Ideomotor Coding: A Transcranial Magnetic Stimulation Study

by

Connor Reid

A thesis submitted in conformity with the requirements

for the degree of Master of Science

Graduate Department of Exercise Sciences

University of Toronto

Copyright © by Connor Reid 2013

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Ideomotor Coding: a transcranial magnetic stimulation study

Connor Reid

Master of Science

Graduate Department of Exercise Science

University of Toronto

2013

Abstract

Ideomotor theory holds that motor plans producing action and the sensory effects of the

actions are cognitively represented in a functionally similar way. The response-effect (R-E)

association is considered bidirectional and automatic in nature. The current research project was

designed to test the hypothesized bidirectional nature of R-E associations by determining if

motor codes were activated following perception of an effect. The automaticity of motor code

activation was investigated via TMS–induced motor evoked potentials (MEPs) following the

presentation an after-effect. To this end, participants completed a training phase in which they

learned a specific R-E association. During the testing phase, the effects were presented prior to

the imperative and TMS stimuli. Behavioural results replicated previous research; participants

preferred to execute the response associated with the presented effect. MEP data, however, did

not support the initial hypothesis. These results are discussed with relation to ideomotor theory

and experimental design.

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Acknowledgments

I would like to thank my supervisor, Dr. Timothy N. Welsh, for his guidance and support

throughout the past two years. I admire and appreciate the enthusiasm with which you approach

the supervisory role, and want to thank you for making this an experience a positive one. I

would like to thank Dr. Luc Tremblay for the significant role he played in shaping my interest in

research during my undergraduate and graduate studies. I would also like to express gratitude to

Dr. Susanne Ferber and Dr. Matthias Niemeier for their helpful comments and suggestions

regarding my thesis.

I would also like to thank Jim and Janis Reid, and Michelle Wood for their support, love and

patience throughout this process. Lastly, I want to thank all of the members of the Action and

Attention Laboratory and the Perceptual-Motor Behaviour Laboratory; especially Gerome

Manson for his help in experimental design, and Kimberley Jovanov for her assistance in data

collection.

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Table of Contents

1   Introduction.................................................................................................................................1  

1.1   The ideomotor principle ......................................................................................................2  

1.2   Behavioural Evidence for Ideomotor Theory ......................................................................4  

1.2.1   A Two-stage Model of Voluntary Action Control ..................................................7  

1.3   Cortical Structures Involved in the R-E Associations .......................................................12  

1.4   Transcranial Magnetic Stimulation ...................................................................................14  

1.5   Cerebral Laterality in Response Planning .........................................................................16  

1.6   Experimental Aims and Rationale .....................................................................................17  

2   Chapter Two: Experiment 1......................................................................................................20  

2.1   Methods .............................................................................................................................20  

2.1.1   Participants ............................................................................................................20  

2.1.2   Procedure ...............................................................................................................21  

2.1.3   Dependent Measures..............................................................................................24  

2.2   Data Analysis and Results .................................................................................................25  

2.2.1   Reaction Time........................................................................................................26  

2.2.2   Response Frequency ..............................................................................................27  

2.3   Discussion..........................................................................................................................28  

3   Experiment 2.............................................................................................................................29  

3.1   Methods .............................................................................................................................31  

3.1.1   Participants ............................................................................................................31  

3.1.2   Equipment..............................................................................................................32  

3.1.3   Procedure ...............................................................................................................32  

3.1.4   Dependent Measures..............................................................................................37  

3.2   Data Analysis and Results .................................................................................................38  

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3.2.1   Reaction Time........................................................................................................39  

3.2.2   Response Frequency ..............................................................................................44  

3.2.3   Normalized MEPs..................................................................................................45  

4   Discussion.................................................................................................................................48  

4.1   Behavioural Results ...........................................................................................................49  

4.2   Neurophysiological Results ...............................................................................................50  

4.2.1   Cortical Structures Involved in the R-E Relationship. ..........................................51  

4.2.2   Considerations Related to Stimulated Hemisphere. ..............................................54  

4.2.3   Task-Related Considerations .................................................................................55  

5   Conclusions...............................................................................................................................57  

References .....................................................................................................................................59  

Appendices ....................................................................................................................................63  

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List of Figures

1.1 A two-stage model of voluntary action control (adapted from Elsner & Hommel, 2001)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.2 Response-effect mapping, and a visual description of valid and invalid trials in

Experiment 1 of Kunde et al., (2002) (adapted from original article). . . . . . . . . . . . . . . 11

2.1 Experimental button board. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2 Schematic of pre- and post-test procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 23

2.3 Mean reaction times separated by time and compatibility. Error bars indicate standard

error of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26

2.4 Mean response frequency separated by time and compatibility. Error bars indicate

standard error of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.1 Schematic representation of the vertex of the scalp and the approximate location of

motor cortex (A) (adapted from Conforto et al., 2002). . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.2 Schematic of Experiment 2 pre- and post-test trials. . . . . . . . . . . . . . . . . . . . . . . . . . . . .36

3.3 Mean reaction time separated by StimTime. Error bars indicate standard error of the

mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

3.4 Mean reaction time separated by Active Hand and Compatibility. Error bars indicate

standard error of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.5 Mean reaction time separated by Task and StimTime. Error bars indicate standard error

of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

3.6 Mean reaction time separated by Task and Compatibility. Error bars indicate standard

error of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

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3.7 Mean response frequency separated by Time and Compatibility. Error bars indicate

standard error of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.8 Mean MEP separated by StimTime. Error bars indicate standard error of the mean. . . .45

3.9 Mean MEP values separated by Time, Active Hand, and Compatibility. Error bars

indicate standard error of the mean. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

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List of Appendices

Appendix A: Medical history questionnaire ……………………………………………………62

Appendix B: Participant and testing session information ……………………………………....70

Appendix C: Experiment 1 mean values ……………………………………………………….73

Appendix D: Experiment 2 mean values ……………………………………………………….74

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1 Introduction

Humans are exposed to countless stimuli every day. Although many of these stimuli do not

require a response, others require voluntary action. When a stimulus is identified that requires a

response, the actor must select, prepare, and then execute an appropriate goal-directed

movement. The appropriate response is the one that effectively brings about some desired

outcome. From this perspective, the stages of information processing appear to be very

unidirectional in nature, beginning with the identification of a stimulus that requires action, and

ending with an actor executing a response. The conventional view of information processing is

that stimulus identification, response selection, and response programming are separate stages

which are distinct from one another. This approach also holds that the stages of information

processing are serial in nature, in that one stage cannot begin until the previous stage has been

completed successfully (see Schmidt & Lee, 2011, for a review). This approach has contributed

greatly (and will continue to contribute) to research methods in the field of motor control and

experimental psychology. There are, however, alternative accounts of information processing

which do not subscribe as rigidly to serial processing, but rather suggest that certain stages of

information processing mutually affect one another.

One such alternative account of information processing is known as ideomotor or

common coding theory (e.g., Prinz, 1997). Ideomotor theory suggests that stimuli, responses,

and their associated response outcomes may not simply be sequential components of a series of

processing events, but are rather a group of interdependent processes which are cognitively

represented in a functionally similar way to one another. The main tenet of ideomotor coding is

that the representations of specific movements are tightly bound to the perceptual codes

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representing the effect that the response generates in the environment. The purpose of the

present research was to test this core tenet by examining response selection biases and

neurophysiological measures of motor system activation following the presentation of effects

that have been associated with responses. Before outlining the specific goals and hypotheses of

the experiments in the current study, a review of ideomotor coding and the neurophysiological

technique used in the present study (transcranial magnetic stimulation – TMS) will be presented.

1.1 The ideomotor principle

Authors such as James (1890), Lotze (1852), and Harleß (1861) initially described the

ideomotor concept by suggesting that in order to select and perform a purposeful action, an actor

must first consider the action effect (consequence) he/she wishes to achieve. When an actor

considers an intended action effect, a common code is thought to be activated consisting of

action effect features, and the associated motor program capable of initiating the action itself.

Thus, early ideomotor concepts forwarded the notion that, due to the common coding of actions

and effects, the conceiving of an effect activates the associated motor code that would bring

about that effect.

More recent models (e.g., Elsner & Hommel, 2001; Prinz, 1997) have built upon this

notion and suggest that these common codes are developed through a learning process in which

producing actions results in perceptual feedback (from multiple modalities), and that repeating

these actions strengthens the cognitive association between the action produced, and the

perceptual feedback the actor receives. The end result is that through experience, the planned

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action becomes cognitively associated with its expected perceptual consequences, and that

actors execute actions with the expectation that they will receive this afferent information upon

movement completion (a goal-directed movement). Although these processes were originally

considered over a century ago, the ideomotor approach has been altered and updated, and

remains a topic of current interest.

Notably, Prinz’s (1997) ideomotor or common coding theory provided a concrete

framework to explain the link between perceived events and planned actions. The theory holds

that afferent information provided by environmental stimuli are cognitively represented as event

codes. This information is used to prepare the motor system to mobilize effectors to execute a

response (cognitively represented as action codes). The critical aspect of ideomotor theory is

that although event codes consist of afferent information and action codes consist of efferent

motor plans, they cognitively share a common representational domain. Because of this

common coding system, it is predicted that there is a bidirectional relationship between effects

and actions such that the activation of one code necessarily activates the other code. This

bidirectional activation facilitates accurate and efficient response selection because it: 1) allows

one to activate the appropriate response by conceiving of a desired effect; or 2) one can predict

the outcome of a specific action when a given action code is activated. By way of example, one

can activate the action code for pressing the brake pedal on a car by conceiving of the desire to

slow the car down, and one can predict that the car they are driving will slow down by

activating the action code to press the brake. Critically, it is only through a number of

experiences (e.g., during driving lessons) in which the action generates a specific effect that this

coupling or binding between actions and effects occur.

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1.2 Behavioural Evidence for Ideomotor Theory

Beyond the theoretical details of ideomotor theory, there is growing behavioural

evidence to suggest that actions are cognitively represented in conjunction with their perceptual

consequences. Such behavioural ideomotor research investigates how associations between

actions and their effects are formed, and what factors most influence the formation of these

associations (e.g., Elsner & Hommel, 2001; Greenwald, 1970a, b; Hoffman, Sebald & Stocker,

2001; Hommel, 1996; Kunde, Hoffmann, & Zellmann, 2002; Stock & Hoffmann, 2002). The

following paragraphs contain a review of a selection of the key studies that have provided

behavioural support for the ideomotor notion that when a certain action R (e.g., brake pedal) has

been learned to be associated with action effect E (e.g., car slowing down), the perception of

action effect E prior to the execution of action R increases the speed and probability of action R.

This pattern of behavioural facilitation suggests a strong bidirectional association between

action and effect codes.

Preliminary evidence for the role of effect anticipation in action control was carried out

by Greenwald (1970a, 1970b). Greenwald designed several experiments to investigate the

assumption that anticipation of voluntary action is based upon response-effect (R-E) learning.

Participants responded to auditory or visual stimuli (which were letters or digits) by reproducing

the stimulus by speaking or writing. This research revealed that when the modality of the

stimulus corresponded with the modality of the produced response, reaction times (RTs) were

shorter than when they did not correspond. Specifically, it was found that RTs for written

answers were shorter in response to visual stimuli than auditory stimuli, and that RTs for spoken

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answers were shorter in response to auditory stimuli than visual stimuli. Consistent with

ideomotor theory, facilitation occurred when the modality of the action effects was consistent

with the modality of the imperative stimuli. Greenwald attributed facilitation to the

compatibility between the action, the modality of the effect of the action, and the modality of the

imperative stimulus. For example, the auditory stimulus facilitated verbal RTs because the

auditory stimulus activated the existing bidirectional R-E relationships between sound and

speech, and primed the action (verbal response) relative to when the verbal response was made

to the visual stimulus.

Hommel (1996) provided additional support for ideomotor theory in a novel task

designed to investigate the relationship between actions and their response-contingent events.

This research consisted of a series of experiments, all of which required participants to make

two-choice responses (left and right hand index finger presses of a shift key on a computer

keyboard). Each keypress response was coupled with a unique auditory stimulus. The

characteristics of auditory stimuli varied across experiments; in Experiments 1-2, one tone

played through either a left or right loudspeaker, while in Experiments 3-5, a high-pitched or

low-pitched tone played simultaneously through a left and right loudspeaker (Experiment 3), or

one central loudspeaker (Experiment 4 and 5). These stimuli served as response-contingent

events in that they were presented only after the response was executed. Participants, however,

were informed that the response-contingent auditory stimuli were irrelevant to the task. In all

experiments, participants completed an acquisition phase of 200 practice trials that were

designed to establish a relationship between each response and its respective auditory after-

effect. The specific methods and aims varied in each experiment, but the consistent purpose was

to investigate the coding of actions and their response-contingent events. For example, during

the acquisition phase of Experiment 3, participants pressed a left key (Response 1; R1), in

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response to the appearance of the letter O (Stimulus 1; S1) on a computer screen, or pressed a

right key (Response 2; R2) in response to the appearance of the letter X (Stimulus 2; S2).

Subsequent to a left keypress, a low tone was played (response-contingent action effect, E1, 200

Hz, 100 ms duration), and a right keypress was coupled with the presentation of a high tone

(response-contingent action effect, E2, 500 Hz, 100 ms duration). Thus, during acquisition,

correctly performed trials occurred as follows: S1 R1 + E1, and S2 R2 + E2. In the testing

phase, effect tones were presented simultaneous to the visual imperative stimuli, and

participants were required to respond to the visual stimuli in the same way as they did in the

acquisition block. The execution of the response would result in the immediate playback of its

corresponding effect tone. The key to the studies was that one of two learned effect tones was

randomly played simultaneous to the onset of the visual target. This created two possible

scenarios for each imperative stimulus; trials were said to be either compatible (e.g., S1 + E1

R1 + E1) or incompatible (e.g., S1 + E2 R1 + E1). The experimental hypothesis was that

experience with each R-E mapping would lead to an association between the motor pattern

responsible for executing a response (R1) and the cognitive representation of the response-

contingent action effect (E1). Generally speaking, the experiments revealed that participants had

shorter RTs to initiate a response when pre-cue auditory stimuli were compatible with the

learned key/tone mapping, than when they were incompatible. This research demonstrated: 1)

that task-irrelevant response-contingent auditory stimuli are automatically integrated into

cognitive codes associated with that response; and, 2) due to the bidirectional relationship

between action and effect codes, perceiving a learned action effect activated an associated action

code. These two characteristics of action-effect associations are thought to contribute to the

rapid retrieval of a motor program capable of executing the associated action. What was unclear

from this research, however, was whether the effects of this relationship translate beyond

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perceptual and response initiation efficiencies (as reflected in RT) to affect response selection

and programming.

1.2.1 A Two-stage Model of Voluntary Action Control

Based upon the behavioural evidence suggesting that R-E relationships play a role in the

efficiency of action selection and execution, Elsner and Hommel (2001) suggested a two-stage

model of voluntary action control, operating upon the framework of the ideomotor principle. As

its name suggests (and consistent with earlier thinking), this more formal model is comprised of

two stages. In the first stage, response-effect (R-E) contingencies are learned, and in the second

stage, response selection occurs based upon learned R-E bidirectional associations (Figure 1.1).

Elsner and Hommel (2001) proposed that when a randomly selected motor pattern (R) executed

by the actor results in a specific perceivable change in the environment, this motor program and

specific effect (E) are represented in the cognitive system. Due to the temporal overlap of the

cognitive codes for R and E, they are thought to become associated in such a way that activating

one code automatically activates the other. Thus, according to the two-stage model, after several

co-occurrences of R and E, this association has been established and actors are able to select this

motor program R in order to bring about a desired effect E. That is to say, the actor is able to

select a movement by mentally retrieving the known effects of the selected movement.

Likewise, perceiving effect E in the movement environment automatically primes the motor

program R. As the R-E relationship is thought to be bidirectional in nature, the selection of a

specific response activates the associated effect code, which can be used to predict the outcome

of that response.

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Figure 1.1: A two-stage model of voluntary action control (adapted from Elsner & Hommel, 2001).

Elsner and Hommel (2001) designed a series of experiments to empirically investigate

their two-stage model of voluntary action control. The experimenters adapted the task from

Hommel (1996) into a free-choice task with response selection/programming implications.

Acquisition trials were similar to that of Hommel (1996), except that there was a single

imperative stimulus (a white rectangle on a computer screen). Upon presentation of the

stimulus, participants were free to choose one of two keypresses. Each keypress (i.e., left or

right) triggered a unique auditory tone lasting 200 ms (i.e., high [800 Hz] or low tone [400 Hz];

symbolically - S R1 + E1 or S R2 + E2). Participants were free to choose and press either

key on each trial, and were asked to execute, as much as possible, a balanced ratio of left and

right responses over 200 acquisition trials. The test phase was similar to acquisition, except that

one of two effect tones was randomly played as the imperative stimulus. Participants were still

free to choose either response on any given trial, and were no longer required to mind a

balanced response ratio. Thus, in the test phase, participants produced either ‘acquisition-

consistent choices’ (e.g., E1 R1 + E1) or ‘acquisition-inconsistent choices’ (e.g., E1 R2 +

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E2). Recall that the critical dependent variable in Hommel (1996) was RT. Instead of evaluating

RT, Elsner and Hommel (2001) focused on response frequency in the test phase as the index of

preference for compatible or incompatible responses. This index of preference (or response

bias) was thought to reflect the activation of action codes via presentation of the effect. Overall,

the results indicated that participants preferred to execute responses which were consistent with

the learned R-E mapping during acquisition phases (e.g., Experiment 3A: 63.8% acquisition-

consistent, 36.2% acquisition-inconsistent; Experiment 3B: 60.1% acquisition-consistent, 39.9%

acquisition-inconsistent). This finding extended the known RT effect (e.g., Hommel, 1996) to

include implications in response selection. Together, these data are consistent with the notion

that R-E relationships are automatically activated upon the presentation of a learned action

effect, and that they have implications for response programming/selection.

Although the aforementioned research provided critical behavioural demonstrations of

the association between action and effect codes, it does not demonstrate this relationship when

effect codes are not perceptually available prior to response selection. In the critical condition of

Hommel (1996) and Elsner and Hommel (2001), one of two auditory effect tones E1/E2,

associated with one of two actions (R1/R2), would be presented prior or simultaneous to the ‘go’

signal. Kunde et al. (2002) argued that it was necessary to show R-E relationships during action

planning without effect codes being perceptually available (i.e., presenting (E1/E2) prior to the

execution of R1/R2). To this end, the researchers designed three experiments to examine the

action/effect relationship in a unique task, where effect codes were not perceptually available

prior to response selection. The task required participants to execute one of four key presses

(Figure 1.2), where pressing keys one and three immediately resulted in the playback of a low-

pitched tone (250 Hz, 600 ms duration), and pressing keys two and four were paired with a

high-pitched tone (650 Hz, 600 ms duration). The digits 1-4 and the colours red, yellow, blue,

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and green were both mapped to four keys in left-to-right order. In the first experiment, a

response cue (a number between 1-4, representing keypresses) was presented in the center of a

computer screen. Participants were instructed to prepare (but not execute) the keypress that

corresponded to the cue. The cue remained on the screen for 1500 ms, at which point a second

cue (one of four colours) was presented to participants that required an immediate response.

Immediately after the key press response had been completed, its corresponding auditory after-

effect would play. Critical trials occurred when the cue was invalid (that is, the prepared

response (e.g., the first cue was “3”) was not congruent with the produced response (e.g., the

second cue was red [key “1”], yellow [key “2”], or green ([key “4”]). On these invalid trials, the

second cue could indicate a response whose auditory effect either corresponded with (e.g., red)

or did not correspond with (e.g., yellow or green) the effect produced by the initially cued

response (e.g., “3”). The experimental prediction, termed the collateral facilitation hypothesis,

was that preparation of one motor program (corresponding to an auditory action effect) would

facilitate the execution of another response that shared the auditory action effect of the prepared

response. For example, if key 3 (mapped with a low-pitched tone) was initially cued, and the

second stimulus required a key 1 response (also mapped with a low-pitched tone), the authors

expected to see shorter RTs than if the second stimulus corresponded with a high-pitched effect

tone (the effect for keys “2” and “4”). This effect was confirmed in the second half of

Experiment 1 (after participants had learned the association between stimuli) in that RTs were

faster for corresponding invalid trials, than non-corresponding invalid trials.

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Figure 1.2: Response-effect mapping, and a visual description of valid and invalid trials in Experiment 1 of Kunde et al., (2002) (diagram adapted from original article).

In Experiment two, participants were presented with a stimulus S1, and were required to

prepare (but not execute) a corresponding response R1. Before they executed this action, another

stimulus S2 was presented, which was to be immediately responded to, followed by the

execution of the originally prepared action R1. The collateral facilitation hypothesis was also

demonstrated in this task, in that an action R2 was initiated with significantly shorter RTs when

the originally prepared action R1 corresponded with the same auditory effect as action R2.

Experiment 3 replicated these findings with a more complex, pen transport task.

Taken together, the behavioural research reviewed here supports ideomotor theory in

that it suggests an important role of action effects in the cognitive retrieval and selection of

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motor codes. Specifically, learned R-E associations (e.g., when a certain action R1 has been

learned to be associated with action effect E1) increase the efficiency (shorter RTs - Greenwald,

1970a, b; Hommel, 1996; Kunde et al., 2002) and probability (response frequency - Elsner &

Hommel, 2001) of initiating action R1, when its corresponding effect E1 is perceived prior to the

execution of action R1. These patterns of behavioural facilitation provide strong evidence in

support of the strong bi-directional association between action and effect codes predicted by

ideomotor theory.

1.3 Cortical Structures Involved in the R-E Associations

Evidence suggesting that action effects influence the selection and execution of

responses is growing. What is less clear, however, are the neural substrates of this relationship.

A large amount of work on this topic has been compiled on musical tasks. Skilled musicians are

an appropriate population to use when investigating R-E associations, as their practice is

comprised of pressing keys (or combinations of keys), each of which is associated with a unique

auditory effect tone. For example, Drost, Reiger, Brass, Gunter, and Prinz (2005) demonstrated

that expert pianists were slower (in terms of RT) to execute responses when pre-cue tones were

incongruent with the chord to be played. This data is consistent with the ideomotor notion that

perception of effects and executing actions are cognitively represented in a functionally similar

manner, and that the R-E association is bidirectional in nature.

Neurophysiological research with musical populations suggests that the frontoparietal

motor-related network may play a role in R-E associations. Evidence suggests that this common

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network is comprised of an area related to action, including premotor cortex, and dorsolateral

and inferior frontal cortex areas, as well as a strip related to perception, including the superior

temporal gyrus and supramarginal gyrus (Bangert & Altenmuller, 2003; Bangert et al., 2006;

Lotze, Scheler, Tan, Braun, & Birbaumer, 2003; Meister et al., 2004). These regions have also

been implicated in perception of visual effect codes, where presenting learned action effects

leads to premotor cortex activity when observing other actors (Calvo-Merino, Glaser, Grezes,

Passingham & Haggard, 2005; Tai, Scherfler, Brooks, Sawamoto, & Castiello, 2004).

Non-expert musicians have also been examined via fMRI while they acquire R-E

associations between pressing keys and receiving auditory feedback through experience. Lahav,

Saltzman, and Schlaug (2007) had non-musicians learn a piano piece during an acquisition

phase, followed by a test phase where participants were required to passively monitor a variety

of tone sequences. Some of the tone sequences were novel, but the critical trials were those in

which part of the piece they had learned in the acquisition phase was replayed. Consistent with

the notion that the R-E associations involve the motor system, fMRI results demonstrated

activation in the frontoparietal motor-related network when participants listened to sequences

consistent with those that they learned in the acquisition phase. Specifically, activation was

found in Broca’s area, the premotor region, the intraparietal sulcus, and the inferior parietal

region.

Taken together, this research suggests that R-E associations involve the motor system

and related perceptual and cognitive areas. What has yet to be demonstrated, however, is more

direct evidence that perception of a learned action effect automatically primes the motor code

associated with that action in the motor system (and specifically in primary motor cortex [M1]).

To address this issue, the current research project will use transcranial magnetic stimulation

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(TMS) to probe the excitability of the nervous system at the effector level, subsequent to the

presentation of a learned action effect.

1.4 Transcranial Magnetic Stimulation

TMS is a safe, non-painful, and non-invasive method of stimulating the cerebral cortex.

When TMS is applied over an area of motor cortex which corresponds to a specific area of the

motor homunculus, muscular contractions can be elicited (a technique first demonstrated by

Barker, Jalinous, & Freeston, 1985). By analyzing the characteristics of selectively elicited

muscular contractions, one can gain insight into the excitability of the corticospinal tract.

A modern TMS unit consists of a magnetic stimulator and an electrical capacitor. Its

operation and function is based upon Faraday’s Law, and the related principle of

electromagnetic induction. The electrical capacitor stores and delivers a large amount of

electrical current to the magnetic stimulator (consisting of a coil of wire that is connected to the

electrical capacitor - Maeda & Pascual-Leone, 2003; Rothwell, 1997). The brief electrical

current induces an equally brief magnetic field in the volume surrounding the coil. This transient

magnetic field can induce an electrical current in any nearby electro-conductive medium. If the

induced magnetic field is close enough to the brain, the field induces an electrical current in the

extracellular fluid of the brain which, if it is of sufficient magnitude, can generate action

potentials in nearby neurons. When M1 is stimulated, the change in electrical potential can

activate the cortical interneurons which synapse to the descending pyramidal neurons. When

enough of the neurons are activated, the descending action potentials can activate the alpha-

15

motorneurons and cause a brief muscle contraction. In typical TMS research where M1 is

stimulated, surface electromyographic (EMG) electrodes are placed on the skin to monitor

electrical activity in the effector muscles of interest and record the induced muscle contraction

(Wasserman, 1997). The induced contraction is termed the motor evoked potential (MEP).

When the amount of stimulation is kept constant, the amplitude of the MEPs is an indicator of

corticospinal excitability (Maeda & Pascual-Leone, 2003). The amplitude of the MEP is

typically calculated as the difference in voltage between the largest negative and positive peaks.

Larger amplitude MEPs for a given level of stimulation are thought to reflect an increase in

corticospinal excitability, while relatively smaller amplitude MEPs for a given level of

stimulation are thought to reflect decreased excitability or inhibition.

Although TMS has many potential diagnostic and therapeutic uses, the current research

project used TMS as an investigative tool. Specifically, TMS was used as an indicator of

corticospinal excitability while participants perform an experimental task. As a methodological

example, Fadiga, Fogassi, Pavesi, and Rizzolatti (1995) used TMS to elicit MEPs in this manner

to determine if the observation of another person’s response activates the motor system in a

response/muscle specific manner. This study was based on research with monkeys suggesting

that a subset of neurons in motor area F5 becomes active similarly when the monkey executes

goal-directed movements and when it observes similar movements (di Pellegrino, Fadiga,

Fogassi, Gallese & Rizzolatti, 1992). To determine whether a similar phenomenon occurs in

humans while avoiding the invasive procedures of primate studies, Fadiga et al. monitored

TMS-induced MEPs in the hand muscles of human participants while the participants: 1)

observed an experimenter grasping 3-D objects; 2) simply looked at the same 3-D objects; 3)

observed an experimenter tracing geometrical figures in the air; or, 4) detected the dimming of a

light. Fadiga et al. found that MEP amplitudes were highest in the condition where participants

16

observed an experimenter grasping 3-D objects. The authors suggested that this increase in

MEP amplitude indicated that the excitability of the motor system increases when humans

observe a goal-directed action performed by another individual. The method used by Fadiga et

al. of recording and analyzing TMS-induced MEPs as an index of motor system excitability

while participants perform an experimental task was adapted for use in the current research

project.

1.5 Cerebral Laterality in Response Planning

The areas that contribute to motor planning and control are spread throughout each

hemisphere, and the two cerebral hemispheres seem to contribute different aspects to the motor

plan. For the typical right-handed individual, the data suggest that the left hemisphere plays a

critical role in planning the timing and amplitude of muscle contractions, and the right

hemisphere plays a dominant role in organizing the spatial components of the action (see Elliott

& Roy, 1996; Sainburg, 2010 for reviews). Due to its dominant role in planning muscle

contractions, it is generally accepted that response planning typically involves the left

hemisphere regardless of which limb is involved. This conclusion is based on a series of

neurophysiological (e.g., Frey, 2008; Haaland et al., 2000; Janssen et al., 2011; Johnson-Frey et

al., 2004; Kim et al., 1993) and behavioural studies (see Elliott & Roy, 1996; Sainburg, 2010 for

reviews). For example, Johnson-Frey et al. (2004) reported findings from fMRI studies of right-

handed adults examining the cortical structures involved in planning (defined by the authors as

identifying, retrieving, and preparing actions with commonly used tools), verses executing tool

actions using either the dominant or non-dominant hand. Regardless of the limb in question,

17

planning to use a tool activated a left hemisphere network, composed of: the posterior superior

temporal sulcus, proximal middle and superior temporal gyri, inferior frontal and ventral

premotor cortices, anterior and posterior supramarginal gyri, angular gyrus, and prefrontal

cortex.

Further, results from studies of intermanual transfer of learning (in which participants

learn a sequence of movements with one hand and then perform it with the other hand) indicate

that there is greater transfer of learning when the participant learns the task with their left hand

and then performs it with the right hand, then vice versa (e.g., Taylor & Heilman, 1980). This

greater left-to-right than right-to-left transfer is thought to occur because the left-hemisphere is

involved in the planning of actions for the left hand and, hence, “learns” the motor task even

though the right hand is at rest during acquisition. On the contrary, the right hemisphere is not

involved during the planning and execution of right-handed movements and, hence, the right-

hemisphere/left-hand system does not benefit as much from the training of the right hand.

Overall, the data suggest that the left hemisphere plays an important role in the planning and

learning of movements for both hands. As will be made clearer below, this apparent laterality in

function influenced the location of stimulation in the current research project.

1.6 Experimental Aims and Rationale

The main purpose of the current research project was to use TMS to investigate whether

the perception of a learned after-effect automatically results in the retrieval of its associated

motor code. The underlying hypothesis was that if the after-effect automatically retrieves the

18

motor code, the representation of that motor code in M1 should increase in excitability. Thus,

the automatic retrieval of the motor codes should be reflected by the presence of higher TMS-

induced MEP amplitudes following the presentation of the tone associated with that response

than when a tone associated with a different response is presented.

This project consisted of two experiments. In Experiment 1, participants completed a

task similar to the free-choice button task in Elsner and Hommel (2001). The purpose of this

experiment was to validate the task used in the main experiment by replicating the findings of

Elsner and Hommel (2001). Recall that Elsner and Hommel (2001) reported that, subsequent to

an acquisition phase designed to associate specific R-E (i.e., button-tone) mappings, participants

had a preference to interact with compatible over incompatible buttons following the

presentation of one of the tones. The authors argued that this response selection preference

occurred because the presentation of a specific tone activated the motor code associated with

that tone, biasing the participant to select and execute that response. Experiment 1 was

conducted to ensure that a similar protocol could generate the same pattern of effects before it

was adapted for use in the TMS study.

Experiment 2 (the critical new experiment) involved an adaptation of the free-choice

button press task in Experiment 1 to include a single TMS pulse on each test trial. TMS-induced

MEPs of an effector muscle served as an indicator of corticospinal excitability following the

presentation of effect codes. Based on ideomotor theory’s prediction that common coding

underlies motor planning, and the resulting perception of an after-effect evokes a representation

of its associated action in the motor system (e.g., Elsner & Hommel, 2001; Hommel, 1996;

Kunde et al., 2002), it was predicted that the cognitive retrieval of an effect-evoked response

code should not only be apparent behaviourally (reflected in response selection biases), but also

19

neurophysiologically (reflected in MEP amplitudes). Thus, the main experimental hypothesis

was that MEP amplitudes should be higher than baseline when the effect tone presented prior to

TMS was compatible with the learned R-E relationship for the stimulated muscle, compared to

when the presented effect tone is associated with the contraction of a different muscle. Similar

to the biases in response selection, these differences in MEP amplitude should only be present

after the participant has completed an acquisition phase in which he/she has learned to associate

a response with a specific effect (i.e., the tone should not affect amplitudes in the pre-acquisition

test, but should affect MEP amplitudes in the post-acquisition test). However, if common

coding has a more limited role in motor behaviour, or if after-effect perception does not evoke

response codes, then the presentation of effects should not influence the activity of the motor

system. Also, given that TMS will be directly applied to the hand area of M1 in this experiment,

absence of an MEP effect may also suggest that the R-E relationship is not exclusively localized

to M1, but may be represented in other areas in the brain.

20

2 Chapter Two: Experiment 1

As mentioned above, an initial experiment was conducted without TMS to ensure that

the task used in the main experiment was capable of replicating the results of Elsner and

Hommel (2001). Recall that the dependent variable of interest in the free-choice tasks of Elsner

and Hommel (2001) was response frequency - contrary to previous studies which focused on

RT. To this end, participants completed a free-choice button press task before and after an

acquisition phase designed to establish a link between each response and its associated auditory

action effect. In pre- and post-test tasks, participants were presented with one of two previously

learned action effects prior to the imperative stimulus. The experimental prediction was that

participants in the post-test task would prefer to interact with the button whose learned after-

effect was compatible with the pre-cue auditory stimulus.

2.1 Methods

2.1.1 Participants

Nine volunteers (5 male) from the University of Toronto participated in the experiment.

Testing sessions lasted approximately 30 minutes, and subjects were compensated $5 CAD for

their time. All participants were right handed, and between the ages of 18 and 28 years (M=23;

SD=2.4). Prior to their involvement, participants were provided with a written explanation of

the experiment, and were asked to provide written consent to the procedures. All procedures

21

were approved by the Ethics Review Board at the University of Toronto and complied with the

ethical standards of the 1964 Declaration of Helsinki regarding the treatment of human

participants in research.

2.1.2 Procedure

All experimental stimuli and data recording were controlled by a custom E-Prime

program run on a Dell PC. Participants responded to visual stimuli on a computer monitor (Dell:

REV A00) and provided their responses on a custom built button board situated in front of them

(Figure 2.1). During all experimental trials, participants were seated comfortably in a chair in

front of a computer monitor with their palms facing down, resting on a button board, and their

left and right index fingers positioned 2 cm lateral of their respective buttons. Participants

completed three separate blocks; a pre- and a post-test, which were separated by an acquisition

block.

Figure 2.1: Experimental button board.

22

In the pre-test block, trials began with a white fixation cross appearing in the center of a

computer monitor over a black background for 1000 ms (Figure 2.2). At the end of the fixation

phase, the white cross disappeared and after a brief delay (100 ms) one of three pre-cue tones

were presented for 600 ms (consistent with Kunde et al. 2002); a low tone (200 Hz), a high tone

(800 Hz), or white noise. Low and high tones indicated that participants were required to

execute an action on that trial, whereas white noise indicated a ‘no-go trial’. Following the

presentation of the auditory stimulus, a white square appeared in the middle of the screen

(consistent with Elsner and Hommel, 2001). Participants were instructed to press one of the

buttons by lifting and abducting their either their left or right index finger 2 cm to execute a

button press as quickly as possible. Consistent with Elsner and Hommel (2001), participants

were free to choose either a left or right hand keypress on any given trial, and were told that

auditory tones were irrelevant to the task. Participants were instructed to respond as quickly and

as spontaneously as possible and were advised that exclusively pressing one key was not

acceptable. On all trials, button contact resulted in the disappearance of the white square, and

the screen turned black, indicating the end of the trial. There was a 1000 ms inter-trial interval.

Each of the three pre-cue tones were played on 14 trials, resulting in a total of 42 pre-test trials.

23

Figure 2.2: Schematic of pre and post-test procedures

The acquisition task followed the same general procedure as the pre-test task, with a few

differences. First, there was no pre-cue tone. The trial sequence simply progressed from a

fixation cross (1000 ms), to a black screen (1000 ms), to the imperative stimulus (a white

24

square). Second, pressing either button resulted in the immediate playback of one of two

auditory stimuli (600 ms duration) via two computer speakers (simultaneous playback through

both speakers). Pressing the left button resulted in a high-pitched tone (800 Hz), whereas the

right button resulted in a low-pitched tone (200 Hz). Participants completed 200 trials, and

although they were free to choose either button on any given trial, they were instructed to

maintain a goal of pressing each button an equal number of times. This procedure is similar to

Elsner and Hommel (2001) and has been shown to produce R-E associations.

The post-test task was identical to the pre-test task, except for one difference.

Immediately upon button contact, the corresponding auditory tones that were learned in the

acquisition block were played (lasting 600 ms). The effect tones were played upon button press

during the post-test to ensure that any R-E associations established during acquisition were

maintained. Thus, compatible trials were said to be trials where the participant pressed the

button which corresponded to the initial ‘go/no-go’ tone (e.g., high pre-cue tone, followed by a

left button press, causing a high tone to play), whereas incompatible trials occurred when the

participant chose the opposite response (e.g., high pre-cue tone, followed by a right button press,

causing a low tone). Notably, response-contingent auditory tones remained dependent upon the

keypress, as in acquisition. Each pre-cue tone was played on 14 trials, resulting in a total of 42

pre-test trials.

2.1.3 Dependent Measures

Two main dependent measures were collected and analyzed; reaction time (RT) and

response frequency (RESP). RT was defined as the time between visual stimulus onset (white

25

square) and button press. RESP was defined as the percentage of trials on which a participant

interacted with compatible or incompatible buttons.

2.2 Data Analysis and Results

Temporal, but not location, data for trials with RT values larger than two standard

deviations from each participant’s mean (outliers) or shorter than 100 ms (anticipations) were

removed from the data. Trials were also deleted when participants moved in the ‘no-go’

condition, or failed to respond at all. These criteria resulted in removal of 1-14% of trials per

participant and 5% of total experimental trials. RT and RESP data were submitted to separate 2

(Task: pre-test, post-test) by 2 (Compatibility: compatible, incompatible) repeated measures

ANOVAs. Mauchly’s test was performed to ensure sphericity of the data, but there were no

violations of the assumption of sphericity. The nature of any significant results involving three

or more means was determined using a Tukey’s HSD post-hoc test. Alpha was set to p<.05 for

all tests.

26

2.2.1 Reaction Time

Regarding RT, the analysis revealed a significant main effect of Compatibility, F(1,

8)=13.33, p<.01. Post-hoc testing revealed that participants had shorter RTs when interacting

with compatible targets (M=348 ms; SD=79.3) than to incompatible targets (M=378 ms;

SD=76.8). Although there was no main effect of Task, F(1, 8)=1.11, p>0.1, the interaction

between Task and Compatibility approached significance, F(1, 8)=4.88, p=.058 (see Figure 2.3).

Figure 2.3: Mean reaction times separated by time and compatibility. Error bars indicate standard error of the mean.

27

2.2.2 Response Frequency

The RESP analysis did not reveal significant main effects of Time, F(1, 8)=1.00, p>0.1,

or Compatibility, F(1, 8)=3.23, p>0.1. More theoretically relevant to the experimental

hypothesis, however, the analysis revealed a significant interaction between Task and

Compatibility, F(1, 8)=6.91, p<.05 (Figure 2.4). Post-hoc analysis of this interaction revealed

that there was no difference between compatible (M=49%; SD=1) and incompatible (M=51%;

SD=1) trials in the pre-test, however participants preferred to respond to compatible targets

(M=63%; SD=19.4) over incompatible targets (M=37%; SD=19.4) in the post-test.

Interestingly, the magnitude of the differences found here were similar to those reported by

Elsner and Hommel (2001).

Figure 2.4: Mean response frequency separated by time and compatibility. Error bars indicate standard error of the mean.

*Denotes significance at the p<.05 level

28

2.3 Discussion

Experiment 1 was designed to evaluate the validity of the present experimental design

with respect to that of the original free-choice R-E design in Elsner and Hommel (2001). The

main finding was that, in the post-test task, participants preferred to interact with buttons which

were consistent with R-E mappings learned during the preceding acquisition block. This finding

is consistent with the literature in that 200 acquisition trials are typically sufficient for actors to

establish an R-E association between button presses and their contingent auditory tones, which

is reflected in response selection biases.

29

3 Experiment 2

Because Experiment 1 demonstrated participants’ preference for compatible targets after

200 acquisition trials, a second study was designed to investigate the potential

neurophysiological bases of this action-effect relationship. Experiment 2 employed a similar

paradigm to Experiment 1 with the addition of a single TMS pulse on each pre-and post-test trial.

According to ideomotor theory’s prediction that common coding underlies motor planning and

that the perception of an after-effect evokes a representation of its associated action in the motor

system (e.g., Hommel, 1996; Elsner & Hommel, 2001; Kunde et al., 2002), it was anticipated

that the cognitive retrieval of an effect evoked response code should not only be apparent

behaviourally (as evidenced in response selection biases), but also neurophysiologically.

Specifically, it was expected that MEP amplitudes would indicate increased excitability when the

effect tone was compatible with the learned R-E relationship for the stimulated hand than when it

was incompatible with the learned R-E relationship for the stimulated hand.

To this end, single pulse TMS was provided to the area of the right M1 that represents the

first dorsal interosseous (FDI) muscle of the left hand. TMS was provided after the presentation

of effect tones. As in Experiment 1, participants learned to associate the generation of high tones

with depression of the left button and low tones with depression of the right button. To press the

button, participants had to lift and then abduct the index finger which required activation of the

FDI. Hence, it was predicted that activation of left and right FDI would be part of the motor code

that would be associated with high and low tones, respectively. MEPs in the left FDI were

evoked during the time window after the presentation of the tone, but before the presentation of

the visual imperative stimulus. During this time, participants should be selecting and planning

30

their upcoming response, but should still be at rest. If perception of the effect activates the motor

codes of the action that brings about that effect in the M1, then the high tone should activate

(increase the excitability) of the M1 representation of left FDI in the right hemisphere. The

presentation of the low tone should not influence the activation of the M1 representation of left

FDI because it is associated with a right hand response (which is represented in the left

hemisphere). If presentation of the high tone activates the cortical representation of left FDI in

M1, then MEPs recorded from the left FDI should be of greater amplitude when the high tone is

presented prior to the TMS pulse than when the low tone is presented prior to the TMS pulse (a

within-left-hand difference between MEPs on compatible and incompatible trials). Critically,

this difference should only be present in the post-test task (i.e., only after the acquisition phase in

which the R-E association has been developed). Alternatively, if the effect tone does not activate

the representation of the action in M1 (perhaps because the associations are represented upstream

from M1), then there will be no differences in MEP amplitudes following the presentation of low

and high tones.

Note that the right-hemisphere/left-hand system was chosen for stimulation because of

concerns over the confounding role the left hemisphere plays in programming actions for both

hands. That is, even though the left hemisphere should become most active following the

presentation of the low tone, it could be that the left hemisphere becomes active following both

low and high tones because of its role in planning movements for both limbs in right-handed

people. For this reason, it was thought that a more sensitive index of cortical activation

following presentation of the effect tone would be to test the right hemisphere, which would be

more isolated from the influences of both of the effect tones and/or the general planning process.

31

As an additional testing of the ideomotor coding and the timecourse of response

activation, TMS was provided at 3 time points prior to the imperative stimulus. The three time

points were: 0 ms, 150 ms, and 300 ms after the offset of the 600 ms effect tone (the 300 ms

condition was also simultaneous to the imperative visual stimulus). It was expected that

stimulation at 0 ms may be too early for activation of motor codes to be present, but that there

could be increases in excitability (and larger MEPs) at the 150 ms and 300 ms stimulation times.

3.1 Methods

3.1.1 Participants

Eleven different volunteers (5 male) from the University of Toronto, that did not take part

in Experiment 1, participated in Experiment 2. Testing sessions lasted approximately one hour

and 30 minutes. Participants were compensated $15 CAD for their time. All participants were

right handed, and between the ages of 21 to 28 years (M=24; SD=2.7). Prior to their

involvement, participants were provided with a written explanation of the experiment, and were

asked to provide written consent to the procedures. All procedures were approved by the Ethics

Review Office at the University of Toronto and complied with the ethical standards of the 1964

Declaration of Helsinki regarding the treatment of human participants in research. To ensure that

the use of TMS was not contraindicated, participants completed a medical history questionnaire

(Appendix A) and a mental health questionnaire (Appendix B) prior to their involvement in the

study. Among other things, these questionnaires investigated whether he or she (1) wore a

pacemaker, spinal/bladder stimulator, or acoustic device; (2) had any neurosurgical procedures

32

with large craniotomies; (3) had any other intracranial metallic components; (4) had a history of

seizure; or (5) was taking any medication that may affect the excitability of their nervous system

(i.e., antispastics, anxiolytics, hypnotics, antiepileptics, etc.) (Rossini, Barker, Berardelli,

Caramia, Caruso, Cracco, et al., 1994).

3.1.2 Equipment

The TMS system was a single pulse monophasic stimulator (Magstim 200) with a figure-

8 coil with an internal diameter of 70 mm. An image-guided TMS system (Rogue Research,

Montreal, QC; Brainsight 2) was used to locate cortical landmarks and ensure that TMS pulses

were delivered to the same location on each trial. EMG data from the left FDI of the index finger

was recorded from surface electrodes (Rogue Research, Montreal, QC). All experimental stimuli

and response data were controlled and recorded by a custom E-Prime program on a Dell PC.

EMG was recorded by the Brainsight system (3000 Hz) for a 200 ms period that began 50 ms

prior to the TMS pulse and ended 150 ms after the TMS pulse. These data were stored for offline

analysis using Brainsight system software. Participants perceived stimuli on a computer monitor

(Dell; REV A00) and provided their responses on a keyboard situated at a comfortable arms

reach in front of them, so that they could rest their elbows on the armrests of the chair (Rogue

Research, Montreal, QC; Generation 3 TMS chair).

3.1.3 Procedure

33

During all experimental trials, participants were seated comfortably in a chair in front of

a computer monitor with their palms facing down, resting on a keyboard. Although not

necessarily theoretically relevant, it is worth noting that Experiment 2 was completed using

button presses on a keyboard, rather than a custom button board (as was used in Experiment 1).

Participants’ left and right index fingers were positioned on their respective home positions; the

letter ‘z’ (left hand; target was the letter ‘c’) and the number ‘3’ (right hand, number pad; target

was the number ‘1’). Participants completed four separate blocks; an initial mapping phase, a

pre-test block, an acquisition block, and a post-test block.

During the mapping phase, the experimenters used the TMS and Brainsight 2 systems to

identify the participants’ scalp position over the right motor cortex at which the largest MEP

amplitude was evoked in the FDI of the left hand index finger following a TMS pulse. To locate

the hand motor area, a standard procedure was carried out for all participants (Conforto,

Graggen, Kohl, Rosler & Kaelin-Lang, 2004; Mills & Nithi, 1997). Throughout this procedure,

participants were seated comfortably in a chair, with their arms and hands as relaxed as possible,

resting on the armrest of the chair. The electrodes were placed on the left FDI, and a ground was

positioned on the distal aspect of the ulna. After the electrodes were fixed to the skin, the

participant’s head was registered to the default brain map of the Brainsight system using the

anatomical landmarks of the nasion, the tip of the nose, and the edge of each ear.

In the first step of the mapping, the vertex of the scalp was determined by marking (with

non-permanent marker) the intersection of the naison-inion line and the interaural line (Figure

3.1). Next, a mark was made 5 cm to the left of the vertex along the interaural line, representing

the approximate location of the right motor cortex.

34

Figure 3.1: Schematic representation of the vertex of the scalp and the approximate location of motor cortex (A) (adapted from Conforto et al., 2002).

The coil was oriented 45 degrees to the body midline, and tangential to the scalp in order

to facilitate posterior to anterior flow of the electric current along the primary motor strip. The

experimenter initially placed the TMS coil over point A (Figure 3.1), and continued to move the

coil over the scalp in 1 cm steps until an observable MEP was elicited in the left FDI. As

necessary, stimulus intensity was increased in 5% intervals until an MEP was observed. The

resting motor threshold (rMT) was defined as the minimum stimulus intensity which evoked five

of ten MEPs of at least 50 µV (peak-to-peak) from the left FDI (Rossini et al., 1994). Once the

rMT was identified, the experimenter dropped a virtual target on the scalp of the virtual brain

map using the Brainsight 2 software. This virtual target acted as the reference point to which the

experimenter oriented the TMS coil for the duration of the experiment. Experimental stimulus

35

intensity was 120% of rMT, resulting in an average of intensity of 55% (± 7.9 SD) of the

maximal capacity of the TMS unit across participants.

The acquisition task was identical to Experiment 1, except that responses were made on a

keyboard rather than a button board. The pre- and post-test tasks were identical to the procedure

in Experiment 1, with two changes. First, each trial was initiated by an experimenter mouse

click, replacing the consistent one second intertrial interval in Experiment 1. This change was

implemented so that the experimenter could ensure that the participant was prepared for the

upcoming trial, which would include TMS. This experimenter-initiated trial procedure also

allowed the participant to take a break at any time if they needed a break from TMS (though no

participants took this break within a block). Second, each trial included a single TMS pulse at the

specified testing stimulus intensity determined in the mapping procedure for each participant. On

each trial, this pulse occurred at 0 ms, 150 ms, or 300 ms after the offset of the presentation of

the pre-cue tone. To ensure consistent temporal order of events (i.e., time between pre-cue tone

and onset of visual stimulus), auditory stimuli in the 0 ms condition were followed by a 300 ms

wait period, and in the 150 ms condition a 150 ms wait period before the appearance of the

visual stimuli (white square). Thus, there were three TMS presentation times (0, 150, 300 ms),

and three pre-cue tones (high tone, low tone, white noise), resulting in 9 conditions. Similar to

Experiment 1, the only difference between pre- and post-test procedures was that in the post-test,

each keypress was coupled with its associated auditory after-effect to maintain any R-E

association established during acquisition (Figure 3.2). Participants completed 14 trials in each

condition; a total of 126 total pre- and post-test trials.

36

Figure 3.2: Schematic of Experiment 2 pre- and post-trials.

37

3.1.4 Dependent Measures

Three dependent measures were collected and analyzed; normalized peak-to-peak MEP

amplitudes (MEP), reaction time (RT) response frequency (RESP) to compatible and

incompatible targets. Consistent with Experiment 1, RT was defined as the time between visual

stimulus onset (white square) and button press, and RESP was defined as the percentage of

responses participants executed following compatible and incompatible tones. MEP amplitudes

were recorded as the absolute µV difference between the highest positive and lowest negative

voltage recorded. MEP values were normalized for each participant and condition by dividing

mean values in each condition by the average peak-to-peak MEP amplitude in the control (white

noise, neutral tone) condition because this was a “no-go” rest trial and, hence, there should be no

change in corticospinal activation on these trials. This procedure was completed separately for

pre- and post-test values for each participant. As an example, if a participant had a raw peak-to-

peak MEP amplitude of 1150 µV in the pre-test compatible, left hand (response), 0 ms StimTime

condition, and had an average peak-to-peak amplitude in the control (white noise) condition of

900 µV, their normalized MEP value would be 1.28. Normalized MEP values above 1 (one)

indicate increased corticospinal excitability compared to baseline, whereas values below one

suggest decreased excitability or inhibition relative to baseline. Using the white noise/no-go

trials as the baseline helped to control for such factors as the effect of a tone alone, time from

tone, and time to the visual stimulus on corticospinal excitability.

38

3.2 Data Analysis and Results

Temporal, but not location, data for trials with RT values larger than two standard

deviations from each participant’s mean (outliers) or shorter than 100 ms (anticipations) were

removed from the data. Trials were also deleted when participants moved in the ‘no-go’

condition, or failed to respond at all. These criteria resulted in removal of 2-15% of trials per

participant and 10% of total experimental trials. It should be noted that one participant did not

make any post-test incompatible responses with their right hand at the 0 ms StimTime. For this

reason, this participant did not have RT or MEP values for this condition. Absent RT and MEP

values for this participant were substituted with the mean value for that specific condition from

the other participants. This substitution allowed the participant to be included in all analyses.

Regarding RESP, this issue did not require adjustment; the participant simply chose to execute

0% of their responses in this condition. To ensure that this procedure did not bias the results of

the experiment, all analyses were competed both with and without the added values from the

participant in question. The overall pattern of results was not meaningfully altered by

substituting RT and MEP values.

RT, RESP, and MEP data were submitted to separate 2 (Task: pre-test, post-Test) by 2

(Compatibility: compatible, incompatible) by 2 (Active Hand: left, right) by 3 (StimTime: 0 ms,

150 ms, 300 ms) repeated measures ANOVAs. Note that the factor Active Hand refers to the

hand that actually completed the response upon presentation of the visual imperative stimulus.

Mauchly’s test was performed to ensure sphericity of the data; regarding RT, a main effect of

StimTime violated the assumption of sphericity, and as such, a Greenhouse-Guisser correction is

39

reported for that one effect. The nature of any significant results involving 3 or more means

were determined using a Tukey’s HSD post-hoc test. Alpha was set to p<.05 for all tests.

3.2.1 Reaction Time

Regarding RT, the analysis revealed a significant main effect of Active Hand, F, (1,

10)=22.52, p<.001. Post-hoc analysis revealed that participants were faster when responding

with the right (non-stimulated) hand (M=433 ms; SD=117.5), than the left (M=482 ms;

SD=120.2).

40

There was also a main effect of StimTime, F (1.1, 11)=15.36, p<.01 (see Figure 3.3).

Post-hoc comparisons revealed that participants responded faster in the 0 ms condition (M=413

ms; SD=112.4) than both the 150 ms (M=473 ms; SD=109.5) and 300 ms conditions (M=502

ms; SD=122.2). There were no significant differences between RTs in the 150 ms and 300 ms

conditions.

Figure 3.3: Mean reaction time separated by StimTime. Error bars indicate standard error of the mean.

*Denotes significance at the p<.05 level

41

Additionally, there was a significant interaction between Compatibility and Active Hand,

F(1, 10)=9.66, p<.05 (Figure 3.4). Post-hoc analysis revealed that RTs were shorter when

participants executed compatible responses with the right hand than compatible responses with

the left (stimulated) hand. There were no differences between incompatible responses with the

left hand and incompatible responses with the right hand.

Figure 3.4: Mean reaction time separated by Active Hand and Compatibility. Error bars indicate standard error of the mean.

*Denotes significance at the p<.05 level

42

Lastly, there was a significant interaction between Time and StimTime, F(2, 20)=4.15,

p<.05. Of particular interest within this interaction was whether RTs for a given StimTime

differed across Time. Pre- versus post-test RTs were not significantly different from one another

in either the 150 or 300 ms StimTimes. However, participants had shorter RTs at the 0 ms

StimTime in the post-test (M=370 ms; SD=102.6) than in the pre-test (M=456 ms; SD=16.1)

(Figure 3.5).

Figure 3.5: Mean reaction time separated by Task and StimTime. Error bars indicate error of the mean.

*Denotes significance at the p<.05 level

43

Consistent with Elsner and Hommel (2001) and Experiment 1 of the current

investigation, the interaction between Time and Compatibility did not approach significance,

F(1, 10)=1.2, p=0.3 (Figure 3.6). That is, there was no RT advantage when participants executed

compatible responses, compared to incompatible responses. Potential explanations for this

finding are discussed in more detail in Section 4.1.

Figure 3.6: Mean reaction time separated by Task and Compatibility. Error bars indicate standard error of the mean.

44

3.2.2 Response Frequency

The RESP analysis revealed a main effect of Active Hand, F(1, 10)=5.61, p<.05,

indicating that participants preferred to perform the task with their left hand (M=54.9%; SD=1)

rather than their right hand (M=45.9%; SD=1). This slight bias might have emerged due to the

extra excitation provided via the TMS. Critically, there was a significant interaction between

Time and Compatibility, F(1, 10)=6.27, p<.05 (Figure 3.7). Post-hoc analysis revealed that in the

post-test condition, participants preferred to interact with compatible trials (M=59%; SD=21.4)

over incompatible trials (41%; SD=21.4). No differences were present in the pre-test condition.

This finding is consistent with the experimental prediction, and replicates results from both

Elsner and Hommel (2001) and Experiment 1 of the current project.

Figure 3.7: Mean response frequency separated by Time and Compatibility. Error bars indicate standard error of the mean.

*Denotes significance at the p<.05 level

45

3.2.3 Normalized MEPs

An analysis of MEP data revealed a significant main effect of Active Hand, F(1, 10)=

48.37, p<.001. MEP values were significantly higher when responses were made with the left

hand (M=1.47; SD=0.68) than the right hand (M=1.05; SD=0.57).

Additionally, there was a main effect of StimTime, F(2, 20)= 4.41, p<.05. Post-hoc

analysis revealed that MEP values were higher in the 0 ms condition (M=1.4; SD=0.66) than in

the 150 ms (M=1.19; SD=0.64) and 300 ms (M=1.18; SD=0.65) conditions, which were not

different from one another (Figure 3.8).

Figure 3.8: Mean MEP separated by StimTime. Error bars indicate standard error of the mean.

*Denotes significance at the p<.05 level

46

Lastly, there was a significant interaction between Time, Hand, and Compatibility F(1,

10)=5.15, p<.05. Post-hoc analysis of this interaction revealed only one marginally theoretically-

relevant significant difference: MEP values were higher in post-test compatible responses made

with the left hand (M=1.56; SD=0.57), compared to the post-test compatible condition in the

right hand (M=0.93; SD=0.48) (Figure 3.9). It is important to note, however, that there were no

significant pre/post differences between compatible and incompatible responses when the left

hand subsequently responded (i.e., no within-left-hand differences).

Figure 3.9: Mean MEP values separated by Time, Active Hand, and Compatibility. Error bars indicate standard error of the mean.

*Denotes significance at the p<.05 level

47

The critical four-way interaction of Time, Hand, Compatibility, and StimTime did not

approach significance F(2, 20)=0.27, p=0.77. This result suggests that the pre/post differences in

the amplitudes of the MEPs on compatible trials did not increase as a function of time from the

presentation of the effect tone.

48

4 Discussion

The present study was designed to investigate the neural basis of R-E relationships in a

task in which participants built an association between an action and its response-contingent

auditory consequence. In Experiment 1, the results of Elsner and Hommel (2001) were

replicated. Specifically, in a free-choice task following a 200 trial R-E acquisition phase,

participants preferred to interact with buttons that were compatible with a learned R-E

contingency rather than one that was incompatible with the learned R-E contingency (reflected in

response frequency results). In Experiment 2, the pre- and post-tests included a single TMS pulse

at one of three different timepoints after the presentation of a learned after-effect. As expected,

the behavioural results of Experiment 1 (preference for compatible buttons in the post-test) were

replicated. Of critical theoretical importance, it was hypothesized that perception of an effect-

evoked response code should be apparent not only behaviourally, but also neurophysiologically -

as evidenced by higher MEP values when the effect tone was compatible with the learned R-E

relationship for the stimulated hand, compared to a control condition. This hypothesis was based

upon the ideomotor notion that the perception of an after-effect learned to be associated with a

particular action automatically evokes a representation of that associated action in the motor

system (e.g., Elsner & Hommel, 2001; Hommel, 1996; Prinz, 1997; Kunde, et al., 2002). MEP

data did not confirm this hypothesis. Specifically, MEP amplitudes were not affected in a

meaningful way by the presentation of a learned after-effect in that there was no apparent within-

hand difference in corticospinal excitability in compatible trials after a 200 trial R-E acquisition

phase. Overall, the behavioural results of the current research project support ideomotor theory

49

and will be discussed first. Second, the neurophysiological results, which did not compliment the

behavioural results, will be considered. MEP results will be discussed regarding potential

complications related to the stimulated cortical hemisphere in Experiment 2, followed by a

consideration of the cortical structures thought to be involved in the R-E relationship. Lastly,

alterations to the current experimental task will be considered.

4.1 Behavioural Results

Neither Experiment 1 or 2 demonstrated a compatibility advantage for RT subsequent to

the acquisition phase. Although some action-effect studies report differences in RT (e.g.,

Hommel, 1996; Kunde et al., 2002), the nature of the current task may not necessarily have set

the conditions needed to garner RT differences. Specifically, due to the free-choice nature of the

task, participants may have chosen to initiate their response more freely in time, thus negating

any RT bias that may have existed. The generally long RT for a simple task supports the notion

that participants may not have completed the task with a sense of urgency. The lack of

theoretically relevant RT results is consistent with the findings of the free-choice experiments of

Elsner and Hommel (2001).

More theoretically relevant to the current experiment, the initial hypotheses pertaining to

R-E relationships were supported by RESP data in both Experiments 1 and 2. When given the

choice, participants preferred to interact with buttons that were compatible with the R-E

contingencies learned in the acquisition task. These results replicated the findings of Elsner and

Hommel (2001) and are consistent with the notion that learned relationships between self-

50

produced movements, and movement-contingent events lead to an automatic and bidirectional

integration of the motor code responsible for producing movement. The integration of these

codes are maintained in a common cognitive code which stores the event-related information. In

the case of the present studies, the presentation of an effect tone activated the associated response

code, and this activated response code subsequently biased response selection in favour of the

“compatible” over the “incompatible” response.

4.2 Neurophysiological Results

It was anticipated that there would be increases in normalized MEP values in the post-test

task when the pre-cue tone presented was compatible with the learned R-E contingency for the

left hand, and when participants executed the compatible response. This effect was predicted to

increase in time from the onset of the tone. Such was not the case; in fact, the critical interaction

(Task x Hand x Compatibility x StimTime) did not approach significance. It was observed,

however, that normalized MEP values recorded in the left hand were higher in the post-test

condition for compatible responses subsequently made with the left hand, than compatible

responses subsequently made with the right hand (see Figure 3.9). Upon initial assessment, this

result appears promising; it was expected that the difference between mean MEP values would

be highest in the post-test on trials where the pre-cue tone corresponded with the left (stimulated)

hand, compared to the right hand. Upon investigation of the data, however, these results are

likely due to higher overall mean MEP values for left hand response trials, compared to right

hand trials. Specifically, rather than the aforementioned difference being consistent with the

experimental hypothesis, these two points (the highest of four means for left hand responses,

51

compared to the lowest of four means for right hand responses) were likely different due to

lateral differences in excitability, based upon which hand responded. Simply put, the to-be-

executed response was selected prior to the TMS pulse and any subsequent difference is likely an

artifact. This claim is corroborated by the fact that a main effect of Active Hand was found;

across all conditions, trials where participants responded with their left hand yielded higher MEP

values than those where they responded with the right hand. Further, there were no significant

differences between pre/post values within the left hand. Overall, these data suggest that,

although the presentation of the effect tones influenced the selection of the responses the

participants executed, the effect tones did not significantly influence corticospinal excitability.

Although the MEP data do not support the hypothesis that the ideomotor coding would affect M1

excitability, they are not necessarily contradictory to ideomotor theory, broadly speaking,

because it is possible that the common codes underlying the response selection biases exist

upstream from M1.

4.2.1 Cortical Structures Involved in the R-E Relationship.

It may have been the case that stimulating the hand area of M1 did not result in a

neurophysiological expression of the R-E relationship because the stimulated area was

downstream of the critical cortical areas responsible this association. Although M1 is known to

have a major role in the planning of actions and is the main source of the corticospinal neurons

that deliver the action signals to spinal efferent neurons, there is contradicting evidence

suggesting that the R-E relationship may exist elsewhere in the brain.

52

For example, Elsner et al. (2002) conducted a positron emission tomography (PET) study

to cortically evaluate ideomotor principles. In this study, participants underwent a typical R-E

task - they first learned that self-initiated keypresses were paired with specific tones (i.e., high or

low tones). After 200 trials of learning, participants were put in a PET scanner, and were

required to passively listen to sequences of action-effect tones and neutral tones, and were not

required to execute a response. Consistent with previous research suggesting that the motor

system plays a role in R-E relationships, the researchers found that the caudal supplementary

motor area increased in activation as the frequency of action-effect tones increased. However,

these researchers also found that the right hippocampus also increased its activity in a similar

manner to the caudal supplementary motor area. Thus, the researchers suggested that not only

does the perception of an after-effect automatically retrieve the associated motor code, but they

also suggested that the increase in hippocampal activation likely indicates that there is a memory

retrieval of the learned associations. That the hippocampus and supplementary motor area may

have a role in the R-E relationship may also explain the lack of a critical interaction in the

current project. The design of Experiment 2 necessitated that the majority of R-E relationships

take place (or at least are expressed) in M1, because the TMS pulse was localized to M1 in order

to generate a muscular contraction at the level of the effector. The results of Elsner et al. (2002)

suggest that M1 might not be activated following the presentation of action effect; at least not

when the person is not required to execute the response.

Similarly, Melcher, Weidema, Eenshuistra, Hommel, and Gruber (2008) replicated and

extended the results of Elsner et al. (2002) using fMRI. Participants actively learned R-E

associations between keypresses and auditory tones, and then passively perceived learned action

effects inside an fMRI scanner. Consistent with the behavioural effects in typical R-E research,

this experiment demonstrated that passive perception of learned action effects results in

53

activation of a number of motor-related brain regions (SMA, premotor cortex, somatosensory

cortex, and cerebellum). Notably, this effect was only observed when left hand action effects

were passively observed, whereas right hand action effects did not demonstrate this trend.

Although this lateralized trend suggests a novel asymmetry in R-E associations that might have

been consistent with our predictions, the authors could only speculate as to what may have

produced this result.

Lastly, Mutschler et al. (2007) provided evidence for R-E associations in cortical

locations that are alternative to the motor system. The researchers used fMRI, and had

participants passively listen to piano melodies which had been actively learned (participant

learned to play the piece), or passively learned (participant listened to the piece) for 30 minutes.

The results indicated that there was a significant increase in activation for the active learning

group in the sensorimotor hand area of the insular cortex, compared to the passive learning

group. These results suggest that the perception of learned action-effect associations may be

stored in the insular sensorimotor cortex.

Taken together, there is a considerable amount of evidence suggesting that R-E

associations may exist within an extensive fronto-partietal network of motor-related areas see

also (see Section 1.3), R-E associations might not involve M1 directly. If this is the case,

stimulating M1 would not necessarily index response code activation, even if R-E associations

exist cortically. Note, however, that in all the previous studies reviewed here the task involved

passive listening. Passive listening might have been used as the experimental task to potentially

control for the confounding effects and extra cortical activity associated with the planning and

selection of a movement. The absence of M1 activity in those studies would be consistent with a

passive listening condition in which no movement was required. The present research was based

54

on the assumption that requiring execution of the task might increase the potential for M1

because the motor system would be engaged to a higher degree. This assumption might have

been faulty and M1 might not be involved in R-E associations, even when a response is needed.

In addition, it is possible that 200 trials of acquisition, or only a single day of acquisition, were

not sufficient to result in significant changes in the coding of M1 (e.g., Ungerleider, Doyon, &

Karni, 1996; Karni et al. 1998). The bias observed in response selection suggests that some

neural plasticity occurred somewhere in the system, but it is possible that M1 was not affected.

Thus, there might not have been a change in MEP amplitude following the presentation of the

different tones because M1 may not be involved in the coding of R-E associations.

4.2.2 Considerations Related to Stimulated Hemisphere.

Another possible reason for the absence of an influence of effect tone on MEPs may have

to do with the stimulated hemisphere/hand system. As discussed earlier in the document (see

Section 1.5), a conscious decision was made to stimulate the right-hemisphere/left-hand system

to potentially isolate the effects of the tone to a single hemisphere and not confound the influence

the left hemisphere plays in planning movements of both limbs. It has been repeatedly reported

that cortical structures in the left hemisphere play a role in movement planning, regardless of

which limb is involved (e.g., Frey, 2008; Haaland, et al., 2000; Kim et al., 1993; Janssen et al.,

2011; Johnson-Frey et al., 2004). Thus, regardless of whether participants chose to respond with

the left or right limb, it was possible that the left hemisphere would be involved in the planning

of that action. Because of the role of the left hemisphere in action planning for both limbs, the

present study adopted a more conservative approach, and chose to stimulate right M1. It was

55

believed that if the left hemisphere was stimulated, MEP results may have changed similarly for

both right and left hand responses because it is involved in planning response for both hands. It

may have been the case that this approach was too conservative. That is, it is possible that the

right hemisphere played too little a role in action planning, and this limited role may have been

responsible for the absence of critical differences in MEP values. In other words, because the

role of the right hemisphere in movement planning is more limited than the left hemisphere (e.g.,

Johnson-Frey et al., 2008), its level of activation may not have been sufficient to bring about

observable differences in the MEP data. It is difficult to know if this was a contributing factor to

the lack of modulation in MEP amplitude, but future work should repeat this study with

stimulation of the left hemisphere.

4.2.3 Task-Related Considerations

It is also possible that the lack of neurophysiological support for the R-E relationship

stems from task design limitations, which may result in participants pre-planning their responses

to a certain extent even prior to the onset of the tone. Recall that test trials in Experiment 2

involved a fixation cross, followed by a delay, followed by a 600 ms pre-cue tone, and a 300 ms

time span including a TMS pulse and a delay (delay time was variable depending upon

StimTime). It may have been the case that due to the timing of task events/stimuli, participants

had already planned which of the two movements they would execute on the upcoming trial by

the time that the TMS pulse was delivered. The pre-planning of the responses may be reflected

in the main effect for Active Hand observed for MEP amplitudes.

56

Additionally, participants may not have completed the task with the sense of urgency

which was expected, but rather they may have selected an action and retrieved the motor plan

subsequent to the delivery of the TMS pulse. The latter suggestion is consistent with the lack of

an RT bias for compatible trials in the post-test condition; it appeared as though participants

were executing their responses more freely in time, without a sense of urgency. If this were the

case, neurophysiological differences would also not have been expected. The task design was

based upon the assumption that participants would be responding to stimuli as quickly and as

accurately as possible. If they executed their responses more freely in time, the timing of TMS

pulses may not have been a true indicator of excitability during the cognitive retrieval of motor

plans bound to the presented effect tone.

Lastly, there may be issues with the timing of stimulation and the length of the effect

tones. In Experiment 2, the earliest StimTime condition was immediate upon offset of the effect

tone. Thus, all three StimTime conditions stimulated M1 after the offset of the effect tone (600

ms after the onset of the tone). A necessary adaptation to the task would be to stimulate M1

simultaneous to or during the effect tone to get a better sense of the timecourse of M1 activation

following the tone. This may provide further insight regarding the corticospinal excitability of

the motor system while the effect tone is perceptually available.

57

5 Conclusions

The purpose of the research reported in this thesis was to test if the perception of a

learned after-effect automatically resulted in the retrieval of its associated motor code. This

purpose was investigated over two experiments. In Experiment 1, participants completed an

acquisition phase where they learned R-E mapping between two keypresses and their response-

contingent auditory effect tones. In a free-choice testing phase after acquisition, where one of

two effect tones was presented prior to the imperative stimulus, participants demonstrated a

preference to interact with the button that was compatible with the effect tone that had played

following the response during the learned R-E mappings in the acquisition phase. In Experiment

2, participants completed a similar acquisition task, but the pre- and post-test tasks were adapted

to include a single TMS pulse at one of three timepoints on each trial. The amplitude of the

MEPs evoked via TMS was used as an index of corticospinal excitability following the

perception of a learned after-effect. Behaviourally, the results from Experiment 2 were consistent

with Experiment 1; participants preferred to interact with the button that was compatible with the

pre-cue effect tones after an acquisition phase. The neurophysiological results did not reflect this

response bias. The critical interaction did not reach significance, and differences which did

emerge were likely due to underlying biases caused by differential activation when the

stimulated versus non-stimulated hand responded.

Three potential (not mutually exclusive) explanations may account for the absence of

MEP differences in Experiment 2. First, the experimental design involved stimulating the right

hemisphere. This conservative approach was taken because the left hemisphere is thought to be

involved in response planning for both limbs, and it was thought that stimulating the right

58

hemisphere would avoid potential confounds associated with this role of the left hemisphere.

This approach, however, may have been too conservative, as the right hemisphere may not play a

substantial enough role in response planning to see differences in excitability. Second, TMS was

applied to M1. Growing evidence suggests that R-E associations may take place outside of the

motor system – or at least upstream from M1. If this were the case, stimulating M1 would not

necessarily reflect increased excitability as reflected in MEP data. Lastly, trial sequences need to

be adapted. Specifically, the timecourse of test trials may have enabled participants to pre-plan

their responses, which would result in less excitability at the stimulation times, and TMS

stimulation must be adapted so that participants are stimulated simultaneous to the playback of

the auditory effect tone. Overall, the present research suggests that presentation of the effect

tones influence the selection of the responses the participants executed, but the effect tones did

not significantly influence corticospinal excitability. Further research is necessary to determine

whether this is due to the stimulated hemisphere, non-stimulated cortical structures playing a role

in R-E associations, or timing of task events.

59

References

Bangert, M., Peschel, T., Schlaug, G., Rotte, M., Drescher, D., Hinrichs, H., et al. (2006). Shared networks for auditory and motor processing in professional pianists: Evidence from fMRI conjunction. NeuroImage, 30, 917-926.

Bangert, M., & Altenmuller, E.O. (2003). Mapping perception to action in piano practice: a longitudinal DC-EEG study. BMC Neuroscience, 4, 1-14.

Barker, A.T., Jalinous, R. & Freeston, I.L. (1985). Non-invasive magnetic stimulation of the

human motor cortex. Lancet, 2, 1107.

Calvo-Merino, B., Glaser, D.E., Grezes, J., Passingham, R.E., & Haggard, P. (2005). Action observation and acquired motor skills: an fMRI study with expert dancers. Cerebral Cortex, 15, 1243-49.

Conforto, A. B., Graggen, W. J. Z., Kohl, A. S., Rosler, K. M., & Kaelin-Lang, A. (2004). Impact of coil position and electrophysiological monitoring on determination of motor thresholds to transcranial magnetic stimulation. Clinical Neurophysiology, 115, 812-19.

di Pellegrino, G., Fadiga, L., Fogassi, L., Gallese, V., & Rizzolatti, G. (1992). Understanding motor events: a neurophysiological study. Experimental Brain Research, 91, 176-80.

Drost, U.C., Rieger, M., Brass, M., Gunter, T.C., & Prinz, W. (2005). Action-effect coupling in

pianists. Psychological Research, 69, 223-41.

Elliott, D., & Roy, E.A. (1996). Manual Asymmetries in Motor Performance. USA: CRC-Press.

Elsner, B., & Hommel, B. (2001). Effect anticipation and action control. Journal of Experimental Psychology: Human Perception and Performance, 27, 229-40.

Elsner, B., Hommel, B., Metschel, C., Drzezga, A., Prinz, W., Conrad, B. et al. (2002). Linking

actions and their perceivable consequences in the human brain. Neuroimage, 17, 364-72.

Fadiga, L., Fogassi, L., Pavesi, G., & Rizzolatti, G. (1995). Motor facilitation during action observation: A magnetic stimulation study. Journal of Neurophysiology, 6, 2608-11.

60

Frey, S.H. (2008). Tool use, communicative gesture and cerebral asymmetries in the modern human brain. Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 1951-57.

Greenwald, A. G. (1970a). A choice reaction time test of ideomotor theory. Journal of Experimental Psychology, 6, 20-25.

Greenwald, A.G. (1970b). Sensory feedback mechanisms in performance control: with special

reference to the ideo-motor mechanism. Psychological Review, 77, 73-99.

Haaland, K.Y., Harrington, D.L., & Knight, R.T. (2000). Neural representations of skilled movement. Brain, 123, 2306-13.

Harleß , E. (1861). Der Apparat des Willens [The apparatus of will]. Zeitschrift fuer Philosophie

und philosophische Kritik, 38, 50-73.

Hoffmann, J., Sebald, A. & Stocker, C. (2001). Irrelevant response effects improve serial learning in SRT tasks. Journal of Experimental Psychology: Learning, Memory & Cognition, 27, 470-82.

Hommel, B. (1996). The cognitive representation of action: automatic integration of perceived action effects. Psychological Research, 59, 176-86.

Hommel, B. (2009). Action control according to TEC (theory of event coding). Psychological

Research, 73, 512-26.

James, W. (1890). The principles of psychology (Vol. 2). New York: Dover Publications.

Janssen, L., Meulenbroek, R.G.J., & Steenbergen, B. (2011). Behavioural evidence for left-hemisphere specialization of motor planning. Experimental Brain Research, 209, 65-72.

Johnson-Frey, S.H., Newman-Norlund, R., & Grafton, S.T. (2004). A distributed left hemisphere

network active during planning of everyday tool use skills. Cerebral Cortex, 15, 681-95.

Karni, A., Meyer, G., Rey-Hipolito, C., Jezzard, P., Adams, M.M., Turner, R. et al. (1998). The acquisition of skilled motor performance: Fast and slow experience-driven changes in primary motor cortex. Proceedings of the National Academy of Sciences of the United States of America, 95, 861-68.

61

Kim, S.G., Ashe, J., Hendrich, K., Ellerman, J.M, Merkle, H., Urgurbil, K., & Georgopoulos,

A.P. (1993). Functional magnetic-resonance-imaging of motor cortex-hemispheric-asymmetry and handedness. Science, 261, 615-17.

Kunde, W., Hoffmann, J., & Zellmann, P. (2002). The impact of anticipated action effects on

action planning. Acta Psychologica, 109, 137-55.

Lahav, A., Saltzman, E., & Schlaug, G. (2007). Action representation of sound: audiomotor recognition network while listening to newly acquired actions. The Journal of Neuroscience, 27 308-14.

Lotze, R.H. (1852). Medicinische Psychologie oder die Physiologie der Seele [Medical psychology or physiology of the soul]. Leipzig, Germany: Weidmann’sche Buchhandlung.

Lotze, R.H., Scheler, G., Tan, H.R., Braun, C., & Birbaumer, N. (2003). The musician’s brain: functional imaging of amateurs and professionals during performance and imagery. NeuroImage, 20, 1817-29.

Maeda, F., & Pascual-Leone, A. (2003). Transcranial magnetic stimulation: Studying motor neurophysiology of psychiatric disorders. Psychopharmacology, 168, 359-76.

Meister, I.G., Krings, T., Foltys. H., Boroojerdi, B., Muller, M., Topper, R., & Thron, A. (2004).

Playing piano in the mind: An fMRI study on music imagery and performance in pianists. Cognitive Brain Research, 19, 219-28.

Melcher, T., Weidema, M., Eenshuistra, R.M., Hommel, B., & Gruber, O. (2008). The neural

substrate of the ideomotor principle: An event-related fMRI analysis. Neuroimage, 39, 1274-88.

Mills, K.R., & Nithi, K.A. (1997). Corticmotor threshold to magnetic stimulation: Normal values

and repeatability. Muscle & Nerve, 20, 570-576.

Mutschler, I., Schulze-Bonhage, A., Glauche, V., Demandt, E., Speck, O, et al (2007). A rapid sound-action association effect in human insular cortex. PLoS One, 2, e259.

62

Prinz, W. (1997). Perception and action planning. European Journal of Cognitive Psychology, 9, 129-54.

Rossini, P. M., Barker, A. T., Berardelli, A., Caramia, M.D., Caruso, G., Cracco, R. Q., et al.

(1994). Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: Basic principles and procedures for routine clinical application. report of an IFCN committee. Electroencephalography and Clinical Neurophysiology, 91, 79-92.

Rothwell, J.C. (1997). Techniques and mechanisms of action of transcranial stimulation of the human motor cortex. Journal of Neuroscience Methods, 74, 113-22.

Sainburg, R.L. (2010) Lateralization of Goal-directed Movement. In Elliott, D & Kahn, M.A.,

(Eds.), Vision and Goal-Directed Movement: Neurobehavioural Perspectives, (219-237). USA: Human Kinetics.

Schmidt, R.A., & Lee, T.D. (2011). Motor Control and Learning. A Behavioural Emphasis (5th

Edition). USA: Human Kinetics.

Stock, A. & Hoffmann, J. (2002). Intentional fixation of behavioural learning or how R-E learning blocks S-R learning. European Journal of Cognitive Psychology, 14, 127-53.

Tai, Y.F., Scherfler, C., Brooks, D.J., Sawamoto, N., & Castiello, U. (2004). The human

premotor cortex is ‘mirror’ only for biological actions. Current Biology, 14, 117-20.

Taylor, H.G., & Heilman, K.M. (1980). Left hemisphere motor dominance in right-handers. Cortex, 16, 587-603.

Ungerleider, L.G., Doyon, J., & Karni, A. (2002). Imaging brain plasticity during motor skill

learning. Neurobiology of Learning and Memory, 78, 553-64.

Wassermann, E. M. (1998). Risk and safety of repetitive transcranial magnetic stimulation: Report and suggested guidelines from the international workshop on the safety of repetitive transcranial magnetic stimulation. Electroencephalography and Clinical Neurophysiology, 108, 1-16.

.

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Appendices

APPENDIX A: MEDICAL HISTORY QUESTIONNAIRE

FOR VOLUNTEERS PARTICIPATING IN STUDIES INVOLVING TRANSCRANIAL MAGNETIC STIMULATION

SURNAME:............................ GIVEN NAMES:.................................

DATE OF BIRTH:............................ SEX:......................

ADDRESS:..............................................................................................

HOME PHONE:....................... WORK PHONE:.......................

1. When was the last time you had a physical examination?

2. If you are allergic to any medications, foods or other substances, please name them.

3. If you have been told that you have any chronic or serious illnesses, please name them.

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4. Have you been hospitalized in the past three years? Please give details.

5. During the past twelve months:

Has a physician prescribed any form of medication for you? Y/N

Have you experienced any faintness, light-headedness, blackouts? Y/N

Have you occasionally had trouble sleeping? Y/N

Have you had any severe headaches? Y/N

Have you experienced unusual heartbeats such as skipped beats or

palpitations? Y/N

Have you experienced periods in which your heartbeat felt as though it

were racing for no apparent reason? Y/N

6. At present:

Do you experience shortness of breath or loss of breath while

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walking? Y/N

Do you experience sudden tingling numbness or loss of feeling in

your arms, hands, legs, feet or face? Y/N

Do you get pains or cramps in your legs? Y/N

Do you experience pain or discomfort in your chest? Y/N

Do you experience any pressure of heaviness in your chest? Y/N

Do you have diabetes? Y/N

If yes, how is it controlled (please circle one)? dietary means... insulin injector...

oral medication... uncontrolled...

7. Have you ever been told that your blood pressure was abnormal? Y/N

8. How often would you characterize your stress level as being high (please circle one)?

never…occasionally... frequently... constantly...

9. Have you ever undergone electro-convulsive-therapy (ECT)? Y/N

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10. If you are female, are you or is there a chance you might be pregnant? Y/N

11. Have you ever experienced seizures or fainting spells? Y/N

12. Have you ever been told that you have any of the following illnesses?

(please circle all that apply)

myocardial infarction... arteriosclerosis... heart disease... heart block...

coronary thrombosis... rheumatic heart... heart attack... aneurism...

coronary occlusion... angina... heart failure... heart murmur...

13. Has any member of your immediate family been treated for or suspected

of having any of the following conditions? Please identify their relationship

to you (e.g., father, mother, etc.)

(a) Epilepsy-

(b) Stroke-

(c) Diabetes-

(d) Heart disease-

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(e) High blood pressure-

(f) Memory loss-

(g) Dementia-

14. Please list all operations or surgical procedures of any kind performed

in the last 15 years.

1.

2.

3.

4.

5.

6.

15. Have you ever been injured by any metallic foreign body

(e.g., nail, bullet, shrapnel, etc.)? Y/N

16. Have you ever engaged in metal grinding? Y/N

If yes, could metal fragments be present near your eyes? Y/N

17. Is there any history of head trauma with loss of consciousness? Y/N

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18. Please indicate if you have any of the following:

Cardiac pacemaker Y/N

Aneurysm clips Y/N

Implanted cardiac defibrillator Y/N

Any type of biostimulator Y/N

Any type of internal electrodes (e.g., cochlear implant) Y/N

Insulin pump Y/N

Any type of electronic, mechanical or magnetic implant Y/N

Hearing aid Y/N

Any type of intravascular coil filter or stent (e.g., IVC filter) Y/N

Artificial heart valve prosthesis Y/N

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Orbital/eye prosthesis Y/N

Any type of surgical clip or staple Y/N

Intraventricular shunt Y/N

Artificial limb or joint Y/N

Dentures Y/N

Any implanted orthopaedic item (eg. pins, rods, screws, nails,

clips, plates, wire) Y/N

Any other implanted item Y/N

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I certify that the above information is correct to the best of my knowledge. I have read and understand the entire contents of this form and I have had the opportunity to ask questions regarding the information on this form.

Volunteer's name

______________________________________

Volunteer's signature

______________________________________ Date: _____________________

Witness's name

______________________________________

Witness's signature

______________________________________ Date: _____________________

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Appendix B: Participant and Testing Session Information

Date: __________________ Participant Number:____________ Age: ____________

Gender (circle one): Female / Male

Vision (circle one): Normal / Corrected-to-normal

Do you wear corrective lenses of any kind?

If so, please wear them when you participate in the study.

Assessment of Mental Health

1) With a “yes” or “no” answer, please tell me if you have any pre-existing mental illnesses or disorders of the central nervous system. If your answer is “yes”, please do not provide any details.

2) With a “yes” or “no” answer, please tell me if you have had a concussion or other closed-head injury within the last 3 months. If your answer is “yes”, please do not provide any details.

Note: Potential participants must answer “no” to both of these questions in order to be allowed to

enter into the study. When they arrive for the testing session, they will also be required to

complete a more thorough Health Questionnaire.

Determination of Hand Dominance

Please answer these questions using the following scale:

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Always the left hand - Mostly the left hand - Either hand – Mostly the right hand – Always the

right hand

1) Which hand do you use to write with when you are writing with a pen? 2) Which hand do you use to throw a ball? 3) Which hand do you use to eat soup with a spoon? 4) Which hand do you use to brush your teeth? 5) Which hand do you use to hold a hammer when hammering a nail into a wall?

Stimulation and Recording Details

EMG Channel 1: Muscle_____________ Estimate MEP P2P amplitude at rMT: ______µV

EMG Channel 2: Muscle_____________ Estimate MEP P2P amplitude at rMT: ______µV

Resting motor threshold: ___________% Number of MEPs >100 µV P2P: _________/10

Testing level (115% rMT): _________%

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APPENDIX C: EXPERIMENT 1 MEAN VALUES

Mean RT values (in ms) ± standard deviation

Mean RESP values (%) ± standard deviation

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APPENDIX D EXPERIMENT 2 MEAN VALUES

Mean RT values (in ms) ± standard deviation

Mean RESP values (%) ± standard deviation

Mean normalized MEP values ± standard deviation