load_risky_choices#

load_risky_choices()[source]#

Load the risky choices dataset from a CSV file.

Returns:
pd.DataFrame

A pandas DataFrame containing the risky choices data.

See also

cpm.applications.decision_making.PTSM

simplified version of the Prospect Theory-based Softmax Model (PTSM) for decision-making tasks based on Tversky & Kahneman (1992).

cpm.applications.decision_making.PTSM1992

full version of the Prospect Theory-based Softmax Model (PTSM) for decision-making tasks based on Tversky & Kahneman (1992).

cpm.applications.decision_making.PTSM2025

Prospect Theory Softmax Model loosely based on Chew et al. (2019).

Notes

The data are from a risky decision-making task (similar to e.g., Rutledge et al., 2014), a two-alternative forced-choice task, which is implemented as a smartphone-compatible gamified application available in the BrainExplorer app (https://brainexplorer.net/) as the game called “Scavenger”. On each trial, participants are presented with two stimuli and asked to select one of them. After they make their choice, they receive feedback in the form of rewards (coins). The stimuli are cartoonish depictions of stranded spaceships that participants have to salvage in order to obtain coins. In addition, participants are shown a radar, two circles drawn at the bottom of the screen, revealing the possible outcomes for each spaceship and their associated probabilities. One of the spaceships always returns an outcome with 100% probability (i.e., the safe option). The other spaceship has a 50% probability of returning an either a 0 or non-zero outcome (i.e., the risky option). In gain trials, all possible outcomes are positive (except the outcome of 0). In loss trials, all possible outcomes are negative (except the outcome of 0). Furthermore, forming an additional within-participant manipulation, trials can be either ambiguous or non-ambiguous. In ambiguous trials, the radar provides only partial information about the probabilities of the possible outcomes of the risky choice option. In these trials, the risky choice option is shown to have at least a 25% probability of returning an outcome of 0, and at least a 25% probability of returning a different outcome. Unbeknownst to the participant, the true probability of both outcomes is always 50/50 (which is why the risky_probability values in the dataset are always 0.5). Participants play one block with 40 trials, including 10 non-ambiguous gain trials, 10 non-ambiguous loss trials, 10 ambiguous gain trials, and 10 ambiguous loss trials.

The dataset includes the following columns:

  • ppt: the participant number.

  • safe_magnitudes: the magnitude of the safe option.

  • risky_magnitudes: the non-zero outcome magnitude of the risky option.

  • risky_probability: the probability of the risky option.

  • choice: the choice of the participant (1 for risky, 0 for safe).

  • optimal: the optimal choice based on expected value (1 for risky, 0 for safe).

  • feedback: the feedback (reward) given to the participant.

  • ambiguity: whether the trial was ambiguous (1 for yes, 0 for no).

  • attempt: the number of the experimental session for a given participant (e.g. 2 here means that participant is completing the experiment the second time).

  • rt_flip: the reaction time for the choice, measured in seconds, multiplied by -1 if the choice is not an optimal choice. It is used for fitting various drift diffusion models.

References

Rutledge, R. B., Skandali, N., Dayan, P., & Dolan, R. J. (2014). A computational and neural model of momentary subjective well-being. Proceedings of the National Academy of Sciences of the United States of America, 111(33), 12252–12257. https://doi.org/10.1073/pnas.1407535111

Examples

>>> data = load_risky_choices()
>>> print(data.head())
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