All about noise masking

So, a few years ago I got interested in noise masking. It’s a widely used paradigm, where you measure detection thresholds for targets buried in various amounts of external noise (kind of like the ‘snow’ on an untuned TV set).

Some 2D white noise, increasing in contrast from left to right. Stronger noise (right) will impede detection of a target.

The technique is useful because it allows sensitivity deficits, sometimes due to clinical conditions like amblyopia, to be characterised better. For example, it might show that internal noise is greater in some condition, and this in turn could inform new models, treatments or therapies.
For a long time people have sort of half realised that there are some funny things about noise masking. The data don’t always come out the way the dominant model of noise masking says they should, and different labs sometimes come up with quite different results. This has been noted in passing in several papers, but there was clearly a need for someone to really nail the problem, and get to the bottom of things.
After a few years of pilot experiments (this has never been my main focus, always an exciting side project), Tim and I came up with a series of experiments that we thought would shed some light on what’s going on in noise masking. Our hypothesis was that the white noise masks that people typically use might actually cause most of their masking through a process of suppression (and not by injecting noise into the decision variable like they’re supposed to). We came up with four tests for this, each of which appeared to support our conjecture.
What really convinced us though was when we developed a new type of noise, that was much ‘purer’, and didn’t produce any suppressive masking on our four tests. It’s actually a very simple idea that appears in the literature way back about 40 years ago, but in experiments on detecting luminance instead of contrast. All you do is take your target stimulus and add or subtract a little bit of its contrast, randomly, in both intervals of a 2IFC experiment. Here’s an example trial sequence:

Demo of 0D noise: Each column represents a trial, with the upper row showing the stimulus presented in the first interval, and the lower row the stimulus in the second interval. Each stimulus is a log-Gabor, with its contrast drawn from a zero-mean Gaussian distribution. When the contrast is positive, the stimulus has a bright bar in the centre. When it is negative it has a dark bar in the centre. The top row has also had a positive target contrast added (in this example, always 30%). An observer in the experiment selects the interval with the highest positive contrast, as this is the one most likely to contain the target. In the first column, this is Interval 1, as it has high positive contrast, whereas Interval 2 has high negative contrast. So, the observer’s choice is correct. However, in trial 2, the displayed contrast in Interval 1 is actually negative (despite the target contrast being added to it) whereas in Interval 2 it is positive. In this situation, the observer will pick the ‘wrong’ (e.g. non-target) interval, even though they are behaving sensibly. But, on average over many trials, they will tend to choose the target interval when the target contrast is high enough to overcome the variance of the ‘noise’.

From the perspective of a mechanism tuned for detecting the target, this ‘contrast jitter’ behaves in exactly the same way as the more widely used ‘snow’ type white noise. Crucially though, it doesn’t activate any other nearby mechanisms that might cause suppression. Because it has no spatial or temporal dimensions, we call this new stimulus zero-dimensional (0D) noise.
I think the 0D noise technique is a much cleaner way of doing noise masking experiments. What we’re saying is quite controversial though, so whether anyone else agrees remains to be seen! But at least the paper is out there now. It’s probably my favourite project, despite (or because of) the level of nerdiness involved in parts of it. It’s open access, and you can read the paper here:
Zero-dimensional noise
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