Published February 18, 2025
| Version v1
Dataset
Open
Behavioural data, A Task-Invariant Prior Explains Trial-by-Trial Active Avoidance Behaviour Across Gain and Loss Tasks.
Authors/Creators
-
1.
Karolinska Institutet
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2.
University of Cambridge
- 3. Central European University
- 4. Psykiatri Nordvast
Description
# Bayesian priors in active avoidance
Author: Tobias Granwald (orcid: 0009-0002-0208-6891)
The folder contains the extracted data used in the analysis with the manuscript "A Task-Invariant Prior Explains Trial-by-Trial Active Avoidance Behaviour Across Gain and Loss Tasks." (Granwald et al., 2025).
Data was collected and analysed in accordance with our preregistration: https://osf.io/rej74
The extracted data is the raw choice data from the robber task and the factory task in with all participants choices in one file.
The following changes has been made in this file compared to the raw data file beyond putting all the data in to a single file:
- Participant IDs have been anonymized by removing participant's prolific IDs and instead adding a numbers between 1 and 279.
- Participant's responses in the PANAS and and STAI have been extracted and added as a separate row to the data.
- Demographic data extracted from Prolific has been added as separate rows in the data.
- Default data registered by jsPsych (de Leeuw, 2015) that was not of relevance to the tasks has been removed (trial_type, trial_index, time_elapsed, stimulus, response, timeout, failed_images, failed_audio, failed_video, question_order, correct)
- Data about other unrelated tasks was removed (oMST; Stark et al., 2023).
- Data from practice trials were removed.
- Data from instruction trials or non-choice parts of the trials were removed.
extractedData.csv contains the following collumns of data:
- subjID_anon (integer, anonomized participant IDs, 1 to 279)
- rt (integer; response time in ms)
- response (string, keyboard response made by the participants "arrowleft" and "arrowright")
- category (string, category data in the row, here selected only category "choice")
- success (integer, binary indicating if the active action was successful, success = 1, loss or passive action = 0, participant failed to make a response = "noResponse")
- robberTotPoints (integer, cummulative points earned during the current block of the robber task, trials of the factory task = 0 and start each block at = 0)
- factoryTotPoints (integer, cummulative points earned during the current block of the factory task, trials of the robber task = 0 and start each block at = 0)
- trial (integer, trial number of the tasks separtatly 0 to 59 for the first block and 60 to 119 for the second block)
- task (string, in which task the current trial is played, robber task = "robber" and factory task = "factory")
- offers (integer, offers presented to the participants in the tasks. In the robber task, the offer was the amount of the tip that the participants may earn at the end of the night before encountering the robber. In the factory task, the offers was the possible loss that the participant incurs if they let the machine break.)
- cost (integer, the cost of the active action on the current trial)
- noActValue (integer, the value of the passive action, in the robber task = 0, in the factory task = -offers)
- stimNo (string, file name of the stimulus presented on the current trial robber task ex. "img/robber1.png" and factory task ex. "img/factory1.png")
- probWin (floating-point, success probability used on the current trial to calculate based on a binomial distribution whether the participant would be successful or not in avoiding the negative outcome when chosing the active action)
- act (integer, binary indicating whether the participant chose the active = 1 or the passive action = 0 or failed to make a response = "noResponse")
- PosAffect_tot (integer, sum score for the participant's score in the positive affect subscale of the PANAS (Watson et al., 1988))
- STAIT_tot (integer, sum score for the participant's score in the trait subscale of the STAI Y-form (Spielberger et al., 1983))
- demograph_age (integer, the "Age" variable extracted from the demographic data provided by prolific.com age in years)
- demograph_sex (string, the "Sex" variable extracted from the demographic data provided by prolific.com, male = "Male" and female = "Female")
- demograph_nationality (string, "Nationality" variable extracted from the demographic data provided by prolific.com)
- demograph_approvals (integer, "Total.approvals" variable extracted from the demographic data provided by prolific.com)
- failed_AC_Q (integer, number of failed attention checks the participant had in the questionnaires (0 to 6), if more than 2 participants were excluded)
extractedData_FU.csv contain the same collumns as the data above except for "demograph_approvals" and it instead includes:
- demograph_dateDif (integer, the number of days between the first session and the follow-up session defined by "Completed.at" - "Completed.at" for the two sessions extracted from the demographic data provided by prolific.com)
The task was programmed by Tobias Granwald at Karolinska Institutet utilizing JavaScript, html and css to ensure that data can be collected online, it also uses the toolbox: jsPsych v 7.1.0 (de Leeuw, 2015).
The task can be found at https://github.com/Granwald/healthyPriors
## References
de Leeuw, J. R. (2015). jsPsych: A JavaScript library for creating behavioral experiments in a Web browser. Behavior Research Methods, 47(1), 1–12. https://doi.org/10.3758/s13428-014-0458-y
Granwald, T., Dayan, P., Lengyel, M., & Guitart-Masip, M. (2025). A Task-Invariant Prior Explains Trial-by-Trial Active Avoidance Behaviour Across Gain and Loss Tasks. Communications Psychology, 82(3) 1-14. https://doi.org/10.1038/s44271-025-00254-1
Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). State-Trait Anxiety Inventory for Adults Sampler Set Manual, Instrument and Scoring Guide. www.mindgarden.com.
Stark, C. E. L., Noche, J. A., Ebersberger, J. R., Mayer, L., & Stark, S. M. (2023). Optimizing the mnemonic similarity task for efficient, widespread use. Frontiers in Behavioral Neuroscience, 17. https://www.frontiersin.org/articles/10.3389/fnbeh.2023.1080366
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales. Journal of Personality and Social Psychology, 54(6), 1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063
Files
extractedData.csv
Additional details
Related works
- Is published in
- Journal article: 10.1038/s44271-025-00254-1 (DOI)
Funding
- Marianne and Marcus Wallenberg Foundation
- MMW 2020-0013
Dates
- Collected
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2024-01-22Start of data collection for "extractedData.csv"
- Collected
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2024-01-26End of data collection for "extractedData.csv"
- Collected
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2024-02-07Start of data collection for "extractedData_FU.csv"
- Collected
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2024-02-13End of data collection for "extractedData_FU.csv"
Software
- Repository URL
- https://github.com/Granwald/healthyPriors
References
- Granwald, T., Dayan, P., Lengyel, M., & Guitart-Masip, M. (2025). A Task-Invariant Prior Explains Trial-by-Trial Active Avoidance Behaviour Across Gain and Loss Tasks. Communications Psychology, 82(3) 1-14. https://doi.org/10.1038/s44271-025-00254-1