Richard John Tunney

ARTIFICIAL GRAMMAR LEARNING AND THE TRANSFER OF SEQUENTIAL DEPENDENCIES Richard John Tunney A thesis submitted for the degree of Doctor of Philosophy University of York Department of Psychology July 1999 By degrees I made a discovery of still greater moment. I found that these people possessed a method of communicating their experience and feelings to one another by articulate sounds. I perceived that the words they spoke sometimes produced pleasure or pain, smiles or sadness, in the minds and countenances of the hearers. This was indeed a godlike science, and I ardently desired to become acquainted with it... had first; but, by degrees, ... reading puzzled me extremely at I discovered that he uttered many of the same sounds when he read as when he talked. I conjectured, therefore, that he found on the paper signs for speech which he understood, and I ardently longed to comprehend these also; but how was that possible, when I did not even understand the sounds for which they stood as signs? p 108/110 Mary Shelley (1818,1992) for Rachel 11 ABSTRACT Exposure to sequences of elements constrained by an artificial grammar enables observers to classify new sequences as being either well- or ill-formed according to that grammar. Moreover, participants are also able to transfer their knowledge of the grammar to sequences composed of novel vocabulary elements. Two principle theories have been advanced to account for these effects. The first argues that participants learn grammatical rules that are abstract in the sense of being independent of vocabulary; even in a new vocabulary sequences can be classified on the basis of rule-adherence (e. g. A. S. Reber, 1989). The second argues that participants memorise the exemplars and subsequently classify new sequences on the basis of how similar they are to those exemplars (e. g. L. R. Brooks & J. R. Vokey, 1991). ' To assess the relative contributions of these two modes of representation this Thesis re-defines these theories in terms of the information that can be used to classify sequences in a novel vocabulary. The rule-based account of transfer is predicated on the abstraction of sequential dependencies between both repeating and non-repeating elements. These can be applied in a novel vocabulary by inducing the correspondences between vocabularies across sequences. The exemplar-based account of transfer is predicated on memory for dependencies between repeating elements alone. The similarity of new sequences can be determined on the basis of this form of dependency on a sequence- by-sequence basis. A review of the empirical literature suggests that both kinds of information can be transferred to a novel vocabulary. Seventeen experiments confirm this conclusion, but demonstrate that participants are not equally sensitive to the different forms of sequential dependence. Finally, the contributions of these two modes of classification can be dissociated by altering the frequency distribution of the training exemplars. These findings inform both theories and computational models of artificial grammar learning and general cognitive processes. 111 LIST OF CONTENTS Abstract iii List of figures xi List of tables xii Acknowledgements xiv Authors declaration xv CHAPTER ONE ABSTRACT KNOWLEDGE AND IMPLICIT COGNITION 1 1.1 INTRODUCTION 1 1.2 THE REPRESENTATION OF KNOWLEDGE 7 1.2.1 On the nature of grammatical dependence 7 1.2.2 Abstract knowledge of artificial languages 12 1.2.3 Episodic memory for fragments of exemplars 18 1.2.4 Episodic memory for whole exemplars 29 1.2.5 Interim Summary 41 1.3 COMPUTATIONAL MODELS 43 1.3.1 Fragment based models 43 1.3.2 Exemplar models 51 1.3.3 Neural Networks 53 1.3.4 Conclusions 58 1.3 DISCUSSION 59 CHAPTER TWO REPLICATING THE BASIC EFFECT 64 2.1 INTRODUCTION & OVERVIEW 64 2.2 EXPERIMENT 1 A FAILURE TO REPLICATE THE TRANSFER OF GRAMMATICAL KNOWLEDGE TO A NOVEL VOCABULARY. 65 iv 2.2.1 Introduction 64 2.2.2 Method 66 2.2.3 Results 69 2.2.4 Discussion 71 2.3 EXPERIMENT 2 A REPLICATION OF ALTMANN ETAL. (1995, EXPERIMENT 4). 72 2.3.1 Introduction 72 2.3.2 Method 73 2.3.3 Results 75 2.3.4 Discussion 76 2.4 EXPERIMENT 3 A SECOND FAILURE TO REPLICATE THE TRANSFER OF 77 GRAMMATICAL KNOWLEDGE 2.4.1 Introduction 77 2.4.2 Method 77 2.4.3 Results 78 2.4.4 Discussion 79 2.5 EXPERIMENT 4 THE PROPORTION OF ILLEGAL BIGRAMS INFLUENCES CLASSIFICATION IN THE SOURCE BUT NOT THE NOVEL VOCABULARY 81 2.5.1 Introduction 81 2.5.2 Method 81 2.5.3 Results 82 2.5.4 Discussion 84 2.6 CHAPTER SUMMARY 84 CHAPTER THREE TRANSFERRING THE IDENTITY OF THE FIRST ELEMENT 89 3.1 INTRODUCTION 89 3.1.1 The transfer of two forms of sequential dependence 91 V 3.1.2 Overview of Experiments 95 3.2 EXPERIMENT 5 SENSITIVITY TO FIRST ELEMENT ILLEGALITY CARRIES THE TRANSFER EFFECT IN THE ABSENCE OF ILLEGAL REPETITION STRUCTURE. 97 3.2.1 Introduction 97 3.2.2 Method 98 3.2.3 Results 100 3.3.3 Discussion 103 3.3 EXPERIMENT 6 SENSITIVITY TO FIRST ELEMENT ILLEGALITY IS NOT PREDICATED ON SENSITIVITY TO MISPLACED REPEATING ELEMENTS 104 3.3.1 Introduction 104 3.3.2 Method 105 3.3.3 Results 106 3.3.4 Discussion 107 3.4 EXPERIMENT 7 MASKING THE FIRST ELEMENT 109 3.4.1 Introduction 109 3.4.2 Method 109 3.4.3 Results 110 3.4.4 Discussion 114 3.5 EXPERIMENT 8 NO EVIDENCE OF TRANSFER WHEN THE FIRST ELEMENT AND REPETITION STRUCTURES ARE HELD CONSTANT 115 3.5.1 Introduction 115 3.5.2 Method 116 3.5.3 Results 117 3.5.4 Discussion 118 3.6 EXPERIMENT 9 vi TRANSFERRING THE IDENTITY OF THE FIRST ELEMENT 120 3.6.1 Introduction 120 3.6.2 Method 121 3.6.3 Results 122 3.6.4 Discussion 123 3.7 CHAPTER SUMMARY 123 CHAPTER FOUR THE TRANSFER OF SECOND-ORDER SEQUENTIAL DEPENDENCE 129 4.1 INTRODUCTION 129 4.1.1 Can fragmentary knowledge account for transfer across vocabularies? 131 4.1.2 On sensitivity to first- and second order dependencies 133 4.1.3 Is fragmentary knowledge implicit? 135 4.1.4 Overview of experiments 136 4.2 EXPERIMENT 10 THE TRANSFER OF SECOND-ORDERSEQUENTIAL DEPENDENCE 138 4.2.1 Introduction 138 4.2.2 Method 139 4.2.3 Results 142 4.2.4 Discussion 146 4.3 EXPERIMENT 11 TRANSFER OF SECOND ORDER SEQUENTIAL DEPENDENCE WITH ONLY ONE POSSIBLE MAPPING BETWEEN VOCABULARIES 147 4.3.1 Introduction 147 4.3.2 Method 148 4.3.3 Results 150 4.3.4 Discussion 152 vii 4.4 EXPERIMENT 12 DIRECT AND INDIRECT TRANSFER OF SECOND-ORDER SEQUENTIAL DEPENDENCE 152 4.4.1 Introduction 152 4.4.2 Method 153 4.4.3 Results 154 4.4.4 Discussion 157 4.5 EXPERIMENT 13 AN INCIDENTAL ORIENTING TASK FACILITATES THE TRANSFER OF SECOND ORDER SEQUENTIAL DEPENDENCE 157 4.5.1 Introduction 157 4.4.2 Method 159 4.5.3 Results 159 4.5.4 Discussion 162 4.6 CHAPTER SUMMARY 162 CHAPTER FIVE A FUNCTIONAL DISSOCIATION BETWEEN THE CLASSIFICATION OF TWO FORMS OF SEQUENTIAL DEPENDENCE 168 5.1 INTRODUCTION 168 5.1.1 Similarity and rule-based processing are predicated upon different forms of sequential dependence. 169 5.1.2 How might knowledge of two forms of sequential dependence be dissociated? 170 5.1.3 Overview of experiments 171 5.2 EXPERIMENT 14 NOISE INHIBITS THE TRANSFER BUT NOT THE 173 ACQUISITION OF SEQUENTIAL DEPENDENCIES BETWEEN NON-REPEATING ELEMENTS 5.2.1 Introduction 173 5.2.2 Method 175 5.2.3 Results 178 viii 5.2.4 Discussion 183 5.3 EXPERIMENT 15 PARTICIPANTS DO NOT APPLY DEPENDENCIES BETWEEN NON-REPEATING ELEMENTS WHEN THE LANGUAGE ALSO CONTAINS REPEATING ELEMENTS. 183 5.3.1 Introduction 183 5.3.2 Method 183 5.3.3 Results 186 5.3.4 Discussion 188 5.4 EXPERIMENT 16 PARTICIPANTS DO NOT ENCODE DEPENDENCIES BETWEEN NON-REPEATING ELEMENTS WHEN THE LANGUAGE ALSO CONTAINS REPEATING ELEMENTS. 190 5.4.1 Introduction 190 5.4.2 Method 191 5.4.3 Results 193 5.4.4 Discussion 194 5.5 EXPERIMENT 17 THE ENCODING AND SUBSEQUENT CLASSIFICATION OF REPETITION STRUCTURES IS UNAFFECTED BY NOISE 196 5.5.1 Introduction 196 5.5.2 Method 196 5.5.3 Results 198 5.5.4 Discussion 201 5.5 CHAPTER SUMMARY 203 CHAPTER SIX GENERAL DISCUSSION 207 6.1 The representation of artificial languages: Summary of empirical work 209 6.2 Implications for theories of artificial grammar learning 216 ix 6.5 Concluding remarks 230 APPENDIX A LIST OF STIMULI 232 DIRECT TESTS USED IN CHAPTER 4 265 APPENDIX B DISTRIBUTIONS OBSERVED IN CHAPTER 4 267 REFERENCES 271 LIST OF FIGURES Figure 1.1: Finite-state grammar used by Reber (1967). 4 Figure 1.2: Finite-state grammar used by Altmann et al. (1995). 8 Figure 1.3: The vocabularies used by Altmann et al. (1995). 8 Figure 1.4: A modified Simple Recurrent Network (Dienes et al. 1995, 1999). 53 Figure 2.1: Finite-state grammar used in Experiments 1-4. 67 Figure 2.2: The correspondences between the grammar and the two vocabularies. 67 Figure 4.1: Stimuli used in Experiment 10. 140 Figure 4.2: The mapping between vocabulary elements used in Chapter 4. 141 Figure 4.3: Stimuli used in Experiment 11. 149 Figure 5.1: Stimuli used in Experiment 14. 176 Figure 5.2: The mapping between vocabulary elements used in Chapter 5. 177 Figure 5.3: Stimuli used in Experiment 15.

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