Lee, Van Heuveln, Morrison, & Dietrich (1998) suggest, incorrectly, that I argued (Green 1998a) that connectionist networks will not be scientific models unless and until they capture every aspect of neural activity. What I argued was that unless and until connectionists come to terms with the idea that connectionist networks must model SOMETHING (and neural activity currently seems to be the best candidate, but it need not be the only one) they are not models of anything at all, and therefore may have little role to play in cognitive science.
2. The core of Lee et al.'s argument seems to begin with their assertion that "a model does not have to be isomorphic to the phenomenon concerned in all respects. Instead, all that it is required is to share relational similarities with the phenomenon in certain RELEVANT respects" (para. 5). This is, of course, a truism with which no one -- certainly not I -- could disagree. They then go on to use it as a foundation for their further argument that "from the fact there are many differences between biological neural networks and connectionist networks it does not follow that connectionist models are not scientific models, nor does it follow that the theories in which they participate are not scientific theories" (para. 5). All this is true, but beside the point. My claim was not that connectionist nets aren't "scientific" (I'm not sure I even know what they mean by this) because they aren't "isomorphic" with neural activity. My claim was that unless and until connectionists are willing to accept specifically that their networks are literal models of neural activity (or of any other concrete domain of phenomena) -- e.g., that nodes are neurons, though as I've repeated many times, it needn't be that crude a mapping -- we will not know what they are modeling at all.
3. Lee et al. (para. 6) go on to discuss post hoc statistical analyses. I have already discussed these in a reply to Medler & Dawson (1998; Green 1998b). Lee et al.'s claim that such analyses "are not, in general, ad hoc" is an interesting one. In general, connectionists seem to conduct descriptive rather than confirmatory factor and cluster analyses on their networks, and to that degree they are ad hoc. Indeed, it was the legendary "ad hoc-ness" of such analyses that contributed to the poor reputation such statistical methods now have among many research psychologists, and it was why they have generally opted, in recent years, for LISREL and structural equation modeling techniques instead.
4. Lee et al. also criticize my argument that connectionist networks have too many degrees of freedom by attempting to draw an analogy between connectionist networks and model airplanes. Does the fact that a model airplane in a wind tunnel has many degrees of freedom, they ask, "mean that the model airplane is of no use in understanding the behavior of an actual air plane" (para. 7)? I reject the analogy outright. We have good reason to believe that a model airplane operates on the same aerodynamic principles as does a real airplane: they both have wings, tails, flaps, etc. The whole point of my target article was that we currently have no reason to believe that such a mapping holds between connectionist nets and cognition at all because we don't know what the nodes and connections correspond to in the cognitive domain.
5. Last, Lee et al. claim that Clark (1993) has shown us how to correctly interpret connectionist research; but they do not respond to the criticisms I have made of Clark's position in previous replies (Green 1998b, 1998c). Until they do, I continue to be dubious that Clark has shown us anything of the kind.
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Green, C.D. (1998b) Statistical analyses do not solve connectionism's problem: Reply to Medler & Dawson on Connectionist-Explanation. PSYCOLOQUY 9(15) ftp://ftp.princeton.edu/pub/harnad/Psycoloquy/1998.volume.9/ psycoloquy.98.9.15.connectionist-explanation.12.green
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