- Introduction
- Requirements
In our daily life, different forms of knowledge are involved.
- Rule-based knowledge i.e. When we try to identify whether an integer x is oddd or even, we use a specific rule: x%2==0? Even:Odd
- Association-based knowledge i.e. classical conditioning, operant conditioning
Based on the form of knowledge, two learning and decision making processes are proposed, rule-based learning and association-based learning. In the learning phase, rule-based learning stores abstract rules wherease association-based learning stores associations. When a new case comes, rule-based learning considers whether the new case satisfies a specific rule and then gives a corresponding result. Association-based learning checks whether the new case could trigger stored associations. A judgment depends on the number of triggered associations and the intensity of each association.
The current experiment investigates how a rule-based variable and an association-based variable influence human performance in a simplified experimental setting of learning, the artificial grammar learning paradigm.
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The artifical grammar learning paradigm is a simplified learning-test experimental setting. After exposure to strings derived from a formal grammar, human participants illustrated above-chance accuracy on grammatical judgments of new strings without knowledge in details of the grammar (Reber, 1967). Strings derived from a formal grammar exhibit not only rule-based patterns but also statistical/associative features. AGL research investigates what type of knowledge, rule-based or association-based or both, has been learned
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Prepare a formal grammar (AG): most researchers used finite state grammars
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- Participants will observe/memorize a set of items generated by the AG
- Participants will not be told that strings follow certain rules
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- A list of new items is generated. X% follows the AG (Grammatical-G) and 1-x% does not (Ungrammatical-UG)
- Participants will be told that training strings follow a certain grammar but not details of the grammar
- Participants will be asked to determine whether new items are grammatical or ungrammarical
Learning items and test items use the same grammatical rules but different alphabets. i.e. The learning session uses letter sequences and the test session uses color sequences.
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- Participants exhibit above chance accuracy
- Participants could not articulate how they make grammaticality judgments.
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Rule-Based Interpretations
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Participants have learned complete or partial of the original grammar (Reber,1967,1969, 1989)
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Participants have learned a set of propositional rules with the form {Feature -> Grammaticality} (Dulany, et al, 1984)
Feature refers to a chunk of symbols. Grammaticality refers to "Grammatical" or "Ungrammatical"
i.e. A participant might learned that items with the chunk "XV" are always grammatical and establishes the rule {"XV" -> Grammatical}
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Statistics-Based Interpretations
- Specific Similarity/Edit Distance: Grammatical judgment of a given test item is based on whether the test item is highly similar to a specific learning item (Vokey & Brooks, 1992)
- Generalized Context Model: Grammatical judgment of a given test item is based on the averaged similarity between the test item and all learning items (Pothos & Bailey, 2000)
- Analogical Similarity: Grammatical judgment of a transfer test item is based on structural similarity with learning items (Brooks & Vokey)
- Chunk Strength: Grammatical judgment of a given test item is based on whether the test item contains frequent bigrams or trigrams(Knowlton & Squire, 1996)
- Entropy: Grammatical judgment of a given test item is based on its entropy value according to all learning items (Jamieson et al., 2016)
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- Grammar Complexity:
- Finite State grammar
- Context Free Grammar
- Chunk Strength: For a given item, chunk strength is the averaged frequency of all its bigrams and trigrams in the learning session
Both standard and transfer settings are used
2 (Grammatical vs. Ungrammatical) x 3 (High, Medium & Low Chunk Strength) x 2 (Changed Module vs. Unchanged Module)