Resolving temporal threat uncertainty by observational learning involves the amygdala, hippocampus and anterior insula.
Paper
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The authors' code
MATLAB · 21 lines · 455 B · no license
- %% analysis nemexp
- % S.Knobloch 3/25
- clear all
- addpath '/projects/crunchie/nemo/scripts/fmri/subfcts'
- addpath '/projects/crunchie/nemo/scripts/fmri/subfcts/helpfcts'
- addpath '/projects/crunchie/nemo/scripts/toolboxes/spm'
- rmpath '/common/apps/spm12-7771'
- task = task_nemexp;
- %% preprocessing
- slicetime_correct(task)
- realign_solo(task)
- coregister(task)
- normalize(task)
- %% firstlevel
- firstlevel(task)
- %% secondlevel
- secondlevel(task)
analysis_nemexp.m, no license · at the source
Overview
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
- Department of General Psychology and Cognitive Neuroscience, Charlotte Fresenius University, 20148 Hamburg, Germany
Abstract
By observing others’ experiences, humans learn about threats while avoiding firsthand dangers. Yet, previous neuroscience research has focused on observational threats that are predictable. In firsthand learning, temporally predictable and unpredictable threats can be mechanistically discerned. In this study, we developed a novel observational paradigm in which participants learned from predictable and unpredictable observational threats, as well as a no-threat condition. Participants encountered the same conditions during an expression phase to investigate how the brain encodes predictive and unpredictive threat cues observed in others. Participants in Experiment 1 (n = 20, 14 female, 6 male) and Experiment 2 (n = 23, 9 female, 1 non-binary, 13 male) successfully learned threat contingencies, showing heightened threat expectations for predictive cues and unpredictable condition-onsets. Behavioral findings converged with neural (fMRI, Experiment 2) responses in the anterior insula during the expression phase. Reflecting the dynamic process of learning, amygdala activation in response to predictive threat cues displayed a linear decrease across trials. Interestingly, BOLD responses to others’ pain that was predictable, were enhanced within the amygdala, insula and hippocampus, compared to unpredictable conditions. Our findings suggest that humans can learn to resolve temporal threat uncertainty, through the observation of others. The present work thereby contributes to understanding the social aspects of fear and anxiety disorders.
Reproduced under the paper's license (CC BY), from the paper cited above.
Repositories
Its files are read in the Code ↔ Paper reader above.
OSF 653pr
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
11 files
- code/
analysis_nemexp.m , MATLAB, 21 lines - code/
analysis_nemobs.m , MATLAB, 21 lines - code/
subfcts/ , MATLAB, 37 linescoregister.m - code/
subfcts/ , MATLAB, 196 linesfirstlevel.m - code/
subfcts/ , MATLAB, 4 lineshelpfcts/ normit.m - code/
subfcts/ , MATLAB, 38 linesnormalize.m - code/
subfcts/ , MATLAB, 36 linesrealign_solo.m - code/
subfcts/ , MATLAB, 148 linessecondlevel.m - code/
subfcts/ , MATLAB, 32 linesslicetime_correct.m - code/
task_nemexp.m , MATLAB, 157 lines - code/
task_nemobs.m , MATLAB, 173 lines
OSF 5ny9u
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
- 28 September 2026: the link answers (HTTP 200)
The paper's code and data availability statement is in the Data section.
Tracing map
Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.
What the map holds:
- 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
- 11 scripts, each with its path and the digest of its content;
- no match between paragraphs and code yet;
- neither the text of the paper nor the code itself.
Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.
Data
No dataset and no data link were found in the paper.
Data availability
Raw behavioral data from both experiments and code from the fMRI analysis is available in this online repository: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 28 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 2 keywords, 14 MeSH terms, 1 funder, 45 references, 7 RRIDs.
Cite
This paper
Knobloch, S., Jansen, T., Hille, L. C., Müller, M., Rumpf, L., Bauer, C. F. L., & Haaker, J. (2026). Resolving temporal threat uncertainty by observational learning involves the amygdala, hippocampus and anterior insula. Translational psychiatry, 16(1), 488. https://
BibTeX
@article{knobloch2026res
author = {Knobloch, Simon and Jansen, Theresa and Hille, Lara Caroline and Müller, Madeleine and Rumpf, Leonie and Bauer, Christian Francis Lewis and Haaker, Jan},
title = {{Resolving temporal threat uncertainty by observational learning involves the amygdala, hippocampus and anterior insula}},
journal = {Translational psychiatry},
year = {2026},
month = sep,
volume = {16},
number = {1},
pages = {488},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/
url = {https://
pmid = {42800807},
pmcid = {PMC13615975}
}
RIS
TY - JOUR
AU - Knobloch, Simon
AU - Jansen, Theresa
AU - Hille, Lara Caroline
AU - Müller, Madeleine
AU - Rumpf, Leonie
AU - Bauer, Christian Francis Lewis
AU - Haaker, Jan
TI - Resolving temporal threat uncertainty by observational learning involves the amygdala, hippocampus and anterior insula
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/
VL - 16
IS - 1
SP - 488
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/
UR - https://
LA - en
ER -
CSL-JSON
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