Justification in Judgment

This dataset contains the data from two experiments investigating to what degree process accountability motivates decision makers to shift from retrieval of past exemplars to rule-based integration processes. The first experiment concerns retrieval-based configural judgement tasks, while the second concerns elemental judgement tasks requiring weighing and integrating information. The data includes results from both training trials and test trials, the images used for pictorial stimuli, the code needed to run the experiments in PsychoPy, and the code used to analyse the results in R.


Cite this dataset as:
Hoffmann, J., von Helversen, B., Gaissmaier, W., 2017. Justification in Judgment. Open Science Framework (OSF). Available from: https://doi.org/10.17605/OSF.IO/7NMWS.


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Janina Hoffmann
University of Bath; University of Basel; University of Konstanz

Bettina von Helversen
University of Basel; University of Zurich

Wolfgang Gaissmaier
Max Planck Institute for Human Development; University of Konstanz


Data collection method:

For details of the data collection methodology, see the associated publication: J. Hoffmann, W. Gaissmaier, and B. von Helversen. "Justifying the judgment process affects neither judgment accuracy, nor strategy use." Judgment and Decision Making, Vol. 12, No. 6, November 2017, pp. 627–641.

Technical details and requirements:

Experiments were run in PsycoPy (https://www.psychopy.org/) and analyses were conducted in R.


Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (FNS)

Modeling Human Judgment: Integrating Memory and Rule-based Processes

Publication details

Publication date: 4 September 2017
by: Open Science Framework (OSF)

Version: 1

DOI: https://doi.org/10.17605/OSF.IO/7NMWS

URL for this record: https://researchdata.bath.ac.uk/id/eprint/803

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Janina Hoffmann


Faculty of Humanities & Social Sciences