{"success":true,"database":"eegdash","data":{"_id":"6953f4249276ef1ee07a3338","dataset_id":"ds004330","associated_paper_doi":"10.1523/jneurosci.1546-22.2022","authors":["Johannes J.D. Singer","Radoslaw M. Cichy","Martin N. Hebart"],"bids_version":"1.7.0","contact_info":null,"contributing_labs":null,"data_processed":false,"dataset_doi":"doi:10.18112/openneuro.ds004330.v1.0.0","datatypes":["meg"],"demographics":{"subjects_count":30,"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://www.jneurosci.org/content/jneuro/43/3/484.full.pdf"},"funding":["ERC-StG-2021-101039712","CI241/1-1","CI241/3-1","CI241/7-1","ERC-StG-2018-803370"],"ingestion_fingerprint":"1cf701d0b0f9aac35d07e251ffbc7c8699c881a64945355bc522df119641c3bc","license":"CC0","n_contributing_labs":null,"name":"The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)\n","readme":"This dataset contains the raw MEG data accompanying the paper \"The spatiotemporal neural dynamics of object recognition for natural images and line drawings\" (Link to preprint: https://biorxiv.org/cgi/content/short/2022.08.12.503484v1). Please cite the above paper if you use this data.\nThe dataset includes:\nMEG data for 9 runs for each subjects. Events files that contain the onsets, durations and trial types for each trial in the experiment (excluding catch trials).\nFor a full description of the paradigm and the employed procedures please see the manuscript.\nResults for the first-level analyses for this data can be found on OSF (https://osf.io/vsc6y/). Code for the analysis of the data can be found on Github (https://github.com/Singerjohannes/object_drawing_dynamics/).","recording_modality":["meg"],"senior_author":null,"sessions":["01"],"size_bytes":165028307499,"source":"openneuro","study_design":null,"study_domain":null,"tasks":["main"],"timestamps":{"digested_at":"2026-05-31T16:14:28.202919+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":270,"storage":{"backend":"s3","base":"s3://openneuro.org/ds004330","raw_key":"dataset_description.json","dep_keys":["CHANGES","README","participants.json","participants.tsv"]},"tagger_meta":{"model":"gpt-6-sol","taxonomy":"v2","config_hash":"39931253008fe2df","tagged_at":"2026-10-06T13:13:11Z","source":"eegdash-llm-tagger"},"tags":{"pathology":["Healthy"],"modality":["Visual"],"type":["Perception"],"confidence":{"pathology":0.6,"modality":0.8,"type":0.8},"reasoning":{"few_shot_analysis":"The visual discrimination example labels moving-dot recognition as Visual and Perception, providing the closest task-level convention for object recognition. The auditory music-versus-speech example also labels sensory-response research Perception rather than Language. Unlike the clinical examples, this dataset names no recruited condition.","metadata_analysis":"The title states \"object recognition for natural images and line drawings,\" and the readme describes \"the raw MEG data accompanying the paper\" with that title. The participants overview says \"Subjects: 30\" but gives no diagnosis. The readme mentions trial events and catch trials without describing another research aim.","paper_abstract_analysis":"The abstract says participants \"viewed images of objects depicted as photographs, line drawings, or sketch-like drawings\" and identifies the aim as \"neural dynamics of object recognition.\" This supports Visual and Perception. It reports no clinical recruitment.","evidence_alignment_check":"Pathology: Metadata says \"Subjects: 30\" and names no condition; healthy-population few-shot patterns suggest Healthy. They align insofar as neither indicates clinical recruitment, but health is inferred rather than stated. Modality: Metadata says \"natural images and line drawings\"; the visual discrimination example suggests Visual. They align. Type: Metadata says \"object recognition\"; the visual discrimination example suggests Perception. They align. No metadata fact is overridden by a few-shot pattern.","decision_summary":"Pathology: Healthy versus Unknown. No disorder is identified in \"Subjects: 30\" or the object-recognition title; Healthy is the stronger contextual inference, but absence of an explicit health statement limits confidence to 0.6. Modality: Visual versus Unknown. \"Natural images and line drawings\" in the title, the matching paper title in the readme, and the abstract's \"viewed images of objects\" favor Visual; alignment with the visual-task example supports 0.8. Type: Perception versus Language. \"Object recognition\" in the title and readme and \"neural dynamics of object recognition\" in the abstract favor Perception; the abstract's mention of communicating meaning does not establish a language task. The visual-discrimination example supports 0.8."}},"nemar_citation_count":1,"computed_title":"The spatiotemporal neural dynamics of object recognition for natural images and line drawings (MEG)","nchans_counts":[{"val":310,"count":270}],"sfreq_counts":[{"val":1000.0,"count":270}],"stats_computed_at":"2026-05-31T19:34:32.598836+00:00","total_duration_s":132057.73,"author_year":"Singer2022","canonical_name":null,"references_and_links":["https://biorxiv.org/cgi/content/short/2022.08.12.503484v1"],"bad_channels_info":null,"associated_paper_meta":{"channel":"search","confidence":"high","author_overlap":3,"is_oa":true,"oa_status":"hybrid","source":"paper_resolver"}}}