What is selectivity?

TL;DR: Claiming that something is “selective” implies knowledge of the stimulus dimension to which it is tuned. It also does not apply to simple intensity codes because selectivity requires a tuning preference.

TuningVsSensitivity
Unless you can tell me where on the x-axis “pain” is relative to “rejection” or whatever else dACC may respond to, you can’t really know that this brain area behaves according to either relationship.

This post is a just little rant about an age-old pet peeve of mine: neuroscience studies claiming they found that some brain area is selective for some stimulus/task/mental state etc. This issue recently resurfaced in my mind because of an interesting exchange between Tal Yarkoni on the one hand and Matt Lieberman and Naomi Eisenberger on the other. The latter recently published a study in PNAS suggesting that dACC is “selective for pain”. Yarkoni wrote a detailed rebuttal to their claims criticising the way they inferred selectivity. I recommend following this on-going discourse (response by Lieberman & Eisenberger and a  response to their response by Yarkoni). I won’t go into any depth on it here. Rather I want to make a more general comment on why I feel the term selectivity is frequently misused.

Neuroimaging methods like fMRI allow researchers to localise brain regions that respond preferentially to particular stimuli or tasks. This “blobology” is becoming less common now that our field has matured and many experiments are more sophisticated. Nevertheless, localising brain regions that respond more to one experimental condition than another will probably remain a common sight in the neuroimaging literature for a long time to come, even if the most typical such use is probably functional localisers to limit the regions of interest in more complex experiments.

Anyway, in blobological studies, results are frequently reported as showing that your blob is “selective” for something or other: not only are we supposed to believe that the dACC is selective for pain, but also that the FFA is selective for images of faces, the LOC is selective for intact objects, and the MT+ complex is selective for motion. While some of these claims may be correct, they are not demonstrated by blobology using any kind of method. In fact, Lieberman and Eisenberger write in their response to Yarkoni:

“We’ve never seen a response to one of these papers that says they were wrong to make these claims because they didn’t test for the thousands of other things the region of interest might respond to.  Thus the weak form of selectivity, the version we were using, can be stated this way:

Selectivityweak: The dACC is selective for pain, if pain is a more reliable source of dACC activation than the other terms of interest (executive, conflict, salience).”

Perhaps there has never been a response saying that these were wrong but I think there should have. What is selectivity? While the dictionary defines “selective” as a synonym of

“discriminating, particular, discerning”

the term has long been established in neuroscience. Take the Nobel Prize winning research by Hubel and Wiesel in the 1960s for example. They discovered that neurons in the visual cortex are selective to the orientation of simple bar stimuli. So for instance a horizontal bar may drive a neuron to fire strongly while a vertical one would not. In the neurophysiology literature one would say that this particular neuron has an “preference” for horizontal bars.

This, however, does not mean that the neuron is “selective” for horizontal bars. The firing rate in response to oblique bars is likely somewhere between the rate for horizontal and vertical ones. The distinction between preference and selectivity may seem like semantic quibbling but it’s not. They are referring to very different concepts and, as I will try to argue, this distinction is important. But let’s not jump ahead.

“Strong” selectivity (to use Lieberman and Eisenberger’s terminology) implies that the neuron only responds to horizontal bars but not much else. In the following images, the contrast of each oriented grating denotes how much of a neuronal response it would produce.

High
“Strong” orientation selectivity

A “weakly” selective neuron might respond similarly across a wide range of orientations:

Low
“Weak” orientation selectivity

An even less selective neuron would show very similar responses to all orientations and a completely non-selective neuron would respond at the same rate to any visual stimulus regardless of its orientation.

None
Almost no orientation selectivity

This means we can measure responses of the neuron across the whole range of orientations (or, more generally speaking, across a range of different stimulus values). Thus we can determine not only the preferred orientation but also how strongly the exact orientation modulates neuronal responses. In other words, we can quantify how discerning, how selective the neuron is for orientation. The key point here is that in all these examples the stimulus dimension for which the neuron is selective is the orientation of bar stimuli. The neuron prefers horizontal. It is selective for orientation.

So at least according to how neurophysiologists have defined selectivity over the past half century, the term refers to how much varying the stimulus along this dimension affects the neuronal response. I hope you will agree now that this does not apply to many of the claims of selectivity in the neuroimaging literature: an experiment that shows stronger responses in FFA to faces than houses shows that FFA prefers faces. It does not show that it doesn’t also prefer other stimuli, a point already discussed by Tarkoni and Lieberman and Eisenberger. More importantly, it does not demonstrate that FFA is selective for faces.

Rather the stimulus dimension for which there may be selectivity here could be loosely categorised as “visual objects” or even just “images”. Face-selectivity implies that the region is sensitive to changing the face identity (or possibly also some other attribute specific to faces). Now, I believe there is fairly good evidence that FFA does just that – however, simply comparing the response to faces to non-face images does not and cannot possibly demonstrate this. Only comparing responses (or response patterns) to different faces can achieve that.

Why does this matter? Is this really not mere semantics? No, because from all of this follows that to demonstrate selectivity requires systematic manipulation of the stimulus space. You must map out how changing the stimulus modulates responses by a neuron or brain region or whatever. You cannot make any claim about selectivity without any concept of how different stimuli relate to each other. For orientation that is simple but for more complex objects it is not. Comparing faces to houses or body parts or animals or tools or whatnot cannot achieve that, not unless you can tell me exactly why one category (say, houses?) should be more similar to faces than another (say, cars?). There are many possible models that could relate different categories. It could be based on low-level visual similarity, semantic similarity, conceptual similarity, etc. There are studies investigating exactly that, for example by using representational similarity analysis – but a discussion of this is outside the scope of this post. The point is that simply randomly comparing different object categories, no matter how many thousands, does not by itself tell you about selectivity.

Hopefully, by now I have convinced you why the claim that the dACC is selective for pain cannot possibly be correct, at least not if it is based only on a blobological method comparing responses to an arbitrary set of stimuli. I am not even sure that selectivity for pain is even conceptually possible. It would imply that there are mental states or tasks that are just not quite pain but not really something else yet either, and that there is a systematic relationship between that and dACC responses. Perhaps this is possible, I don’t know. Either way, no fMRI or NeuroSynth or other analysis comparing pain and rejection and conflict resolution or whatever can demonstrate this.

While we’re at it, showing that responses in dACC covary with the intensity of pain would not confirm selectivity for pain either. All that this shows is that dACC is responsive to pain. Because stimulus selectivity and preference go hand in hand. Selectivity for pain would imply that this region preferentially responds to a particular pain level but less so to pain that is stronger or weaker.

I added another paragraph because of a discussion I had about this last point on social media: selectivity implies that a neuron or brain area is tuned to a stimulus space and it can only exist if there is also a stimulus preference. An intensity code merely implies that responses increase as the stimulus quantity is increased. While the steepness of this increase can vary and tells you about sensitivity, such a neuron has no stimulus preference because the response either saturates (thus losing sensitivity beyond a certain level) or increases linearly (which is probably biologically implausible). This is mechanistically different from selectivity with different consequences on how the stimulus dimension is represented and how it affects behaviour. A brain area may very well be sensitive to contrast, to pain intensity, or confidence – but unless the code allows you to infer the exact stimulus level from the response it isn’t selective.

One thought on “What is selectivity?

  1. On 12 Dec 2015 I added a paragraph at the end for clarification and also a summary at the top. I also added an illustration of tuning curves and intensity-response curves at the beginning. The categorical experiments discussed about the “selectivity for pain” in the dACC would not fit either of these examples. The reason for this is that by measuring the response to a set of different categories without any clear model of how these categories relate to one another you cannot really estimate selectivity or even sensitivity of a neuron/ROI.

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