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VR – Iowa Gambling Task

In this demo, participants follow the Iowa Gambling Task, a classic experiment in which participants choose between decks of cards with different payoffs. The skin conductance response before and after making a choice can be easily analyzed due to the marking of events from the experiment in the physiological record. In addition, the appearance of the decks, assigned probabilities of winning and losing, can be modified.

Usage Guidelines

This demo is also intended to serve as a tutorial on how to construct a virtual reality experiment. It can be fully modified and has been designed in a modular format with extensive comments to allow reuse of parts in other experiments. Code is written in the Python programming language and extensive support on programming with Python is provided in the software package and user forums. 3D models from the demo can be reused within the VR platform (only).

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Details

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Objectives

  1. To use the Iowa Gambling Task template, a hypothesis testing tool modeled after the work of Bechara et al (1994).
  2. To record skin conductance before and after the choice.

Overview

Quote and graph taken from Bechara et al (2005):

 ‚The participants are given four decks of cards, a loan of $2000 facsimile US bills, and asked to play so as to win the most money. Turning each card carries an immediate reward ($100 in decks A and B and $50 in decks C and D). Unpredictably, however, the turning of some cards also carries a penalty (which is large in decks A and B and small in decks C and D). Playing mostly from decks A and B leads to an overall loss. Playing from decks C and D leads to an overall gain. The players cannot predict when a penalty will occur, nor calculate with precision the net gain or loss from each deck. They also do not know how many cards must be turned before the end of the game (the game in fact ends after 100 card selections).‚

Taken from: The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers
A. Bechara, H. Damasio, D. Tranel and A.R. Damasio  TRENDS in Cognitive Sciences Vol.9 No.4 April 2005

Anticipatory SCR levels change as a function of the number of trials experienced and result in an increasing disparity between levels observed prior to selecting good vs. bad decks. Bechara et al have proposed that this change in somatic response occurs even before the participants have adequate conscious knowledge of the situation.

Data analysis

With minor customization, this demo application can be directly applied for research or teaching purposes. It allows the user to test the following:

  1. Are somatic responses different before and after good vs. bad decks?
  2. Are somatic responses different for more vs. less predictable decks (defined as low vs. high variance in outcome)?
  3. How do 1, 2 change as a function of time?

Support

Application Notes

Knowledge Base

Hardware Packages   |   VR – Iowa Gambling Task

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fNIRS Data Analysis

In this fNIRS training, you will see a typical analysis workflow for fNIRS data in experiments featuring both event-based and continuous stimulation. Learn about a powerful yet easy-to-use software package with both a graphical user interface and scripting capability (for automation).

What You Will Learn:
• Preparing data for analysis
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• Spatial visualization
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