R.W. Kentridge, (2001) Cognition, Chaos and Non-deterministic Symbolic Computation: the Chinese Room Problem Solved?. Psycoloquy: 12(050) Symbolism Connectionism (17)

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PSYCOLOQUY (ISSN 1055-0143) is sponsored by the American Psychological Association (APA).
Psycoloquy 12(050): Cognition, Chaos and Non-deterministic Symbolic Computation: the Chinese Room Problem Solved?

Commentary on Harnad on Symbolism-Connectionism

R.W. Kentridge,
Psychology Department,
University of Durham,
Durham DH1 3LE, UK
web: www.dur.ac.uk/~dps0rwk



Symbolic descriptions of the behaviour of continuous dynamical systems are, of necessity, approximations. In some cases, where the behaviour of the system is very orderly or completely random, then a single symbolic representation may provide an adequate description of the system. Dynamical systems which perform non-trivial computation do not, however operate in either of these regimes. One reason why symbol-grounding is such a tricky issue may be that no single symbolic description adequately describes the behaviour of computationally powerful dynamical systems like brains over a range of spatial or temporal scales. A symbolic description in which the 'contents' of symbols must sometimes be examined is, of course, not truly symbolic since its 'symbols' are not truly atomic.

    REPRINT OF: Kentridge, R.W. (1993) Cognition, chaos and
    non-deterministic symbolic computation: The Chinese room problem
    solved?. Think 2: 12-78 (Special Issue on "Connectionism versus
    Symbolism" D.M.W. Powers & P.A. Flach, eds.).


1. In this paper I offer an explanation of how the grounding of stimuli in an initial analog world can effect the interpretability of symbolic representations of the behaviour of neural networks performing cognition. I make two assertions about the form of networks powerful enough to perform cognition, first that they be composed of non-linear elements and second that their architecture is recurrent. As nets of this type are equivalent to non-linear dynamical systems I then go on to consider how the behaviour of such systems can be represented symbolically. The crucial feature of such representations is that they must be non-deterministic, they therefore differ from deterministic symbol systems such as Searle's Chinese Room. A whole range of non-deterministic symbol systems representing a single underlying continuous processes can be produced at different levels of detail. Symbols in these representations are not indivisible, if the contents of a symbol in one level of representation are known then the subsequent behaviour of that symbol system may be interpreted in terms of a more detailed representation in which non-determinism acts at a finer scale. Knowing the contents of symbols therefore effects our ability to interpret system behaviour. Symbols only have contents in a grounded system so these multiple levels of interpretation are only possible if stimuli are grounded in a finely detailed world.


2. What is the relationship between symbolic computation and that performed by connectionist networks? Harnad (2001) points out a few minor perceived differences between the capabilities of nets and symbol systems and then goes on to concentrate on solutions to Searle's 'Chinese Room' problem (Searle, 1980). I think he is correct in his argument (Harnad, 1990) that symbol systems which are not grounded in an initially analog world are indeed incomplete models of cognition. In this paper I will point out an important difference between deterministic symbol systems like the Chinese Room and neural networks which may explain formally why grounded and ungrounded systems are not equivalent.


3. I will start by making two assertions about the power of networks required to model cognition. First there is the well known requirement that nets must be composed of non-linear elements if they are to be capable of learning arbitrary classifications (Minsky and Pappert, 1969). The second requirement is that networks which are intended to model cognition must be capable of time-dependent information processing (see e.g. Edelman, 1978; Kentridge, 1990). If the units in these networks are not to have infinite memories of their prior states and time is not sliced up arbitrarily by an all-knowing external clock (it would have to be omniscient in order to predict the longest window needed to detect the determining antecedents of future events) then the architecture of these nets must be recurrent. If these assertions are accepted then we can consider networks capable of modelling cognition as non-linear dynamical systems from now on.


4. The behaviour of non-linear dynamical systems can be reconstructed in terms of symbolic languages defined by finite-state, stack or nested-stack automata (Crutchfield and Young, 1990) so a basis exists for directly comparing the symbolic computation of the Chinese Room with that of a neural net and hence of understanding symbol-grounding. When we consider how symbols are constructed when modelling the behaviour of a dynamical system symbolically, following the method of Crutchfield and Young, a fundamental difference between computation in the Chinese Room and computation in a neural network becomes apparent. When constructing a language from a stream of dynamical system states we wish to produce a minimal representation which consists of sets of states mapped onto discrete symbols together with rules specifying the allowable transitions between sequences of those symbols. In the process of producing a discrete symbolic description from a continuous stream of analog system states we must make an initial discrete partition of the data stream by dividing the state space of the dynamical system into a number of regions and then recording which of those regions the system is in at regular intervals. The aim of the language reconstruction is then to reduce long sequences of these discretely partitioned system states into individual symbols which nevertheless still predict the subsequent behaviour of the system at the symbolic level.

5. If the stream of states was in fact produced by a formal language such as that of the Chinese Room then any sufficiently long sequence of states could be modelled by a reduced set of symbols and deterministic transition rules between those symbols, that is, by a deterministic grammar (see e.g. Aho, Sethi and Ullman (1986) for methods of achieving this). This is not the case when the stream of partitioned states is produced by a non-linear dynamical system. We can see, just by considering the initial stream of discretely partitioned states, that any sequence of states cannot be modelled deterministically by an automaton simpler than the sequence itself. The hallmark of non-linear dynamical systems is chaos. Chaos is defined by the sensitive dependence of a system's evolution on its initial states (Crutchfield, Farmer, Packard and Shaw, 1986). In other words, the eventual behaviour of a chaotic system is unpredictable unless its initial position in state space is known to infinite precision. When we partitioned the state space of our dynamical system into discrete regions the precision of the state space positions used in our language reconstruction necessarily became finite. The consequence of this is that any finite linguistic model of a non-linear dynamical system which admits chaotic behaviour cannot be completely deterministic - the rules governing the transitions between symbol sequences must be probabilistic if any ordered behaviour of the system is to be captured by a concise symbolic description.


6. I have described a deep difference between the deterministic symbolic computation of the Chinese Room and non-deterministic symbolic descriptions of neural networks' behaviour. How does this difference relate to the symbol-grounding problem? Consider what happens as we change the size of partitions in our initial discretization of the continuous analog stream of system states. We can produce a whole series of non-deterministic symbolic models at different scales of partition all of which describe the behaviour of the underlying dynamical system. As we decrease the size of our partition the predictability of subsequent system behaviour increases and hence the symbols and transition probabilities produced in our reconstruction change. Sequences of fine-grain states which correspond to symbols are collapsed or broken up in coarsely partitioned models. The transitions between symbols in a coarsely partitioned model are more precisely described in a finely partitioned model. If we know something about the set of system states from which an individual symbol is derived in the coarse model then our predictions of the subsequent symbolic behaviour of the system change. The internal structure of the symbol clearly contributes to system behaviour even at the symbolic level. The behaviour of a system in which the starting state (the sensory input) is analog (grounded) is, in theory, deterministic and in practical symbolic representations is better predicted if we know something of the contents of symbols. These multiple levels of symbolic interpretation are not possible in an ungrounded system in which symbols are indivisible.


7. One question still needs to be addressed: 'Are neural-networks which perform cognition likely to exhibit chaotic behaviour?' If the answer to this question was 'no' then the preceding argument about non-determinism and chaos would be irrelevant. Crutchfield and Young (1990) show, however, that before the onset of chaos the behaviour of non-linear dynamical systems can be modelled symbolically by finite-state automata, whereas their behaviour during the transition to chaos is only adequately modelled by nested-stack automata. In linguistic terms finite-state automata are equivalent to regular grammars (Chomsky, 1963) while nested-stack automata are equivalent to indexed grammars which include all context-free grammars and some context-sensitive ones (Aho, 1969). Regular grammars are inadequate to describe natural language descriptions of the world, some form of recursive grammar (context-free at the least) is required (Chomsky, 1963). We can conclude that a neural network powerful enough to perform the cognitive tasks required by the Chinese Room problem must therefore exhibit chaotic behaviour.

8. It is interesting to note finally that Crutchfield and Young also show that non-deterministic symbol systems achieve a maximum amount of computation in terms of complexity at the phase-transition between predictable and chaotic behaviour. I have recently (Kentridge, forthcoming) provided evidence suggesting that physiologically realistic neural network models are easily maintained at this phase-transition by diffuse background activity through a mechanism of self-organizing criticality (Bak, Tang and Wiesenfeld, 1988). It is therefore not implausible that cognition is achieved by the brain performing efficient non-deterministic symbolic computation at a phase-transition.


9. To return to the Chinese Room Problem, I propose the following explanation: A Chinese speaker in the real world can understand Chinese because he or she has access to a hierarchy of probabilistic symbolic interpretations of the world; Searle in the Chinese Room cannot because he only has access to a single level of symbols and an inadequate set of deterministic rules connecting them.

ACKNOWLEDGEMENT: This research was supported by DRA Fort Halstead, Contract 2051/047/RARDE. I would like to thank Rosemary Stevenson for helpful comments on an earlier draft of this paper.


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