COGNITIVE NEUROPSYCHOLOGY, 2004, 21 (1), 51–56
PROMISES, PROMISES Trevor A. Harley
University of Dundee, UK
In the target article I argued that cognitive neuropsychologists have increasingly deviated from the original goals and methods of the subject. In this reply to the commentators, I argue that future progress using neuropsychological approaches to understanding behaviour is most likely to be made by the use of converging sources of evidence that are garnered by an interdisciplinary methodology. Neuroimaging data may have a role to play in such an enterprise, but are unlikely to be prominent in cognitive psychological theorisation in isolation.
INTRODUCTION In my original article, I argued that cognitive neuropsychologists are increasingly deviating from the manifesto of cognitive neuropsychology as it was envisaged 20 years ago. While much progress is often good, this deviation is unfortunate. The original goals of cognitive neuropsychology (which were to relate brain-damaged behaviour to models of normal processing, and to inform models of normal processing on the basis of neuropsychological data) and the original methods (most importantly, an emphasis on the single-case study, and a lack of emphasis on establishing localisation of function) are still important today. As Vallar points out, cognitive neuropsychology has made a great deal of progress, and (contrary to what McCloskey thinks I said) I believe it is still capable of making much more. It is of course a forlorn—and indeed an undesirable—task to attempt to force scientists to adopt a programmatic approach to science. I neither hope nor expect researchers to change what they are doing and how as a result of one article. The history of the philosophy of science is a long one full of instances of philosophical insight followed by practical neglect. (For example, although the problems
with falsificationism are legion and well known— see Duhem, 1962, Lakatos, 1974, or Quine, 1961—it is in practice the underlying philosophical approach adopted by most cognitive psychologists.) Researchers have many reasons for tackling the problems that they do in the way they do, including the perceived scientific importance of the problem, interest, tractability, convenience, and even fashion. As Shallice points out, I believe that ultracognitive neuropsychology has succumbed to fashion more than to any inherent failing. The responses to the original article raised points that fall into three categories. 1. What is the proper way to study the relation between brain and behaviour? 2. What role can computational modelling play? 3. What use is brain imaging?
WHAT IS THE ROLE OF COGNITIVE NEUROPSYCHOLOGY IN THE STUDY OF THE RELATION BETWEEN BRAIN AND BEHAVIOUR? Cognitive neuropsychology was originally a rather methodologically narrow affair. Indeed, Shallice
I am grateful to Tim Shallice for his valuable comments on a draft of this paper.
Ó 2004 Psychology Press Ltd http://www.tandf.co.uk/journals/pp/02643294.html
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(1988) christened the narrowest conception ultracognitive neuropsychology. People have become rather apologetic about calling themselves ultracognitive neuropsychologists (note, for example, the hint of an apologetic tone in Coltheart’s response). Caplan argues that the field of cognitive neuropsychology should be characterised more widely by not just investigating functional architectures and their disorders, but also by uncovering the neural basis for mental function, and for characterising the relation between mental disease and mental function. I agree that both of these goals are proper ones for neuroscience. I think, though, we should avoid terminological squabbles. I have taken “cognitive neuropsychology” to mean an approach defined by the goals and methodology as stated in my original article. The important question is whether the particular methods and aims of ultracognitive neuropsychology could indeed lead to a greater understanding of human behaviour. I argue that they still can, without recourse to additional sorts of approach. These other approaches (e.g., neuroimaging) may also be useful, but are the alternative approaches (such as those promulgated by Caplan) likely to be as successful as the approach I designate by the shorthand “cognitive neuropsychology”? And has ultra-cognitive neuropsychology really got nothing left to offer? I return to these points below, arguing that all of these approaches have a role to play, but greater progress might be made by their integration. Shallice points out that some arguments in cognitive neuropsychology have degenerated into internecine feuds. An example he provides of such a feud is to be found in the area of neurosemantics; to which we can add the notorious example of the debate about the number of routes involved in reading. The observation that psychology tends to end up in feuds between proponents at extremes of dichotomies has been around for some time (see, for example, Ades, 1981; Newell, 1973). Newell (who called his paper You can’t play 20 questions with nature and win) deplored the mindless accumulation of facts and the mindless application of dichotomies to psychological theorisation. We still have dichotomies, but different ones: For example, we
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now argue about interactive versus independent, serial versus parallel, discrete versus cascaded processing, and single route versus dual route models. (Of course, the change in the dichotomies that are argued may represent some progress.) Shallice hopes that a convergence of types of evidence will lead to a resolution of the feuding. I agree that converging evidence should lead to progress, but I am sceptical that it will lead to a resolution of these feuds. There are always new auxiliary hypotheses we could add to the protective belt to save the hard core of the programme from falsification (Lakatos, 1974). Hence such resolutions have rarely occurred in the past. What is needed is a drastic recasting of the problem that transcends the level of description of the original problem. In practice, old debates tend to be left by the wayside as new theories come to power (or perhaps to fashion). For example, Plaut (2002) showed how a distributed connectionist model of semantics could provide a middle ground between the poles of unitary and multiple semantics. In addition to providing a possible solution to what had previously seemed an intractable debate, the model provides an account of optic aphasia. Whether the old dichotomy will be completely swept away remains, however, to be seen. Rather depressingly, cognitive psychology has a curious property: I can think of no other science where there are so many competing theories of the same phenomena that happily coexist for so long. Lambon Ralph notes several problems with the single-case study approach (for example, it is difficult to tell whether a difference in performance on two tasks is really indicative of a modular dissociation or some other variable, such as task difficulty differences or premorbid variation). He observes that case-series approaches are becoming popular. The case-series approach avoids some of the difficulties of single-case studies, and has additional advantages—for example, we can note how performance varies with severity. The effects of severity may be important in the study of category-specific deficits in dementia (Gonnerman, Andersen, Devlin, Kempler, & Seidenberg, 1997; but see Garrard, Patterson, Watson, & Hodges, 1998, who found no correlation between type of disorder and level of severity). Lambon Ralph notes that this
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approach fits well with the computational neuropsychology approach, which can account for the possible effects of severity (Devlin, Gonnerman, Andersen, & Seidenberg, 1998). Garrard et al., on the other hand, emphasised the importance of different regions of the brain in the origin of the living–nonliving distinction. Although the final resolution of this debate is unclear, it is apparent that we have learned something important by a combination of approaches. What is now necessary is a more inclusive computational model. I agree that the combination of computational neuropsychology and a case-series methodology may be fruitful. Once again, it seems that we are seeking a convergence of the application of methodologies. Finally, contrary to McCloskey’s assertion, I do not attack Rapp’s handbook for being too broad: Indeed, I praise it. A range of topics makes a pleasant relief from reading eight articles on the neuropsychology of semantics (the number published by this journal in 2002). Nor do I assume that an overview of an area should slavishly reflect the exact proportion of effort made by researchers who publish in one journal. A handbook should go beyond such narrow confines and adopt a broader perspective—which Rapp’s book does admirably. As I said in my original article, “it deserves particular praise for its attempt to show cognitive neuropsychologists that there is a world outside the processing of isolated words” (p. 14).
WHAT ROLE CAN COMPUTATIONAL MODELLING PLAY? Coltheart correctly points out that computational modelling does not equal connectionist modelling. Any sort of modelling has the virtue of being explicit, and of specifying the processes in addition to the architecture. The question is whether some types of modelling are likely to be more productive than others. Perhaps Coltheart is right in that such questions might be misguided because different approaches tackle different types of problem. Nevertheless, there is an important
difference between connectionist and nonconnectionist modelling. Nonconnectionist computational modelling makes explicit assumptions about the form of the functional architecture with the goal of testing those assumptions. In contrast, in connectionist modelling, aspects of the functional architecture emerge. I also argued that many cognitive neuropsychologists are obsessed with functional architecture; connectionist modelling tells us about both architecture and process. For both of these reasons, connectionist modelling is currently the prevalent type of modelling. The emergence of the functional architecture when modelling is an important feature that has both an advantage and a disadvantage. Take Coltheart’s analysis of Plaut, McClelland, Seidenberg, and Patterson (1996). He points out that the network might have partitioned itself into two subnetworks. If this were the case, then the conclusion from the modelling would be that the functional architecture for reading exception words and nonwords must indeed contain two routes— even if the modellers tried to impose a single route on the network in the first place. Hence the advantage of connectionist modelling here is that we have learned something about the functional architecture that was not part of our original assumption. One disadvantage of connectionist modelling is that it can be difficult to work out exactly what is going on in a sophisticated network (a point also made by McCloskey). We must examine what architecture the trained network has developed (rather than that which we imposed, or even that which we assumed had developed), and this might not always be easy. Indeed, to reiterate, it might be as difficult as studying the mind. Dell suggests a couple of ways in which this difficulty might be alleviated. First, it is desirable to make the models easy to use by other researchers. Second, it might often be desirable to make models small so that the principles involved in the model are clearly demonstrated. Models are often made large in an attempt to make them more realistic, but this realism comes at a cost. Of course, it is not always possible to keep our models small—the point of interest might be an emergent property of a large vocabulary, for example. COGNITIVE NEUROPSYCHOLOGY, 2004, 21 (1)
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In spite of these problems, connectionist modelling is superior to nonconnectionist modelling, in that we learn what is actually necessary to perform a task, rather than what we assume might be necessary. Hence cognitive neuropsychologists can only ignore connectionist modelling at their peril. Connectionist modelling is full of surprises. It can help resolve those dichotomies that have been the bane of cognitive psychology. I have already mentioned Plaut’s (2002) account of optic aphasia. Furthermore, the account is based on an assumption that connectivity is as local as possible—an assumption that is derived from neurological considerations (see Jacobs & Jordan, 1992). This interplay of methods is further evidence of the usefulness of a convergent, interdisciplinary approach. The replies demonstrate the division in cognitive neuropsychology about the role connectionist modelling should play. While Coltheart and McCloskey are clearly more sceptical, Dell, Lambon Ralph, and Shallice applaud the potential of connectionism in cognitive neuropsychology. Lambon Ralph argues that computational modelling offers a form of theorising where both architecture and processes are explicitly defined. In addition, examination of the processes revealed by modelling may show that architectures need not be as complex as box-and-arrow models might suggest. There is, however, as McCloskey points out, a distinction between a computational model and a computational theory. It is indeed possible to have an explicit model without an explicit theory, by making, for example, arbitrary choices about implementation—which to some extent is unavoidable. It is difficult to imagine a circumstance in which a theory might prescribe the necessity of 666 hidden units rather than 667. But that is not the point. Even arbitrary choices are explicit, and can be criticised, modified, or rejected. For example, Plaut and Shallice (1993) analysed a number of implementational architectures and learning algorithms, and showed that the selection of one rather than another was more or less arbitrary with respect to the key points of the semantic attractor theory of
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reading and deep dyslexia. With a noncomputational model these courses of action are not available. But surely it is desirable to strive to be as explicit as possible about the nature of cognitive processes. McCloskey argues that neither I nor connectionist modellers have made a case that connectionism can deliver. If you believe we have failed, then I offer the same argument as the neuroimagers: Trust me, it will happen in the future.
WHAT USE IS BRAIN IMAGING? To my surprise, my scepticism about the usefulness of neuroimaging proved to be the least controversial point of my article. There were few attempts at the challenge of providing an example whereby it has told us something novel about the way in which the mind works, although we have had plenty of promises that it will. Note that I do not claim that neuroimaging has no uses: For example, it is of exceptional importance in diagnosis; it might be useful in evaluating treatments; it might be essential for distinguishing subtypes of related disorders (e.g., some of the dementias); and it might play a supporting role in an interdisciplinary approach to cognition. There are two strands to the problems with neuroimaging—methodological problems with gathering and interpreting data, and problems in principle concerning the limitations of what neuroimaging can tell us. No one addressed the methodological problems, and so they remain outstanding, as they might do for some time. However, although serious, the methodological problems are less interesting than the theoretical limitations. Let us assume that the methodological problems can be solved. Could neuroimaging, then, tell us anything in principle about the mind? Shallice did rise to the challenge. He described five types of theorising about the mind, arguing that both cognitive neuropsychology and neuroimaging are useful for exploring all five types. He argues that functional imaging has uses in exploring the architecture of the mind and how tasks are learned and operate in real time, but not for how cognitive
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systems are implemented. There are, however, uses. Nevertheless the assumption is still that if two processes are localised in different regions of cortex then they are presumed to be produced by different subsystems. But what is the evidence for this presumption? And how could we tell if it is true? Lambon Ralph also argues that neuroimaging in conjunction with other techniques may prove revealing. In particular, imaging may converge with information from brain-damaged and nonbrain-damaged patients and behavioural studies. I maintain, in the absence of a counterexample, that neuroimaging will at best provide a suggestive role; it cannot play a crucial one. Lambon Ralph suggests that it might be possible to bridge the gap between brain and mind by computational modelling, with the models including models of the neural substrate. I agree that this “let’s build a brain” approach may in the long run be the best bet for psychology, but its realisation is a long way off. Even then, the models may be as mysterious as the brain itself. Vallar argues that reporting background neurological and localisation data now might provide a storehouse of information for the future. He points out that activation-imaging studies of people without brain damage tend to suggest that large areas of the brain may be involved in processing, but not all of this area may be essential; in contrast, data from patients with very localised damage may be important in constraining the localisation of function. This conclusion supports the idea that we should not forget the original methods and goals of cognitive neuropsychology. Caplan reconsiders the Mehler example of the planum temporale. He argues that the conclusion that the planum temporale is in fact involved in the general processing of sequence—rather than grammar or music—is an advance for our understanding of the functional architecture. We now need to introduce this set of cognitive operations. That, however, is the point: We could carry on introducing auxiliary assumptions and new components for ever, given the imaging data. The central problem demonstrated here is that it is difficult to envisage how a cognitive model could ever be
falsified by neuroimaging data in isolation. Because the data involve fundamentally different domains, one cannot be used in the other. Perhaps I too, like Coltheart, just suffer from a lack of imagination. Coltheart is even more pessimistic than I am about the likely contribution of neuroimaging. He distinguishes between attempts to localise cognitive functions in the brain and attempts to adjudicate between competing cognitive theories using neuroimaging data. He argues forcefully that so far neuroimaging has not successfully achieved either goal. Furthermore, like me, he is sceptical whether it can ever do so. With regard to the first goal, it will be difficult to isolate one part of the brain as being responsible for one component of a task in an interactive brain. It might be possible by a proper task analysis, but it is also possible that reasoning about the components of the task and their locations will prove to be so circular that it will be forever impossible. With regard to the second goal, knowing the hardware underlying something tells us nothing about the software that runs upon that hardware. In either case, we need the model before the pictures.
CONCLUSIONS Among these differences, there is some consensus that the hope for the next 20 years lies in converging evidence from different methodologies—including cognitive neuropsychology. Like Lambon Ralph, I have a dream: The future lies in a synthesis—a combination of computational modelling, analysis of single-case studies, case-series approaches, realtime data, and information from the neurosciences. Lambon Ralph provides some examples where converging evidence from interdisciplinary approaches is providing dividends. Another example is from speech production, where evidence from neuroimaging (De Zubicaray, McMahon, Eastburn, & Wilson, 2002; and see Indefrey & Levelt, in press, for a more general review of what imaging can tell us about word production), computational modelling (Harley, 1993), and experimental data (Lupker, 1979) suggests a need for some inhibitory mechanism. Yet another COGNITIVE NEUROPSYCHOLOGY, 2004, 21 (1)
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example is provided by recent work that integrates experimental, neuropsychological, and imaging data to further our understanding of the neural substrate of visual and auditory short-term memory systems, and how short-term memory relates to long-term memory (Ruchkin, Grafman, Cameron, & Berndt, in press). The extent to which neuroimaging can otherwise deliver on the huge investment made in it remains to be seen. Perhaps in 20 years time all these pictures in Cognitive Neuropsychology may turn out to be useful after all. Cognitive neuropsychology is dead; long live cognitive neuroscience.
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