This data pool (N = 617) comes from 10 studies assessing performance of healthy participants (i.e., no known neurological impairments) on the Iowa gambling task (IGT)—a task measuring decision making under uncertainty in an experimental context. Participants completed a computerized version of the IGT consisting of 95 – 150 trials. The data consist of the choices of each participant on each trial, and the resulting rewards and losses. The data are stored as .rdata, .csv, and .txt files, and can be reused to (1) analyze IGT performance of healthy participants; (2) create a “super control group”; or (3) facilitate model-comparison efforts.

A correction article related to the authors of this publication can be found here:

A correction article relating to the authors of this publication can be found here:

2000–2013

This data pool comes from eight independent published studies (

Table

Overview of the studies included in the data pool. See text for a description of the different payoff schemes.

Study | Number of participants | Number of trials | Payoff | Demographics^{a} |
---|---|---|---|---|

Fridberg et al. |
15 | 95 | 1 | M = 29.6 years (SD = 7.6) |

Horstmann^{b} |
162 | 100 | 2 | M = 25.6 years (SD = 4.9), 82 female |

Kjome et al. |
19 | 100 | 3 | M = 33.9 years (SD = 11.2), 6 female |

Maia & McClelland |
40 | 100 | 1 | Undergraduate students |

Premkumar et al. |
25 | 100 | 3 | M = 35.4 years (SD = 11.9), 9 female |

Steingroever et al. |
70 | 100 | 2 | M = 24.9 years (SD = 5.8), 49 female |

Steingroever et al. |
57 | 150 | 2 | M = 19.9 years (SD = 2.7), 42 female |

Wetzels et al. ^{c} |
41 | 150 | 2 | Students |

Wood et al. |
153 | 100 | 3 | M = 45.25 years (SD = 27.21)^{d} |

Worthy et al. |
35 | 100 | 1 | Undergraduate students, 22 female |

^{a}Information that was provided in the original articles. This information consists of the mean age and the standard deviation in brackets, or alternatively the occupation of the participants. In addition, the number of female participants is provided for most datasets.

^{b}Data collected by Annette Horstmann. These data were first published in Steingroever et al.

^{c}Data of the standard condition. Data of three other conditions can be downloaded here:

^{d}The first 90 participants of this dataset are between 18–40 years old (M = 23.04, SD = 5.88), and participants 91–153 are between 61 and 88 years old (M = 76.98, SD = 5.20).

In the traditional payoff scheme, the net outcome of 10 cards from the bad decks (i.e., decks A and B) is −250, and +250 in the case of the good decks (i.e., decks C and D). In addition, there are two decks with frequent losses (decks A and C), and two decks with infrequent losses (decks B and D). In the traditional payoff scheme, there is a variable loss in deck C (i.e., either −25, −50, or −75; classified here as payoff scheme 1). However, some of the included studies used a variant of this payoff scheme in which the loss in deck C was held constant (i.e., −50; classified here as payoff scheme 2). A second difference between payoff scheme 1 and 2 is that payoff scheme 1 uses a fixed sequence of rewards and losses, whereas payoff scheme 2 uses a randomly shuffled sequence.

The payoff scheme introduced by Bechara & Damasio

A computerized version of the Iowa gambling task was applied in all studies. The number of trials varied between 95, 100, and 150 (

Participants completed a computerized version of the IGT after having received the instructions. Participants began the task with a loan of +2000. More details on the procedures can be found in the original articles. The sample sizes presented in Table

The computerized IGTs were based on one of the three payoff schemes described above (see also Table

All studies were administered through a computerized version of the IGT (see original articles for more details).

IRB approval was obtained for each data collection (in accordance with local rules). All participants gave written informed consent before participation in the study. The shared data pool was stripped of any potentially identifying information before being uploaded.

IGTdataSteingroever2014.zip. This zip archive contains the following files:

IGTdata.rdata

choice_95.csv, choice_100.csv, choice_150.csv, wi_95.csv, wi_100.csv, wi_150.csv, lo_95.csv, lo_100.csv, lo_150.csv, index_95.csv, index_100.csv, index_150.csv.

choice_95.txt, choice_100.txt, choice_150.txt, wi_95.txt, wi_100.txt, wi_150.txt, lo_95.txt, lo_100.txt, lo_150.txt, index_95.txt, index_100.txt, index_150.txt.

Processed data

The data are provided in three different formats: .rdata (R), .csv (Excel), and .txt. The .rdata file is called “IGTdata.rdata” and it contains the following 12 matrices:

choice_95, choice_100, and choice_150: These matrices contain the choices of all studies that used a 95-trial, 100-trial, and 150-trial IGT, respectively. The dimension of each matrix corresponds to the number of subjects x number of trials. For example, choice_95 is a 15 x 95 matrix, and the entry of the third row and fifth column corresponds to the choice that the third participant made on the fifth trial (Fig.

wi_95, wi_100, and wi_150: These matrices contain the rewards of all studies that used a 95-trial, 100-trial, and 150-trial IGT, respectively. The dimension of each matrix corresponds to the number of subjects x number of trials. For example, wi_100 is a 504 x 100 matrix, and the entry of the third row and fifth column corresponds to the reward that the third participant received on the fifth trial. The entries of the three reward matrices vary between 40 and 170.

lo_95, lo_100, and lo_150: These matrices contain the losses of all studies that used a 95-trial, 100-trial, and 150-trial IGT, respectively. The dimension of each matrix corresponds to the number of subjects x number of trials. For example, lo_150 is a 98 x 150 matrix, and the entry of the third row and fifth column corresponds to the loss that the third participant received on the fifth trial. The entries of the three loss matrices vary between – 2500 and 0. Thus, the losses are saved as negative numbers.

index_95, index_100, and index_150: These matrices contain the name of the first author of the study that reports the data of the corresponding participant. For example, the third row of index_95 can be used to identify who collected the choices saved in the third row of choice_95, wi_95, and lo_95.

Screenshot of a subset of the choice_95 matrix. Each row contains the sequence of choices from a specific participant. For example, the entry of the third row and fifth column corresponds to the choice that the third participant made on the fifth trial (i.e., “2” – deck B). To determine who collected the data of this particular participant, one needs to refer to the third row of index_95 (cf. Fig.

Screenshot of a subset of the index_95 matrix. Each row can be used to identify who collected the data of a specific participant. The screenshot shows that the data of subjects 1 – 10 who completed a 95-trial IGT were collected by Fridberg et al.

These 12 matrices all saved together in the “IGTdata.rdata” file. In addition, we saved the 12 matrices as separate .csv and.txt files. For example, the matrix choice_95 (Fig.

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05/11/2014

This data pool has several reuse potentials: First, it could be used to more thoroughly investigate healthy participants’ performance on the IGT. Second, it could be reused as a “super control group”. This means that performance of an experimental group can be assessed relative to the performance of healthy participants included in this data pool. Third, the data pool could be reused to compare computational models for the IGT. However, it should be noted that the 10 datasets were collected in different environments, and that the performance of the participants on the IGT may possibly be affected by factors that varied across the included studies (e.g., the use and type of incentives, questions about the IGT during the performance to asses participants’ awareness, randomly shuffled payoff or fixed payoff sequence, the type of task instruction).

We would like to thank Kala Battistelli, Andreas Below, Katie Chamberlain, Courtni East, Noémie J. Eichhorn, Lindsey Ferris, Monica Gamboa, Kaitlynn Goldman, Karolin Gohlke, Karla Gomez, Alexis Gregg, Jordan Hall, Christy Ho, Jonas Klinkenberg, Lauren Laserna, Katja Macher, Samantha Mallec, Megan McDermott, Ramona Menger, Gerard Moeller, Michael Pang, Anthony Schmidt, Candice Tharp, Lucas Weatherall, Michael Wesley, and Christopher Whitlow for their help in collecting the data.