{"success":true,"database":"eegdash","data":{"_id":"69d16e04897a7725c66f4c4e","dataset_id":"ds007521","associated_paper_doi":"10.1101/2025.09.09.674354","authors":["Moerel, Denise","Chenh, Cecilia","Bowman, Sophie","Carlson, Thomas"],"bids_version":"1.0.2","contact_info":null,"contributing_labs":null,"data_processed":true,"dataset_doi":"doi:10.18112/openneuro.ds007521.v1.0.1","datatypes":["eeg"],"demographics":{"subjects_count":23,"ages":[22,62,21,18,21,23,21,20,21,21,22,23,21,22,20,21,20,22,22,21,22,18,21],"age_min":18,"age_max":62,"age_mean":22.82608695652174,"species":null,"sex_distribution":{"f":14,"m":9},"handedness_distribution":{"r":20,"l":3}},"experimental_modalities":null,"external_links":{"paper_url":"https://doi.org/10.1101/2025.09.09.674354"},"funding":["ARC DP200101787 (TAC)"],"ingestion_fingerprint":"4c66a7318e84ada67e09f2565df26a6040a8d94b64b227c72e605d412bbb312d","license":"CC0","n_contributing_labs":null,"name":"The effect of hunger and state preferences on the neural processing of food images","readme":"A preprint of the manuscript can be found on bioRxiv: doi.org/10.1101/2025.09.09.674354\nThe experiment and analysis code can be found via the Open Science Framework: doi.org/10.17605/OSF.IO/ZFD7P\nExperiment Details:\nHuman electroencephalography recordings from 23 participants, who did a letter task and calorie categorisation task. In the letter task, participants viewed rapid streams of overlaid food/non-food images and letters, pressing a button whenever they saw a vowel, while ignoring the images. This setup directed attention away from the visual objects, making them task-irrelevant. In contrast, the calorie categorisation task required participants to actively evaluate each food image and classify it as higher or lower in calories than bread, by pressing a button.\nExperiment length: 1 hour","recording_modality":["eeg"],"senior_author":null,"sessions":["1","2"],"size_bytes":52686910289,"source":"openneuro","storage":{"backend":"s3","base":"s3://openneuro.org/ds007521","raw_key":"dataset_description.json","dep_keys":["CHANGES","README.txt","participants.json","participants.tsv","task-food_eeg.json","task-food_events.json"]},"study_design":null,"study_domain":null,"tasks":["food"],"timestamps":{"digested_at":"2026-05-31T16:29:47.470848+00:00","dataset_created_at":null,"dataset_modified_at":null},"total_files":46,"computed_title":"The effect of hunger and state preferences on the neural processing of food images","nchans_counts":[{"val":64,"count":46}],"sfreq_counts":[{"val":100.0,"count":46}],"stats_computed_at":"2026-04-22T23:16:00.312849+00:00","total_duration_s":null,"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","Attention"],"confidence":{"pathology":0.7,"modality":0.9,"type":0.9},"reasoning":{"few_shot_analysis":"The visual dot-discrimination example maps classification of visual stimuli to Visual and Perception even when participants make button or mouse responses. The music-versus-speech example likewise labels neural processing of sensory stimuli Perception. These conventions support Perception for food-image processing; the explicit comparison of attended and unattended images also supports Attention.","metadata_analysis":"The readme says participants 'viewed rapid streams of overlaid food/non-food images and letters' and, in another task, 'actively evaluate[d] each food image and classify it as higher or lower in calories than bread.' It states that the letter task directed 'attention away from the visual objects.' The title identifies the aim as 'the neural processing of food images.' The cohort is described as '23 participants,' with no recruited clinical condition named.","paper_abstract_analysis":"The abstract identifies 'hunger state,' 'task relevance,' and 'current individual preference' as factors affecting 'the processing of visual food information.' Participants completed sessions 'after fasting overnight' and 'after eating normally.' It contrasts a task in which stimuli were relevant with one in which 'attention was distracted away.' This supports perceptual processing and attention as research aims, not a clinical population or therapeutic intervention.","evidence_alignment_check":"Pathology: Metadata says '23 participants' and describes fasting and eating sessions, but names no diagnosis. The healthy-participant examples suggest Healthy for a nonclinical cohort; these align, with Healthy inferred rather than explicitly confirmed. Modality: Metadata says participants 'viewed' food images and letters and evaluated 'each food image.' Visual matches the visual-discrimination example; they align. Type: Metadata names 'neural processing of food images' and says the letter task directed 'attention away from the visual objects.' Few-shot sensory-processing examples suggest Perception, while the explicit attended-versus-unattended comparison additionally supports Attention; they align. No metadata fact is overridden by a demonstration pattern.","decision_summary":"Pathology top two: Healthy versus Unknown. '23 participants' and the fasting-versus-eating session description support a nonclinical sample; no recruited disorder is identified, so Healthy wins, though only by contextual inference (0.7). Modality top two: Visual versus Multisensory. 'Viewed rapid streams of overlaid food/non-food images and letters,' 'each food image,' and 'visual food information' support Visual; no second sensory channel is described (0.9, three explicit features and a clear few-shot match). Type top two: Perception versus Attention. 'Neural processing of food images,' classification of food images, and the abstract's 'processing of visual food information' support Perception; 'attention away from the visual objects' and the abstract's task-relevance comparison support Attention as a genuine second aim. Both are selected (0.9, multiple explicit phrases and a clear perceptual few-shot match). Calorie categorisation is sensory classification, not evidence that choice policy is the primary aim."}},"canonical_name":null,"name_confidence":0.55,"name_meta":{"suggested_at":"2026-04-14T10:18:35.343Z","model":"openai/gpt-5.2 + openai/gpt-5.4-mini + deterministic_fallback"},"name_source":"author_year","author_year":"Moerel2026","acknowledgements":"The authors acknowledge the University of Sydney HPC service for providing High Performance Computing resources.","how_to_acknowledge":"Moerel, D., Chenh, C., Bowman, S. A., & Carlson, T. A. (2025). The effect of hunger and state preferences on the neural processing of food images. bioRxiv, 2025-09.","associated_paper_meta":{"channel":"text/readme","confidence":"high","author_overlap":4,"is_oa":true,"oa_status":"preprint","source":"paper_resolver"}}}