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Predictive Motor Learning of

Ob j ect Manipulation

Alice Geraldine Witney

Sobell Departm ent of Neurophysiology,

Institute of Neurology,

University College London

Submitted for examination for the PhD degree: November, 2000

Viva: 9th February, 2001

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Abstract

Anticipating the consequences of our m otor com m ands is a fundam en­

tal component of sensorimotor control. For example, w hen one hand pulls

on an object held in the other hand, there is an anticipatory increase in grip

force in the restraining hand that prevents the object from slipping. This an­

ticipation is thought to rely on a forward internal m odel of the m anipulated

object and the m otor system, enabling the prediction of the consequences

of our actions. This thesis examines the learning of prediction using a vir­

tual object paradigm . Subject's held an object in each han d whose prop­

erties were under computer control. This allowed instant changes in the

behaviour of the objects being m anipulated on a trial to trial basis, w ithout

providing any cues to the subject. To investigate the developm ent of the

prediction that a single object is being m anipulated betw een the hands, the

forces on each object were controlled so that the two objects were linked

together, and therefore acted as a single object. Decay of prediction was ex­

am ined after the linkage betw een the forces on the two objects was removed

and they again acted as two independent objects. Prediction of single ob­

ject m anipulation was found to be quick to build up after the occurrence of

a linkage betw een the objects, but slow to decay after this linkage was re­

moved. The effect of previous experience of either linkage or independence

betw een the objects was then examined, w ith as systematic effect of link­

age being found on the current predictive response. W hen the properties of

the virtual object were altered to form objects w ith novel spatial properties,

prediction was found to be specific to the direction that the subject had pre­

viously experienced. The learning of anticipatory grip force m odulation to a

virtual object w ith novel properties w as examined by the addition of a tem ­

poral delay in the linkage between action and effect. A second prediction

developed coupled with the suppression of the earlier predictive response.

These experimental findings have been related to the presence of forward

internal models, and compared with the conditioning of a predictive motor

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I w ould like to thank my supervisor. Dr Daniel W olpert for giving me the

opportunity to do this project, and for his help, enthusiasm , and guidance

throughout the three years.

Thanks to Dr Susan Goodbody for her help.

The model in Chapter 6 was done by Phillip Vetter, and I w ould like to

thank him for this.

Thanks to Chris Seers, Bill Cameron for their technical support.

Thanks to Pierre Baraduc for help w ith ETpXand provision of fine cheeses;

Julie Savvides for extensive photocopying and Ana Caballero for translat­

ing the experimental protocol into Spanish.

Many thanks to the Wolpert-lab and Sobell D epartm ent for making my

time in the departm ent enjoyable.

Thanks to Joan Cinnelly of the National Hospital for securing cheap ac­

commodation for me for three years.

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Contents

1 Introduction 13

1.1 Internal m o d e l s ...13

1 . 2 Uses of forward m o d e l s ...16

1.2.1 Sensory p r e d ic tio n ... 22

1.2.2 Distal supervised le a rn in g ... 24

1.2.3 Mental p r a c tic e ...25

1.2.4 M O SA IC ...26

1.3 Learning and representation of an internal m odel ...27

1.4 Grip force as a paradigm for forward m o d e l s ...29

1.4.1 Reactive grip force m o d u la tio n ... 32

1.4.2 Development of grip force r e s p o n s e ... 34

1.4.3 Learning the appropriate grip force re s p o n s e ...36

1.4.4 Bio-mechanics of grip f o r c e ... 38

1.4.5 Cutaneous afferents ... 38

1.4.6 S l i p ...40

1.4.7 Central c o n t r o l ... 41

1.4.8 Cortical a r e a s ... 43

1.4.9 Anticipatory grip fo rc e ... 44

1.4.10 Earlier theories of predictive grip force m odulation . . 45

1.5 Aims of t h e s i s ...47

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2.1.2 Grip s u r f a c e s ... 52

2.1.3 Force tra n s d u c e rs... 52

2 . 2 A n a l y s i s ... 54

2.2 . 1 Calculation of grip f o r c e ... 54

2.2.2 Calculation of load f o r c e ... 54

2.2.3 Am plitude and t im i n g ... 54

3 Learning and decay of prediction in object manipulation 55 3.1 Abstract ... 55

3.2 In tro d u c tio n ...56

3.3 M e t h o d s ...58

3.3.1 Subjects ... 58

3.3.2 P r o c e d u r e ... 58

3.3.3 One link c o n d itio n ... 59

3.3.4 Three link c o n d it i o n ... 60

3.3.5 Interval c o n d itio n ... 60

3.3.6 Non-physical c o n d it i o n ... 60

3.4 A n a l y s i s ...61

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6

3.5.1 One linked co n d itio n ... 63

3.5.2 Three linked c o n d itio n ... 69

3.5.3 Interval co n d itio n ... 71

3.5.4 Non-physical c o n d itio n ...72

3.6 D isc u ss io n ...73

3.6.1 Development of p r e d ic tio n ...75

3.6.2 Decay of p red ictio n ... 76

3.6.3 Importance of p h y s i c s ...77

4 Predictive motor learning of a temporal delay 78 4.1 Abstract ...78

4.2 In tro d u c tio n ...78

4.3 M e t h o d s ...82

4.3.1 Subjects ... 82

4.3.2 A p p a r a tu s ... 82

4.3.3 P r o c e d u r e ... 83

4.3.4 Non-cued c o n d it i o n ... 84

4.3.5 Self-generated c o n d itio n ...84

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4.5.1 Pre-exposure ...8 8

4.5.2 Self-generated c o n d itio n ... 91

4.5.3 Cued c o n d itio n ... 95

4.6 D is c u s s io n ...98

4.6.1 Performance in the absence of a d e l a y ...99

4.6.2 Performance in the presence of a d e l a y ...102

4.6.3 Modularity and internal m o d e l s ... 104

5 The effect of externally generated and novel consequences on pre­ diction 106 5.1 Abstract ... 106

5.2 In tro d u c tio n ... 107

5.3 M e t h o d s ... 109

5.3.1 P r o c e d u r e ...I l l 5.4 A n a l y s i s ...1 1 2 5.5 R e s u l t s ... 114

5.5.1 Condition LU:- L inked-U nlinked... 114

5.5.2 Condition LE:-Linked-Extem al... 115

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5.5.4 Condition D ecay-External... 117

5.5.5 Condition D eca y -N o v e l... 122

5.6 D isc u s s io n ... 125

6 The effect of previous experience and prior expectation on predic­ tion 129 6.1 Abstract ... 129

6.2 In tro d u c tio n ... 129

6.3 M e t h o d s ... 132

6.4 R e s u l t s ... 135

6.5 D isc u ss io n ... 141

7 Spatial representation of a forward model 145 7.1 Abstract ... 145

7.2 In tro d u c tio n ... 145

7.3 M e t h o d s ... 147

7.4 A n a l y s i s ... 149

7.5 R e s u l t s ... 152

7.5.1 L ^ jjc o n d itio n ... 152

7.5.2 Z/Q c o n d itio n ... 153

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7.5.5 LqL iso condition ... 158

7.6 D is c u s s io n ... 161

8 Discussion 167 8.1 Grip force and forward m o d e l s ...167

8.1.1 Development and decay of a forward m o d e l ... 168

8.1.2 M odularity ... 169

8.1.3 The effect of previous ex p erien ce...174

8.1.4 Spatial representation of a forward m o d e l... 175

8.2 Comparison with classical c o n d itio n in g ...176

8.2.1 A c q u is itio n ... 177

8.2.2 Re-acquisition and E x tin ctio n ...178

8.2.3 The Effect of other s tim u li...178

8.2.4 N eural substrates of predictive m otor le a r n in g 179 8.3 S u m m a ry ... 180

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10

List of Figures

1.1 Grip force ... 30

1.2 Internal model for predictive grip force m o d u la tio n ... 32

2 . 1 Schematic diagram of the virtual object ... 51

2.2 Bandwidth of the virtual object a p p a r a tu s ... 53

3.1 H and positions ... 64

3.2 Grip force responses for linked t r i a l s ...65

3.3 Grip force responses for unlinked trials ...6 6 3.4 Individual grip force profiles ... 67

3.5 Grip force m odulation over linked and unlinked tr ia ls 6 8 3.6 Grip force modulation over batches ...70

4.1 Hypotheses of possible adaptation to d e l a y ...80

4.2 Schematic diagram of apparatus ...83

4.3 Left hand p o s itio n s ... 89

4.4 Grip response in pre-exposure p h a s e ...91

4.5 Grip force m odulation in self-generated c o n d itio n ...92

4.6 Grip force response for individual subjects ...93

4.7 Coefficient of determination of grip w ith load f o r c e ...94

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4.9 Grip force m odulation in cued co n d itio n ...97

5.1 Individual subject's grip force modulation dependent on pre­ vious trial ... 116

5.2 Grip profiles dependent on previous t r i a l ... 118

5.3 Grip force profiles dependent on occurrence of an external trial 121 5.4 Peak grip force dependent on occurrence of an external . . . . 122

5.5 Grip force profilesdinked, unlinked, l in k e d - d o w n ... 123

5.6 Grip force m odulation dependent on occurrence of a linked- dow n trial ... 124

6.1 Grip force m odulation on linked and unlinked t r i a l s ... 136

6.2 Unlinked grip force p r o f ile s ...138

6.3 Binary tree of grip force modulation and sim ulated data . . . 140

6.4 Regression param eters of the history of lin k a g e ... 141

7.1 Paradigm for studying generalization of predictive grip force 150 7.2 Average load force trajectory ...152

7.3 Grip force modulation: Condition ...153

7.4 Grip force profiles during training p h a s e ...154

7.5 Grip force modulation: condition Lq ...155

7.6 Grip force m odulation on first test t r ia l...156

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12

7.8 Grip force modulation: condition LoC/ 4 5 ... 157

7.9 Individual subject's grip force modulation: condition Lot/ 4 5 . . 158

7.10 Grip force modulation: condition L0L4 5 ... 159

7.11 Individual subject's grip force modulation: Condition L0L4 5 . 159

7.12 Grip force modulation: condition LqLi8q ... 160

7.13 Individual subject's grip force modulation: condition L0L1 8 0 . 161

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1

Introduction

We are able to move and interact w ith our environm ent effortlessly. H ow ­

ever, the task of movement and control is deceptively difficult. The simplest

action requires the coordination of m any muscles and joints. Additionally,

during m otor behaviour the problems of coordination, tim ing and interac­

tion between neural, muscular and skeletal structures have to be overcome.

The concept of a motor program to successfully overcome such problems

of coordinating movement has become prevalent (Pew, 1974; Rosenbaum,

1980; Jordan, 1995; Jeannerod, 1988)

1.1 Internal models

An early formulation of the presence of a representation of the m otor system

has existed since H ead's (1926) notion of a schema. Such a representation

is thought crucial for the formation of a m otor program. One of the early

definitions of the motor program was by Keele (1968) w ho stated that the

motor program was "a set of muscle commands that are structured before a

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Introduction 14

out

However, such early conceptualizations of internal representations were

vague, although an im portant development of the concept of a motor pro­

gram was m ade by Held (1962,1965). He suggested that the internal refer­

ence of position during goal-directed movements should be derived from

the program of these movements as a corollary of the m otor output, inde­

pendent from peripheral feedback. This model was based on the effects of

visuomotor adaptation, in situations where the relation betw een the posi­

tion of a visual target in space, and the direction of m ovem ents towards

that target was systematically modified by the use of laterally displacing

prisms. Held argued that in order for the movements to become accurate in

this condition, their program had to be modified on a trial to trial basis, and

rebuilt according to the new visuomotor rules.

A theoretical framework for models of motor representation was pro­

vided by M arr's (1982) levels of functioning for information-processing de­

vices. Three levels of functioning were proposed, the highest level being

the "computational theory" which defines the goal of the computation, and

the strategy that could be used to carry it out. Second, the "representation

level", where the computational theory is im plemented into a representa­

tion of the input and output of the system and into an algorithm for trans­

forming input to output. And finally the "hardware im plem entation" of the

representation and the algorithm. This thesis will examine the "representa­

tion level" of predictive control.

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sys-tern, whose inputs are the motor commands from the 'controller' w ithin the

CNS and outputs are sensory feedback. Knowledge of just the input to a

dynamical system is not enough to predict the output. However, w hen the

'state' of the system is known in addition to the m otor command, the be­

haviour of the system can be predicted. The state therefore contains all the

relevant time-varying information needed to predict or control the future of

the system. For example, to predict the effects of applying a torque around

the knee joint, it is also necessary to know the configuration and m otion of

other body segments. One of the problems the m otor system has to over­

come is how to estimate the state from the m otor com m and and sensory

feedback.

Representations of the sensori-motor transformations betw een m otor and

sensory variables are thought to occur within the CNS. Such internal m od­

els are systems that simulate the behaviour of natural processes and trans­

form sensory signals to motor commands and m otor com m ands to sensory

signals. There are two main groups of internal models, know n as forward

and inverse models (Jordan, 1995; Wolpert, 1997; Wolpert and Ghahram ani,

2000).

The forward model is a causal representation of the m otor apparatus

that represents the behaviour of the motor system in response to outgoing

motor commands (Kawato et al., 1987; Jordan and Rumelhart, 1992; Jordan,

1995; Wolpert et al., 1995; Miall and Wolpert, 1996; Wolpert, 1997). That is,

forward models capture the causal relationship betw een actions an d out­

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Introduction 16

1950; V. Holst, 1954; Jeannerod et al,, 1979).

Inverse models simulate the transformation from outputs to inputs. Un­

like the forward model, the inverse model is not necessarily unique as the

transformation can be one-to-many. Fast feed-forward control can be achieved

through an inverse model of the controlled object, and therefore, an accu­

rate inverse model could act as ideal feed-forward controllers. Due to this,

the training of inverse models is crucial for performance in feed-forward

control.

1.2 Uses of forward models

Forward models can be separated into forward dynamic models and for­

ward sensory models according to their role; forward dynamic models are

responsible for the prediction of the next state given the current state and

motor command; whereas forward sensory models predict the sensory feed­

back given the estimated state.

A forward model, given it simulates the motor-sensory transform ation

can have a num ber of potential fundam ental roles in the m otor system, in­

cluding motor planning, execution and learning (Kawato et al., 1987; Jordan

and Rumelhart, 1992; Jordan, 1995; Wolpert et al., 1995; Miall and Wolpert,

1996; Wolpert, 1997). The importance of a forward m odel to the m otor sys­

tem arises in several areas including state estimation; state prediction; sen­

sory prediction; distal supervised learning; mental practice and m odular

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State estim ation

The State of the motor system forms a compact representation w ith far lower

dimensionality than the full array of sensory feedback. However, as the

state of a system is not directly accessible to the controller, the state has to

be estim ated using the only sources of information available to the CNS,

sensory feedback and the motor commands. A forward m odel is an impor­

tant component in the sensori-motor integration process that occurs during

state estimation. An estimate of the current state is m ade by m onitoring the

inputs, that is the motor commands, and the outputs, the sensory feedback

using an 'observer' (Merfeld et al., 1993; Jordan, 1995; Wolpert et al., 1995).

A Kalman filter m odel can be used to estimate the state. The Kalman fil­

ter produces this estimate using both motor outflow and sensory feedback

combined w ith a model of the motor system. The Kalman filter assumes

that there is both variability in the response of the m otor system to the mo­

tor command, and variability in the sensory feedback. The Kalman filter

uses two components to try and reduce the effects of these variability on

the estimate of the state. The feed-forward component of the m odel uses

the efference copy together with the current state estimate to predict the

next state by simulating the movement dynamics w ith a forw ard model.

The feedback component compares the sensory inflow w ith a prediction of

the sensory inflow based on the current state. This difference betw een the

actual and predicted sensory feedback, the sensory error is used to correct

the state estimate m ade by the forward model. The contributions of both the

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mod-Introduction 18

ulated by the Kalman gain so that optimal state estimates are produced.

Damage to the parietal cortex has been show n to result in the failure

to m aintain state estimates; a patient with a parietal lesion was show n to

experience perceptual fading (Wolpert et al., 1998a).

State prediction

Forward models are im portant for coordination and tim ing in the m otor

system. Prediction from a forward model can help to overcome the prob­

lems associated with feedback delays. In sensori-motor loops, feedback de­

lays are large in the order of 100 ms (Cordo et al., 1994). If rapid m ovements

are attem pted under feedback control instability could result (Miall et al.,

1993). However, a forward model is able to provide internal feedback of the

predicted consequences of an action and instability can be prevented. If an

efference copy of the actual motor command is input to the forw ard model,

the forward model predicts the feedback. The neural com putation time for

this prediction is much shorter than the external visual or proprioceptive

feedback delay. Therefore, if a forward model is used in the internal feed­

back loop, feedback control performance is improved as the large external

feedback delays can be avoided (Ito, 1970). This has been show n w ithin a

Smith predictor model where a forward model is used to provide internal

feedback of the predicted outcome of an action before sensory feedback is

available (Miall et al., 1993).

Forward models are thought to enable predictive responses that can help

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the action of one part of the body on another (Massion, 1992; Lacquaniti,

1992; Nashner, 1982; Gahery and Massion, 1981). Different responses are

m ade to self-produced and externally produced perturbations in unload­

ing, ball catching and other postural adjustments (Dufosse et al., 1985; Mas­

sion, 1992). The predictive response is thought to be dependent on predict­

ing the consequences of the descending motor comm and, as signalled by

efference copy, using an internal model of both one's ow n body and the

object (v. Helmholtz, 1867; Sperry, 1950; v. Holst, 1954; Wolpert, 1997).

Performance of a voluntary movement by one body segment is usu­

ally accompanied by an adjustment of posture aim ed at preventing the im­

balance which w ould otherwise occur. Such anticipatory postural adjust­

m ents have been demonstrated to occur in the back and legs w hen a sub­

ject is standing and makes a rapid arm m ovem ent affecting the equilib­

rium (Bouisset and Zattara, 1987; Cordo and Nasher, 1982; Lee et al., 1987)

The "waiter task" is an example of the use of a forw ard m odel to m ini­

mize postural disturbances. The waiter task illustrates how a waiter is able

to unload plates and bottles from a tray with one han d while the tray is

balanced on the contralateral forearm. In this situation the position of the

tray does not move, although unloading the tray should provoke an u p ­

w ard arm movement. Such adjustments of the limb compliance dependent

on the environmental context are a feature of our m otor system (Lacquaniti

et al., 1992).

The waiter task has been examined extensively experimentally (Dufosse

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Mas-Introduction 20

sion, 1992; Lum et al., 1992). In these unloading tasks, one arm is held in

a horizontal position whilst supporting a 1 kg weight. This weight is u n ­

loaded either by the experim enter's hand (imposed unloading) or by the

subject's other hand in response to a tone (voluntary unloading). The EMC

activity from the brachioradialis of the postural arm, and from the biceps of

the active arm are recorded (Hugon et al., 1982). This enabled a comparison

of the latency of the EMC from the postural arm and from the active arm,

and therefore a measure of the anticipatory nature of the response. The

degree of displacement of the postural forearm is a further m easure used

to determine w hether a predictive response has occurred. Minimal distur­

bances reflect that a prediction of unloading was available whilst larger dis­

turbances suggest anticipation was not possible.

The position of the w eight-supporting forearm w as found to change

very little w hen the weight is taken off by the contralateral hand. EMC

recordings have shown that an inhibition in the brachioradialis muscle pre­

cedes the onset of the change in force caused by the opposite hand. This

indicates that an anticipatory postural adjustm ent occurred prior to the u n ­

loading so that the position of the loaded hand remains unchanged (Du­

fosse et al., 1985; Massion, 1992). W hen the loading w as self-generated,

forearm displacement was found to be 5° ± 2°. In contrast, if the unloading

w as carried out by the experimenter, there was no anticipatory inhibition

of the postural forearm flexors and w hen unloading did occur there was an

upw ard rotation of the postural forearm by 16° ± 2° (Hugon et al., 1982; For­

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by a w arning tone at a fixed interval before removal, there was still no an­

ticipatory response in the forearm muscles. Therefore, self-generated move­

m ent was crucial for the anticipation to occur. Further studies demonstrate

that not every active movement is able to result in an anticipatory adjust­

ment. If the subject simply pressed a button with their thum b to remove the

load, anticipation did not occur even after several h u n d red presentations

of the situation, and the forearm displacement was large at 20° ± 2° (Du­

fosse et al., 1985). Additionally, the anticipatory response did not transfer

between limbs (Ioffe et al., 1996). If subjects were first trained on an un­

loading task with a right active arm and a left postural arm and switched

on testing to the right arm being the postural arm, no anticipatory response

was found to occur. However, w hen the leg rather than the contralateral

arm was used as the postural limb in the unloading task, an anticipatory re­

sponse occurred in the quadriceps, and postural disturbance was minimal

at 6° ± 5° (Forget and Lamarre, 1995)

Further studies have examined the affect of an additional tem poral de­

lay of 100-150 ms between the time between the subject's lifting w ith one

hand and unloading with the other. Subjects were able to leam to predict

this novel situation and anticipation became appropriately tim ed for the

forthcoming unloading (Forget and Lamarre, 1995).

Together these findings suggest that the requirements for a predictive

response are specific.

A further example of the use of forward models is dem onstrated in ball

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Introduction 22

1987; Lacquaniti and Maioli, 1989). W hen w e catch a bail that falls ver­

tically to the hand, both predictive and reactive mechanisms are used to

produce an appropriate catching action. The forw ard m odel needs to pre­

dict time, location and m om entum of the impact so that limb kinematics

and kinetics can be appropriately controlled. This control of limb position

and compliance has to adhere with the timing requirem ents of the task for

catching to be successful. EMC recordings show ed there were early and late

components, the early component is thought to be related to anticipation.

The am plitude of this early EMG response was inversely proportional to the

height of the fall and is thought to correspond to the readiness reaction that

incorporates the fall duration. The role of prediction has been examined by

modification of different variables in the task, for example the height of fall

and mass of ball. W hen balls of identical external appearance were used but

different masses were used, and the mass of ball is unexpectedly changed,

subjects scale their response to the expected m om entum . The prediction

used is thought to be based on a model of the ball's flight combined w ith an

internal model of the current position and m ovem ent of the arm. This for­

w ard model is accessed to predict a target, but is u p d ated using peripheral

information from vision to produce an adapted response.

1.2 . 1 Sensory prediction

Cancelling of sensory reafference is essential in the distinction of self-generated

and external-generated disturbances. The apparent disregard for self-produced

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ac-tions caused by ourselves to be reduced Qeannerod, 1988).

The cancellation of sensory reafference is necessary in the distinction of

m ovem ent of the world from movement made by our ow n m otor system.

This was described in eye movements by Von Holst and M ittelstaedt (1950)

who defined the role of the efference copy of the m otor command. In their

theory, the role of the efference copy is to cancel undesirable sensory mes­

sages resulting from self-generated movements.

This ability to distinguish self from other has been show n in tickling,

where self-generated tickle is perceived by the subject as less ticklish (Blake-

more et al., 1998b); an attenuation, which is argued to be possible by the

cancellation of self-produced stimuli. The problems that occur w hen there

is a failure to monitor the consequences of action include the false attribu­

tion of movement, and a false perception of the world. Grusser and Landis

(1991) report a patient, LM, with akinetopsia after bi-lateral lesions in the

occiptio-temporal region who fails to distinguish m ovem ent caused due to

her own action from movement in the outside world. According to their re­

port "w hen walking across the garden or along the street, she reported that

the objects in her extra-personal space were moving up and dow n". This

m ay be caused by a difficulty in discriminating visual image m otion origi­

nating from external sources and motion from the patient's ow n activity.

Differences in the response to external and self generated disturbances

are reflected neurophysiologically in the differing response that are m ade

betw een self and externally generated touch. Cells in the tem poral lobe,

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move-Introduction 24

m ent generally, and respond to the type of m ovem ent rather than the form

of movement. In particular, they have been shown to respond selectively

to unexpected movements. If the monkey itself creates m ovem ent in the

w orld as a consequence of its own actions, the cells are silent. For example,

these cells have been found to respond to the sight of an object moved by

the experimenter but do not respond w hen the same object is moved by the

monkey (Hietanen and Perrett, 1993; Hietanen and Perrett, 1996b; Hietanen

and Perrett, 1996a). These cells are thought to contribute to the m onitoring

of the consequences of action (Mistlin and Perrett, 1990). In one experiment,

a monkey was trained to move a grating by rotating a striped drum . This

was compared w ith the response to a grating moved by an experimenter.

Therefore, the link between the m onkey's actions and the changes in the

world were correlated. After training the nervous system registered the

correlation between action and consequence, so that changes in the world

generated by the monkey no longer activated the STPa neurones.

Forward models are thought to be an im portant p art of systems that use

an efference copy of the motor comm and to anticipate and cancel the sen­

sory effects of a given movement. Forward sensory m odels are responsible

for this prediction that is derived from the state prediction of a forward dy­

namic model.

1.2.2 D istal supervised learning

A problem faced by the motor system is that of translating goals and errors

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controller. Jordan and Rumelhart (1992) have show n how a forw ard model

can be used to estimate the motor errors during perform ance by the back-

propagation of sensory errors through the model. This is know n as distal

supervised learning as the supervision of the task, that is the detection of

errors is distal to the desired detection of m otor signal errors. A forward

model could be used to transform errors between the desired and actual

sensory outcome of a movement into the corresponding errors in the motor

comm and (Jordan, 1995).

1.2.3 M ental practice

Mental practice can lead to improved performance at a m otor task (Yaguez

et al., 1998), particularly in the early stages of learning a task (Bohan et al.,

1999). This improvement could be due both the m onitoring of performance

and m otor learning taking place in the absence of actual movement. A

forward model could be useful in mental practice, as it could be used to

predict the outcome of different imagined actions (Jordan and Rumelhart,

1992; Wada and Kawato, 1993; Miall and Wolpert, 1996). This w ould en­

able the controller to select between the imagined actions, or allow the

controller to adapt. The notion that the underlying m echanisms for both

physical and imagined practice are similar is supported by im aging (De-

cety et al., 1994; Decety, 1996); the compliance of im agined m ovem ents to

FitTs Law (Decety et al., 1989; Decety and Jeannerod, 1996; Decety, 1996);

patient studies (Kagerer et al., 1998) and a similarity in the interference ef­

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Introduction 26

during m otor imagery have been suggested to be related to the functional

and neural changes that are observed in the consolidation of motor m em ­

ories (Shadmehr and Brashers-Krug, 1997; Shadm ehr and Holcomb, 1997).

This alteration in stability could be due to increased learning by motor im­

agery (Bhushan and Shadmehr, 1999).

1.2.4 MOSAIC

The MOSAIC model (MOdular Selection A nd Identification for Control)

(Kawato and Wolpert, 1998) proposes the use of paired forward and in­

verse m odels for control. The forward models compete to predict the sen­

sory feedback given a m otor command, so that each becomes specialized for

a different dynamic situation, such as m anipulating different objects. The

prediction of each model can be compared to the actual feedback to provide

a set of prediction errors. The smaller the prediction error of a particular

model, the more likely that model is to model the current dynamics. The

set of forward models, therefore, act as a set of hypothesis testers and their

errors can be used to identify the current dynamics. The errors are then

used and to gate the contribution of a set of paired controllers to the final

motor command. Such m odularity has a num ber of benefits to the motor

system. Firstly, this architecture enables the retention of previously learned

behaviours to each situation. Secondly, a response to novel situations can

be derived from combinations of previously experienced context. Thirdly,

as the world is m odular and we act in a variety of different contexts, this

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1.3 Learning and representation of an internal m odel

Both forw ard and inverse models simulate properties of the m otor system.

For such models to be functional, it is essential that they are not static, and

can be modified as new environments are encountered, for example novel

objects are m anipulated, and also to take into account changes that occur

in the properties of the arm due to growth and injury. The ability to leam

and use new internal models is, therefore, a fundam ental property of the

m otor system (Shadmehr and Brashers-Krug, 1997; Wolpert, 1997; Wolpert

and Ghahramani, 2000).

An example of this learning occurs if subject's are asked to move a 2.5 kg

weight, where after training the trajectory is the same as before the weight

w as added (Lacquaniti et al., 1982). Dynamic learning has been used to

study the development of internal models appropriate for novel force fields

(Shadmehr and Mussa-Ivaldi, 1994; Gandolfo et al., 1996). Such paradigm s

for studying the development of inverse models of the m otor system are

useful frameworks for the examination of learning of forw ard m odels of

m otor control; the focus of this thesis. Within the studies examining the

adaptation of inverse models, subject's were trained to m ake point-to-point

m ovem ents in a novel force field. Initially the force field caused a signif­

icant divergence from the normally observed trajectory (Shadmehr and

Mussa-Ivaldi, 1994; Gandolfo et al., 1996). With practice in the novel en­

vironment, the trajectory eventually converged to the trajectory before the

introduction of the field. This dynamic learning was assessed by the exam­

(29)

Introduction 28

during one point-to-point movement. This enabled the aftereffects of the

exposure to the force field to be examined. It was found that the trajec­

tories occurring during these "catch trials" were the mirror-images of the

observed trajectories w hen the subjects were initially exposed to the force

field, w ith the m agnitude of these after-effects gradually increasing over

the training period. The presence of such after-effects w hen no force field

was present shows that the CNS had constructed an internal model of the

force field which was generating patterns of force that anticipated the pre­

viously encountered perturbing forces (Shadmehr and Mussa-Ivaldi, 1994).

The learning of such internal models has been show n to be initially fragile,

but becomes robust after a few hours (Brashers-Krug et al., 1996). W hen

subjects learned to make reaching movements under 2 conflicting m echan­

ical environments it was found that two motor m aps could be learned and

retained. However, it was only w hen the training of the two tasks w as sep­

arated by approximately 5 hours that performance on both tasks was not

impaired. If the training separation was less than this, the performance on

the first task was impaired; that is reteroactive interference occurs. Once

successfully learned, the memory was present over a 5 m onth period. This

suggests that the learning of the internal models had tw o components; ini­

tially memory was fragile but after consolidation of this internal m odel with

practice, memory became robust.

Generalization paradigm s have been used to examine the representa­

tion of internal models. The generalization paradigm can be sum m arized

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re-gion of input space. Secondly, subjects are exposed to a novel input-output

rem apping over a limited region of input space. Finally, subjects are once

again tested on their input-output m apping on the full region tested in the

first phase. The pattern of generalization outside of the learned region re­

flects the structure and constraints underlying the internal m odel (Ghahra­

m ani and Wolpert, 1997; Ghahramani et al., 1996). Such paradigm s have

been used to examine the representation of inverse m odels (Shadmehr and

Mussa-Ivaldi, 1994; Gandolfo et al., 1996).

In this thesis we will examine the learning of forward models, including

the use of catch trials to assess the development of an appropriate predic­

tion. In Chapter 7, a generalization paradigm will be used to examine the

spatial representation of a forward model.

1.4 Grip force as a paradigm for forward m odels

Grip-force m odulation provides an ideal paradigm to study internal model

learning, due to the differing latency of response that occurs to alterations

in load force that are self-generated compared to adjustm ents in load that

are externally generated. These differences are thought to be due to the in­

volvem ent of a forward model of both the m otor system and the controlled

object.

W hen we hold an object in a precision grip betw een the thum b and fore­

finger, sufficient grip force (perpendicular to the surfaces) m ust be gener­

ated to prevent the object from slipping (Johansson and Westling, 1984; Jo­

(31)

Introduction 30

Acceleration (a)

iGrip Force

Load Force (mg +ma)

Figure 1.1 — Grip force is generated perpendicular to the object surfaces. The level of grip force required is dependent on the load force that the object exerts on the fingers.

Johansson, 1996). The level of grip force required depends on the load force

(tangential to the surfaces) the object exerts on the fingers, that is its weight

w hen at rest, and the frictional properties of the surface of the object. Fig­

ure 1.1.

W hen the object is moved by the subject the load force on the fingers

m ust change to accelerate the gripped object. W ithout a corresponding

change in grip force the object w ould slip. In this self-generated condition

the grip force tends to parallel load force with negligible delay.

Such anticipatory m odulation is seen in discrete self-generated move­

ments w hen pulling on an object in different directions and at different

speeds (Johansson and Westling, 1984; Johansson et al., 1992). Anticipa­

tion has also been shown to occur during continuous self-generated move­

m ents (Flanagan and Wing, 1993; Flanagan and Wing, 1995). W hen an ob­

ject is m oved by the arm, grip force modulates in phase w ith load force.

Grip force increases as the load increases, and also falls as the load de­

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Grip force m odulation has been examined in other grips to assess the

specificity of these parallel m odulation between grip and load force to the

precision grip (Flanagan and Tresilian, 1994). Norm al, inverted and bi­

m anual grips were examined. In the inverted grip, or "Pirg", outw ard

rather than inw ard force was required to hold the object. Additionally the

direction was varied to produce different patterns of load force changes

during the movement. It was found that regardless of the grip used, m od­

ulation of grip force occurred in parallel with alterations in load force. In­

creased variability that was present in the pirgs w as explained by the re­

duced sensory feedback from cutaneous afferents available in these grips.

This coupling of grip and load has also been show n in m ultidigit grips (Burst-

edt et al., 1999).

Anticipation was found not to be specific to increases in load force caused

by arm movements and was present w hen subjects were instructed to jum p

so that the object was actively m oved by the subject w ithout arm move­

m ent (Flanagan and Tresilian, 1994). Similar parallel m odulation of grip

and load have been found w hen the movement on the object is caused by

w alking and running (Kinoshita et al., 1993).

This invariance of the grip-load force coupling across grip types and

m odes of transport suggest a general strategy for coordinating grip and

load forces during active transport of a held object. The forw ard model

incorporates movements of the arm and body. Grip is m odulated w hen the

net result of all movements leads to object acceleration and therefore fluctu­

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Introduction 32

Motor C om m and

Predicted Hand

Internal Acceleration Internal

Model of --- ► Model of

Arm Object

Anticipatory Grip Force

Efference Copy

Figure 1.2 — Internal model for predictive grip force modulation

Despite the relatively rapid response of cutaneous afferents, such an­

ticipatory control cannot be explained as a reaction to peripheral feedback

(Johansson and Westling, 1984; Flanagan and Wing, 1995) due to unavoid­

able feedback delays (Johansson and Westling, 1984; Forssberg et al., 1992).

A system based solely on feedback control w ould be ineffective for m anip­

ulative actions w ith frequencies above about 1 Hz, which w ould exclude

some complex skills (Kunesch et al., 1989; Johansson and Cole, 1994).

This suggests that the skilled m anipulation of objects requires the cen­

tral nervous system (CNS) to use the motor command, in conjunction with

internal models of both the body and the object, to anticipate the resulting

load force and thereby adjust grip force appropriately (Flanagan and Wing,

1997b; Wing et al., 1997; Blakemore et al., 1998a), Figure 1.2.

1.4.1 Reactive grip force m odulation

The predictive response present in response to self-generated loads can be

contrasted w ith the response when the load force is unexpectedly increased,

for example by someone else tapping on the object. In this externally-generated

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fingertips w ith a latency of around 60-100 ms after load force (Johansson

and Westling, 1988; Cole and Abbs, 1988; Johansson et al., 1992; Blakemore

et al., 1998a).

This reactive grip force response m ust depend on the feedback from cu­

taneous afferents as afferents from intrinsic and extrinsic han d muscles and

the interphalangeal joints do not respond to alterations in load force fast

enough (Macefield and Johansson, 1996).

The automatic nature of adjustments m ade to slip, and the reliance of

feedback from cutaneous afferents to signal alterations in object properties

have been examined in three ways (Westling and Johansson, 1987; Johans­

son and Westling, 1990). Firstly, alterations in force coordination that usu­

ally w ould follow object slip could also be elicited by afferent volleys in tac­

tile units (Westling and Johansson, 1987). W hen the tactile afferents inner­

vating the skin areas in contact with the object were electrically stimulated,

a m otor response occurred that was similar to that following a slip.

Secondly, latency data shows that the m otor responses to natural slips, as

well as the similar responses to weak cutaneous electrical stim ulation were

automatic in nature and not voluntarily initiated. The latency betw een a

slip and the onset of the force ratio change w as twice the latency of the

most rapid spinal reflex in intrinsic hand muscles at approximately, 75 ms,

but is less than half the voluntary reaction time. This latency is associated

with long-latency reflex responses to suddenly im posed finger or thum b

movements (Johansson and Westling, 1987).

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Introduction 34

is illustrated by the observed 'm agnet phenom enon' observed w hen sub­

ject's attem pt to slowly separate their fingers while holding the object in

the air. The regulation of the g rip /lo ad ratio by signals from cutaneous

afferents mechanisms appears to interfere w ith the voluntary drive, and

subject's are reported to experience the sensation of the object sticking to

their fingers. The effort required to overcome this 'm agnet phenom enon'

becomes stronger the closer the grip force is to the slip force, ie high fric­

tional surfaces (Johansson and Westling, 1987).

1.4.2 Development of grip force response

Object properties are learnt through development, indicated by increas­

ing ability to correctly parameterize grip force to the object being m anip­

ulated (Forssberg et al., 1991; Forssberg et al., 1992; Eliasson et al., 1995;

Forssberg et al., 1995).

In contrast to the highly program m ed and invariant grip and load forces

generated by adults in separate lifts with objects, children exhibit substan­

tial variability in both the force am plitudes generated and the tem poral

response (Newell and McDonald, 1997). This variability of m otor output

in children is not restricted to the control of grip force and is present in

a num ber of other tasks including postural control, reaching and locomo­

tion (Manoel and Connolly, 1997; Horak, 1996; Konczak et al., 1995; Ashby

et al., 1997; Schmitz et al., 1999). It has been suggested that this increased

variability in force production, which w ould norm ally be viewed as a mal­

(36)

Schmitz et al., 1999). This variability may allow the CNS to evaluate dif­

ferent response patterns and relate the intended m ovem ent to the motor

consequences (Newell and McDonald, 1997). Therefore variability w ould

facilitate the establishment of relations between the m ovem ent param eters

and interactions within the environment. Additionally such variability may

also reflect constant adaptation to a system that is rapidly changing due to

changes in limb length and mass during growth.

Grip force m odulation in children is less precisely tim ed than in adults,

w ith the tight correlation between grip and load force increases absent dur­

ing loading. Instead, the strategy used is to initiate a grip force increase

before the increase in loading due to object movement. Therefore, once the

object has been accelerated, the children were already gripping the objects

substantially, w ith any further increases grip force not in parallel w ith the

load force. In experiments where both adults and children were required to

perform a task where load force ram ped up, and then rem ained constant,

show ed that in children the movement phases of the lift are m uch longer

than in adults (Gordon and Forssberg, 1997). This increase is thought to be

partly due to prolonged delays from the mechanical events signaling the

term ination of one phase and the onset of the motor activity beginning the

next phase.

By the end of 2 years, children's grip and load forces begin to increase

more in parallel, with this coupling increasing to adult values by 8 years,

through improvements can occur as late as 14 years (Forssberg et al., 1991;

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Introduction 36

1.4.3 Learning the appropriate grip force response

Grip force levels can be set w ithout somatosensory feedback, anticipating

the physical properties of the object, that include the object's weight, shape

and friction at its surface (Johansson and Westling, 1984; Johansson and

Westling, 1988; Johansson and Cole, 1994; Jenmalm and Johansson, 1997).

Gordon et al.(1993) examined the grip force scaling to commonly m anip­

ulated objects, including crispbread packages and tins. It was found that

w hen lifting these 'common objects', grip force is program m ed prior to ini­

tiation the lift to match the expected weight of the object based on previous

experience. Test objects were then presented to subjects; objects w ith a mass

that had not previously been experienced. W hen subject's lifted these test

objects, grip force became correctly scaled to load force w ithin the first few

trials. Once learned, the subject was able to correctly scale their grip force

appropriate for this test object after a delay of 24 hours. However, if a novel

object, that is an object w ith the visual appearance of a "common object"

but differing in density, was presented to the subject, the default grip force

based on the visual appearance of the object was used, w ith up to ten trials

needed to correctly adjust the grip force.

Flanagan and Wing (1997) show ed that the anticipatory increase in grip

force that occurs w hen a subject accelerates an object is adaptable to novel

force environments. W hen the subject was exposed to inertial, viscous or

composite force fields during point-to-point m ovem ents it w as found that

the level of grip force quickly updated to m odulate in parallel w ith the new

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Wing and Flanagan, 1998).

Similar rapid adaptations to novel environments have been show n w hen

the gravitational force has been altered (Hermsdorfer et al., 2000). Load

force changes on an object occur during parabolic flights, where gravity

ranges from hypergravity (2g) and a short period of m icrogravity (Og). Sub­

ject's m anipulating an object under different gravitational loads quickly

leam to m odulate grip as the novel load force changes. The predictive cou­

pling betw een grip and load force occurred in transitions betw een different

gravity levels, illustrating the rapid adaptation to changing load conditions.

These findings are strong evidence for the existence of a forw ard internal

model that is predicting the properties of both the m otor system and the ob­

ject being manipulated. If subject's were unable to adapt to the new loads,

it w ould be suggestive of a simple association between the m otor command

and grip force that leads to appropriate m odulation of grip force during ob­

ject acceleration. However, this is clearly not the case, and the m otor system

is able to compensate for changes in the environmental context, resulting in

appropriate predictions being generated.

The importance of previous m anipulative experience in correctly scaling

grip force is illustrated by the force development pattern that can be seen

in children where m ulti-peaked force rate profiles can be seen (Forssberg

et al., 1991). This strategy is similar to the probing strategy used by adults

w hen lifting a novel object, or w hen they are uncertain as to the physical

properties of the object. The dependence of anticipatory control is reduced,

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Introduction 38

breakage is avoided.

1.4.4 Bio-mechanics of grip force

The precision grip is an example of the redundancy of the muscular sys­

tem, and requires the activity of both intrinsic and extrinsic hand m us­

cles (Smith, 1981; Muir, 1985; Maier et al., 1993). At least 15 muscles are

thought to have a contribution in the exertion of force w hen holding an ob­

ject with a precision grip (Napier, 1956; Napier, 1962). The grip requires

stabilization of the three joints of each finger, which can be considered as

a bio-mechanical bar linkage system (Hepp-Raymond et al., 1996). It also

requires that the compression force, that is the static equilibrium, as well

as the arch-like posture is m aintained between the thum b and index finger.

According to biomechanical constraints the extrinsic muscles w ith tendons

spanning all four links are best suited for the provision of a continual force

output, whereas the intrinsic muscles can adjust and m odulate grip force.

Long and Brown (1964) showed that the extrinsic muscles provide the m ain

compression force, assisted by intrinsic muscles that include the first dorsal

and palm ar interossei (IDI and 1 PI). Of the thenar muscles, the flexor polli-

cis brevis (FPB) exerts compresion force by the m etacarpophalangeal flexion

of the thum b w ith the adductor pollicis (ADP) providing compression force.

1.4.5 C utaneous afferents

In a precision grip task, cutaneous input is crucial for the updating of grip

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Westling, 1987; Westling and Johansson, 1987). If an object is slippery, for

example silk material rather than sandpaper, the ratio betw een the grip

force and load force has to be higher. W hen the surface m aterial changes

on a trial by trial basis, the motor output is influenced by the new frictional

conditions at different phases of the lift (Johansson and Westling, 1987).

The glabrous skin of the hand, especially the fingertips are densely in­

nervated by mechanoreceptors (Johansson and Vallbo, 1979; Johansson and

Vallbo, 1983; Lofvenberg and Johansson, 1984). Early studies show ed the

basic differences in tuning between mechanoreceptors in monkeys and com­

pared these characteristics to hum an psychophysical data (Mountcastle et al.,

1969). Four types of mechanoreceptors have since been characterized (FAI,

FAII, SAI, SAII) (Knibestol and Vallbo, 1970). Mechanoreceptors are subdi­

vided into two categories dependent on their response to a sustained step

indentation of the skin. 44% of the units are slowly adapting, that is they re­

spond with a sustained discharge, and 56% of the units are fast adapting, re­

sponding w ith a burst of impulses at the onset and removal of the stimulus.

Within these two broad subdivisions, two different units are distinguish­

able on their receptive fields. Fast adapting Type I (FA I) and Slow adapting

Type 1 (SA I) have small and discrete receptive fields. This is in comparison

w ith the FA II and SA II units, which have wider receptive fields. The den­

sity of glabrous skin receptors is highest in the fingertip, where the density

of FA I units and SA I units is about 140 units/cm ^ and 70 units/cm ^. At

a given point, the m ean overlap of the receptive fields of the FA I units is

(41)

Introduction 40

for the SAI units the overlaps are 16,6, and 2 respectively (Lofvenberg and

Johansson, 1984; Johansson and Vallbo, 1983).

The different mechanoreceptors also differ in their potential functions

for the characterization of object properties.

The contribution of each type of mechanoreceptor has been examined

at different stages of lifting, holding, releasing objects, and during abrupt

changes in tangential forces (Cole and Johansson, 1993; Johansson and West­

ling, 1987; Johansson and Westling, 1984; Macefield et al., 1996). FA 1 units

are found to respond optimally to small force changes related to initial con­

tact, slip and release of the object and are sensitive to the location and edge

contours of objects in contact with the skin. FAll units are highly responsive

to mechanical transients, so are especially active during the initial acceler­

ation and deceleration of the object. SAI units are found to be sensitive to

changes in grip force, and SAll units are particularly responsive to changes

in tangential load force. The coupling of grip and load forces in m anipula­

tion forms the basis for an efficient sensory control of the m otor output to

accommodate the physical properties of the object. Such synergies represent

strategies to simplify the dem ands on the control m echanism by reducing

the num ber of degrees of freedom of the musculoskeletal system that have

to be explicitly controlled (Bernstein, 1967).

1.4.6 Slip

Once the object is touched the most relevant afferent inform ation for the fi­

(42)

the object is provided by the mechanoreceptors located in the glabrous skin

of the digits (Lofvenberg and Johansson, 1984). The importance of input

from cutaneous afferents can be illustrated in people w ith im paired sensi­

bility in the fingers. Damage to the cutaneous afferents lead to difficulties in

holding and gripping objects, even with visual compensation. This crucial

role was dem onstrated by Mott and Sherrington (1895) who show ed that

w ith spared distal cutaneous innervation, little functional im pairm ent was

evident in the forelimb even if the rest of the limb was deafferented.

In general, subjects avoid excessive grip forces; applying enough to pre­

vent the object slipping w ith grip force rarely exceeding the m inim um grip

force by more than 30% safety margin (Johansson and Cole, 1992). This

strategy avoids both object breakage and unnecessary muscle fatigue. There­

fore, as the load force increases, the grip force m ust also increase to prevent

slippage and to maintain this safety margin.

1.4.7 Central control

The precision grip, with opposition of the thum b and index finger is charac­

teristic of m anual skill and dexterity (Wiesendanger, 1999).This grip allows

the skilful m anipulation of objects coupled w ith the precise control of pre­

hensile force (Napier, 1956; Napier, 1962). Based on anatomical, physiolog­

ical and behavioural studies, the motor cortex and cortico-motomeuronal

pathw ays are essential for such precision grip and object m anipulation (Lemon,

1993; Lemon et al., 1995; Lemon et al., 1996).

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fin-Introduction 42

ger m ovem ents was first demonstrated by Lawrence and Kuypers (1968)

who show ed that when the tract was lesioned in the m onkey the ability to

make individual finger movements was lost. Across species comparisons

highlight how the development of the corticospinal tract is mirrored by an

increasing ability to perform individual finger movements, culminating in

the ability to perform a precision grip (Lemon, 1993). A lthough the hands

of m ost prim ates are physically similar they substantially differ in the de­

gree of dexterity. N apier (1961) classified primates by an index of dexterity

for prim ates that reflect these substantial differences in grasping. For ex­

ample, tree shrews w ith a dexterity index of 3 have whole arm control w ith

a basic grasping action. These animals are lacking cortico-motoneuronal

projections. The lemur, with an index of 5 is skilled at prehension but not

at object m anipulation. In contrast, the old world monkeys w ith an index

of 6 can perform pow er and precision grips. These prim ates have substan­

tial cortico-motoneuronal projections. Heffner and M asterton (1975,1983)

show ed that there were two factors that were highly correlated w ith m an­

ual dexterity; firstly, the level that the corticospinal tract penetrated, and

secondly the presence of cortico-motoneuronal projections that reach into

the motoneuronal cell groups of lamina IX i.e. the deepest spinal lamina.

The developm ent of the precision grip in hum ans parallels the development

and m aturation of cortico-motoneuronal connections. Initially infants grasp

is m ainly caused by tactile and proprioceptive reflexes (Eliasson et al., 1995).

Gradually, these reflexive grasping patterns become more stereotyped as

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children w ith Cerebral Palsy, who fail to develop the corticomotomeuronal

p ath have impaired m anual dexterity and tend to exhibit m anipulative pat­

terns similar to those in young infants (Eliasson et al., 1991; Eliasson et al.,

1995).

1.4.8 Cortical areas

Four m ain cerebral areas are thought to have an im portant role in the control

of hand movements; prim ary motor cortex (Ml); the supplem entary motor

area (SMA); prem otor cortex (PM) and cingulate m otor areas (CM) (Ehrs-

son et al., 2000; Kinoshita et al., 2000). The output from low-threshold

mechanoreceptor afferents is transmitted to the cerebral cortex where they

contribute to the updating of the grip load force ratios (Johansson and Cole,

1992) w ith the somatosensory cortex sending direct projections to the motor

cortex (Jones and Powell, 1970). Reversible inactivations of the somatosen­

sory cortex by injection w ith muscimol was found to disrupt fine finger

movements, w ithout any disturbance of reaching and hand shaping (Hikosaka

et al., 1985). The participation of the m otor cortical neurons w ith cutaneous

receptive fields in adapting grasping and lifting forces to different surface

textures and weights has been dem onstrated (Picard and Smith, 1992a; Pi­

card and Smith, 1992b).

The primate motor cortex, known to play a key role in the control of

fine finger movements, receives detailed information over very rapid path­

ways from the mechanoreceptive units of the glabrous skin. Lemon (1981)

(45)

Introduction 44

a m otor cortex neurone during grasping. In this study m ost of the neurones

w ith cutaneous inputs were more highly activated during a task which in­

volved the use of a precision grip to reach rewards than during a lever-

pulling task.

A large area of M l is involved in the selection of distal m otor muscles of

the contralateral forelimb that can evoke movements of the fingers or wrist.

Each muscle and type of movement has been found to be m ultiply repre­

sented. Therefore, M l is thought to be primarily involved in the selection

of the appropriate muscles to perform the movement, w ith the other corti­

cal areas influencing output from motor cortex indirectly, especially by the

dense projections from these areas to the hand representation of M l (Porter,

1972). SMA is thought to be particularly relevant to the control of bim anual

tasks (Wiesendanger, 1986; Viallet et al., 1992)

1.4.9 Anticipatory grip force

Muller and Dichgans (1994a,1994b) reported patients w ith degenerative cere­

bellar lesions who they found show ed a lack of co-ordination of grip and

load forces w hen performing lifting tasks using a precision grip. Grip and

load force had become decoupled in these patients, so that the forces did

not consistently change in parallel as has been observed in norm al patients

could adjust grip force rates, their ability to do so was significantly lower

than controls. Particular problems were observed in adjusting grip force to

the differing load forces associated w ith objects of differing weight. They

(46)

ad-just grip force in parallel w ith load force was due to a failure of anticipa­

tory parameterization. Patients w ith unilateral cerebellar dam age showed

a selective impairm ent in ability to m odulate grip force on the affected

side (Muller and Dichgans, 1994b). Further cerebellar patient studies have

examine w hether adjustments in grip force occur to fluctuations in iner­

tial load force during arm movements (Babin-Ratte et al., 1999). This study

show ed that unlike normal subjects who adapt to fluctuations in load force,

a patient with cerebellar degeneration was unable to accurately m odulate

grip force with load force. Grip force was found to be increased in m ag­

nitude, and there were no timing differences between upw ards and dow n­

w ards movements. The total load force is the sum of inertial load forces

and gravitational load force. Therefore, the point of m axim um loading dif­

fers betw een upw ard dow nw ards movements and cerebellar patients were

not appropriately scaling their grip force.

Evidence for the involvement of the cerebellum in grip force m odulation

also comes from electrophysiology; neurones in the cerebellum as well as

prim ary m otor cortex have been reported to fire related to object weight and

texture prior to the onset of movement (Espinosa and Smith, 1990; Picard

and Smith, 1992a).

1.4.10 Earlier theories of predictive grip force modulation

The presence of forward models of the motor system to account for the

predictive m odulation of grip force to self-generated loads helps to qual­

Figure

Figure 1.1 — Grip force is generated perpendicular to the object surfaces. The level
Figure 2.1 — Schematic diagram of the apparatus used to create a virtual object.
Figure 2.2 — Bode plot to calculate the bandwidth of the virtual object apparatus. The bandwidth is defined as the point where the gain dropped t o i / \/2.
Figure 3.1 — Hand positions for the a) One Link b) Three Link c) Interval d) Non­
+7

References

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