Assessing the Nature of Knowledge that is acquired in an Artificial Grammar Learning Paradigm Open Access

Murugan, Rohini (Summer 2023)

Permanent URL: https://etd.library.emory.edu/concern/etds/c247dt38v?locale=en%255D
Published

Abstract

Languages follow grammatical rules, which determine the order of words in sentences. One of the ways in which infants learn language is by learning these rules implicitly from the speech that they are exposed to. This process is called implicit learning and has been studied using artificial grammar learning paradigms. Typically, in these experiments, learning is assumed to be implicit. However, much of our formal education involves a different process called explicit learning. Though implicit and explicit learning has been studied in humans, the measures used to identify these mechanisms have certain limitations. Further, very little is known about how nonhuman animals learn similar rules in an AGL task and these measures are difficult to be adapted to nonhuman animal tasks. Thus, we combined an AGL task with a metacognitive measure that has been previously used in nonhuman animals to assess the nature of knowledge in humans. Participants were initially exposed to sequences of visual symbols generated by an artificial grammar, followed by a testing phase in which they classified sequences as either grammatical or ungrammatical. On a subset of trials (choice trials), participants were given the option to either take or skip this classification. If participants had explicit knowledge of the grammar, they should be more likely to take trials when they are confident and skip when they are not. Participants learned the grammar and reported explicit knowledge of the grammar. However, they performed similarly in forced and choice trials, suggesting that this metacognitive measure does not measure the explicitness of their knowledge of this grammar.

Table of Contents

Introduction. 1

Implicit and Explicit Learning. 2

Artificial Grammar Learning Paradigms. 3

Limitations of methods used in Artificial Grammar Learning Paradigms. 4

Metacognition as a measure to assess the explicitness of knowledge. 5

Experiment 1. 7

Participants. 7

Stimuli 8

Artificial Grammar 8

Stimulus Sequences. 9

The Experimental Setup. 11

Results. 20

Participants learnt the within-chunk transitions of the grammar in the AGL task: 20

Participants showed evidence of explicit knowledge in verbal reports: 21

Participants did not show evidence of explicit knowledge in accuracy difference between forced and choice conditions: 23

Participants did not show evidence of explicit knowledge in the Sequence Completion task: 24

Discussion. 26

Experiment 2. 31

Participants. 31

Changes in Experiment 2. 31

Results. 36

Participants learnt the grammar: 36

Participants showed evidence of explicit structural knowledge of within-chunk transitions: 37

Participants showed evidence of explicit judgement knowledge in performance-confidence correlation: 38

Validation of the forced/choice paradigm: 40

Participants did not show evidence of explicit judgement knowledge through the performance difference between forced and choice trials: 41

Discussion. 46

References. 49

Appendix. 56

Figures

Figure 1: 19

Figure 2: 29

Figure 3: 30

Figure 4. 35

Figure 5: 44

Figure 6: 45

Tables

Table 1. 18

Table 2. 34

About this Master's Thesis

Rights statement
  • Permission granted by the author to include this thesis or dissertation in this repository. All rights reserved by the author. Please contact the author for information regarding the reproduction and use of this thesis or dissertation.
School
Department
Subfield / Discipline
Degree
Submission
Language
  • English
Research Field
Keyword
Committee Chair / Thesis Advisor
Committee Members
Last modified

Primary PDF

Supplemental Files