very quickly to allow you to interpret the world. We’re years away from building a computer that can do the same job anywhere near as fast or as well. So, the aim of vision science is to understand the processing (or algorithms) that the brain uses to simplify and interpret the input from the eye. If we can understand this, we’ll know a lot more about how the brain works in general, and also be able to reproduce its functions in a computer program or even a robot. There are many potential applications for an accurate computer model of human vision, in areas such as advertising, image and movie compression, airport security, photography – even building artificial eyes for the blind.
Psychophysical methods: overview
Some vision research uses direct methods, such as single cell recording (usually in animals) or imaging (e.g. fMRI). However, a less invasive (and expensive!) method is to present observers with visual stimuli (pictures or movies) and ask them questions about them. These questions are not typically open-ended (how does this picture make you feel . . .) but are instead very simple. For example, we might show an observer two images of some stripes, and ask which has the highest contrast (i.e. the biggest difference in brightness between the dark and light regions). In the example below, it’s clear that the stimulus on the left is higher in contrast, though in real experiments the judgement might be much more difficult. Such simple questions have the advantage that they take very little time to respond to, and responses can often be given via a mouse or computer keyboard and repeated many times (often many thousands of times).
Using these responses, we can gain insight into how the brain is working. Sometimes, an experiment might be designed to distinguish between two alternative theories. Other experiments might report on a surprising new finding, and still others collect data to inform the construction of computer models of the visual system. By systematically varying the stimuli in a controlled manner, we can find out about the limitations on performance (i.e. how good subjects are at doing a task) as well as their subjective experience of perception. This works for all sorts of stimulus dimensions: contrast, luminance, colour, motion, depth, size, tilt and many others. Psychophysical methods are used for other senses too – hearing and touch in particular.
A simple detection experiment
A fundamental question we can ask is how intense a target stimulus must be before we can see it. This intensity could be along lots of possible dimensions (luminance, colour, motion etc.), but the example here will be for contrast. So, how high must the contrast of a stimulus be before it can be reliably detected? We can find this out by presenting the target at a range of contrasts, and seeing how accurate an observer’s performance is at each contrast level. When the contrast is high, they should get it right all the time, as the target will be clearly visible. When the contrast is very low (definitely invisible) the observer will be guessing, so performance will be at chance levels. Somewhere in between these two extremes we should be able to find the contrast level where the target is just able to be detected.
One possible method is to show observer a single presentation, and ask whether or not they saw the target (and repeat many times for different contrast levels). This is called a yes/no task (for obvious reasons) and under some circumstances it is an OK method to use. However, there are a few technical reasons why it might not be the best choice of task. Instead, a technique called two-alternative forced choice (2AFC) is often better. This is very similar, except that there are two intervals (indicated by beeps), one of which contains the target, and the other contains a blank screen. The observer says which interval they thought contained the target (actually, this can also be done by using different areas of the screen, like left and right sides, instead of different temporal intervals), and the computer records their response. We repeat this for many many trials, over a range of contrasts, and calculate on what percentage of trials the observer was correct at each contrast level.
The psychometric function
The graph above shows some example data (circles) from an experiment like the one described. The y-axis tells us what percentage of trials were answered correctly at each contrast level (given on the x-axis in logarithmic (dB) units). The function the data describe is called a psychometric function. Because there are two intervals in our task, even when the target is invisible and the observer is guessing they will still be right half (50%) of the time. You can see that at the left hand side of the graph, where the contrast is low, the data points cluster around 50% correct. At the other extreme, when contrast is high, the observer is right all of the time – on 100% of the trials. The intermediate contrasts are the interesting ones, as here performance is somewhere between chance and perfect.
We usually decide to call a specific level of performance the ‘threshold’. A good level (for 2AFC) is 75% correct, as it is half way between chance and perfect performance – it’s when the observer can just see the target. You might notice that there is a data point very close to 75% correct, at about 6dB of contrast. If we didn’t care about details, this would be a good approximation of threshold. However, sometimes we want to be a bit more exact, so we fit a curve to the data points (using a computer program) and find out where the curve passes through 75% correct. For this example, it’s just under 6dB – this is our threshold.
Thresholds for different targets
Measuring just one threshold on its own isn’t really very interesting. But if we vary something about the target we can see how performance gets better or worse by measuring lots of thresholds. A classic example is the contrast sensitivity function (CSF). This measures thresholds (or sensitivity, which is 1/threshold) for grating stimuli (like the ones above) at a range of bar sizes (spatial frequencies). Low spatial frequencies are big, with wide bars, whereas high spatial frequencies are small, with very narrow bars. The graph below plots sensitivity as a function of spatial frequency. You can see there is a peak between 1 and 4 c/deg – these are the frequencies at which we are most sensitive. On either side of this, sensitivity falls off, meaning we need more contrast to reach threshold. The contrast sensitivity function is our window of visibility on the world – stimuli within the window are visible, those outside it are not. It is often used in clinical research to understand the source of a visual problem, or the limitations it causes for a patient.