The question of whether functions are localizable to distinct parts of the brain, aside from its obvious importance to neuroscience, bears on a wide range of philosophical issues—reductionism and mechanistic explanation in philosophy of science; cognitive ontology and mental representation in philosophy of mind, among many others. But philosophical interest in the question has only recently begun to pick up (Bergeron, 2007; Klein, 2012; McCaffrey, 2015; Rathkopf, 2013).
I am a “contextualist” about localization: I think that functions are localizable to distinct parts of the brain, and that different parts of the brain can be differentiated from each other on the basis of their functions (Burnston, 2016a, 2016b). However, I also think that what a particular part of the brain does depends on behavioral and environmental context. That is, a given part of the brain might perform different functions depending on what else is happening in the organism’s internal or external environment.
Embracing contextualism, as it turns out, involves questioning some deeply held assumptions within neuroscience, and connects the question of localization with other debates in philosophy. In neuroscience, localization is generally construed in what I call absolutist terms. Absolutism is a form of atomism—it suggests that localization can be successful only if 1–1 mappings between brain areas and functions can be found. Since genuine multifunctionality is antithetical to atomist assumptions it has historically not been a closely analyzed concept in systems or cognitive neuroscience.
In philosophy, contextualism takes us into questions about what constitutes good explanation—in this case, functional explanation. Debates about contextualism in other areas of philosophy, such as semantics and epistemology (Preyer & Peter, 2005), usually shape up as follows. Contextualists are impressed by data suggesting contextual variation in the phenomenon of interest (usually the truth values of statements or of knowledge attributions). In response, anti-contextualists worry that there are negative epistemic consequences to embracing this variation. The resulting explanations will not, on their view, be sufficiently powerful or systematic (Cappelen & Lepore, 2005). We end up with explanations that do not generalize beyond individual cases. Hence, according to anti-contextualists, we should be motivated to come up with theories that deny or explain away the data that seemingly support contextual variation.
In order to argue for contextualism in the neural case, then, one must first establish the data that suggests contextual variation, then articulate a variety of contextualism that (i) succeeds at distinguishing brain areas in terms of their distinct functions, and (ii) describes genuine generalizations.
Usually, in systems neuroscience, the goal is to correlate physiological responses in particular brain areas with particular types of information in the world, supporting the claim that the responses represent that information. I have pursued a detailed case study of perceptual area MT (also known as “V5” or the “middle temporal” area). The textbook description of MT is that it represents motion—it has specific responses to specific patterns of motion, and variations amongst its cellular responses represent different directions and velocities. Hence, MT has the univocal function of representing motion: an absolutist description.
However, MT research in the last 20 years has uncovered data which strongly suggests that MT is not just a motion detector. I will only list some of the relevant data here, which I discuss exhaustively in other places. Let’s consider a perceptual “context” as a combination of perceptual features—including shape/orientation, depth, color, luminance/brightness, and motion. On the traditional hierarchy, each of these features has its own area dedicated to representing it. Contextualism, alternatively, starts from the assumption that different combinations of these features might result in a given area representing different information.
- Despite the traditional view that MT is “color blind” (Livingstone & Hubel, 1988), MT in fact responds to the identity of colors when color is useful in disambiguating a motion stimulus. Now in this case, MT still arguably represents motion, but it does use color as a contextual cue for doing so.
- Over 93% of MT cells represent coarse depth (the rough distance of an object away from the perceiver. Their tuning for depth is independent of their tuning for motion, and many cells represent depth even in stationary These depth signals are predictive of psychophysical results.
- A majority of MT cells also have specific response properties for fine depth (depth signals resulting from the 3‑d shape and orientation of objects) features of tilt and slant, and these can be cued by a variety of distinct features, including binocular disparity and relative velocity.
How do these results support contextualism? Consider a particular physiological response to a stimulus in MT. If the data is correct, then this signal might represent motion, or it might represent depth—and indeed, either coarse or fine depth—depending on the context. Or, it might represent a combination of those influences.
The contextualism I advocate focuses on the type of descriptions we should invoke in theorizing about the functions of brain areas. First, our descriptions should be conjunctive: the function of an area should be described as a conjunction of the different representational functions it serves and the contexts in which it serves those functions. So, MT represents motion in a particular range of contexts, but also represents other types of information in different contexts—including absolute depth in both stationary and moving stimuli, and fine depth in contexts involving tilt and slant, as defined by either relative disparity or relative velocity.
When I say that a conjunction is “open,” what I mean is that we shouldn’t take the functional description as complete. We should see it as open to amendment as we study new contexts. This openness is vital—it is an induction on the fact that the functional description of MT has changed as new contexts have been explored—but also leads us precisely into what bothers anti-contextualists (Rathkopf, 2013). The worry is that open-descriptions do not have the theoretical strength that supports good explanations. I argue that this worry is mistaken.
First, note that contextualist descriptions can still functionally decompose brain areas. The key to this is the indexing of functions to contexts. Compare MT to V4. While V4 also represents “motion” construed broadly (in “kinetic” or moving edges), color, and fine depth, the contexts in which V4 does so differ from MT. For instance, V4 represents color constancies which are not present in MT responses. V4’s specific combination of sensitivities to fine depth and curvature allows it to represent protuberances—curves in objects that extend towards the perceiver—which MT cannot represent. So, the types of information that these areas represent, along with the contexts in which they represent them, tease apart their functions.
Indexing to contexts also points the way to solving the problem of generalization, so long as we appropriately modulate our expectations. For instance, on contextualism it is still a powerful generalization that MT represents motion. This is substantiated by the wide range of contexts in which it represents motion—including moving dots, moving bars, and color-segmented patterns. It’s just that representing motion is not a universal generalization about its function. It is a generalization with more limited scope. Similarly, MT represents fine depth in some contexts (tilt and slant, defined by disparity or velocity), but not in all of them (protuberances). Of course, if the function of MT is genuinely context sensitive, then universal generalizations about its function will not be possible. Hence, insisting on universal generalizations is not an open strategy for an absolutist—at least not without question begging.
The real crux of the debate, I believe, is about the notion of projectability. We want our theories not just to describe what has occurred, but to inform future hypothesizing about novel situations. Absolutists hope for a powerful form of law-like projectability, on which a successful functional description tells us for certain what that area will do in new contexts. The “open” structure of contextualism precludes this, but this doesn’t bother the contextualist. This situation might seem reminiscent of similar stalemates regarding contextualism in other areas of philosophy.
There are two ways I have sought to break the stalemate. First is to define a notion of projectability that informs scientific practice, but is compatible with contextualism. Second is to show that even very general absolutist descriptions fail to deliver on the supposed explanatory advantages of absolutism. The key to a contextualist notion of projectability, in my view, is to look for a form of projectability that structures investigation, rather than giving lawful predictions. The basic idea is this: given a new context, the null hypothesis for an area’s function in that context should be that it performs its previously known function (or one of its known functions). I call this role a minimal hypothesis, and the idea is that currently known functional properties structure hypothesizing and investigation in novel contexts, by providing three options: (i) either the area does not function at all in the novel context (perhaps MT does not make any functional contribution to, say, processing emotional valence); (ii) it functions in one of its already known ways, in which case another context gets indexed to, and generalizes, an already known conjunct, or (iii) it performs a new function in that context, forcing a new conjunct to be added to the overall description of the area (indexed to the novel context, of course). While I won’t go into details here, I argue in (Burnston, 2016a) that this kind of reasoning has shaped the progress of understanding MT function.
One option open to a defender of absolutism is to attempt to explain away the data suggesting contextual variation by changing the type of functional description that is supposed to generalize over all contexts (Anderson, 2010; Bergeron, 2007; Rathkopf, 2013). For instance, rather than saying that a part of the brain represents a specific type of information, maybe we should say that it performs the same type of computation, whatever information it is processing. I have called this kind of approach “computational absolutism” (Burnston, 2016b).
While computational neuroscience is an important theoretical approach, it can’t save absolutism. My argument against the view starts from an empirical premise—in modeling MT, there is not one computational description that describes everything MT does. Instead, there are a range of theoretical models that each provide good descriptions of aspects of MT function. Given this lack of universal generalization, the computational absolutist has some options. They might move towards more general levels of computational description, hoping to subsume more specific models. The problem with this is that the most general computational descriptions in neuroscience are what are called canonical computations (Chirimuuta, 2014)—descriptions that can apply to virtually all brain areas. But if this is the case, then these descriptions won’t successfully differentiate brain areas based on their function. Hence, they don’t contribute to functional localization.
On the other hand, suggesting that it is something about the way these computations are applied in particular contexts runs right into the problem of contextual variation. Producing a model that predicts what, say, MT will do in cases of pattern motion or reverse-phi phenomena simply does not predict what functional responses MT will have to depth—not, at least, without investigating and building in knowledge about its physiological responses to those stimuli. So, even if general models are helpful in generating predictions in particular instances, they don’t explain what goes on in them. If this description is right, then the supposed explanatory gain of CA is an empty promise, and contextual analysis of function is necessary. My view of the role of highly general models mirrors those offered by Cartwright (1999) and Morrison (2007) in the physical sciences.
Some caveats are in order here. I’ve only talked about one brain area, and as McCaffrey (2015) points out, different areas might be amenable to different kinds of functional analysis. Perceptual areas are important, however, because they are paradigm success cases for functional localization. If contextualism works here, it can work elsewhere, as well as for other units of analysis, such as cell populations and networks (Rentzeperis, Nikolaev, Kiper, & van Leeuwen, 2014). I share McCaffrey’s pluralist leanings, but I think that a place for contextualist functional analysis must be made if functional decomposition is to succeed. The contextualist approach is also compatible with other frameworks, such as Klein’s (2017) focus on “difference-making” in understanding the function of brain areas.
I’ll end with a teaser about my current project on these topics (Burnston, in prep). Note that, if the function of brain areas can genuinely shift with context, this is not just a theoretical problem, but a problem for the brain. Other parts of the brain must interact with MT differently depending on whether it is currently representing motion, coarse depth, fine depth, or some combination. If this is the case, we can expect there to be mechanisms in the brain that mediate these shifting functions. Unsurprisingly, I am not the first to note this problem. Neuroscientists have begun to employ concepts from communication and information technology to show how physiological activity from the same brain area might be interpreted differently in different contexts, for instance by encoding distinct information in distinct dynamic properties of the signal (Akam & Kullmann, 2014). Contextualism informs the need for this kind of approach. I am currently working on explicating these frameworks and showing how they allow for functional decomposition even in dynamic and context-sensitive neural networks.
 The high proportion and regular organization of depth-representing cells in MT resists the temptation to try to save informational specificity by subdividing MT into smaller units, as is normally done for V1. V1 is standardly separated into distinct populations of orientation, wavelength, and displacement-selective cells, but this kind of move is not available for MT.
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