Supporting data and code from 'The time-course of spatial structure and semantic processing in real-world scenes'
If there are any issues, contact Matt Anderson: Matt.Anderson@soton.ac.uk

The files AggData/SEM_categorization.txt and AggData/STR_categorization.txt contain trial-by-trial response data from the semantic and spatial structure tasks respectively. The columns should be intuitively named, but here is a brief description of each:

- Ppt_No: unique participant identifier
- Scene: scene identifier in the SYNS database (see https://syns.soton.ac.uk/)
- View: view identifier in the SYNS database
- cat_agreement: proportion of participants who selected the ground-truth category, using unlimited viewing durations (see Anderson et al., 2021)
- GT_Category: Ground-truth category. In the semantic file, the numbers 1->6 correspond to Nature, Road, Residence, Farm, Beach, and Car Park / Commercial respectively. In the spatial structure file, numbers 1->4 correspond to Cluttered, Closed Off, Flat, and Tunnel respectively. 
- TrialNumber: order in which the image was presented for a given participant
- imageID: unique identifier for each image
- distance_*: The next 6 variables compute various statistics of the image using coregistered ground-truth LiDAR data. These are the (i) mean across the image, standard deviation, and range. These statistics are also computed for a small patch around the fixation location. 
- distance_bin: median split by the column distance_mean
- Colour_GrayScale: 1 = Grayscale, 2 = Colour
- Stereo_Cond: 1 = Mono, 2 = Stereo, 3 = Stereo-Reversed
- Pres_Time: number of frames, as a multiple of 13.33 msecs (75 Hz refresh rate)
- SceneCat: participant-selected category
- DepthCat: participant-selected depth
- ResponsePeriod: Response time (dunno why I called it period)
- Elapsed: empirical measure of computer flip interval (psychtoolbox)
- Cat_correct: 1 = correct, 0 = incorrect
- Depth_Correct: 1 = correct, 0 = incorrect


The folder AnalysisScripts contains:

mafc_dprime.m
- This script computes dprime for a task with >2 categories. For tasks with 2 categories, it produces d-prime estimates identical to the standard method

RapidSceneCatLMMs_CategoryAnalyses.R
- R script that runs the linear mixed models on the category data. This works on data from both tasks by changing which txt file you load in. 

RapidSceneCatLMMs_DepthAnalyses.R
- Same as above, but for the depth responses