
Data relates to a visual-psychophysics experiment ran in psychopy.  The aim of the experiment was to investigate the impact of self-motion of visual spatio-temporal integration.  The data was collected in Novemeber and December 2023.  However, there is currently only partial data for one of the participants (pt2), with further data to come.  

This is a 4AFC target dtection task where participants identify which one of four known locations did NOT contain a target, against a moving background.  Each target is composed of two small probes which are separated by four diagonal pixels.  Probes always appear for one frame (8.33ms).  The probes are either presented concurrently (at the same time) or are separated by one of six interstimulus-intervals (ISIs) of 0 to 100ms.  (The total presentation times, excluding the concurrent condition, correspond to the target duration in the direction-detection task).  The two probes composing each target are aligned radially, such that the second probe is either inward (closer to the centre of the screen) or outward (closer to the edge of the screen) than the first probe.  The alignment of the three targets is the same such that for each target, the second probe is outward (or inward) of the first probe.  

The background is an array of ~1000 dots which move in an expanding or contracting pattern to simulate forwards or backwards self-motion.  Staircase proceedures adjust the luminance of the target, that is, if a participant correctly detects the target, the luminance will be lower on the next trial from this condition.  If they respond incorrectly, the lumiance will be higher.  The main manipulation is the relationship between the apparent motion of the probes (inwards or outwards) and the background motion (expanding or contracting).  The relationship can either be congruent (both in same direction) or incongruent.  This does not apply to the concurrent condition, which is included as a dummy condition, where were predict no effect.  There are seven target durations (e.g., concurrent or the six ISI conditions) giving 14 conditions in total.  There are 25 trials per conditions.   A full set of data is 12 runs of the experiment.

Analysis fits a psychometric function to the 25 trials from each condition to estimate the luminance threshold where participants are able to detect the target with 75% accuracy.  

When a participant has complete 12 runs of the experiment, we calculate trimmed means (e.g., the 2 highest and lowest values are removed and the mean of the remaining eight values is calculated).  Four of the 5 participants here have completed 12 runs, and their results csvs are labelled with TM2 (trimmed means), pt2 has had no values trimmed from their data.

 
Experimental data was collected by running Target_detection_Dec23.py.  This script calls kestenSTmaxVal.py, an adaptive staircase procedure sets the luminance levels.  To run this script you will also need psychopy, which can be downloaded from https://www.psychopy.org/download.html. You will need to enter your monitor details into psychopy's monitor centre. 

Data is analysed with the run_analysis.py script.  To fit the psychometric curves you will need psignifit which can be downloaded from https://github.com/wichmann-lab/python-psignifit/wiki/Install.


