Temporal alignment of pupillary response with stimulus events via deconvolution
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README.md
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characterize-freq-content.py
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subject-demographics-voc.tsv

README.md

Pupillometry deconvolution methods

This is a methods paper on deconvolution of pupillometry data for auditory experiments.

McCloy D, Larson E, Lau B, & Lee AKC (2016). Temporal alignment of pupillary response with stimulus events via deconvolution. The Journal of the Acoustical Society of America, 139(3), EL57–EL62. doi: 10.1121/1.4943787

Raw data is cleaned and aggregated with analyze-data.py (for the pupil impulse response experiment) and analyze-voc-data.py (for the vocoded letters experiment). These should create the summary data objects needed to create the figures (avg_data.npz for Figure 1; voc_data.npz and voc_data_wierda.npz for Figure 3).

Once those are in place, the makefile for the article accepts directives pre (for prepress version formatted similar to the final formatting in JASA-EL), sub (for the JASA-EL submittable version: with double spacing, line numbers, list of figures, etc), or web (for a prepub manuscript with my preferred formatting for posting on the web).

NB: Figure generation is not 100% automated for the make sub directive, because it requires opening the auto-generated PDF figures in Adobe Illustrator and saving them as EPS files in order to avoid rasterization of the semi-transparent regions of the plot (only strictly necessary for Figure 3).