{"success":true,"database":"eegdash","data":{"_id":"6953f4239276ef1ee07a32e3","dataset_id":"ds003682","associated_paper_doi":"10.1126/sciadv.abf9616","authors":["Toby Wise","Yunzhe Liu","Fatima Chowdhury","Raymond J. Dolan"],"bids_version":"v1.5.0","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"10.18112/openneuro.ds003682.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":28,"ages":[],"age_min":null,"age_max":null,"age_mean":null,"species":null,"sex_distribution":null,"handedness_distribution":null},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1126/sciadv.abf9616"},"funding":[],"ingestion_fingerprint":"3cbea1ccffa007b78a78c8a376e2d2f0d27af88c20f32021bce0221d38cb1dd3","license":"CC0","n_contributing_labs":null,"name":"Model-based aversive learning in humans is supported by preferential task state reactivation","readme":"This dataset contains raw and processed MEG data for the paper \"Model-based aversive learning in humans is supported by preferential task state reactivation\" by Toby Wise, Yunzhe Liu, Fatima Chowdhury & Ray Dolan.\nRaw data is provided as `.fif` files, although it was acquired on a CRF system.","recording_modality":["meg"],"senior_author":null,"sessions":["01"],"size_bytes":324312031420,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["AversiveLearningReplay"],"timestamps":{"digested_at":"2026-05-31T16:12:59.819757+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":336,"storage":{"backend":"s3","base":"s3://openneuro.org/ds003682","raw_key":"dataset_description.json","dep_keys":["CHANGES","README"]},"tagger_meta":{"model":"openai/gpt-4o","tagged_at":"2026-06-10T08:19:41Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Unknown"],"type":["Affect"],"confidence":{"pathology":0.8,"modality":0.5,"type":0.8},"reasoning":{"few_shot_analysis":"In the few-shot examples, when datasets involved learning tasks with emphasis on aversive or affective dimensions, 'Affect' was used as type. For instance, a dataset titled 'Probabilistic Learning with Affective Feedback' was categorized as 'Learning' under 'Type', reflecting a learning paradigm perhaps focused on feedback processing under certain emotional or reward conditions. The key was linking the learning task type to its affect dimension matching the current dataset description about 'aversive learning'.","metadata_analysis":"The dataset is titled 'Model-based aversive learning in humans is supported by preferential task state reactivation,' indicating that it involves a learning paradigm. The term 'Model-based aversive learning' emphasizes the affective (aversive) component in the learning process used in the study.","paper_abstract_analysis":"No useful paper information.","evidence_alignment_check":{"Pathology":{"metadata_says":"\"This dataset contains raw and processed MEG data for the paper 'Model-based aversive learning in humans'...\" indicates a focus on humans with no specific pathology.","few_shot_pattern_suggests":"Default to 'Healthy' in absence of pathology.","alignment":"These align as the focus seems to be on a healthy cohort for learning paradigms.","conclusion":"Healthy as it directly aligns with absence of any specific pathology focus."},"Modality":{"metadata_says":"No specific sensory modality is detailed. It's MEG data as opposed to sensory modality-driven like 'Visual' or 'Auditory'.","few_shot_pattern_suggests":"When unspecified, category will default to 'Unknown' if not explicit.","alignment":"They align to some extent as there is no explicit indication of sensory modality.","conclusion":"Unknown due to lack of explicit sensory modality in metadata."},"Type":{"metadata_says":"'Model-based aversive learning...' suggests a learning paradigm, with the aversive (affective) component potentially indicating emotional processing.","few_shot_pattern_suggests":"Similar datasets with affective components labeled as 'Affect'.","alignment":"They align by focusing on the type of learning with an emphasis on affective ('aversive') outcomes.","conclusion":"Affect is chosen due to explicit mention of 'aversive learning,' integral to study, aligning with few-shot examples."}},"decision_summary":"For 'Pathology', Healthy is justified by absence of specific pathology mention, mirrored by few-shot default behavior when not specified, yielding 0.8 confidence based on 2 explicit metadata quotes. 'Modality' remains Unknown due to lack of explicit information, thus defaults at 0.5 given ambiguity without specifics. 'Type' clearly aligns with Affect due to affect-laden learning paradigm, supported by few-shot and metadata, securing a 0.8 score, based on strong pointers in title and scope."}},"nemar_citation_count":1,"computed_title":"Model-based aversive learning in humans is supported by preferential task state reactivation","nchans_counts":[{"val":414,"count":336}],"sfreq_counts":[{"val":1200.0,"count":336}],"stats_computed_at":"2026-05-31T19:34:32.518004+00:00","total_duration_s":114318.24,"canonical_name":null,"name_confidence":0.89,"name_meta":{"suggested_at":"2026-04-14T10:18:35.342Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Wise2021","generated_by":[{"Name":"Custom Python scripts","Description":"Custom python scripts"}],"bad_channels_info":null,"associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":4,"is_oa":true,"oa_status":"gold","source":"paper_resolver"}}}