Executive control of Attention in Alzheimer's Disease :
A modelling approach

Thesis for the degree of   Master of Science in Psychiatric Theory and Research Methods

by Dr. Michael Moutoussis    fzsemmo@gn.apc.org
at the Department of Psychiatry, University College London Medical School,  Wolfson Building, Middelesex Hospital, London W1 8AA ,
Supervised by  Dr. Martin Orrell, Dept. of Psychiatry  and  Dr. George Houghton, Dept. of Psychology

Dedication :
To Prof. Walter Freeman, for the teaching, and to Pam, for everything.


I. Abstract

II.  Introduction
1. The importance of executive functions in Alzheimer's disease
2. Dual Attention and the Central Executive System
3. Dual Attention and the Central Executive System
4.  Functions preserved in AD - Implications for Attention and Planning
5. Dementia, education and executive function
6. Mathematical modelling in Psychiatry : some difficulties
7. Modelling efforts in Alzheimer's disease
8. Mathematical models of Attentional Control
9. Cerebral Localisation of Supervisory Attentional / Central Executive processes
10. Neurochemical findings
11. Physiologically based modelling
12. Dynamical aspects of biological models
III. Aims of the present study
1. To develop the theory of normal 'central executive' function
2. To simulate dual task performance deficits demonstrated in AD
3. To identify important limitations of the proposed models and to suggest ways to overcome them
IV. Methods
1. Methods used for Systematic Review of literature
2. Criteria for setting model structure
    A. Parameter setting
    B. Estimation of model success
3. Effective use of programming environments
    A. Programming Language
    B. Operating systems
    C. Hardware
4. Core model structure and programming
5. Auxiliary programming & software
6. Program quality control
    A. Code integrity
    B. Program design
7. Mathematical Analysis
V. Results
1. Models directly based on the Houghton attentional control theory
    A. Direct lateral inhibition - only model
    B. Descending attentional control
    C. Descending and ascending 'matching' attentional system
2. Direct lateral inhibition plus descending/ascending match models
3. Revised model - preliminary results
    A. Alternative neural unit equations
    B. Alternative matching mechanism
VI. Discussion
1. Rationale of the present study
2. Critique of methods
3. Interpretation of findings
    A. Models directly based on Houghton's theory
    B. The success of lateral inhibition : Interpretation and novel predictions of the augmented model.
4. Next step models
5. Reflections in the light of other findings
6. New directions for further research
    A. Structure of a revised model
    B. Evaluation of the revised model
    C. Education and Alzheimer's disease
7. Summary & Conclusion
VII. Acknowledgements
VIII. References
IX. Appendices
1. Appendix I: C++ code samples
    A. Example of 'main' program code
    B. Example of a C++ class hierarchy
2. Appendix II: Example of Literature search strategy

I. Abstract


Introduction : In Alzheimer's disease (AD) executive and working memory deficits often compromise the safety and independence of patients. Such executive functions are understood to organise working memory. The latter has been described as consisting of "slave" systems co-ordinated by a Central Executive System (CES) that controls attention. The CES is particularly affected in AD. Computer models have contributed greatly to the understanding of selective attention, but not with reference to AD. We explore the potential for an understanding of the loss of executive function in AD using computer modelling and we review recent research from a variety of fields whose integration is central to this approach. Research has suggested that high educational achievement protects against the development of AD, possibly by helping preserve such executive functions. This finding is explored in the light of the modelling studies. Executive function in AD has been investigated experimentally, most notably by the group of Baddeley and colleagues. Dual task performance in normal elderly controls has been compared with that of AD patients. In the AD group, performance in any task was reduced by the presence of a concurrent task. With the passage of time the effect of any concurrent task on any primary one increased for the AD group but not for the controls. This demonstrated a specific executive deficit in the patient group.

Aims : It is feasible to develop rigorous models of 'central executive' function. The main hypothesis of our study is that the application of the computational model of attentional control of Houghton and co-workers could account for the pattern of deficit observed in AD patients.

Methods : Dual task performance data was simulated by using models of attentional control derived from those of Houghton and co-workers. In these models for each sensory modality bottom-up and top-down signals are compared in 'match-mismatch' modules. The output of these modules can excite or inhibit lower level sensory modules towards particular input features. Two attentional control systems, one for each modality, were combined in the present simulations to account for dual task performance. Sensory and 'match-mismatch' modules were linked by cross modality inhibitory attentional control whose implementation here was guided by the neuropathology of early AD. Key building blocks of the original Houghton model were analysed mathematically to explain the behaviour of the overall models. Alternative building blocks and structural modifications were explored.

Results :

1. The models adequately simulated performance of controls.

2. Reduction in single task performance in AD was also successfully simulated.

3. Reduction in dual relative to single task performance in AD was only simulated for narrow parameter ranges (was not robust).

4. Model capacity for gradual performance deterioration was limited.

5. Positive feedback loops involving units such as those used by Houghton have drawbacks such as spurious activated states and limited capacity for increases in gain.

6. These problems can be overcome by using units with a flatter response at their rest state.

7. Incorporation of direct lateral inhibition at the level of sensory cortex greatly improves model performance.

8. Preliminary implementation of the matching mechanism as synchronisation in the oscillatory activities of two brain areas and of lateral inhibition as oscillatory noise interference between modalities is successful in qualitatively simulating single and dual task performance for normal controls and for sufferers of Alzheimer's disease.

Discussion : The models described here were successful in simulating many features of attentional control. However, they do not robustly predict the experimental results without substantial modification. When Houghton's theory is reviewed most of the conceptual essentials are retained, but many important details of implementation are rejected. More physiological details of neural masses help improve models. It is argued that direct lateral inhibition is unlikely to be a biological default between modalities: its essential point is likely to be the presence of pathway interference at specific levels (rather than across levels) which is preserved or even augmented in AD. In the light of recent neuroscientific findings, a modified theory is suggested that postulates 'Match / mismatch' function to involve oscillatory synchronisation. Interference within levels (similar to direct lateral inhibition) is hypothesised to impair synchronisation between levels. Preliminary simulations support this theory but more extensive exploration is required. It is envisaged that in the future the study of executive function may aid the assessment of the ability for independent living of patients and the understanding of the protective role of education in AD.

II. Introduction

1. The importance of executive functions in Alzheimer's disease

Dementia is one of the most frightening and costly diseases to affect humanity, one that eventually destroys most mental abilities. Memory deficits are prominent but not necessarily sufficient to impose severe restrictions in the patient's lifestyle and independence. When, however, executive as well as mnemonic functions are affected, so that the patient cannot carry out the schemas necessary for daily activities, the patient puts herself in danger. An example would be having difficulty coordinating the sequence of actions required to make a cup of tea. It is therefore of great practical importance to understand and support those mental mechanisms concerned with executive and attentional functions.

Research into Alzheimer's disease (AD), the commonest cause of dementia, has therefore explored in recent years the 'executive' deficits (Baddeley et al, 1991). The term `executive' is used in different ways by different researchers and in different contexts, in line with the ignorance characterising new fields of enquiry. Jaak Panksepp (1998) defines: " 'Executive system' implies that a neural system has a superordinate role in a cascade of hierarchical controls ". 'Executive' is applied to planning, sequencing, control of attention and of working memory and specific functions such as cognitive set shifting or suppression of dominant tendencies.

2. Psychological theories of executive control of attention

Specific functional modules, such as the Supervisory Attentional System or the related Central Executive System (CES) have been proposed to malfunction in AD in order to account for executive deficits (Baddeley 1996). According to such psychological models a 'Supervisory Attentional System' is hypothesised to co-ordinate the lower level, or 'slave', components of working memory, i.e. the 'Phonological Loop' and the 'Visuospatial Scratchpad'. Since Baddeley's pioneering work there has been active debate as to the modularity and brain localisation, of functional modules that subserve and co-ordinate working memory (Wickelgren, 1997).

3. Dual Attention and the Central Executive System

Baddeley and colleagues (1991) examined executive function in a series of experiments which form the basis for the present analysis. They attempted to test whether AD disproportionately affects Central Executive allocation of attention, so that dual task performance is affected more than the constituent tasks. They compared patients with mild AD with normal elderly controls and followed them up over one year. In these experiments a primary tracking task, following a randomly moving white square, was combined with graded secondary tasks. The difficulty of the tracking task was first individually adjusted in the absence of secondary tasks so that subjects managed to stay on the square for 40-60% of the time. The difficulty was then fixed. The simplest concurrent secondary task was 'articulatory suppression': the subject counted repeatedly from 1 to 5 during pursuit tracking. The next stage was reaction time to tones : the subject had to press a foot switch as soon as an auditory stimulus was presented. Reaction times and percentage of missed tones were recorded. The final stage was a memory span task. In this the maximum length of a random digit sequence that the subject could reliably repeat back was determined in the absence of pursuit tracking prior to each of the three testing sessions. Sequences of this 'subject-tailored' maximum length were then presented during pursuit tracking and recall performance recorded.

The results obtained were consistent with the hypothesis. In the AD group, performance in any task was reduced by the presence of a concurrent task. A key result was that with the passage of time the effect of any concurrent task on any primary one increased for the AD group but not for the controls, supporting the hypothesis of a specific executive deficit in the patient group.

4. Functions preserved in AD - Implications for Attention and Planning

In a series of experiments complementing those of Baddeley, Simone & Baylis (1997) showed that executive function was impaired in AD using a paradigm involving suppression of a dominant response tendency. Subjects had to quickly respond to a green light, but ignore a yellow one. AD subjects showed many false positive responses, consistent with weak executive control. To test for the cognitive level at which errors occurred, subjects were asked how sure they were that they responded correctly. AD patients were aware of their erroneous choices. The authors concluded that the executive deficit did not involve early information processing but rather the efficient implementation of a response.

5. Dementia, education and executive function

Recent epidemiological findings in dementia are related to the above concerns. Low educational achievement appears to be an independent risk factor for the development of dementia (Orrell & Sahakian, 1995). The aged of lower educational achievement more commonly fail tests of activities of daily living (Zhang et al, 1990), while the better educated require greater damage to cortical areas important for executive function to get as impaired (Alexander et al, 1997). The mechanisms underlying the protective effect of education are however poorly understood. Mathematical modelling could help clarify relevant hypotheses.

6. Mathematical Modelling in Psychiatry: some difficulties

In most sciences modelling has an important role between experimental studies and theoretical analysis. It is not, however, obvious that mathematical modelling has much place in the neurosciences in general and in psychiatry in particular. Psychiatric theories are often conceived in qualitative terms and expected predictions are derived on the basis of semantic inference and common sense. However, many theories are not precise enough (they may be underspecified) to derive accurate predictions from; or the system involved may be too complicated for one to derive predictions by common sense (the theory may be intractable).

A rigorously specified model has many advantages : First, it forces a more complete description of the problem. The key variables have to be defined and theoretical assumptions become explicit. Second, explicit alternative explanations of the data on which the model is based can be formulated. Third, detailed predictions can be made: therefore falsification of a rigorously defined model is easier i.e. the theory provides better means for its own falsification (Notturno, 1984). Fourth, counterintuitive predictions of the theory may become apparent. These may explain already existing data difficult to understand on the grounds of common sense. It may also be rigorously tested whether data seemingly contradicting the theory could in fact be compatible with it. Fifth, ignorance about the theory can be quantified, e.g. in terms of model parametres. Results of further experiments can be anticipated and such experiments planned. Finally, once successful models are developed, numerical experiments can be performed that would be too difficult to carry out in vivo.

Although mathematical modelling is thus indispensable in the hard sciences, it has not fared as well in psychiatry. There are several reasons for this. Biological systems are so complicated that people think that a lot more needs to be known about them before meaningful modelling can take place. It is often unclear that biological systems have laws possessing explanatory power independent of the system's fine structure. It is also unclear how components and their interactions at any particular level of description give rise to collective properties in a non-trivial manner. Indeed, the theory of evolution favours a top-down, 'how is this feature teleologically reasonable', view over the reductionist, bottom up modelling approach. In addition it is sometimes possible to construct several different models that explain the data. These models may be difficult to tell from each other by experiment or may be poor at making novel predictions.

Mathematical modelling is culturally alien to psychiatrists; most importantly the questions that could be investigated by modelling simply do not occur to us. Our heuristics of science do not include the values of rigour, unification of ideas, economical description of phenomena and beauty of mathematical structure that guide the hard sciences.

Finally, biological phenomena usually involve self-organising structures that consume energy. In such systems measured variables do not change in proportion each other, i.e. the systems are non-linear (Nicolis, 1991; Nicolis, 1989; Kelso, 1995). The theory of nonlinear systems has only recently emerged and its neurobiological applications are still at an early stage.

7. Modelling efforts in Alzheimer's disease

Despite these obstacles, considerable inroads have been made in the modelling of AD. Connectionism has aided the understanding of psychiatric disorders and of AD in particular. A connectionist model consists of interconnected units representing neurones or groups of neurones. Units can have simple internal structure yet their networks can perform complex functions, made possible by appropriate patterns of synaptic interconnections (Jeffery & Reid, 1997). This is an anatomically inspired approach that can be used to model brain function at the level of groups of cells (Traub et al, 1997) or of individual cortical areas (Freeman, Yao & Burke, 1988). The role of neurotransmitters can be elucidated. Models can be used to investigate the transition from the level of nerve activities to that of objects of perception and action (Freeman, 1991). Contextual meaning and hence emotion can be taken into account (Armony et al, 1995). At the 'highest' (symbol manipulation) level connectionist models can be used to simulate objects of cognition such as memories (Hartley & Houghton, 1996). At the neurotransmitter level they can help explain observed psychopathology (Cohen & Servan-Schreiber, 1992).

The first goal of modelling in AD has been the understanding of dysmnesia. Pioneering studies (Carrie, 1993) used highly abstract connectionist models. These explained the distributed storage of memories, their initial resistance in the face of gradual neuronal loss (simulated by unit deletion in the network) and their subsequent smooth decline. The use of fairly realistic learning algorithms allowed important shortcomings of the model to be identified. The greater impairment in new learning relative to memories laid before 'atrophy' could not be explained, and gradual deletion of neurones from biologically more realistic networks failed to produce a gradual decrease in recall. Performance remained intact until a large proportion of network elements were lost, then dropped catastrophically. To simulate the gradual course of the illness it was necessary to introduce synaptic compensation as found in the real brain (Ruppin & Reggia, 1995). Synaptic compensation is a biological constraint which permits the models to explain the gradual degradation of recall performance, the differential sparing of remote memories and the increased rate of false positive retrieval errors found in AD.

In contrast to these sophisticated studies of memory little attention has been paid to the modelling of executive function, despite its clinical importance. Modelling loss of synapses might explain weakened executive control while synaptic compensation might help explain the excess of false positive responses, accounting for the findings of both Baddeley and Tipper discussed above.

8. Mathematical models of Attentional Control

The starting point for the present simulations is the theory of attentional control of Houghton and colleagues (Houghton & Tipper 1994; Houghton 1995). In these models each sensory modality consists of low level sensory modules, high level modules where the behavioural goals or targets of the organism are stored and intermediate, 'match-mismatch', modules that compare percepts with targets (fig 1a).

Figure 1a. Layout and component units of the Attentional Control mechanism. The Target field (A) carries a partial description of objects to be sought during a given task. Their activation signifies some priority, such as 'look for this'. The Object field (C) represents sensory areas. It carries Property Units representing features of sensory stimuli. Each Property Unit gives excitatory input to two 'gain control' units ('On' and 'Off'). The 'On' gain unit gives positive feedback to the property unit, the 'Off' negative. The 'On' makes the property unit more responsive to the presence of the corresponding feature, the 'Off' less so. The equations governing these units are discussed in the Results section. The Match / Mismatch module (B) consists of pairs of elements. Match units fire (and Mismatch stay silent) when a feature activated in the 'object' field is also present in the top down description of the Target field. If the feature is present only in the Target field, only the Mismatch units fire. Output units correspond to the response schemata relevant to different percepts.


Figure 1b. Connectivity that implements the Attentional Control mechanism. Filled circles are inhibitory synapses, arrowheads excitatory (as in following figures). The Target field units feed to the corresponding Match/Mismatch unit pairs. For each target feature (see 7) there is a unit pair (6) calculating whether the feature is present, on the basis of input by the corresponding Property unit (e.g. unit d gives bottom up input 5). If a match is present the corresponding Match unit activates the On unit of the Property unit coding the same feature (1) and inhibits its sister Off unit (2), thus increasing the responsiveness of the Property unit. If a mismatch is detected the Mismatch unit becomes active and reduces responsiveness (3, 4). Negative feedback tends to reduce the activation of Property units subject to mismatch. Coactivated features then cooperate to dominate perception and activate an appropriate response. If for example property units a and b belong to the same object, and unit a is activated, it tends to facilitate activation of b both directly (12) and indirectly (10,11). Finally an emergent assembly of features, signifying an object, activates a response schema.

The output of the 'match-mismatch' modules can excite or inhibit the lower level sensory modules towards particular input features, and thus attend or ignore these particular features: this is the crucial mechanism of attentional control in the model. The activated features are then combined into attended percepts.

The models of Houghton and co-workers can simulate a large number of experimental data on selective attention. These include perceptual distracter processing, negative priming, response binding in the presence of distractors and inhibition of return. The models are built around important principles such as attentional control of perceptual gain regulation, opponent processing and competitive-cooperative intra-module interactions. Drawbacks of the model include first, its loose basis on physiology. This is most evident in the match / mismatch module. Its units perform logical computations whose physiological implementation is quite unclear. Secondly, the whole model is constructed on the basis of information-processing constraints. This is no bad thing in itself but it ignores how dynamical constraints can give rise to structure. Levine, Parks & Prueitt (1993) in their review of the methodology of simulation of `frontal' cognitive functions expect that functionally and structurally distinct levels of brain activity are separated by underlying dynamical constraints. Thirdly, the Houghton model is over-stable. Once perceptual input is terminated object assemblies tend to persist. This necessitates the introduction of decaying synaptic weights between units of the Object field (connections (9) and (10) in fig. 1b). Target node activities also have to be made to decay. Freeman (1992) has discussed how positive and negative feedback loops in neural systems need not convey excessive stability if allowed to operate within oscillatory regimes.

Context - dependent allocation of attention has been studied by Cohen and coworkers (1992) using a model of the Stroop task. In this task subjects are presented with dual stimuli, e.g. the name of a colour spelt out and the ink colour in which the word is written. They are asked to either read what the word says or to name its ink colour as quickly as possible. Each 'modality' (reading vs. colour naming) has its own 'pathway' in the model. Both are influenced by a 'task demand' module which the authors identify with a function of the prefrontal cortex. Cohen finds that 'attentional selection can be thought of as the mediating effects that the internal representation of context [here, of the instruction the subject has received from the experimenter] has on processing' (fig. 2).

Compared to the theory of Houghton and coworkers, the model of Cohen et al includes a simpler 'Matching' mechanism (another way of looking at the function of the hidden units !) capturing essential biological plausibility. It doesn't, however, include any cross-modality interaction except at the output level; Its simplest modification to conform to the dual task layout would therefore completely decouple modalities. It could therefore not predict dual-task interference effects. An important finding from the work of Cohen and coworkers is that 'the degree to which a process relies on attention is determined by the strength of the underlying pathway'. This may mean that a disproportionate attentional deficit may be not because a 'Central Executive' is particularly affected in AD but because eroded underlying pathways would take stronger attentional modulation to perform.

Figure 2a. Modular structure of the model of attention in the Stroop task according to Cohen and coworkers. External input is received by the input units, C ; In order for it to activate the output units, D, activity passes through the intermediate, 'hidden' units B. These are in part activated by units A which express the context to which the organism must give priority. These are similar to the 'target' units of the Houghton models (fig. 1) in that they are independently and externally activated.

Figure 2b. Detailed structure of Cohen's Stroop task model. All the synapses in or by the 'reading pathway' are shown. The connections of the colour naming pathway are similar but weaker. Note the absence of cross modality inhibition.

Figure 2c. The 'hidden' units of the Cohen model perform a graded AND (or 'match') function by becoming activated, and therefore allowing signal to propagate along their respective pathways, when not only bottom-up excitation is present from the stimulus but also descending facilitation.

9. Cerebral Localisation of Supervisory Attentional / Central Executive processes

The concurrent tasks involved in the Baddeley experiments were chosen so as to minimise competition for local resources and thereby to highlight the possibly shared 'Central Executive' requirements. Thus the primary task - tracking - is presumed to involve the 'visuospatial scratchpad' slave system while the secondary the 'phonological loop' for example. Of course both are shown to depend on attention 'allocated' to them by the presumed central executive. While earlier imaging studies showed little overlap between the brain areas activated by verbal and non verbal working memory tasks (Raichle, 1993), more recent studies have concluded that the dorsolateral prefrontal cortex contributes to the maintenance of both verbal and nonverbal information (Fiez et al, 1996). Recent imaging studies indicate that rCBF decreases in sensory areas that are not to be attended (Drevets et al, 1995), in agreement with theoretical studies that place emphasis on inhibitory mechanisms in attention.

10. Neurochemical findings

The deterioration of the cholinergic system in AD is thought to affect attention significantly. Sahakian has demonstrated that when cholinergic function enhancing drugs improve neuropsychological performance in AD this is attributable more to an improvement of attentional rather than memory function (Lawrence & Sahakian, 1995).Imaging studies indicate that Scopolamine, a cholinergic antagonist, attenuates memory-task-induced increases of rCBF in the right anterior cingulate but also bilaterally in the prefrontal cortex.

Taken together these findings support a model of AD where elements corresponding to particular aspects of prefrontal function are either damaged or functionally dysconnected (Schreiter-Gasser et al, 1993; Morris, 1994) to the rest of the model. The primary candidates for this, in Houghton's terms, would be the 'target field' and the 'match-mismatch' (fig. 1a). Damage to either would result in reduced attentional control on lower-level units, a priori equally impairing excitatory and inhibitory control.

11. Physiological basis for modelling

In most connectionist psychological models units are very simplified in biological terms, being derived more on the basis of constraints from psychology. This does not by itself imply that the functional approximation is crude, as biological systems are self-organising, far from equilibrium and hence their emergent properties at any one level may be robust within a functionally important range of conditions. However the adequacy of the approximation is always a matter of concern.

Numerous abstract models are used to provide tractable equations for modelling neuronal elements. Often used is the (logistic) sigmoid neuron where output is a sigmoid function of the sum of all inputs to the neuronal soma within a preceding short time interval. Continuous time models usually include a differential operator acting on neuronal state variables equated with a sigmoid function of inputs, which may involve delays. The prototype is the model of Wilson & Cowan (1973). The units used in the work of Houghton et al involve first order operators and no delays. More physiological alternatives range from the second-order-operator models of neuronal populations of Freeman (1975) to simulations of large number of neurons with details of their ionic currents and other biophysical properties (Traub et al, 1997). I shall consider the logistic sigmoid neuron as a minimum requirement for physiological relevance and more complicated models when detailed time evolution of the system is simulated.

12. Dynamical analysis and biological models

It is important to analyse the performance of the neural systems involved in attention not just in terms of facilitation and inhibition of units, but in terms of attractor dynamics. This involves considering what patterns of activity are open to the network, what is the stability of such patterns and how the 'landscape' (phase space) of all such available patterns changes under different conditions. We may, for example, think that a stable performance of the primary task corresponds to an attractor set of the entire network but one with specific directions of relative instability. Perturbations along such directions (corresponding, for example, to the auditory signal) can 'flip' the system into another, possibly transient, 'attractor' set (corresponding to auditory perception, match and output). The motivation for thinking of this model in terms of attractors and their stability comes from several sources.

First, the model of Houghton et al is, as discussed, over-stable. Its authors thus introduced modifications with little psychological or physiological support. Dynamical analysis of the model's stability could, alternatively, clarify the causes and solutions to the problem in a less ad hoc way.

Second, while the attractors that are used in most psychological models are point attractors, the ones found experimentally in investigations of thalamocortical interaction and those extensively studied in limbic structures (e.g. entorrhinal cortex, heavily involved in AD) are par excellence oscillatory (Kay, 1996; Gray, 1994). There is evidence for thalamic and limbic areas being involved in attention (Forstl & Sahakian, 1993) and particularly in the matching process. Freeman and co-workers have demonstrated dramatically the effect that attention and behavioural significance have on the patterns of oscillatory activation of the olfactory cortex (Grajski & Freeman, 1989, Eeckman & Freeman, 1991, Kay,1996). An important component of the binding of sensory features into visual percepts also relies on oscillatory attractors (Gray, 1994; Bressler, 1996). In the present models oscillations would arise naturally if realistic synaptic delays were incorporated in the existing negative feedback loops. Superposition of oscillatory attractors, switching between such attractors and global modulation of oscillatory cortical activity differs from equilibrium (point attractor) dynamics as applied to the same brain functions. It is thus important to consider our present models in terms of attractor dynamics to prepare the ground for incorporation of the above findings. Introducing oscillatory dynamics must however be necessitated by both the probable applicability of the physiological findings and the need to use that level of description to overcome limitations of the information processing approach used so far.

Another reason to consider analysis in terms of attractors is illustrated by the dynamical analysis of errors in a neural network model of dyslexia. This has demonstrated that dynamical analysis can explain some counterintuitive experimental findings that are replicated by neural network models (Hinton, Plaut & Shallice, 1993). Burgess and Hitch (1996) claim that a model that replicates experiment but "cannot be mapped onto a conceptual understanding of the processes giving rise to behaviour is useless". The dyslexia model demonstrated that this conceptual understanding, and therefore the usefulness of such a model, may well depend on the understanding of the attractors involved.


Modelling Executive & Attentional function in Alzheimer's Disease - 1999 MSc Thesis - M. Moutoussis

III. Aims



1. To develop the theory of normal 'central executive' function.

Attentional control may depend on the matching between sensory representations of perceived objects and partial, 'target 'representations of sought objects (Houghton & Tipper, 1994). The central hypothesis of the present work is that models of this matching process can simulate executive attentional control during dual tasks.

I aimed to simulate the influence of attention in normal subjects during single- and dual- task conditions by bringing together previously developed models of attentional control ( Houghton & Tipper,1994) with the dual pathway concept of Cohen and co-workers (1992) .

Baddeley's reaction time experiment could be simulated with a model of attentional control with modality specific pathways (fig. 3) . This will be used as the nucleus of the present study as it includes all the important experimental findings. Once this is adequately modelled, other dual task experiments could be simulated as further tests of the main hypothesis.

Figure 3 Basic Auditory Reaction time / Tracking simulation architecture. The cross modality attentional control projections are shown in the a priori most likely site.


2. To simulate dual task performance deficits demonstrated in AD.

By introducing neuropsychologically plausible 'lesions' in the above model I aim to replicate the experimental findings in mildly demented subjects. AD lesions can be simulated as an impairment of the match / mismatch field. The modules situated closer to the sensory and motor interfaces will in this approximation be treated as intact, in line with the localisation of such modules within sensory cortices, preserved in early DAT.

As in the Baddeley experiments, a greater attentional control deficit (corresponding to more advanced AD) should lead to increasing divergence between single-task and dual-task performance. I aim to show that progressive 'damage' would suffice to account for the deterioration of DAT subjects over time.

'Damage' to the model proposed here does not simulate the AD effects in a trivial manner. Reducing the efficiency of cross modality inhibition might decouple modalities, and dual task performance may improve relative to single task performance. This possibility makes the model proposed more falsifiable.

3. To identify important limitations of the proposed models and to suggest ways to overcome them.

When model components or architecture are found to limit ability to simulate experimental results, I aim to improve on these not only by introducing the minimum sufficient modifications but also by guiding such modifications by appropriate physiological considerations.

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  Contents   Abstract      Introduction     Aims   Methods    Results     Discussion
 Acknowledgements    References     Appendices

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