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Slot Machine Two-Step Sequential Learning Task

This is an adaptation of Nathaniel Daw's two-step sequential learning task.

  • Author: Alexander Breslav (Duke)
  • Collaborators: Dr. Scott Huettel (Duke), Dr. Nancy Zucker (Duke), Dr. John Pearson (Duke)
  • Original code and stimuli were generously shared by Dr. Arkady Konovalov (UZH) [citation] and Dr. Nicolette Sullivan (LSE) [citation].

This version of the task uses slot machine stimuli and is designed for adults with no background/stastical knowledge. I have made a number of major changes for the purposes of my work:

For this version of the task:

  • I overhauled and iteratively improved the tutorial through three rounds of qualitative testing. The goal of this overhaul and testing was to ensure users could understand the rules and complex dynamics of the game.

  • I created higher quality stimuli and expanded the stimuli set. This was done so that each block (practice, money, and food) had entirely different stimuli. The stimuli color sets and symbols are randomized between subjects; the color sets are fully accessible.

  • There are practice rounds, a block (150 trials) where subjects are rewarded with money, and a block (150 trials) where subjects were rewarded with food.

Dependencies:

  • MatLab 9.4
  • Psychtoolbox 3
  • Statistics and Machine Learning Toolbox 11.3
  • Robotics System Toolbox 2.0

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[In Production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of reward prediction for food with college undergraduates.

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