Connectionists: Brain-like computing fanfare and big data fanfare

[Mark H. Bickhard]

I would like to offer a couple of observations and comments.

I am in strong agreement with the emphasis on and necessity for theories, in interaction with empirical data and methodology, though I do find it useful in general to differentiate between theories and models — both are useful and needed, but not identical.  This is not a consensual distinction in the philosophy of science, but one way to think of the difference I have in mind is to consider theories as resources and frameworks for the construction of models.

For example, does the brain function in terms of the processing of semantic information, or via endogenously oscillatory processes (at multiple spatial and temporal scales, within complex topologies) that engage in modulations amongst themselves?  The former assumption is almost universal today, but there are serious problems with it.  I advocate the latter (paper available, for those who might be interested: http://www.lehigh.edu/~mhb0/ModelCNSFunctioning19Jan14.pdf).

This raises some related issues.  E.g., is there a possibility that the field is stuck in a framework with a false presupposition?  If so, we might be in a position similar to that of attempting to determine (model?) the mass of fire (phlogiston).  That example is from a while ago (and the assumption that fire is a substance goes back millennia, at least in the Western tradition), but more recently we find people devoting careers to figuring the nature and properties of "associations" or of how two-layer Perceptrons could engage in full human level pattern recognition (and, later, overlooking for a decade or so that the in-principle proof of impossibility did not apply to multiple layer connectionist models).  False presuppositions are rather common in history.  I argue that the framework of semantic information processing is such a false presupposition.

Even without the problems of false presuppositions, there are also the problems of missing conceptual frameworks.  You could not "reverse engineer" a table without notions of atoms, molecules, van der Waals forces, and so on — it is not a problem solvable via reverse engineering alone without regard for the conceptual resources available.  Presumably, a similar point holds for understanding how the brain works, only more so, and it seems likely that we are in such a position of conceptual impoverishment, if not (also) in a position of working within a false framework of presuppositions.

I have argued that we are in fact working with false presuppositions and with an impoverishment of conceptual resources, and have made some attempts to contribute to resolving those problems.  The history of science provides strong "inductive" support that all science has been and still is caught in such problems and that major advances require changes at those levels.  Including science(s) of the brain.  But my arguments have tended to be more "in-principle" (and, thus, have driven me into theory, and even [horrors!] philosophy).

This speaks to the points made in the discussion about theory as a differentiated subdomain within physics, and that it perhaps should also be recognized as such in studying the brain (and in psychology and cognitive science, etc. more generally).  I fully agree.  Recall, however, that the first Nobel for purely theoretical work in physics was for Dirac in 1933 (they would not and did not award the Nobel to Einstein for his purely theoretical work).  So, physics is a positive model in this regard, but it too suffered from a serious hesitancy to make and honor the distinction.

Mark

Mark H. Bickhard
Lehigh University
17 Memorial Drive East
Bethlehem, PA 18015
mark at bickhard.name
http://bickhard.ws/

On Jan 28, 2014, at 7:52 PM, Ping Li wrote:



Hi John, 
In psychology, it's often the opposite -- when the theory (or model, in this case) and experiment don't agree, it's the theory that's to blame. Hence we have to "simulate the data", "replicate the empirical findings", and "match with empirical evidence" (phrases used in almost all cognitive modeling papers -- just finished another one myself)... That's why Brad pointed out it's so hard to publish modeling papers without corresponding experiments or other empirical data.

Best,
Ping


 That's not the whole story.  For modern physics, a common happening is that when theory and experiment disagree, it is the experiment that is wrong, at least if the theory is well established.  (Faster-than-light neutrinos are only one example.)

John Collins






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