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Commentary on “Cortical Activity and the Explanatory Gap” by J. G. Taylor
CONSCIOUSNESS AND COGNITION ARTICLE NO.
7, 214–215 (1998)
CC980351
Commentary on ‘‘Cortical Activity and the Explanatory Gap’’ by J. G. Taylor Derek J. Smith School of Environmental and Human Sciences, University of Wales Institute, Cardiff, Wales
The general thrust of this commentary is that although Taylor has undoubtedly brought consciousness studies appreciably closer to the edge of the explanatory gap he has not led us far across it. This is not to say that there is any great fault with his lists of the objectively testable criteria of PE (Tables 1 and 2), for these provide a commendably clear statement of PE design principles for those attempting to build such a property into a silicon substrate. However, there is a world of difference between building demonstrable artificial PE—should that ever be achieved—and explaining it. After all, human parents ‘‘build’’ many thousands of new experiencing units every day, but we are still at a loss to explain how they work. The distinct risk remains, therefore, that—were we ever to be given a machine we could hold a deep and meaningful conversation with—we would still be unable to fathom out ‘‘the go’’ of it (Craik, 1966). That said, we turn specifically to the concept of locally recurrent connections (Section 4.1). Taylor sees these as being essential to ‘‘store the relation between successive pairs of patterns,’’ and describes them as ‘‘localized ‘bubbles’ of activity which persist independent of input.’’ We are totally at ease with this, but feel it only fair to point out that a cognate device has been used for many years within conventional data processing. In Smith (1997a,b), for example, we have ourselves promoted the potential of a software device known as the IDMS set currency for explaining certain aspects of biological cognition. This is a device invented by data-base systems programmers to help applications programmers ‘‘keep their place’’ at a given point in a complex data manipulation while the process is sent off to do something important elsewhere. It works by holding in memory what is effectively a snapshot of search pattern (n) for the duration of an excursion into search patterns (n ⫹ 1) . . . (n ⫹ m), so that pattern (n) can be restored as soon as the excursion is over. The device comes as standard with what are known as ‘‘Codasyl’’ data bases, such as Computer Associates’ Integrated Database Management System (IDMS), and is actually nothing more than a small address table held in RAM and constantly updated. The point is that in data-base systems of any size, the applications programmers typically need to access far more than just one record. Their updates and inquiries involve fragments of data gleaned from hundreds, if not thousands, of points within the data network. (Such systems may not be very parallel, therefore, but they are extremely distributed.) The logic by which the target data is progressively accumulated is known as a ‘‘traversal,’’ and it is vital that each traversal is meticulously thought out in advance. Each access must be made in precisely the right order and Commentary on J. G. Taylor (1998). Cortical activity and the explanatory gap. Consciousness and Cognition, 7(2), 109–148. 214 1053-8100/98 $25.00
Copyright 1998 by Academic Press All rights of reproduction in any form reserved.
COMMENTARY
215
from precisely the right direction. And this is where the set currency comes in. To use a generic example, what the IDMS programmer must do when traversing ‘‘red widgets,’’ looking for, and acting upon, a particular subset thereof, is to relocate the last red ‘‘widget’’ every time the next one is required. This slightly counter-intuitive action is required because each record contains the address of the next on a chain pointer principle, and this address may have been corrupted during the main process. There is thus a clear prima facie similarity between the recurrent connections of Taylor’s linked neural networks and the data-base currency concept. Both rely on a concise but relatively durable form of short term memory, and both involve areas of local overlap between two otherwise disparate data patterns. Our central criticism is therefore that Taylor is overly hasty when he dismisses relational models of the mind as being ‘‘unrelated to their neural underpinnings’’ (Section 2). While this may commonly be true, it is far from inevitable. Indeed, we submit without hesitation that a properly implemented relational model (by which we mean logically related but physically networked) need suffer no explanatory gap at all. This is because even the most complex data-base traversals can be fully and safely reduced to sequences of individual machine instructions. (Massive sequences, it is true, but totally predictable nonetheless.) This, after all, is the nature of conventional computer programming: you could write your programs in machine code, but you choose not to do it that way because it is many orders of magnitude more difficult. You write instead in a high-level language and use a compiler to do the low-level code generation for you. Moreover, it is precisely this ability to switch at will between macro and micro considerations which biology lacks most. The very essence of the explanatory gap is that we are notoriously bad at seeing how wholes can ever become greater than the sums of their parts. Every time we dump out our low-level neural activity traces we see not the slightest evidence of concept nor will nor self, nor indeed of any recognizable fragment thereof. We simply have no means of tracing the line of emergence of those holistic properties we know must be emergent. The explanatory gap, in other words, would perhaps be better described as a ‘‘compiler gap.’’ We close by pointing out (a) that data networks remain one of the frontrunners in biological memory theory, and (b) that Codasyl data bases are nothing more than psychology’s old Associationist networks made incarnate. This being so, it surely follows that traversal skills—the skills of explaining why a certain problem is best solved in a certain way given the distribution of the relevant memories—should be to an extent transportable between the worlds of biological, conventional, and connectionist data processing. In other words, data-base programmers have been coping with the explanatory gap for 30 years, and it would be nice to see their work mentioned more often in the consciousness literature. REFERENCES Craik, K. J. W. See Sherwood, S. L. Sherwood, S. L. (1966). The nature of psychology: A selection of papers, essays and other writings by the late Kenneth J. W. Craik. Cambridge, UK: Cambridge University Press. Smith, D. J. (1997a). The IDMS set currency and biological memory. Cardiff, UK: UWIC. Smith, D. J. (1997b). The magical name Miller, plus or minus the umlaut. In D. Harris (Ed.), Engineering psychology and cognitive ergonomics (Volume 2). Aldershot, UK: Ashgate.
7, 214–215 (1998)
CC980351
Commentary on ‘‘Cortical Activity and the Explanatory Gap’’ by J. G. Taylor Derek J. Smith School of Environmental and Human Sciences, University of Wales Institute, Cardiff, Wales
The general thrust of this commentary is that although Taylor has undoubtedly brought consciousness studies appreciably closer to the edge of the explanatory gap he has not led us far across it. This is not to say that there is any great fault with his lists of the objectively testable criteria of PE (Tables 1 and 2), for these provide a commendably clear statement of PE design principles for those attempting to build such a property into a silicon substrate. However, there is a world of difference between building demonstrable artificial PE—should that ever be achieved—and explaining it. After all, human parents ‘‘build’’ many thousands of new experiencing units every day, but we are still at a loss to explain how they work. The distinct risk remains, therefore, that—were we ever to be given a machine we could hold a deep and meaningful conversation with—we would still be unable to fathom out ‘‘the go’’ of it (Craik, 1966). That said, we turn specifically to the concept of locally recurrent connections (Section 4.1). Taylor sees these as being essential to ‘‘store the relation between successive pairs of patterns,’’ and describes them as ‘‘localized ‘bubbles’ of activity which persist independent of input.’’ We are totally at ease with this, but feel it only fair to point out that a cognate device has been used for many years within conventional data processing. In Smith (1997a,b), for example, we have ourselves promoted the potential of a software device known as the IDMS set currency for explaining certain aspects of biological cognition. This is a device invented by data-base systems programmers to help applications programmers ‘‘keep their place’’ at a given point in a complex data manipulation while the process is sent off to do something important elsewhere. It works by holding in memory what is effectively a snapshot of search pattern (n) for the duration of an excursion into search patterns (n ⫹ 1) . . . (n ⫹ m), so that pattern (n) can be restored as soon as the excursion is over. The device comes as standard with what are known as ‘‘Codasyl’’ data bases, such as Computer Associates’ Integrated Database Management System (IDMS), and is actually nothing more than a small address table held in RAM and constantly updated. The point is that in data-base systems of any size, the applications programmers typically need to access far more than just one record. Their updates and inquiries involve fragments of data gleaned from hundreds, if not thousands, of points within the data network. (Such systems may not be very parallel, therefore, but they are extremely distributed.) The logic by which the target data is progressively accumulated is known as a ‘‘traversal,’’ and it is vital that each traversal is meticulously thought out in advance. Each access must be made in precisely the right order and Commentary on J. G. Taylor (1998). Cortical activity and the explanatory gap. Consciousness and Cognition, 7(2), 109–148. 214 1053-8100/98 $25.00
Copyright 1998 by Academic Press All rights of reproduction in any form reserved.
COMMENTARY
215
from precisely the right direction. And this is where the set currency comes in. To use a generic example, what the IDMS programmer must do when traversing ‘‘red widgets,’’ looking for, and acting upon, a particular subset thereof, is to relocate the last red ‘‘widget’’ every time the next one is required. This slightly counter-intuitive action is required because each record contains the address of the next on a chain pointer principle, and this address may have been corrupted during the main process. There is thus a clear prima facie similarity between the recurrent connections of Taylor’s linked neural networks and the data-base currency concept. Both rely on a concise but relatively durable form of short term memory, and both involve areas of local overlap between two otherwise disparate data patterns. Our central criticism is therefore that Taylor is overly hasty when he dismisses relational models of the mind as being ‘‘unrelated to their neural underpinnings’’ (Section 2). While this may commonly be true, it is far from inevitable. Indeed, we submit without hesitation that a properly implemented relational model (by which we mean logically related but physically networked) need suffer no explanatory gap at all. This is because even the most complex data-base traversals can be fully and safely reduced to sequences of individual machine instructions. (Massive sequences, it is true, but totally predictable nonetheless.) This, after all, is the nature of conventional computer programming: you could write your programs in machine code, but you choose not to do it that way because it is many orders of magnitude more difficult. You write instead in a high-level language and use a compiler to do the low-level code generation for you. Moreover, it is precisely this ability to switch at will between macro and micro considerations which biology lacks most. The very essence of the explanatory gap is that we are notoriously bad at seeing how wholes can ever become greater than the sums of their parts. Every time we dump out our low-level neural activity traces we see not the slightest evidence of concept nor will nor self, nor indeed of any recognizable fragment thereof. We simply have no means of tracing the line of emergence of those holistic properties we know must be emergent. The explanatory gap, in other words, would perhaps be better described as a ‘‘compiler gap.’’ We close by pointing out (a) that data networks remain one of the frontrunners in biological memory theory, and (b) that Codasyl data bases are nothing more than psychology’s old Associationist networks made incarnate. This being so, it surely follows that traversal skills—the skills of explaining why a certain problem is best solved in a certain way given the distribution of the relevant memories—should be to an extent transportable between the worlds of biological, conventional, and connectionist data processing. In other words, data-base programmers have been coping with the explanatory gap for 30 years, and it would be nice to see their work mentioned more often in the consciousness literature. REFERENCES Craik, K. J. W. See Sherwood, S. L. Sherwood, S. L. (1966). The nature of psychology: A selection of papers, essays and other writings by the late Kenneth J. W. Craik. Cambridge, UK: Cambridge University Press. Smith, D. J. (1997a). The IDMS set currency and biological memory. Cardiff, UK: UWIC. Smith, D. J. (1997b). The magical name Miller, plus or minus the umlaut. In D. Harris (Ed.), Engineering psychology and cognitive ergonomics (Volume 2). Aldershot, UK: Ashgate.