PsychoPy: Code for Cognitive Tasks

Active vs Passive Multiple Object Tracking (AP-MOT) task

The AP-MOT task is used to compare how people sustain attention when they must actively track information versus when they simply monitor for occasional, task-relevant events (aka vigilance).

Both tasks use the same basic MOT display: several identical objects appear on the screen, move unpredictably for several seconds, and then stop so the participant can respond.

• Active MOT (A-MOT): At the start of each trial, a subset of the objects is briefly highlighted as targets. Once the highlighting disappears, all objects begin to move. Participants must continuously track the target objects throughout the motion period. When the objects stop, they are asked to identify which items were the original targets. This version requires sustained, moment-to-moment attentional engagement.

• Passive MOT (P-MOT): The visual display is identical, but no targets are assigned. Participants simply watch the objects move. At the end of the trial, a single object is probed, and they respond whether it performed a small “jump” (an abrupt displacement) during motion. Because no continuous tracking is required, attention is mostly disengaged until the critical event occurs, making this version function like a vigilance task.

Complex Card Matching Task (CCMT)

Task used to investigate the Exploration-Exploitation tradeoff. While it was inspired by the Wisconsin Card Sorting Task, key aspects were changed to make the CCMT more difficult as to tap into higher-order cognitive processes. 

In this task, participants classify cards based on unspecified rules, learning through trial and error with correct-incorrect feedback. It assesses cognitive flexibility by requiring them to alternate between exploration (testing new rules) and exploitation (applying learned rules). Task difficulty is manipulated across two versions: the easy version involves three features (color, shape, number), with six possible classification rules, while the difficult version introduces a fourth feature (size).

This task is available here to download on GitHub, along with more details about the task itself, and Python notebooks to pre-process data.

Vigilance Clock Task

Measure of vigilance (sustained attention). In this task, participants monitor a clock-like display where a hand moves at regular intervals, but occasionally skipping a beat. Feedback is provided through a red flash for incorrect responses and a green flash for correct ones. Contrary to the Mackworth Clock Task, for which reaction time is the primary outcome of interest, the Vigilance Clock Task focuses on accuracy. Every characteristic of the task can be easily changed. 

Click here to access the code files on GitHub.