LEARNING
WITH AND WITHOUT CONSCIOUSNESS
Empirical and computational explorations
Antoine Pasquali
Dissertation présentée en vue de l’obtention du grade de Docteur en Sciences Psychologiques, préparée sous la
direction de Monsieur Axel Cleeremans, Directeur de recherches au F.R.S.-FNRS.
June 2009
LEARNING WITH AND WITHOUT CONSCIOUSNESS Empirical and computational explorations
Written by Antoine Pasquali Cover design by Antoine Pasquali Copyright 2009 by Antoine Pasquali
All rights reserved. No part of this dissertation may be reproduced in any form without prior written permission of the author.
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This dissertation is not for sale.
Antoine Pasquali
Consciousness, Cognition & Computation Group (CO3) Faculty of Psychology and Educational Sciences
Université Libre de Bruxelles (ULB) Brussels, Belgium
ABSTRACT
Is it possible to learn without awareness? If so, what can learn without awareness, and what are the different mechanisms that differentiate between learning with and without consciousness? How can best measure awareness?
Here are a few of the many questions that I have attempted to investigate during the past few years. The main goal of this thesis was to explore the differences between conscious and unconscious learning.
Thus, I will expose the behavioral and computational explorations that we conducted during the last few years. To present them properly, I first review the main concepts that, for almost a century now, researchers in the fields of neuroscience have formulated in order to tackle the issues of both learning and consciousness. Then I detail different hypotheses that guided our empirical and computational explorations. Notably, a few series of experiments allowed identification of several mechanisms that participate in either unconscious or conscious learning. In addition we explored a computational framework for explaining how one could learn unconsciously and nonetheless gain subjective access to one’s mental events. After reviewing the unfolding of our investigation, I detail the mechanisms that we identified as responsible for differences between learning with and without consciousness, and propose new hypotheses to be evaluated as potential investigation roadmap for the future.
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ACKNOWLEDGEMENTS
Although there is only one name written on the cover of this dissertation, it is nothing but the result of a team work. In fact, I should certainly write an entire thesis in order to thank everyone properly, and I am just afraid that these small paragraphs will not reflect the importance of everyone in the accomplishment of this work.
My most sincere gratitude goes first to my family, my friends and most particularly the woman I love, for their continuous support, help and patience over the last few years. Especially I thank them for having both shared my passion and kindly reminded me where Earth was when I was lost somewhere in the interstellar space (I guess on my own planet). I also express all my thankfulness to all those who, coming either from Brussels (of course!), Paris (and its surroundings), Oxford, Limoges, Nîmes, Lyon, Marseille, Genova, Krakow or even Hong- Kong, found the time to visit us in Brussels. We were glad to share our experience with you and transmit a bit of what this beautiful city has to offer. Apparently at least two of you were fully convinced for they decided to live here for a while.
I wish to thank the members of the jury, Alain Content, Axel Cleeremans, Robert M. French, Thierry Meulemans and Arnaud Destrebecqz for allowing me to borrow a bit of their precious time for the difficult task of evaluating my work. They all became my mentors since I arrived for the first time in Brussels in order to follow this course in Cognitive Sciences. I particularly thank you for having opened my
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eyes on such passionate issues, guided me at my very first steps, and I am hopeful that this thesis will at least reflect a small part of the great quality of your teaching.
My thanks go out to the many who participated the investigation reported in this thesis. Among them, I may in particular refer to Willem Verwey, Peter F. Dominey, Alain Content, Régine Kolinsky, Katsuyuki Sakai, David Rosenthal, Jay L. McClelland, Tânia Fernandes, Navindra Persaud for having provided substantial insights in regards to the theoretical investigation, and especially to Axel Cleeremans, Luis Jiménez, Willem Verwey, Bert Timmermans, Stéphanie Schambaron, Domininque Ginhac, Arnaud Destrebecqz, Anne Atas, and Marek Zajac for having supported the day-to-day investigation. I also particularly thank Axel Cleeremans, Bert Timmermans, Stéphanie Schambaron and Luis Jiménez who actively participated in the writing and the correction of the thesis.
I also address a special thought to the (either past or present) members of the CO3 lab, of the LiraLab, and of the CSAIL, for their kind welcome, for our passionate discussions, and for the many unforgettable moments we shared.
If I had written such a dissertation to thank everyone, the larger section would certainly be dedicated to my Research Director, Axel Cleeremans. I just do not know how to express my gratitude for all the support, trust, wisdom, patience, humor, heartening, and inspiration that you have displayed. You surely know a lot about man’s true nature, for you helped me to better understand who I was, to discover both my
9 weaknesses and the path to follow in order to learn to not let them overcome me. Perhaps the silliest thing is that I am not even conscious of all the knowledge that I acquired thanks to you.
Finally, the accomplishment of this work would never have been possible without the support of the National Fund for Scientific Research (NFSR), Brussels, Belgium, and of the European Science Foundation (ESF), Strasbourg, France, FRFC / ESF Grant #2.4577.06 “Mechanisms of serial action”.
Antoine Pasquali
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TABLE OF CONTENTS
Abstract ...5
Acknowledgements...7
Table of figures...15
Introduction ...23
Objectives...23
Plan ...24
Part I: Context ...29
Introduction...31
Critical issues ...31
Methodology ...35
Learning without consciousness ...39
Principles...39
Incidental learning of spatiotemporal contingencies...43
Incidental learning of frequency-of-occurrence contingencies...49
Discussion ...55
Learning with consciousness ...57
Principles...57
Intentional learning of spatiotemporal contingencies...61
Intentional learning of frequency-of-occurrence contingencies....64
Discussion ...68
Measures of consciousness...70
Principles...70
Signal detection theories...72
Process dissociation procedures ...77
Discussion ...81
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Computational interpretations...84
Principles...84
Learning of spatial, temporal and frequency-of-occurrence contingencies...86
Computational architectures...93
Discussion ...100
Hypotheses...102
Introduction ...102
Speculations ...105
Investigations...109
Part II: Empirical and computational explorations...113
Learning without consciousness ...115
Introduction ...115
Anticipation in sequence learning: A computational account of automatic processes ...116
Impact of temporal variability on sequence learning ...142
Advancement...179
Learning with consciousness ...180
Introduction ...180
Chunking in serial action: Objective measures of automatic and controlled processes...181
Cross-domain interaction between linguistic and sensory-motor levels in implicit learning of serial actions ...248
Advancement...311
Learning to be conscious ...313
Introduction ...313
Consciousness and metarepresentation: A computational sketch ...314
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Metacognitive networks and measures of consciousness ...342
Advancement...372
General discussion ...375
Introduction...377
Advancement...377
Mechanisms underpinning differences between learning with and without consciousness ...385
Future investigations...388
References ...391
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TABLE OF FIGURES
(NB: There is no distinction between figures and tables in the dissertation)
Figure I.1. Is Pavlov’s dog learning consciously?...32
Figure I.1.1. The serial reaction time task...44
Figure I.1.2. Measures of RTs along training obtained in SRT task ...45
Figure I.1.3. Artificial Grammar used by Reber in 1967...52
Figure I.2.1. The two grammars used by Jiménez et al. (1996)...65
Figure I.2.2. Questionnaire used by Maia et al. (2004) ...67
Figure I.3.1. Signal detection by Goldiamond (1958) ...73
Figure I.3.2. Signal detection by McMillan (1986)...74
Figure I.3.3. Metacognition by Lau (2007)...76
Figure I.4.1. Processing architecture by Cooper & Shallice (2000)...87
Figure I.4.2. Spatiotemporal learning by Jordan (1986)...90
Figure I.4.3. Recurrent Kohonen map by Voegtlin (2002)...93
Figure I.4.4. SOAR by Newell (1990)...94
Figure I.4.5. Forward Model by Jordan (1992)...96
Figure I.4.6. ARN architecture by Dominey & Ramus (2000)...97
Figure I.4.6. CLARION architecture by Sun (2002)...98
Figure I.4.7. Hybrid architecture by Riga, Cangelosi, & Greco (2004) .99 Figure II.1.1.1. Effect of pace in SRT task by Destrebecqz & Cleeremans (2003)...121
Figure II.1.1.2. Effect of pace in generation task by Destrebecqz & Cleeremans (2003)...122
Figure II.1.1.3. AA/SRN by Destrebecqz & Cleeremans (2003)...124
Figure II.1.1.4. AA/SRN simulation by Destrebecqz & Cleeremans (2003)...127
Figure II.1.1.5. Auto-SRN ...130
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Figure II.1.1.6. Auto-SRN results in SRT task ...133
Figure II.1.1.7. Auto-SRN results in post-test generation tasks...134
Figure II.2.1. Evolution of Reaction Times across Training Blocks....156
Figure II.2.2. Mean reaction Times on Transfer Block ...158
Figure II.2.3. Main recognition scores for old and new six-element fragments...160
Figure II.2.4. Reaction Times in the recognition task ...164
Figure II.3.1.1. Reaction times per block in Experiment 1...200
Figure II.3.1.2. Variance Learning per block in Experiment 1 ...201
Figure II.3.1.3. Generation task in Experiment 1...203
Figure II.3.2.1. Reaction times per block and per element in Experiment 2 ...207
Figure II.3.2.2. Variance Surface per block and per element in Experiment 2 ...208
Figure II.3.2.3. Variance Abstract per block and per element in Experiment 2 ...209
Figure II.3.2.4. Generation task in Experiment 2 ...212
Figure II.3.3.1. Reaction times per block and per chunk keys in Experiment 3 ...216
Figure II.3.3.2. Variance Automatic per block and per chunk keys in Experiment 3 ...218
Figure II.3.3.3. Variance Control per block and per chunk keys in Experiment 3 ...219
Figure II.3.3.4. Generation task in Experiment 3...220
Figure II.3.4.1. Reaction times per block and per chunk keys in Experiment 4 ...224
Figure II.3.4.2. Variance Automatic per block and per chunk keys in Experiment 4 ...228
17 Figure II.3.4.3. Variance Control per block and per chunk keys in
Experiment 4 ...229
Figure II.3.4.4. Generation task in Experiment 4...231
Figure II.4.1.1. Reaction times in Experiment 1 ...263
Figure II.4.1.2. Variance Learning in Experiment 1 ...265
Figure II.4.2.1. Reaction Times in Experiment 2...271
Figure II.4.2.2. Variance Learning in Experiment 2 ...273
Figure II.4.2.3. Completion scores in Experiment 2 ...274
Figure II.4.2.4. Recognition scores in Experiment 2...276
Figure II.4.3.1. Reaction Times per Block in Experiment 3...285
Figure II.4.3.2. Reaction Times per Element in Experiment 3 ...287
Figure II.4.3.3. Variance Surface in Experiment 3...291
Figure II.4.3.4. Variance Abstract in Experiment 3 ...293
Figure II.4.3.5. Word Types Discrimination task in Experiment 3...296
Figure II.4.3.6. Word Types Abstraction task in Experiment 3...297
Figure II.5.1. Architecture of the first network...325
Figure II.5.2. Error proportion for the first-order network ...326
Figure II.5.3. Architecture of the second network...329
Figure II.5.4. Error proportion for the first-order network and for both higher-order networks...330
Figure II.5.5. Performance of the first-order network and of the higher- order networks...332
Figure II.6.1. Network architecture for Blindsight and AGLT simulations ...349
Figure II.6.2. Network architecture for the IGT simulation...351
Figure II.6.3. Results of the Blindsight simulation ...353
Figure II.6.4. Results of the AGLT simulation ...354
Figure II.6.5. Results of the IGT simulation...356
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Figure II.6.6. Additional results of the Blindsight simulation ...364
