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Clearly, the crucial issue here is whether or not the methods adopted in such enquiries are adequate to tackle the phenomena in question.

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The overall tendency in cognitive science is, if one may so phrase it, to remove cognition from its natural human setting in order to study it in the abstract. The problem is, that once the abstraction has been effected, it is difficult to see how the findings of cognitive science are to be applied to the concrete world of psychology. Of course, this is not just a problem for computational psychology. It is a recurrent difficulty for all empirical approaches within the discipline.

However, it is particularly troublesome for researchers in the fields of language compre- hension and problem solving. Perhaps this reflects the fact that protocols are easier to gather, and simulations easier to write, for well-defined tasks, such as chess- playing and theorem-proving.

This suggests that simulation research is method-driven rather than topic-driven. Another methodological issue concerns the equivalence of a computer simulation to that which it is alleged to simulate. Matlin pointed out that human goals tend to be complex and fluid. Accordingly, simulations which fail to represent these phenomena may be spurious. In a similar vein, the alleged preci- sion of simulations may be challenged.

Such ad hoc programming decisions undermine the precision of the resulting simulation. Yet another methodological issue is raised by the possibility that an apparently plausi- ble simulation of behaviour may beguile us into believing that we have discovered how the mind works in a given area. Overall, then, the suspicion lingers that theory in computational psychology is merely an externalisation of intuitions Kline, Clearly, we must distinguish between the articulation of intuitions and the production of an explanatory theory.

In the articulation of intuitions, a phase that usually precedes explanation, the elements of the articulated intui- tion are not independently verified. Explanation, by contrast to intuitive articulation, involves a necessary commitment at least in principle to an objective criterion of confirmation or refutation. Theoretical Reservations. Apart from the preceding methodological reservations, can computational psychology, in principle, explain the higher mental processes? As Haugeland pointed out, according to Hobbes, thinking consists of symbolic operations in which thoughts are not spoken or written symbols but special brain tokens.

This is the tip of an enormous iceberg that deserves close attention, for it is profoundly relevant to the eventual plausibility of Artificial Intelligence. The basic question is: How can thought parcels mean anything? Hobbes and his latter-day computational disciples appear to have had no answer to this question. Haugeland devoted a lot of space in his book to this topic but he was ultimately unable to come to a satisfactory resolution. Briefly, Searle asked us to imagine sitting alone in a room with a basket which contains a collection of Chinese symbols.

If one had a rule- book in English which explained how to manipulate these symbols, one could appear to be capable of answering questions in Chinese, posed from outside the room, despite the fact that one could not understand Chinese. The point of this story is to show that from the perspective of an outsider e. In other words, a system can have input and output capacities which duplicate those of a native Chinese speaker still not understand Chinese. What is lost in the Al simulation of language comprehension, according to Searle , is the vital dis- tinction between syntax shuffling the Chinese symbols according to given rules and semantics knowing what the symbols mean.

Unlike other critics of the computational model, however, Searle was willing to allow that machines can encompass the feat of generating original meaning, but only if they are biological machines! If Fodor is to be believed, the prospects for scientific psychology are bleak. Does cognitive science constitute a revolutionary new approach to the study of human beings? Not according to Westcott It was his opinion that there has been no revolutionary transition from behaviourism to cognitivism; rather, there has been a change in terminology coinciding with a stable and unchanging ideology.

The computer has simply been substituted for the rat, the pigeon and dog as the laboratory subject of choice. Connectionism Parallel Distributed Processing : A new paradigm? Connectionism, also known as Parallel Distributed Processing PDP or neural networks, is the new wave in cognitive science. This new approach challenges the current computational assumption that mental processes can be represented and modelled as serial computer programs. Instead, it proposes that the mind is best understood in terms of massive, dynamic networks of interconnected units which resemble neurons.

Whereas the conventional computational model would represent a concept as a single node, connectionists regard it as a pattern of activation distributed over a neural network. The precise level of activa- tion depends on the weighted sum of the states of activation of the units with which it is connected. Learning occurs when the weights strength of connections are adjusted in accordance with rules derived from environmental influences.

The revolutionary aspects of this approach are threefold. Secondly, the computer metaphor of mind seems to have supplanted by a neurological metaphor of mind. Thirdly, connec- tionist model of the mind differ radically from their symbolic predecessors in regard to the assumption of decomposability of mental processes.

Whereas the conventional computational models have sought to decompose cognitive tasks into rules for manipu- lating representations, PDP systems explain rule-like behaviour as an emergent product of excitations and inhibitions between unit Bechtel.

Table 2. Contrasting approaches of the Classical and Connectionist models of mind. Classical Model Connectionist Model Mental processes modelled as programs run- Mental processes modelled as large-scale dy- ning on a digital computer namic networks of simple, neuron-like proc- Palmer As with the Classical model, reservations have also been expressed about the adequacy of the new Connectionist model.

Can network models be constructed to perform cognitive tasks in the same way that people do? Fodor and Pylyshyn concluded that when the argumentative dust has settled, the Classical approach still remains in position. The classical computational architecture resembles Hobbesian ratiocination and the PDP approach seems like Lockean associa- tionism. Conclusion In this paper, we have offered brief characterisations of cognitive psychology and cogni- tive science, sketched the IP approach to cognition common to them both, and related them to Al.

We articulated the computational metaphor, outlined its advantages, and expressed our reservations about it in some detail. We concluded with a sketch of the recent Connectionist paradigm. Although over half the paper has expressed reservations in respect of the computa- tional metaphor, we do not propose these criticisms in a Luddite spirit. The IP approach to cognition, with its accompanying computational metaphor, has stimulated some of the most interesting research in psychology in recent years.

Even if it were finally to be found wanting and there is as yet no overall consensus as to its ultimate value it would, nonetheless, have advanced our knowledge of human cognition beyond its previous limits. There is still the embryonic Connectionist PDP paradigm to be investigated and who knows what time, ingenuity, and effort will eventually bring to birth from it? Patterns and actions: Cognitive mechanisms are content- specific.

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Positivism and the Prospects for Cognitive Science

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