The second review of brain symbols

Below if the full text of the 'review' I got about brain symbols, and here is my e-mail to the editor.

The editor sent my reponse to the reviewr. In reply, the reviewer said (from meory, I dont have the actual message):

The editor, however, apparently has too much respect to this reviewer to admit that he (the reviewer) is talking nonsense and is too biased for giving an objective assessment of the paper, and rejected the paper anyway.

related texts


Review of Yehouda Harpaz, "The Neurons in the Brain Cannot Implement Symbolic Systems"

I do not recommend this paper for publication. The main problem in the paper concerns the failure to recognize the importance of levels of organization and the different properties in nature are only found inspecific levels. This paper has much the character of vitatlist arguments in the 19th centiry--bacuase properties like self regulation are not exhibited by individual chemical substances, you cannot implement life processes in them, but require a vital force. The premise is correct, but there are other options for the conclusion if one moves up levels to biochemical pathways, complete with many regulatory feedback loops. Likwise, one has to look at the right level in the nervous system to explain how, if it happens, symbol systems get implemented in the brain. No one that I know thinks it is at the level of individual neurons. The same applies, by the way to connectionist accounts--units in a network are not stand-ins for individual neurons. This seems to undermine the author's main argument: Stochastic relations at the level of individual neurons do not mean that it is stochatic all the way up, and that there is not a suitable higher level of organization at which one can implement symbolic processes. (One manifestation of my contention is that most cognitive computational work that does not link itsel to neuroscience occurs at the level of systems neuroscience, not at the level of cellular neuroscience-- these neurscientists don;t think that the cellular level is irrelevant, but recognize new properties that arise when one look at the systems level).

I am very worried about the paper that relies on textbook knowledge of neuroscience. But if one is to that, why not go to the bible: Kandel and Schwartz? The same applies to the author's characterization of the cognitive literature.

More specific comments:

P. 3, Bottom: There are some other characteristics that I think are more salient in the choice of symbolic approach: ability to account for productivity and systematicity, ability to solve binding problems, etc.

P. 3, top: This seems to ignore the point of the cognitive modeling. The point is to provide a framework that can handle what the brain DOES. This does require that the framework can be implemented, but that the choice of characteristics in a cognitive architecture is hardly made because the characteristics are relevant to the brain if that means we can simply find the characteristics are, say, the single neuron level.

P. 4, top : here a crucial ambiguity begins to emerge--is the problem that these characteristics are not implementable by neurons, or by anything built out of neurons?

P. 6, top: There is growing evidence of some limited nerve regeneration in different parts of the brain; this seems to be a case of changes in connectivity.

p. 6, middle: I am not sure what is meant by "a well-defined structure". Results from neuroimaging and lesion studies do suggest patterns of structure-function relations. Is that what is meant?

P. 7, bottom: "The stochastic nature of the low-level connectivity is almost never mentioned explicitly in neurobiological textbooks, probably because it is regarded as a non-fact." This is an extremely dangerous inference. I doubt anyone in mainstream neuroscience would deny a high degree of stochastic organization, and would certainly bring it up in development context. However, what is of more interest is the higher level regularities that emerge. This would be like charging someone working at the biochemical level of denying quantum effects because they don't talk about them.

pp. 8-9: This argument is not at all clear to me, and hence not compelling. Why must the analysis of dynamic properties be at the individual neuron level? I urge the author to take a look at the work of Scott kelso (no friend of a symbolic approach himself) and other dynamicists who work generally at much higher level. As the argument moves on, the confusion of levels continues to develop: the notion of 'location' in a high-level symbolic analysis does not have to refer to a discrete location in the brain --e.g, specific neurons.

p. 11, middle: as far as I know, common-sense usually makes no predictions about human behavior at the level that it is usually modeled in cognitive simulations. The fact that simulations has made many predictions which have been tested by complex behavioral experiments (and then often falsified) suggests the positive role of these simulations.

P. 12: The claim: "Until the evaluation of models will be based solely on brain related parameters, realistic models of the brain will never get noticed" seems obviously false. First of all, the author seems to call for rejecting behavioral parameters. Many neuroscientists would balk at that. Moreover, the modeling community is far less hegemonic than the author suggests. There are lots of different modeling frameworks in place, some clearly brain motivated (e.g., Freeman's dynamic models grounded on studies of the olfactory cortex).

P. 14, top: There seems to a perfectly obvious reading of "not wholly constrained" which has nothing to do with the author's interpretation: my word-processor is not wholly constrained by the chip running in it: clever design at the software level and my input change what it does.

P. 15: I think a more thorough review of the literature is needed to make the charge that symbolic modeling has "hardly ever" generated hypotheses worthy of empirical testing. This seems to be just blatantly wrong. A lot of fruitful cognitive experimentation has been motivated by computational models, and now the same is applying to cognitive neuroscience. (for some examples, consider the work of Dom Massaro, Roger Ratcliff, Max Coltheart and pinker and prince among huge list of others).