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See the readme files included in the data folders and the associated article for more details. Data are stored in the following formats: NIFTI (.nii), MATLAB (.mat) and Excel (.xlsx). This project was supported by Olga Mayenfisch Foundation and Swiss National Science Foundation (320030_149586 to DRB, 320030_188737 to AT, and 320030_184784 to AL), the Wellcome Centre for Human Neuroimaging receives core funding from the Wellcome Trust (091593/Z/10/Z). DRB is also supported by funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. ERC-2018 CoG-816564 ActionContraThreat). AT is supported by the Interfaculty Research Cooperation "Decoding Sleep: From Neurons to Health & Mind" of the University of Bern. BAP is supported by Netherlands Organization for Scientific Research (NWO) VIDI 016.178.052 and by partial funding from R01 MH111444/MH/NIMH NIH. AL is supported by the ROGER DE SPOELBERCH Foundation.
This data set includes selected functional magnetic resonance imaging (fMRI) data supplementing an article. The data include: Untresholded Statistical Parametric Maps (SPMs) and beta images (BOLD signal estimates) for relevant contrasts for the GLMs reported in the article Region-of-interest (ROI) masks: anatomical ROIs with combined hemispheres, and masks created from significant BOLD signal clusters from the whole-brain analyses Summary data files for mean beta (BOLD signal estimate) values and their within-subject errors for each ROI A compilation Excel sheet of condition-wise BOLD parameter estimates and standard errors from previous axiomatic aversive prediction error studies, and associated effect sizes as well as sample sizes from a power analysis. Details of the experimental paradigm as well as of the fMRI data acquisition and analysis can be found in the associated article.
reinforcement learning, normative Bayesian learning, fMRI, axiomatic conditions, aversive prediction errors, threat learning, fear conditioning
reinforcement learning, normative Bayesian learning, fMRI, axiomatic conditions, aversive prediction errors, threat learning, fear conditioning
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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