- Email: [email protected]
The Organisation of Mind
c o r t e x 4 8 ( 2 0 1 2 ) 1 3 6 6 e1 3 7 0
Available online at www.sciencedirect.com
Journal homepage: www.elsevier.com/locate/cortex
Discussion forum
The Organisation of Mind Tim Shallice a,* and Richard P. Cooper b a b
SISSA, Trieste, Italy; Institute of Cognitive Neuroscience, University College, London, UK Department of Psychological Sciences, Birkbeck, University of London, UK
Synopsis of book published by Oxford University Press, 2011 Cognitive neuroscience stems from two rather different sets of disciplines. The brain-based set of disciplines e neuroanatomy, neurophysiology and those based on the physical techniques involved such as cellular recording and functional imaging e are rooted in the biomedical sciences and medical physics. On the other hand information-processing psychology, modern linguistics and artificial intelligence are derived directly from the origins and effects of the computer revolution of the 1940s and more distantly from philosophical disciplines related to the intelligent experiencing human being, such as logic. The two sets of disciplines differ considerably in their social and intellectual styles, the former tending more to a big science pragmatism, the latter more to ideological and theoretical debates. But why should both sets of disciplines be relevant to our attempt to understand mind? The answer, according to The Organisation of Mind, has three aspects. First, it is held that theories of how particular cognitive functions are implemented in the mind have primarily been derived from the second set of disciplines, with the partial exception of neural network theory to which we return. Second, it is argued that for both theoretical development and falsification, behavioural data is relatively weak by comparison with data in the ‘harder’ sciences. Thus, the typical conclusion of an empirical cognitive neuroscience study in human experimental psychology is that of an ordinal relation between performance, as measured, say, in terms of accuracy or reaction time, across different conditions or types of stimuli. However there can be a variety of plausible theories of the cognitive function being investigated and any more formal models can have many variables, so merely ordinal behavioural data can
frequently be accommodated by more than one of the theories. In compensation, the third point is that brain-related findings provide more powerful evidence, as they sharpen up the basic empirical findings, as in neuropsychology, or provide an additional dimension of evidence, as in the functional imaging of normal subjects. But maybe one does not need cognitive type theories at all. Using modern imaging methods could we not build our conception of cognition anew bottom-up in an analogous way to say how neuroscience research currently operates for a structure like MT/V5? There are two strong arguments against this. First, in practice both systems neuroscience and cognitive neuroscience research is dominated by a conception of a particular brain region having a specific function within the cognitive system as a whole, and this can be far from transparent. Thus the example can be given of the hippocampus, which for the last 40 years has been viewed in empirical studies either from the perspective of it as a repository e of knowledge of the local environment, as in O’Keefe and Nadel’s (1978) cognitive map approach, or as a store of episodic memories. Yet there is still no generally agreed way of integrating these two approaches to the function of the hippocampus but nearly all research can be situated under the overall theme of one function or the other. And, moving to a higher-level, it would be foolish in the extreme to investigate language, for instance, without using some conceptual frameworks coming from linguistics; consider tackling language processes without making a division between semantics, syntax and phonology/morphology. In any case, the behaviour of experimental subjects is far more complex to characterise than that of a neuron being recorded by an implanted electrode. Moreover, while a structure like the hippocampus can be studied in animals using
* Corresponding author. Cognitive Neuroscience Sector, SISSA, Via Bonomea 256, 34136 Trieste, Italy. E-mail address: [email protected] (T. Shallice). 0010-9452/$ e see front matter ª 2011 Published by Elsevier Srl. http://dx.doi.org/10.1016/j.cortex.2011.07.004
c o r t e x 4 8 ( 2 0 1 2 ) 1 3 6 6 e1 3 7 0
cellular recording, this technique is very rarely available in humans, the only organism in which higher-level cognitive functions, with which the book is primarily concerned, can be easily studied. Turning to higher-level cognitive processes in the typical human subject, not only is it often not possible to characterise performance more precisely than in a merely ordinal fashion across conditions, it is also nearly always the product of a cognitive task, a characteristic which may seem straightforward but often has hidden complexities. Take lexical decision, for example, which is apparently a very simple task. Behaviour is influenced by the preceding trials. The decision can be based on some combination of the visual form of a letter string, the derived phonology, and/or its semantics, if any. The decision process will also be influenced by the types of non-word stimuli that have occurred in the experiment so far. Moreover, performing a task typically depends upon the subject first understanding instructions and then consciously trying to carry them out, both very mysterious processes indeed. To understand and manipulate these different factors one needs a conceptual framework in which they can be discussed. Currently this is the framework of cognitive science. It seems most unlikely that an unrelated conceptual framework could replace it. We believe then that cognitive-level theorising is essential if one is to understand the more complex aspects of mind. There are a number types of cognitive theory, for instance information-processing models, represented in a more formal fashion by the use of the COGENT framework (Cognitive Objects in a Graphical Environment: Cooper and Fox, 1998), neural network models of the connectionist type, which in turn can be either localist or distributed, and certain symbolic artificial intelligence models. Each of these four styles of theorising is held to be appropriate for particular cognitive domains. Connectionist models are treated within the cognitive science group of theories for two reasons. Sociologically they mainly originated from artificial intelligence, engineering and physics, in a culture more typical of cognitive science than biomedicine. Moreover they do not rely on detailed findings from neuroanatomy and neurophysiology, but rather only an abstract and simplified notion of the neural elements. Given these preliminary considerations, one is faced with the issue of how such cognitive models can be confronted by brain-related findings. In this respect, the book considers two main types of brain-related empirical evidence, neuropsychology and functional imaging. Both intuitively seem to provide potentially very valuable information for unravelling the cognitive puzzle. Neuropsychology, in its cognitive mode, helps to isolate and characterise the operations of specific subcomponents of the cognitive system by the study of selective disorders. Functional imaging in turn seems to provide an even more direct and anatomically precise isolation of the said components. However, the issue is then posed of how findings from these two methods can be related to the four types of theory discussed above, how one can bridge between the two conceptual systems. If one makes the implicit assumptions of the two approaches explicit, then those assumptions can be more easily tested. The general approach in cognitive neuroscience, especially functional imaging, is for inferences from brain-related evidence to cognitive generalisations to be at best
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characterisable as intuitive and at worst as intellectually slovenly. An exception can be made for cognitive neuropsychology, where since the landmark paper of Caramazza (1986) a number of attempts have been made to develop a more formal framework (see e.g., Shallice, 1988; Glymour, 1994; Vallar, 2000). Moreover, in cognitive neuropsychology in practice, there is a fairly standard procedure for relating patient findings to at least three of the types of model discussed earlier. Thus, in information-processing models one or more components or connections need to exist within the relevant model which, if functioning at less than normal levels, would give rise to the observed behaviour. Or if one takes a neural network model, whether it be distributed or localist, the observed behaviour needs to be explicable in terms, say, of removing a certain proportion of the hidden units in a layer, or setting the weights of connections between layers to zero, or by adding noise to the weights of such connections. When one moves to functional imaging the contrast is stark. There is no natural conceptual relation between the pattern of activation realised in the BOLD signal of blood flow changes in fMRI and an aspect of the operation of a neural network. And even the link to information-processing models is far from transparent. A consequence of this lack of a conceptual relation, combined with the sociological difference between on the one hand imaging as a craft and imagers as scientists and on the other modelling and modellers, is that it is rare to find in the literature an interpretation of functional imaging findings couched in terms of cognitive theories. Theoretical conclusions tend to be framed either in reductionist concepts in terms of the properties of the activity of neurons or alternatively in the vagaries of everyday language. When one moves to other imaging methodologies such as EEG or MEG the conceptual problems in making bridge relations to cognitive models are even more severe. Our aim is to provide a conceptual framework that allows the move from brain-related data to cognitive theories to be made. The assumptions embodied in this framework are far from revolutionary, but by making them explicit, their plausibility can then considered. The basic assumption, as far as information-processing theories are concerned, is that the cognitive system is weakly modular in the sense that different components can be separately modified. Each processing component is held to compute a particular type of inputeoutput relation. The set of processing components is assumed to be the same for all members of the reference group, typically members of the same culture. Each processing component is presumed to be located in its “brain region”. At any given time a certain level of its resource is held to be available, this being monotonically related to its effectiveness. It is argued that as a first approximation the effect of a lesion can be characterised as a reduction in one or more resources or as a disconnection between resources. Again to a first approximation the BOLD signal is held to be viewable as a measure of the resource being used by a subsystem. Critically, it is argued that these assumptions may be invalid in certain cases, and it is difficult if not impossible a priori to know when they are. However, as the sets of assumptions are relatively independent of each other, any theoretical conclusion which fits with results obtained using both methods separately can be considered to be strongly supported.
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The framework can then be applied more specifically. As far as neuropsychology as an empirical method is concerned, it has long been a basic premise within cognitive neuropsychology that the ideal approach is the single-case study. “It is deeply characteristic of cognitive neuropsychology that it studies symptoms rather than syndromes and carries out single-case studies rather than group studies” (Caramazza and Coltheart, 2006, p. 5). The position taken in the book is more eclectic. First the example is given of reading without semantics (Schwartz et al., 1980). The pattern of performance shown by patients suffering from this syndrome has been held to pose severe difficulties for so-called division of labour theories (such as that of Plaut et al., 1996) in which the material substratum of reading aloud is spread across two different routes e an all encompassing phonological one and a semantic one (Coltheart, 2006). For when the semantic contribution is much reduced, as it must be in reading without semantics, surface dyslexia should result. However, Plaut et al. had responded by making an initially ad hoc addition to their division of labour theory; subjects were held to differ premorbidly in the relative weight put on the two routes. This was shown to be a very plausible way of explaining the overall set of findings on reading aloud when semantics begins to be lost. The method employed was the case series approach, applied in semantic dementia in a study carried out by Woollams et al. (2007), in which the reading of 50 patients with semantic dementia was examined over time as the atrophy became more severe. Patients initially showing the pattern of reading without semantics became surface dyslexic later in the course of their dementing condition. We do not however take the opposite position to that of Caramazza and Coltheart as do Patterson et al. (2006) who say that “we nevertheless argue that selective emphasis on these exceptional cases is misguided” (p. 179). Instead, we support the use of four different procedures e single-case studies, disease-based case series where the disease process is reasonably well localised, anatomically based case series and classical anatomically based group studies. The four methods are held to have complementary advantages and disadvantages. We argue that a major aim of each of these neuropsychological methods should be the characterisation of a functional syndrome, a large set of properties of a particular patient or group of patients which can be used to confront the theoretical models in a particular domain. The models then have to predict the set of properties using a single lesion pattern. The idea is that the rich variety of properties of the functional syndrome will compensate for the relatively weak falsifying power of any one property. A second more minor aim of the neuropsychological approach, but one which is very antithetical to traditional cognitive neuropsychology, is to provide a potential localisation for any hypothetical subsystems involved in the lesion. This can then provide converging evidence with functional imaging findings for the localisation of the relevant subsystem. If one turns to functional imaging, the situation is more difficult. The BOLD signal appears to correspond more to local field potentials than to the spiking activity of neurons (Goense and Logothetis, 2008). To assume for the correspondence to the cognitive level that the level of the BOLD signal in a brain region is monotonically related to the degree of resource
required by a subsystem localised in the region is not as transparently plausible as is loss of resource for neuropsychological data. However, the concept of processing resource was originally derived from behavioural dual task studies (Norman and Bobrow, 1975). It has been shown by Carpenter et al. (1999) and Just et al. (2001) among others, that in dual task studies the BOLD signal behaves in a way one would expect from the behavioural dual task literature. This makes the bridge relations between BOLD and resource level more plausible. With this framework the strengths and limitations of a variety of different designs of functional imaging experiment, including subtraction, factorial methods, cognitive conjunctions and behaviour-to-BOLD correlations, are considered. It is then argued that, as far as inferences to informationprocessing models are concerned, to draw conclusions from both neuropsychological data and functional imaging data rests on assumptions which in any particular case may not be valid. Moreover whether this is the case could well not be known a priori. However, the nature of the assumptions is qualitatively so different between the two domains that if both methodologies support the same theoretical conclusion then the plausibility of the assumptions made in each of the extrapolations is indirectly strengthened, as is the overall theoretical conclusion. Thus the converging operations methodology of Garner et al. (1956) is especially powerful in this area because of the independence of the critical assumptions made for the two methodologies. As an example the approach is applied to the processing of the visual wordform as in the work of Cohen et al. (2003). With this set of basic principles the book examines core cognition, namely the set of processes that are most relevant for understanding thought. We approach core cognition on two levels. First we consider the basic cognitive computational engine, involving representations, short-term or working memory and operations. Then we consider broadly supervisory processes which modulate the operation of the cognitive computational engine. More specifically we consider prefrontal supervisory processes, episodic memory, consciousness and thinking itself. In general the chapters are structured around one or more computational model. The discussion of representations has as its core the concept of attractor neural networks. These can be internally distributed but have a discrete set of effects on other parts of the cognitive system. We consider in particular the semantic memory model of Rogers et al. (2004), which is strongly supported by studies of semantic dementia, and indirectly by functional imaging evidence, at least as far as the localisation of any semantic hub is concerned. We argue that its adequacy in dealing with category specificity is however less clear, and moreover propose that quite distinct systems will underlie the processing of abstract concepts. The retention of information over short periods of time is a key aspect of computation. We argue that three different types of system are involved e buffers in the sense of Baddeley and Hitch (1974), priming and a more central attentional focus possibly related to the associated buffers of a central production system. From the computational point of view, most emphasis is given to models of the operation of individual buffers relating to input and output phonological stores
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(e.g., Burgess and Hitch, 2005; Hartley and Houghton, 1996) and the grapheme output system (e.g., Glasspool and Houghton, 2005), and how they explain the disorders of individual patients. The central aspect of a cognitive computational engine is the “operation.” We consider two quite different domains of operations e that of the control of action and that of the ordering of units in language e morphology and syntax. As far as action is concerned, we concentrate on how basic actions are combined into larger well-learned operations, such as making coffee, particularly in the context of disorders such as ideational apraxia. Our contention scheduling model (Cooper and Shallice, 2000) is compared with the recurrent network model of Botvinick and Plaut (2004). In language we consider how adequate, from a cognitive neuroscience perspective, are single-route models of morphological operations such as that of Joanisse and Seidenberg (1999), arguing for the importance of neuropsychological data in showing the inadequacy of the approach. As far as syntactic operations are concerned we tentatively support unification ideas, such as those proposed by Hagoort (2005) on the basis of the model of Vosse and Kempen (2000). Turning to supervisory processes, we take a twodimensional approach. The first is the hierarchical view of increasingly abstract processing, as one moves more anteriorly in prefrontal cortex, such as that presented by Badre et al. (2009). However, we argue that this needs to be complemented by a heterarchical approach of a set of qualitatively different processing systems each implementing a different type of control process on lower-level processing systems of the contention scheduling type. This is supported conceptually by consideration of the Domino expert system of Fox and Das (2000) and neuropsychologically by analysis of the group studies of the ROBBIA (Rotman-Baycrest Battery to Investigate Attention) type where the performance of four frontal subgroups e left lateral, right lateral, superior medial and inferior medial e is compared on a variety of reaction time tasks (e.g., Stuss et al., 2005). It is held to be cognitively important on this approach that the anatomical groups remain the same from one study to the next so that a single set of loss of resources has to explain the full behavioural pattern. This means that approaches like Voxel-based Morphometry and Voxel-based Lesion-Symptom Mapping do not easily provide relevant findings for cognitive-level theorising. Episodic memory is seen as having a critical function in providing raw material for case-based reasoning when routine processes based on semantic memory fail. From the cognitive neuroscience perspective this approach is compatible with the multiple trace theory of the function of the hippocampus (e.g., Moscovitch et al., 2006), which is considered both in the context of selective lesions of the hippocampus with respect particularly to long-term retrograde amnesia and in the context of the functional imaging evidence on the contrasting functions of the hippocampus and the perirhinal cortex. The recollection of autobiographical events is further considered in the context of the syndrome of spontaneous confabulation. Various positions are considered such as that of Gilboa et al. (2006) where the ventromedial prefrontal cortex is held, in normal functioning, to set a preconscious ‘feeling of rightness’, and that of Schnider et al. (1996) on the possible role of
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a similar region in preventing temporal confusion between events. Preference is given to a novel proposal that such confabulation arises from a so-called “latching” process between attractor basins each representing a part of an episodic memory; this is held to arise from an ultra-retrieval state e in the sense of the general approach of Hasselmo et al. (1996) e being realised in the hippocampus due to loss of acetylcholine input. The discussion of consciousness and its disorders considers a number of very general types of conceptual approach. Thus philosophical positions relating consciousness to quantum mechanics are rejected, as are activation strength and microconsciousness approaches. Most consideration is given to the approach of Dehaene et al. (2006), which is held to be the first proper scientific theory of consciousness yet proposed in that it links empirical data with a computational model. Various problems with the model are discussed. Most critically, while the model holds that dorsolateral prefrontal and inferior parietal cortices are essential regions for the global workspace, it does not accord any specific functional role to these individual regions with regard to consciousness. We propose instead that consciousness is better characterised in terms of the effect of supervisory processes on other processing systems. The book culminates in a consideration of whether cognitive neuroscience can yet make progress in the domain of thinking. Research has tended to focus on rather specific areas each representing a different type of thought processes. So initially we consider separately cognitive neuroscience research on reasoning with relations, reasoning by analogy, deductive inference, goal-directed problem-solving, rule induction, hypothesis generation and insight. In none of these areas is there yet a solid convergence between neuropsychological and neuroimaging findings. However we find unduly pessimistic the conclusion of Goel (2007) that “these data (i.e., the functional imaging and patient data) are telling us that the brain is organised in ways not anticipated by cognitive theory” (p. 440). Instead, we argue that it is possible to build on the foundational structure provided by simpler investigations of supervisory processes, discussed in an earlier chapter, to move towards a more unified account of thinking and reasoning. Overall we would argue that at a more basic informationprocessing level of theorising there is now good convergence in a variety of areas between neuropsychological and neuroimaging findings. However, if one considers deeper computational models of the connectionist or symbolic architecture type then in virtually no area is one of the models clearly superior to its competitors. To achieve this, we think it will be necessary to combine cognitive neuroscience methods in novel and more powerful ways.
references
Baddeley AD and Hitch GJ. Working memory. In Bower G (Ed), Recent Advances in Learning and Motivation. New York: Academic, 1974: 47e89. Badre D, Hoffman J, Cooney JW, and D’Esposito M. Hierarchical cognitive control deficits following damage to the human frontal lobe. Nature Neuroscience, 12(4): 515e522, 2009.
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Botvinick M and Plaut DC. Doing without schema hierarchies: A recurrent connectionist approach to normal and impaired routine sequential action. Psychological Review, 111(2): 395e429, 2004. Burgess N and Hitch G. Computational models of working memory: Putting long-term memory into context. Trends in Cognitive Science, 9(11): 535e541, 2005. Caramazza A. On drawing inferences about the structure of normal cognitive systems from the analysis of patterns of impaired performance: The case for single-patient studies. Brain and Cognition, 5(1): 41e66, 1986. Caramazza A and Coltheart M. Cognitive neuropsychology twenty years on. Cognitive Neuropsychology, 23(1): 3e12, 2006. Carpenter PA, Just MA, Keller TA, Eddy W, and Thulborn K. Graded functional activation in the visuospatial system with the amount of task demand. Journal of Cognitive Neuroscience, 11(1): 9e24, 1999. Cohen L, Martinaud O, Lemer C, Lehericy S, Samson Y, Obadia M, et al. Visual word recognition in the left and right hemispheres: Anatomical and functional correlates of peripheral alexias. Cerebral Cortex, 13(12): 1313e1333, 2003. Coltheart M. Acquired dyslexias and the computational modelling of reading. Cognitive Neuropsychology, 23(1): 96e109, 2006. Cooper RP and Fox J. COGENT: A visual design environment for cognitive modeling. Behaviour Research Methods, Instruments Computers, 30(4): 553e564, 1998. Cooper RP and Shallice T. Contention scheduling and the control of routine activities. Cognitive Neuropsychology, 17(4): 297e338, 2000. Dehaene S, Changeux JP, Naccache L, Sackur J, and Sergent C. Conscious, preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Science, 10(5): 204e211, 2006. Fox J and Das S. Safe and Sound. Artificial Intelligence in Hazardous Applications. Menlo Park, California: AAAI Press/MIT Press, 2000. Garner WR, Hake HW, and Eriksen CW. Operationism and the concept of perception. Psychological Review, 63(3): 149e159, 1956. Gilboa A, Alain C, Stuss DT, Melo B, Miller S, and Moscovitch M. Mechanisms of spontaneous confabulations: A strategic retrieval account. Brain, 129(Pt 6): 1399e1414, 2006. Glasspool DW and Houghton G. Serial order and consonant-vowel structure in a graphemic output buffer model. Brain and Language, 94(3): 304e330, 2005. Glymour C. On the methods of cognitive neuropsychology. The British Journal for the Philosophy of Science, 45(3): 815e835, 1994. Goel V. Anatomy of deductive reasoning. Trends in Cognitive Science, 11(10): 435e441, 2007. Goense JB and Logothetis NK. Neurophysiology of the BOLD fMRI signal in awake monkeys. Current Biology, 18(9): 631e640, 2008. Hagoort P. On Broca, brain, and binding: A new framework. Trends in Cognitive Science, 9(9): 416e423, 2005. Hartley T and Houghton G. A linguistically constrained model of short-term memory for nonwords. Journal of Memory and Language, 35(1): 1e31, 1996.
Hasselmo ME, Wyble BP, and Wallenstein GV. Encoding and retrieval of episodic memories: Role of cholinergic and GABAergic modulation in the hippocampus. Hippocampus, 6(6): 693e708, 1996. Joanisse M and Seidenberg M. Impairments in verb morphology following brain injury: A connectionist model. Proceedings of the National Academy of Sciences, 96(13): 7592e7597, 1999. Just MA, Carpenter PA, Keller TA, Emery L, Zajac H, and Thulborn KR. Interdependence of nonoverlapping cortical systems in dual cognitive tasks. NeuroImage, 14(2): 417e426, 2001. Moscovitch M, Nadel L, Winocur G, Gilboa A, and Rosenbaum RS. The cognitive neuroscience of remote episodic, semantic and spatial memory. Current Opinion in Neurobiology, 16(2): 179e190, 2006. Norman DA and Bobrow D. On data-limited and resource-limited processes. Cognitive Psychology, 7(1): 44e64, 1975. O’Keefe J and Nadel L. The Hippocampus as a Cognitive Map. Oxford: Clarendon, 1978. Patterson K, Lambon Ralph MA, Jefferies E, Woollams A, Jones R, Hodges JR, et al. “Presemantic” cognition in semantic dementia: Six deficits in search of an explanation. Journal of Cognitive Neuroscience, 18(2): 169e183, 2006. Plaut D, McClelland J, Seidenberg M, and Patterson K. Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103(1): 56e115, 1996. Rogers TT, Lambon Ralph MA, Garrard P, Bozeat S, McClelland JL, Hodges JR, et al. Structure and deterioration of semantic memory: A neuropsychological and computational investigation. Psychological Review, 111(1): 205e235, 2004. Schwartz MF, Saffran EM, and Marin OSM. Fractionating the reading process in dementia: Evidence from word-specific print-to-sound associations. In Coltheart M, Patterson KE, and Marshall JC (Eds), Deep Dyslexia. London: Routledge, 1980: 259e269. Schnider A, von Da¨niken C, and Gutbrod K. The mechanisms of spontaneous and provoked confabulations. Brain, 119(4): 1365e1375, 1996. Shallice T. From Neuropsychology to Mental Structure. Cambridge, UK: Cambridge University Press, 1988. Stuss DT, Alexander MP, Shallice T, Picton TW, Binns MA, Macdonald R, et al. Multiple frontal systems controlling response speed. Neuropsychologia, 43(3): 396e417, 2005. Vallar G. The methodological foundations of human neuropsychology: Studies in brain-damaged patients. In Boller F and Grafman J (Eds), Handbook of Neuropsychology. Amstredam: Elsevier Science, 2000: 305e344. Vosse T and Kempen G. Syntactic structure assembly in human parsing: A computational model based on competitive inhibition and a lexicalist grammar. Cognition, 75(2): 105e143, 2000. Woollams AM, Ralph MA, Plaut DC, and Patterson K. SD-squared: On the association between semantic dementia and surface dyslexia. Psychological Review, 114(2): 316e339, 2007.
Available online at www.sciencedirect.com
Journal homepage: www.elsevier.com/locate/cortex
Discussion forum
The Organisation of Mind Tim Shallice a,* and Richard P. Cooper b a b
SISSA, Trieste, Italy; Institute of Cognitive Neuroscience, University College, London, UK Department of Psychological Sciences, Birkbeck, University of London, UK
Synopsis of book published by Oxford University Press, 2011 Cognitive neuroscience stems from two rather different sets of disciplines. The brain-based set of disciplines e neuroanatomy, neurophysiology and those based on the physical techniques involved such as cellular recording and functional imaging e are rooted in the biomedical sciences and medical physics. On the other hand information-processing psychology, modern linguistics and artificial intelligence are derived directly from the origins and effects of the computer revolution of the 1940s and more distantly from philosophical disciplines related to the intelligent experiencing human being, such as logic. The two sets of disciplines differ considerably in their social and intellectual styles, the former tending more to a big science pragmatism, the latter more to ideological and theoretical debates. But why should both sets of disciplines be relevant to our attempt to understand mind? The answer, according to The Organisation of Mind, has three aspects. First, it is held that theories of how particular cognitive functions are implemented in the mind have primarily been derived from the second set of disciplines, with the partial exception of neural network theory to which we return. Second, it is argued that for both theoretical development and falsification, behavioural data is relatively weak by comparison with data in the ‘harder’ sciences. Thus, the typical conclusion of an empirical cognitive neuroscience study in human experimental psychology is that of an ordinal relation between performance, as measured, say, in terms of accuracy or reaction time, across different conditions or types of stimuli. However there can be a variety of plausible theories of the cognitive function being investigated and any more formal models can have many variables, so merely ordinal behavioural data can
frequently be accommodated by more than one of the theories. In compensation, the third point is that brain-related findings provide more powerful evidence, as they sharpen up the basic empirical findings, as in neuropsychology, or provide an additional dimension of evidence, as in the functional imaging of normal subjects. But maybe one does not need cognitive type theories at all. Using modern imaging methods could we not build our conception of cognition anew bottom-up in an analogous way to say how neuroscience research currently operates for a structure like MT/V5? There are two strong arguments against this. First, in practice both systems neuroscience and cognitive neuroscience research is dominated by a conception of a particular brain region having a specific function within the cognitive system as a whole, and this can be far from transparent. Thus the example can be given of the hippocampus, which for the last 40 years has been viewed in empirical studies either from the perspective of it as a repository e of knowledge of the local environment, as in O’Keefe and Nadel’s (1978) cognitive map approach, or as a store of episodic memories. Yet there is still no generally agreed way of integrating these two approaches to the function of the hippocampus but nearly all research can be situated under the overall theme of one function or the other. And, moving to a higher-level, it would be foolish in the extreme to investigate language, for instance, without using some conceptual frameworks coming from linguistics; consider tackling language processes without making a division between semantics, syntax and phonology/morphology. In any case, the behaviour of experimental subjects is far more complex to characterise than that of a neuron being recorded by an implanted electrode. Moreover, while a structure like the hippocampus can be studied in animals using
* Corresponding author. Cognitive Neuroscience Sector, SISSA, Via Bonomea 256, 34136 Trieste, Italy. E-mail address: [email protected] (T. Shallice). 0010-9452/$ e see front matter ª 2011 Published by Elsevier Srl. http://dx.doi.org/10.1016/j.cortex.2011.07.004
c o r t e x 4 8 ( 2 0 1 2 ) 1 3 6 6 e1 3 7 0
cellular recording, this technique is very rarely available in humans, the only organism in which higher-level cognitive functions, with which the book is primarily concerned, can be easily studied. Turning to higher-level cognitive processes in the typical human subject, not only is it often not possible to characterise performance more precisely than in a merely ordinal fashion across conditions, it is also nearly always the product of a cognitive task, a characteristic which may seem straightforward but often has hidden complexities. Take lexical decision, for example, which is apparently a very simple task. Behaviour is influenced by the preceding trials. The decision can be based on some combination of the visual form of a letter string, the derived phonology, and/or its semantics, if any. The decision process will also be influenced by the types of non-word stimuli that have occurred in the experiment so far. Moreover, performing a task typically depends upon the subject first understanding instructions and then consciously trying to carry them out, both very mysterious processes indeed. To understand and manipulate these different factors one needs a conceptual framework in which they can be discussed. Currently this is the framework of cognitive science. It seems most unlikely that an unrelated conceptual framework could replace it. We believe then that cognitive-level theorising is essential if one is to understand the more complex aspects of mind. There are a number types of cognitive theory, for instance information-processing models, represented in a more formal fashion by the use of the COGENT framework (Cognitive Objects in a Graphical Environment: Cooper and Fox, 1998), neural network models of the connectionist type, which in turn can be either localist or distributed, and certain symbolic artificial intelligence models. Each of these four styles of theorising is held to be appropriate for particular cognitive domains. Connectionist models are treated within the cognitive science group of theories for two reasons. Sociologically they mainly originated from artificial intelligence, engineering and physics, in a culture more typical of cognitive science than biomedicine. Moreover they do not rely on detailed findings from neuroanatomy and neurophysiology, but rather only an abstract and simplified notion of the neural elements. Given these preliminary considerations, one is faced with the issue of how such cognitive models can be confronted by brain-related findings. In this respect, the book considers two main types of brain-related empirical evidence, neuropsychology and functional imaging. Both intuitively seem to provide potentially very valuable information for unravelling the cognitive puzzle. Neuropsychology, in its cognitive mode, helps to isolate and characterise the operations of specific subcomponents of the cognitive system by the study of selective disorders. Functional imaging in turn seems to provide an even more direct and anatomically precise isolation of the said components. However, the issue is then posed of how findings from these two methods can be related to the four types of theory discussed above, how one can bridge between the two conceptual systems. If one makes the implicit assumptions of the two approaches explicit, then those assumptions can be more easily tested. The general approach in cognitive neuroscience, especially functional imaging, is for inferences from brain-related evidence to cognitive generalisations to be at best
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characterisable as intuitive and at worst as intellectually slovenly. An exception can be made for cognitive neuropsychology, where since the landmark paper of Caramazza (1986) a number of attempts have been made to develop a more formal framework (see e.g., Shallice, 1988; Glymour, 1994; Vallar, 2000). Moreover, in cognitive neuropsychology in practice, there is a fairly standard procedure for relating patient findings to at least three of the types of model discussed earlier. Thus, in information-processing models one or more components or connections need to exist within the relevant model which, if functioning at less than normal levels, would give rise to the observed behaviour. Or if one takes a neural network model, whether it be distributed or localist, the observed behaviour needs to be explicable in terms, say, of removing a certain proportion of the hidden units in a layer, or setting the weights of connections between layers to zero, or by adding noise to the weights of such connections. When one moves to functional imaging the contrast is stark. There is no natural conceptual relation between the pattern of activation realised in the BOLD signal of blood flow changes in fMRI and an aspect of the operation of a neural network. And even the link to information-processing models is far from transparent. A consequence of this lack of a conceptual relation, combined with the sociological difference between on the one hand imaging as a craft and imagers as scientists and on the other modelling and modellers, is that it is rare to find in the literature an interpretation of functional imaging findings couched in terms of cognitive theories. Theoretical conclusions tend to be framed either in reductionist concepts in terms of the properties of the activity of neurons or alternatively in the vagaries of everyday language. When one moves to other imaging methodologies such as EEG or MEG the conceptual problems in making bridge relations to cognitive models are even more severe. Our aim is to provide a conceptual framework that allows the move from brain-related data to cognitive theories to be made. The assumptions embodied in this framework are far from revolutionary, but by making them explicit, their plausibility can then considered. The basic assumption, as far as information-processing theories are concerned, is that the cognitive system is weakly modular in the sense that different components can be separately modified. Each processing component is held to compute a particular type of inputeoutput relation. The set of processing components is assumed to be the same for all members of the reference group, typically members of the same culture. Each processing component is presumed to be located in its “brain region”. At any given time a certain level of its resource is held to be available, this being monotonically related to its effectiveness. It is argued that as a first approximation the effect of a lesion can be characterised as a reduction in one or more resources or as a disconnection between resources. Again to a first approximation the BOLD signal is held to be viewable as a measure of the resource being used by a subsystem. Critically, it is argued that these assumptions may be invalid in certain cases, and it is difficult if not impossible a priori to know when they are. However, as the sets of assumptions are relatively independent of each other, any theoretical conclusion which fits with results obtained using both methods separately can be considered to be strongly supported.
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The framework can then be applied more specifically. As far as neuropsychology as an empirical method is concerned, it has long been a basic premise within cognitive neuropsychology that the ideal approach is the single-case study. “It is deeply characteristic of cognitive neuropsychology that it studies symptoms rather than syndromes and carries out single-case studies rather than group studies” (Caramazza and Coltheart, 2006, p. 5). The position taken in the book is more eclectic. First the example is given of reading without semantics (Schwartz et al., 1980). The pattern of performance shown by patients suffering from this syndrome has been held to pose severe difficulties for so-called division of labour theories (such as that of Plaut et al., 1996) in which the material substratum of reading aloud is spread across two different routes e an all encompassing phonological one and a semantic one (Coltheart, 2006). For when the semantic contribution is much reduced, as it must be in reading without semantics, surface dyslexia should result. However, Plaut et al. had responded by making an initially ad hoc addition to their division of labour theory; subjects were held to differ premorbidly in the relative weight put on the two routes. This was shown to be a very plausible way of explaining the overall set of findings on reading aloud when semantics begins to be lost. The method employed was the case series approach, applied in semantic dementia in a study carried out by Woollams et al. (2007), in which the reading of 50 patients with semantic dementia was examined over time as the atrophy became more severe. Patients initially showing the pattern of reading without semantics became surface dyslexic later in the course of their dementing condition. We do not however take the opposite position to that of Caramazza and Coltheart as do Patterson et al. (2006) who say that “we nevertheless argue that selective emphasis on these exceptional cases is misguided” (p. 179). Instead, we support the use of four different procedures e single-case studies, disease-based case series where the disease process is reasonably well localised, anatomically based case series and classical anatomically based group studies. The four methods are held to have complementary advantages and disadvantages. We argue that a major aim of each of these neuropsychological methods should be the characterisation of a functional syndrome, a large set of properties of a particular patient or group of patients which can be used to confront the theoretical models in a particular domain. The models then have to predict the set of properties using a single lesion pattern. The idea is that the rich variety of properties of the functional syndrome will compensate for the relatively weak falsifying power of any one property. A second more minor aim of the neuropsychological approach, but one which is very antithetical to traditional cognitive neuropsychology, is to provide a potential localisation for any hypothetical subsystems involved in the lesion. This can then provide converging evidence with functional imaging findings for the localisation of the relevant subsystem. If one turns to functional imaging, the situation is more difficult. The BOLD signal appears to correspond more to local field potentials than to the spiking activity of neurons (Goense and Logothetis, 2008). To assume for the correspondence to the cognitive level that the level of the BOLD signal in a brain region is monotonically related to the degree of resource
required by a subsystem localised in the region is not as transparently plausible as is loss of resource for neuropsychological data. However, the concept of processing resource was originally derived from behavioural dual task studies (Norman and Bobrow, 1975). It has been shown by Carpenter et al. (1999) and Just et al. (2001) among others, that in dual task studies the BOLD signal behaves in a way one would expect from the behavioural dual task literature. This makes the bridge relations between BOLD and resource level more plausible. With this framework the strengths and limitations of a variety of different designs of functional imaging experiment, including subtraction, factorial methods, cognitive conjunctions and behaviour-to-BOLD correlations, are considered. It is then argued that, as far as inferences to informationprocessing models are concerned, to draw conclusions from both neuropsychological data and functional imaging data rests on assumptions which in any particular case may not be valid. Moreover whether this is the case could well not be known a priori. However, the nature of the assumptions is qualitatively so different between the two domains that if both methodologies support the same theoretical conclusion then the plausibility of the assumptions made in each of the extrapolations is indirectly strengthened, as is the overall theoretical conclusion. Thus the converging operations methodology of Garner et al. (1956) is especially powerful in this area because of the independence of the critical assumptions made for the two methodologies. As an example the approach is applied to the processing of the visual wordform as in the work of Cohen et al. (2003). With this set of basic principles the book examines core cognition, namely the set of processes that are most relevant for understanding thought. We approach core cognition on two levels. First we consider the basic cognitive computational engine, involving representations, short-term or working memory and operations. Then we consider broadly supervisory processes which modulate the operation of the cognitive computational engine. More specifically we consider prefrontal supervisory processes, episodic memory, consciousness and thinking itself. In general the chapters are structured around one or more computational model. The discussion of representations has as its core the concept of attractor neural networks. These can be internally distributed but have a discrete set of effects on other parts of the cognitive system. We consider in particular the semantic memory model of Rogers et al. (2004), which is strongly supported by studies of semantic dementia, and indirectly by functional imaging evidence, at least as far as the localisation of any semantic hub is concerned. We argue that its adequacy in dealing with category specificity is however less clear, and moreover propose that quite distinct systems will underlie the processing of abstract concepts. The retention of information over short periods of time is a key aspect of computation. We argue that three different types of system are involved e buffers in the sense of Baddeley and Hitch (1974), priming and a more central attentional focus possibly related to the associated buffers of a central production system. From the computational point of view, most emphasis is given to models of the operation of individual buffers relating to input and output phonological stores
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(e.g., Burgess and Hitch, 2005; Hartley and Houghton, 1996) and the grapheme output system (e.g., Glasspool and Houghton, 2005), and how they explain the disorders of individual patients. The central aspect of a cognitive computational engine is the “operation.” We consider two quite different domains of operations e that of the control of action and that of the ordering of units in language e morphology and syntax. As far as action is concerned, we concentrate on how basic actions are combined into larger well-learned operations, such as making coffee, particularly in the context of disorders such as ideational apraxia. Our contention scheduling model (Cooper and Shallice, 2000) is compared with the recurrent network model of Botvinick and Plaut (2004). In language we consider how adequate, from a cognitive neuroscience perspective, are single-route models of morphological operations such as that of Joanisse and Seidenberg (1999), arguing for the importance of neuropsychological data in showing the inadequacy of the approach. As far as syntactic operations are concerned we tentatively support unification ideas, such as those proposed by Hagoort (2005) on the basis of the model of Vosse and Kempen (2000). Turning to supervisory processes, we take a twodimensional approach. The first is the hierarchical view of increasingly abstract processing, as one moves more anteriorly in prefrontal cortex, such as that presented by Badre et al. (2009). However, we argue that this needs to be complemented by a heterarchical approach of a set of qualitatively different processing systems each implementing a different type of control process on lower-level processing systems of the contention scheduling type. This is supported conceptually by consideration of the Domino expert system of Fox and Das (2000) and neuropsychologically by analysis of the group studies of the ROBBIA (Rotman-Baycrest Battery to Investigate Attention) type where the performance of four frontal subgroups e left lateral, right lateral, superior medial and inferior medial e is compared on a variety of reaction time tasks (e.g., Stuss et al., 2005). It is held to be cognitively important on this approach that the anatomical groups remain the same from one study to the next so that a single set of loss of resources has to explain the full behavioural pattern. This means that approaches like Voxel-based Morphometry and Voxel-based Lesion-Symptom Mapping do not easily provide relevant findings for cognitive-level theorising. Episodic memory is seen as having a critical function in providing raw material for case-based reasoning when routine processes based on semantic memory fail. From the cognitive neuroscience perspective this approach is compatible with the multiple trace theory of the function of the hippocampus (e.g., Moscovitch et al., 2006), which is considered both in the context of selective lesions of the hippocampus with respect particularly to long-term retrograde amnesia and in the context of the functional imaging evidence on the contrasting functions of the hippocampus and the perirhinal cortex. The recollection of autobiographical events is further considered in the context of the syndrome of spontaneous confabulation. Various positions are considered such as that of Gilboa et al. (2006) where the ventromedial prefrontal cortex is held, in normal functioning, to set a preconscious ‘feeling of rightness’, and that of Schnider et al. (1996) on the possible role of
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a similar region in preventing temporal confusion between events. Preference is given to a novel proposal that such confabulation arises from a so-called “latching” process between attractor basins each representing a part of an episodic memory; this is held to arise from an ultra-retrieval state e in the sense of the general approach of Hasselmo et al. (1996) e being realised in the hippocampus due to loss of acetylcholine input. The discussion of consciousness and its disorders considers a number of very general types of conceptual approach. Thus philosophical positions relating consciousness to quantum mechanics are rejected, as are activation strength and microconsciousness approaches. Most consideration is given to the approach of Dehaene et al. (2006), which is held to be the first proper scientific theory of consciousness yet proposed in that it links empirical data with a computational model. Various problems with the model are discussed. Most critically, while the model holds that dorsolateral prefrontal and inferior parietal cortices are essential regions for the global workspace, it does not accord any specific functional role to these individual regions with regard to consciousness. We propose instead that consciousness is better characterised in terms of the effect of supervisory processes on other processing systems. The book culminates in a consideration of whether cognitive neuroscience can yet make progress in the domain of thinking. Research has tended to focus on rather specific areas each representing a different type of thought processes. So initially we consider separately cognitive neuroscience research on reasoning with relations, reasoning by analogy, deductive inference, goal-directed problem-solving, rule induction, hypothesis generation and insight. In none of these areas is there yet a solid convergence between neuropsychological and neuroimaging findings. However we find unduly pessimistic the conclusion of Goel (2007) that “these data (i.e., the functional imaging and patient data) are telling us that the brain is organised in ways not anticipated by cognitive theory” (p. 440). Instead, we argue that it is possible to build on the foundational structure provided by simpler investigations of supervisory processes, discussed in an earlier chapter, to move towards a more unified account of thinking and reasoning. Overall we would argue that at a more basic informationprocessing level of theorising there is now good convergence in a variety of areas between neuropsychological and neuroimaging findings. However, if one considers deeper computational models of the connectionist or symbolic architecture type then in virtually no area is one of the models clearly superior to its competitors. To achieve this, we think it will be necessary to combine cognitive neuroscience methods in novel and more powerful ways.
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