Signal detection, thresholds and consciousness

Towards the end of the 2nd World War, engineers in the US Air Force were asked to improve the sensitivity of radar detectors. The team drafted a working paper combining some new math and statistics – the scattering of the target, the power of the pulse, etc. They had no way of knowing at the time, but the theory they were sketching – signal detection theory, or SDT – would be one of the most influential and durable in modern psychology. By the 1960s, psychologists had become interested in applying the engineers’ theory to understand human detection – in effect, treating each person like a mini radar detector, and applying exactly the same equations to understand their performance. Fast-forward to today, and SDT is not done yet. In fact, it is beginning to break new ground in the study of human consciousness.

To understand why, we need to first cover a little of the theory. Despite the grand name, SDT is deceptively simple. When applied to psychology, it tells us that detection of things in the outside world is noisy. Imagine the following scenario. You sit down in one of our darkened testing rooms, and I ask you to carry out a relatively boring task (any reader who has participated in one of our experiments will be on familiar ground here). Each time you see a faint spot of light on the computer monitor, you should press the “yes” key. If there was no light, you should press the “no” key. If the task is made difficult enough, then sometimes you will say “yes” when there is no light present. In radar-detector speak this is equivalent to a “false alarm”. You will also sometimes say “no” when the signal was actually there – a “miss”. Why does this happen?

Consider that on each “trial” of our experiment, the faint flash of light leads to firing of neurons in the visual cortex, a region of the brain dedicated to seeing. Because the eye and the brain form a noisy system – the firing is not exactly the same for each repetition of the stimulus – different levels of activity are probabilistic. When the stimulus is actually present, the cortex tends to fire more than when it is actually absent (this is summarised by the shifted “signal” probability distribution over firing rates, X, below). But on some trials on which the stimulus was absent there will also be a high firing rate, due to random noise in the system (corresponding to the dark grey area in the figure). The crucial point is this: you only have access to the outside world via the firing of your visual cortex. If the signal in the cortex is high, it will seem as though the light flashed, even if it was absent. Your brain has no way of knowing otherwise. You say “yes” even though nothing was there.


The other insight provided by SDT is that how many false alarms you make is partly up to you. If you decide to be cautious, and only say “yes” when you are really confident, then the weaker signals in cortex won’t pass the threshold, and false alarms will be reduced. The catch is that the number of “hits” you make will be reduced too. In fact, the cornerstone of SDT is that the visual system has a constant sensitivity, meaning that any increase in hit rate is also accompanied by an increase in false alarms, as shown by the performance curve above. Perception is never perfect.

When I was learning about this stuff as an undergraduate, the SDT curve confused me. It never seemed to me that perception was noisy and graded. I don’t glance at my coffee cup and occasionally mistake it for a laptop. Instead, the coffee cup is either there, or it’s not. There doesn’t seem to be any graded, noisy firing in consciousness.

Yet, in countless experiments, the SDT curve provides a near-perfect fit to the data. This is a paradox that I think is central to our understanding of consciousness. And a recent paper from Mariam Aly and Andy Yonelinas at the University of California, Davis, has begun to develop a solution. They summarize the paradox thus:

“These examples [such as the coffee cup] suggest that some conscious experiences are discrete, and either occur or fail to occur. Yet, a dominant view of cognition is that the appearance of discrete mental states is an epiphenomenon, and cognition in reality varies in a completely continuous manner, such that some memories are simply stronger than others, or some perceptual differences just more noticeable than others.”

Aly and Yonelinas propose a reconciliation of these points of view. Their experiments hinge on measuring SDT curves in different conditions, and across different thresholds (defined as different confidence levels). In the noisy, graded model, there should never be a point at which it is possible to increase hits without also increasing false alarms (the red curve above). However, a hunch that there is a particular “state” of viewing the coffee cup that is never accompanied by mistakes would correspond to the discrete boxes at either end of the SDT distributions. Adding these boxes instead predicts the blue curve (above). Yonelinas and Aly found that for simple stimuli, such as flashes of light, the red curve was a good fit to the data, indicating a graded, noisy process that differed only in strength. But for complex stimuli, such as deciding whether two photographs were the same or different, the SDT curve indeed showed a discrete “state” effect (below). You either saw it, or you didn’t.

Because most previous SDT experiments used simple stimuli, this explains why the graded curve has come to dominate the literature. Yet for the more complex objects we are used to seeing in everyday life, our intuitions are usually correct – there really is a discrete state of seeing the coffee cup. Could these discrete states be what we associate with consciousness?

To test this hypothesis, Aly and Yonelinas asked subjects to say whether their judgment on each trial was due to a conscious, perceived difference, or an unconscious feeling of knowing. They then extracted parameters describing how curvy or discrete the SDT curves were. Conscious perception was associated with a stronger estimate of the discrete state process, while unconscious knowing was associated with a more curvy, or graded, SDT curve. A separate experiment showed that the discrete change in perception occurs at an abrupt point in time, whereas unconscious knowing emerges only gradually.

The paper is a tour de force, and well worth reading for other findings I don’t have space to cover here. Suffice to say the “discreteness” of an SDT curve might provide us with a powerful tool with which to understand how the brain gives rise to consciousness, and does so by using statistical models that are relatively immune to subjective biases. It also paves the way for computational modeling aimed at understanding why graded and discrete processes arise.

But there is another, deeper insight from the paper that I want to conclude with. SDT can also be applied to memory: instead of detecting visual signals from the outside world, think of detecting a memory signal emanating from somewhere else in the system. Yonelinas was one of the first to quantify the discrete/graded distinction in memory (known as “recollection” and “familiarity”). By applying their state/strength model to a standard long-term recognition task, he and Aly found that discrete states were more common when recognising that a previous scene had been seen before (the black curve below). But by subtly altering the long-term memory task to focus on the detection of changes, they found something striking. Here are the SDT curves for the two tasks:

Both show evidence of the discrete “state” process, bridging two areas of psychology traditionally studied separately. But they do so in opposite directions. Why?

“We propose that the reason is that the detection of similarities and differences tend to play opposite roles in memory and perception. That is, in perceptual tasks, noticing even a small change between two images is sufficient to make a definitive “different” response… [In contrast], in recognition memory tasks, one expects the state of recollection to support the detection of oldness (i.e. a y-intercept) rather than the detection of newness.”

In other words, consciousness in perception and memory might both rely on discrete states. And both appear to share a common architecture optimized for different types of detection. The difference, then, is that memory might be optimized for detecting matches with our past, whereas perception seems concerned with detecting mismatches with our future.

Aly M, Yonelinas AP (2012) Bridging Consciousness and Cognition in Memory and Perception: Evidence for Both State and Strength Processes. PLoS ONE 7(1): e30231. doi:10.1371/journal.pone.0030231


4 thoughts on “Signal detection, thresholds and consciousness

  1. Yesterday I received a very kind email from Mariam Aly, first author on this paper, thanking me for the blog but also gently correcting my precis of the perception/memory experiment towards the end of the post. She agreed to me posting part of her email here:

    “I just wanted to clarify something about the state processes in memory and perception. The final experiment is actually entirely long-term memory (i.e. both of those ROC curves are from memory tasks). The idea is that in lots of memory decisions, the state process supports identification of things that match our past (recollection of oldness, this is the black ROC curve in the last figure). But if you design a task so that identification of differences or mismatches is more useful in memory, then the state process supports identification of newess (green ROC cuve in the last figure). So memory state processes can resemble perception state processes in the right conditions.”

    Which still, I think, points the way to an interesting distinction between perception/memory ROCs, but in the context of a single memory experiment. I’ve also updated the text of the post above to more accurately reflect the experiment that was carried out. Thanks Mariam!

  2. Pingback: Active-controlled, brief body-scan meditation improves interoceptive signal discrimination. « Neuroconscience

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s