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Jan 4, 2020
Jan 4, 2020
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Jan 4, 2020

Psychopy Visual Search

A basic T and L visual search experiment.

Author - Colin Quirk (cquirk@uchicago.edu)

T and L visual search is a common attention paradigm. Unlike other visual search tasks, this task forces participants to search serially, resulting in increasing RT with set size (e.g. Wolfe, Cave, Franzel, 1989). If visualsearch.py is run directly, the defaults at the top of the file will be used. To make simple changes, you can adjust any of these values. For more in depth changes you will need to overwrite the methods yourself.

Note: this code relies on my templateexperiments module. You can get it from https://github.com/colinquirk/templateexperiments and either put it in the same folder as this code or give the path to psychopy in the preferences.

Classes

TLTask -- The class that runs the experiment.

Parameters

  • allowed_deg_from_fix -- The maximum distance in visual degrees the stimuli can appear from fixation
  • data_directory -- Where the data should be saved.
  • instruct_text -- The text to be displayed to the participant at the beginning of the experiment.
  • iti_time -- The number of seconds in between a response and the next trial.
  • keys -- The keys to be used for making a response. Should match possible_orientations.
  • max_per_quad -- The number of stimuli allowed in each quadrant. If None, displays are completely random. Useful for generating more "spread out" displays
  • min_distance -- The minimum distance in visual degrees between stimuli.
  • number_of_blocks -- The number of blocks in the experiment.
  • number_of_trials_per_block -- The number of trials within each block.
  • possible_orientations -- A list of strings possibly including "left", "up", "right", or "down"
  • questionaire_dict -- Questions to be included in the dialog.
  • response_time_limit -- How long in seconds the participant has to respond.
  • set_sizes -- A list of all the set sizes. An equal number of trials will be shown for each set size.
  • stim_path -- A string containing the path to the stim folder
  • stim_size -- The size of the stimuli in visual angle.

Additional keyword arguments are sent to template.BaseExperiment().

Methods

  • chdir -- Changes the directory to where the data will be saved.
  • display_break -- Displays a screen during the break between blocks.
  • display_blank -- Displays a blank screen.
  • display_search -- Displays the search array.
  • generate_locations -- Helper function that generates locations for make_trial
  • get_response -- Waits for a response from the participant.
  • make_block -- Creates a block of trials to be run.
  • make_trial -- Creates a single trial.
  • run_trial -- Runs a single trial.
  • run -- Runs the entire experiment.
  • send_data -- Updates the experiment data with the information from the last trial.

Hooks

Hooks can be sent to the run method in order to allow for small changes to be made without having to completely rewrite the run method in a subclass.

Available Hooks

  • setup_hook -- takes self, executed once the window is open.
  • before_first_trial_hook -- takes self, executed after instructions are displayed.
  • pre_block_hook -- takes self, block list and block num Executed immediately before block start. Can optionally return an altered block list.
  • pre_trial_hook -- takes self, trial dict, block num, trial num Executed immediately before trial start. Can optionally return an altered trial dict.
  • post_trial_hook -- takes self and the trial data, executed immediately after trial end. Can optionally return altered trial data to be stored.
  • post_block_hook -- takes self, executed at end of block before break screen (including last block).
  • end_experiment_hook -- takes self, executed immediately before end experiment screen.

Hook Example

For example, if you wanted a block of practice trials, you could simply define a function:

# Arbitrary function name
def my_before_first_trial_hook(self):
    # Self refers to the experiment object
    self.display_text_screen('We will now do a practice block.')
    practice_block = self.make_block()
    for trial_num, trial in enumerate(practice_block):
        self.run_trial(trial, 'practice', trial_num)
    self.display_break()
    self.display_text_screen('Good job! We will now start the real trials.')

Then simply pass the hook into run.

exp.run(before_first_trial_hook=my_before_first_trial_hook)

Just like that, you have modified the experiment without having to change anything about the underlying implementation!

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A T and L visual search experiment in psychopy

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