Elliptical Mechanism Sketches

That the models from cognitive psychology I’ve discussed are not, as they currently stand, complete models of biological or neural mechanisms should come as no surprise. Piccinini and Craver(2011) claimed that these are elliptical mechanism sketches, not complete models. The question then is whether models that start out as data flow or control flow models lend themselves to being filled in with more detail to become full-fledged neural mechanisms, or whether they tend to remain distinct.

It certainly is possible in principle to transform one sort of model or diagram into another. To turn a control flow into a data flow diagram in the case of a computer program, one would have to draw a new diagram with arrows going back and forth between various types of storage devices, peripheral devices, and the CPU. Some additional knowledge about the architecture of the system would be needed in order to do this, and such a diagram would no longer tell you much about the program’s dynamics or its function. Knowledge about how the program works would be lost, in exchange for knowledge about the communications channels used. To turn a data flow model into a control flow model would be very difficult, unless you already knew what the contents of the memory locations accessed were, and what sorts of operations were being done. Again, with sufficient additional knowledge, the transformation could be made. In this case, knowledge about the dynamics of data flow would

be lost in exchange for knowledge about the order of operations. A control flow diagram does not include information about data sources and recipients. Depending on the system being described, this sort of transformation could take a formerly informative, explanatory model and turn it into something uninformative and non-explanatory. For systems with different architectures than a computer, this transformation process might be more or less complicated, but in any case, additional knowledge would be needed, and some would be lost.

Turning either type of flowchart into a sketch of a neural mechanism is similarly compli- cated and in many cases not very useful, but could be done. The processes in a control flow model could be taken as the activities of a central processor. One could certainly identify this central processor with a part of the brain, or the brain in its entirety, but this does not make for a very informative neural mechanism. A decomposition of the processor into sub-entities, such that its activities could be understood as arising from the organization of those sub-entities and what they do would be the obvious next step in turning the black box into a glass box. But knowing what a CPU, for example, is made of and what the parts do seems irrelevant to the explanation the control flow diagram gives. The same CPU can run any number of programs, and the same program can be run on different kinds of CPUs. Making the model more detailed in this way might explain something else about the system, but it does not add to the explanation of why the program gives a particular sort of result. If knowing the order of operations explains the result, then knowing that the code for the third operation was stored in the 17th register does not make the explanation better.

In a data flow model, the boxes could be taken as entities, and the data itself could be considered another entity not explicitly represented. In Broadbent’s example there is a short term store, a filter, and so on. These might be either glass boxes, if the details about them are known, or what Darden (2005) calls “gray boxes,” that is, hypothetical entities where we know the function they should perform, but not what performs it. The arrows do not represent activities so much as the route taken by data. Here a similar problem arises that these are not what we might call black arrows that need to be turned into glass arrows in order to transform the model from an elliptical sketch into a full-fledged mechanism. Instead a different diagram entirely would be needed to represent the activities that the

entities perform (aside from data’s activity of moving around).

In neither control flow nor data flow diagrams are the causal relationships between en- tities or states represented. What makes one step follow after another in a program is the invisible work of the program counter and the hardware that grabs the next command and follows it. A mechanism sketch could be constructed based on either type of flowchart, but additional knowledge—knowledge about entities and their causal relationships—would have to be added. Additionally, some of the information that is represented in these diagrams— about control flow and data flow—would get lost. In short, these diagrams, and the models they represent could be turned into mechanism sketches, but that isn’t what they are, and isn’t always what they’re intended to be. You don’t typically move from one type of model to the other when making progress in science, although you might have reason to make sev- eral diagrams for several purposes. What you do instead is to refine one type of model or the other type, and the diagrams you use to represent the model demonstrate the progress through the model’s refinements. We’ll see an example of this in the next section.

One obvious counterargument might be to point out that what a diagram shows is always partial. One can’t conclude that a model does not specify causal relationships between entities based on those being absent from a diagram depicting that model. This is not the conclusion I’m drawing. The sorts of flowcharts I’ve been discussing are presented by their authors as representative of the models they depict, and these sorts of diagrams are routinely used for this purpose in cognitive psychology. The verbal descriptions that accompany these diagrams do not detail the missing information about causal relationships and entities either. So it is fair to take these diagrams as indications of the contents and structure of cognitive psychology’s models. The models do not include the sort of information expected in a neural mechanism sketch, suggesting that the explanatory aims of cognitive psychologists may not be to describe mechanisms, or at least not the kind of mechanisms Piccinini and Craver (2011) have in mind.

Another counterargument might be to claim that these are mechanism sketches so ellipti- cal as to consist entirely of black boxes or black arrows. One problem with this is that these diagrams are not empty. Although they’re relatively empty of neural details, they aren’t empty of process or data flow details. As psychological models they are not considered to be

deficient. They are complete enough to support explanations. It seems too easy a move to claim that these models are empty mechanisms, without giving a convincing set of examples where models like this have actually been elaborated from elliptical sketch into mechanism. Below I give an example where this failed over the course of several decades to happen. Piccinini and Craver (2011) do not offer any examples where elliptical mechanism sketches have been filled in with neural details.

One objection that I do think can get traction is that while the neural entities and brain activities that might be involved in a data flow model like Broadbent’s or a process flow model like Anderson’s are systematically left out of these models, there are nevertheless entities and activities represented, so perhaps these are the relevant ones. In a data flow model, the data’s flow might be the most relevant causal process. The phenomena being explained might be the effect of this kind of data flow process alone, regardless of what the specific entities doing the data processing are and how exactly they do it. In a process flow model, the sequence of operations might be the most important explanatory factor, regardless of what kind of computing machinery performs them. These sorts of models could very well be mechanistic then, if we’re content with the entities and activities operative in them being rather more abstract than the sorts of things one might reasonably identify with brain parts and the activities thereof. If this is the route we go, then these models are not elliptical mechanism sketches. Instead they are complete enough, without being specifically neural mechanisms. This is an option I will follow up on in Chapter3.

I’ve argued that the sorts of models cognitive psychologists build are not very much like what we’d expect of neural mechanisms, and that turning them into neural mechanisms would require considerable effort, the addition of different kinds of knowledge, and would involve leaving out the sorts of knowledge that these models do include. In the next section I contrast cases where this sort of cognitive model underwent considerable development, and yet was not filled in with neural details, with a case where a sketch of a neural mechanism was filled in with details in just the wayMachamer et al.(2000),Piccinini and Craver(2011) describe. This is not to say that there are no cognitive models that are amenable to this sort of elaboration, just that this route is not typically how cognitive models develop.

2.4 THEORY DEVELOPMENT IN NEUROSCIENCE AND COGNITIVE