Data and analysis scripts for "Control of Visual Adaptation Depends Upon Task", Vergeer and Engel, PLOS ONE, 2020

The main file is FigShareAdaptTasksPlots:This script recreates the main figures from the paper from data matrices.Run this separately for Experiments 1 and 2. The script loads in data structures for each experiment from the relevant file, either figShareExp1Dat.mat or figShareExp2Dat.mat These files contain two variables, one for the tilt aftereffect, "TAEDat" and one for the secondary task performance, "T2Dat"The code then just averages them to produce time courses for TAE and bar graphs for the secondary task performance. In each case, the end of averaging is a 2 x 2 data structure, where one dimension is adaptive state(baseline vs adaptation) and the other is task (grating vs fixation) The TAE is plotted both as full timecourses and as difference time courses (subtracting out baseline from adaptation). The secondary task performance is plotted averaged within 3 time bins, for all 4 conditions, because it is much noisier. The code also does this first for both sessions averaged together, then separately for each session. Each of these is in its own Figure.The data files are also in this directory as is ShadedErrorBar.m, which makes nice error ribbon timeseries plots.主文件为FigShareAdaptTasksPlots：该脚本可基于数据矩阵复现论文中的核心图表。需分别针对实验1与实验2运行此脚本。脚本会从对应文件figShareExp1Dat.mat或figShareExp2Dat.mat中加载各实验的数据结构。上述文件包含两个变量，分别为倾斜后效（tilt aftereffect, TAE）数据"TAEDat"，以及次级任务表现数据"T2Dat"。随后代码仅对二者取平均，以生成倾斜后效的时程曲线与次级任务表现的柱状图。每种情况下，平均操作完成后将得到一个2×2的数据结构，其中一个维度为自适应状态（基线 vs 适应态），另一个维度为任务类型（光栅任务 vs 注视任务）。倾斜后效会同时绘制完整时程曲线与差异时程曲线（即从适应态数据中减去基线数据）。由于次级任务表现的噪声水平较高，因此会将其划分为3个时间箱取平均后，再针对全部4种实验条件绘制图表。代码会先对两次会话的数据合并取平均后执行上述操作，随后再分别针对单次会话执行相同流程，每种结果均会生成独立的图表。本目录下还包含数据文件以及用于生成美观的误差带时序图表的ShadedErrorBar.m工具脚本。