Scripts for analyzing the results from the DIVA Intensity Shift Experiment (CadLab)
Main program: intShift_analysis_1.m
You will need to modify lines 15 and 24 for
- where the two data folders (DN and UP) are and
- where the individual-subject plots will be saved
Usage: intShift_analysis_1(bCD, nStresses, reverseOpt, otherOpts) Inputs: bCD - a 0/1 variable that indicates whether contrast distance (1) or asbolute values (0) are shown. nStresse - vector for specifying the stress positions to include in the analysis. e.g., [1, 2] - both sentences with stress on word 1 and sentences with stress on word 2 [1] - only sentences with stress on word 1
reverseOpt - option to reverse stressed and unstressed, for looking at the absolute values from the unstressed words. NOTE: this option only works under bCD = 0. Do not use this option with bCD = 1. For example, if you want to look at the absolute values from the unstressed words (words 1 or 2) of all sentences Do: intShift_analysis_1(0, [1, 2], 'reverse') Or, if you want to look at the absolute values from the unstressed words on position 1, do: intShift_analysis_1(0, [2], 'reverse') Note that we put [2], instead of [1] here, because when word 2 is stressed, word 1 is unstressed.
otherOpts - additional options, such as "showByEpoch" and "showIndS" (see examples below).
Usage exapmles:
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Contrast distance, from stressed words as both position 1 and 2: intShift_analysis_1(1, [1, 2])
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Same as above, but show epoch-by-epoch data: intShift_analysis_1(1, [1, 2], 'showByEpoch')
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Same as above, but show data from individual subjects: intShift_analysis_1(1, [1, 2], 'showIndS')
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Absolute values, from stressed words at positions 1 and 2: intShift_analysis_1(0, [1, 2])
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Absolute values, from only stressed words at position 2: intShift_analysis_1(0, [2])
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Absolute values, from unstressed words at positions 1 and 2: intShift_analysis_1(0, [1, 2], 'reverse')
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Show composite prosody adaptation (CPA) scores, by phase: intShift_analysis_1(1, [1, 2], 'cpa')
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Show CPA scores, by phase and by epoch: intShift_analysis_1(1, [1, 2], 'cpa', 'showByEpoch')
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Show CPA scores and perfor permutation test (10000 times): intShift_analysis_1(1, [1, 2], 'cpa', 'permute', 10000)
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Analyze the correlation between the adaptation measures and the pertContr: intShift_analysis_1(1, [1, 2], 'cpa', 'corrPert')
Note: you may need to revise the "L3_DATA_DIR" variable to set the correct path to the level-3 (in Kevin's parlance) csv files.
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Use the rank-sum and signed rank tests, instead of the default t-tests; perform random permutation for 10,000 iterations, and do not show the uncorrected p-values in the figures: intShift_analysis_1(1, [1, 2], 'cpa', 'loadCache', 'rs', 'permute', 10000, 'noUnc')
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Use subtraction-based normalization, instead of the default divsion-based one. intShift_analysis_1(1, [1, 2], 'subtr', 'loadCache', 'rs', 'permute', 1000)