Bernd Figner, Elke Weber

Risk taking behaviors in everyday-life typically follow a characteristic developmental pattern. They are low during childhood, increase sharply with puberty, peak in adolescence and early adulthood, and decline again during middle and late adulthood. Though well documented, e.g., from accident statistics, the reasons are still not very well understood. Recent neuroscientific research suggested that the competition between distinct neural networks determines risk taking. Only when affective processes are triggered, adolescents tend show more impulsive risk taking and suboptimal information use than both children and adults due to a usually transient dominance of the affective over the cognitive-control network.

We developed the “Columbia Card Task” (CCT) to investigate developmental changes and individual differences in healthy individuals across the life span and in populations such as substance users. The CCT enables us to compare affect-based versus deliberative risky decisions and their triggering mechanisms as well as predictors of risk taking, such as inhibitory control, need-for-arousal, and impulsivity. Besides behavioral methods, we are using physiological measures, brain imaging, and brain stimulation techniques.

For more information (including CCT demo versions), please visit CCT Webpage

(Also known as ‘DMIDI’)

www.sjdm.org/dmidi

With Kirstin Appelt, Kerry Milch and Michel Handgraaf

The DMIDI is a catalogue of over 200 individual difference measures commonly used in judgment and decision-making research.Basic descriptive information (including references & scale information) is available for all measures. Measures that are publicly available are posted for easy downloading for research and educational use only. Detailed information on history of use (including significance and consistency of results) is available for a subset of measures.

View PDF of a paper describing the instrument.

Click here to access the GitHub repository containing the full demo and files.

Thank you for your interest in Query Theory (QT) and the aspect-listing paradigm!

Query Theory is a prominent model of the cognitive processes that guide decision-making. According to QT, people evaluate options by sequentially “querying” their memory for thoughts that support or oppose particular choices. For example, when deciding between receiving a $40 Amazon gift card today or waiting a week for $45, you might first retrieve a thought favoring immediacy (“I want to read that new book right away”) or instead retrieve a thought supporting delay (“I don’t need anything now, so the extra $5 is worth it”). The order and content of these queries can meaningfully shape the final decision.

*Aspect listing*—a structured thought-listing or “think-aloud” method—allows researchers to observe these internal queries as they unfold. Participants type out their thoughts while evaluating decision options, and our Qualtrics setup automatically saves each thought. This enables researchers to later ask targeted follow-ups (e.g., “On a scale of 1–7, how strongly does Thought J support Option K?”) and to analyze the sequence and structure of thought generation.

This tool offers a flexible approach for studying how preferences are constructed across different contexts. In foundational work, Weber (2007) showed that people tend to choose the option for which they first list supportive thoughts. Subsequent research demonstrated that instructing participants to reverse their natural thought order can even eliminate the endowment effect (Johnson et al., 2017).

A demonstration of the aspect-listing interface is shown below. To explore the full interactive setup yourself, visit the GitHub repository and download the Aspect_Listing_Demo.qsf file.