TrajTracker Paradigms 1.2

TrajTracker offers two ready-to-use experimental paradigms - number-to-position mapping and discrete-choice (two buttons forced choice). Each of them is a set of functions that allow creating your own experiment with almost no programming.

For each of the two paradigms, you can create your own experiment in two ways:

• For most common features, you will only have to change the program configuration.
• If your experiment requires features that are not directly supported by the configuration offered, you can change the python code. To help you in that, see below the documentation of the functions that implement each of the two paradigms.

The list of trial is defined in a CSV file. The format of this files, including the possible things you can configure via this file, are explained here.

The results of each experiment session are saved in 3 files: a file with general data, a file with trials information, and a trajectory file. See details here.

Supported paradigms

Number-to-position mapping

This paradigm shows a number line and various possible stimuli. The response is indicated by dragging the finger to a location on the number line.

For an overview of this paradigm under TrajTracker, see the number-to-position page.

• Configuration: the Config class
• Technical: the software design
• Technical: the ExperimentInfo class
• Technical: the TrialInfo class

Discrete choice

This paradigm shows two response buttons (in the top corners of the screen) and various possible stimuli. The response is indicated by dragging the finger to one of the buttons.

• Configuration: the Config class
• Technical: the software design
• Technical: the ExperimentInfo class
• Technical: the TrialInfo class

How to use these paradigms

Creating a simple experiment (that uses only the supported features)

Several features are already supported by the paradigm we wrote. These features can be used with almost no programming. To use them, you should:

• Create your main program by copy one of the existing sample scripts. The simplest ones are number_to_position_1 for number-to-position experiments and dchoice_1 for discrete-choice experiments.
• In your script, set the experiment’s general configuration parameters. This is done by updating the Config object (see here its documentation for number-to-position and discrete-choice experiments.
• Create a CSV file with the per-trial data. See here a detailed description of this file format.

Even in this experiment, you may need to write minimal code - e.g., if you use non-text stimuli, you would have to create them yourself (e.g., see the quantity_to_position sample experiment we created, where the stimuli are sets of dots).

Making advanced changes by modifying the code

If your experiment requires features that are not supported via the above configuration, you can modify the relevant python functions. To help you on this, the following pages describe how the experiment software is designed: for number-to-position experiments and for discrete-choice experiments.

The simplest way to do such modifications is to copy the relevant functions into your own script. You can see an example for the way it is done in the number_to_position_2 sample script.

Events

TrajTracker Paradigms works with events, which help you run your custom code at predefined times. To learn how to plug your code to run when specific events occur, please read the events and check out the EventManager.register_operation method.

TrajTracker Paradigms supports the following events per trial:

• trajtracker.events.TRIAL_INITIALIZED: Dispatched when the trial information is initalized. This typically happens right after the previous trial has ended.

• trajtracker.events.TRIAL_STARTED: Dispatched when the trial starts, which is when the finger touches the screen.

  when Config.stimulus_then_move = True, the timing of target onset/offset is specified relatively to this event.

• trajtracker.events.TRIAL_SUCCEEDED: Dispatched when the trial ends successfully. If the trial required an additional task after the main task (e.g., confidence rating), the event will be dispatched after the additional tasks are completed too.

• trajtracker.events.TRIAL_FAILED: Dispatched when an error occurs. “An error” does not mean an incorrect response, but that the subject did not comply with one of the experiment rules (e.g. lifted the finger, moved too slowly, etc.).

• trajtracker.events.TRIAL_ENDED: This event dispatched both with TRIAL_SUCCEEDED and with TRIAL_FAILED.

• trajtrackerp.common.FINGER_STARTED_MOVING: Dispatched when the finger leaves the start point.

  when Config.stimulus_then_move = False, the timing of target onset/offset is specified relatively to this event.

  In such case, if you set the target’s onset_time to be 0, it will be presented right after this event. The delay between the event and the target onset may vary within the range of 1 frame (delay <= 17 ms for a 60Hz monitor). If you want to write a custom function that is very accurately syhcoronized with the target’s onset, it is better to register the function to FINGER_STARTED_MOVING with delay. For example, registering your function to FINGER_STARTED_MOVING+0.1 will make it run 100 ms after the target’s onset, and registering it to FINGER_STARTED_MOVING+0.001 will make your function run in the frame immediately following the target’s onset.

Resource files

TrajTracker includes the source code for the two paradigms above, and also some resource files:

• Sounds (to indicate successful / incorrect trial)
• Images for simple stimuli

These files are in the trajtrackerp_res directory (if your installed with pip, find this directory in your site-packages folder).

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