Chunks are not enough: the insufficiency of feature frequency-based explanations of artificial grammar learning (in special issue on music cognition and performance)

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Chunks are not enough: the insufficiency of feature frequency-based explanations of artificial grammar learning (in special issue on music cognition and performance)

Philip A. Higham-1997-01-01-ePrints Soton (University of Southampton)

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TL;DRAbstract

Two experiments tested chunk frequency explanations of artificial grammar learning which hold that classification performance is dependent on some metric derived from the frequency with which certain features occur within the letter string stimuli. Experiment 1 revealed that classification performance was affected by close graphemic similarity between specific training (e.g., MXRVXT) and test strings (e.g., MXRMXT), despite the fact that similar strings did not contain frequently occurring features (e.g., bigrams or trigrams). This effect was replicated in Experiment 2a and Experiment 2b demonstrated that substituting letters to make the consonant strings pronounceable (e.g., substituting X, R, and T, in the consonant string MXRMXT with Y, A, I, to produce MYAMYI) affected classification performance, despite the fact that objective measures of feature frequency were not altered. It is argued that models of classification that focus entirely on the frequency of features within the liter

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Two experiments tested chunk frequency explanations of artificial grammar learning which hold that classification performance is dependent on some metric derived from the frequency with which certain features occur within the letter string stimuli. Experiment 1 revealed that classification performance was affected by close graphemic similarity between specific training (e.g., MXRVXT) and test strings (e.g., MXRMXT), despite the fact that similar strings did not contain frequently occurring features (e.g., bigrams or trigrams). This effect was replicated in Experiment 2a and Experiment 2b demonstrated that substituting letters to make the consonant strings pronounceable (e.g., substituting X, R, and T, in the consonant string MXRMXT with Y, A, I, to produce MYAMYI) affected classification performance, despite the fact that objective measures of feature frequency were not altered. It is argued that models of classification that focus entirely on the frequency of features within the liter

Keywords

TrigramBigramConsonantSpeech recognitionArtificial intelligenceGrammarNatural language processingComputer science

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