{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:R4OYYRVECHGNLFPGLN5J3N4PIZ","short_pith_number":"pith:R4OYYRVE","schema_version":"1.0","canonical_sha256":"8f1d8c46a411ccd595e65b7a9db78f464f643f23273975f9feeb9d2d2266b047","source":{"kind":"arxiv","id":"1512.00899","version":1},"attestation_state":"computed","paper":{"title":"Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Michael J. Tarr, Robert E. Kass, Ying Yang","submitted_at":"2015-12-02T23:02:58Z","abstract_excerpt":"Magnetoencephalography (MEG) has a high temporal resolution well-suited for studying perceptual learning. However, to identify where learning happens in the brain, one needs to ap- ply source localization techniques to project MEG sensor data into brain space. Previous source localization methods, such as the short-time Fourier transform (STFT) method by Gramfort et al.([Gramfort et al., 2013]) produced intriguing results, but they were not designed to incor- porate trial-by-trial learning effects. Here we modify the approach in [Gramfort et al., 2013] to produce an STFT-based source localizat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1512.00899","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-12-02T23:02:58Z","cross_cats_sorted":[],"title_canon_sha256":"5b2db0cc493aa87f00dc6a19e452cf6770f90613c640f3db471bdfc6fdcaeac6","abstract_canon_sha256":"5367d886f2be42c9c81d16f77be003b794074ea2bd4265987d3c2ddee5b85bc7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:25:23.882831Z","signature_b64":"NBY5KTwKl0LFx6oReV06tutIbEeeIhgIN7+JE+4Rf4sIRh68Qc9X0R9b5bYnsfwl6kq/1cMo+x5G2f3l/PcdDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8f1d8c46a411ccd595e65b7a9db78f464f643f23273975f9feeb9d2d2266b047","last_reissued_at":"2026-05-18T01:25:23.882421Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:25:23.882421Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Estimating Learning Effects: A Short-Time Fourier Transform Regression Model for MEG Source Localization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Michael J. Tarr, Robert E. Kass, Ying Yang","submitted_at":"2015-12-02T23:02:58Z","abstract_excerpt":"Magnetoencephalography (MEG) has a high temporal resolution well-suited for studying perceptual learning. However, to identify where learning happens in the brain, one needs to ap- ply source localization techniques to project MEG sensor data into brain space. Previous source localization methods, such as the short-time Fourier transform (STFT) method by Gramfort et al.([Gramfort et al., 2013]) produced intriguing results, but they were not designed to incor- porate trial-by-trial learning effects. Here we modify the approach in [Gramfort et al., 2013] to produce an STFT-based source localizat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1512.00899","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1512.00899","created_at":"2026-05-18T01:25:23.882480+00:00"},{"alias_kind":"arxiv_version","alias_value":"1512.00899v1","created_at":"2026-05-18T01:25:23.882480+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1512.00899","created_at":"2026-05-18T01:25:23.882480+00:00"},{"alias_kind":"pith_short_12","alias_value":"R4OYYRVECHGN","created_at":"2026-05-18T12:29:39.896362+00:00"},{"alias_kind":"pith_short_16","alias_value":"R4OYYRVECHGNLFPG","created_at":"2026-05-18T12:29:39.896362+00:00"},{"alias_kind":"pith_short_8","alias_value":"R4OYYRVE","created_at":"2026-05-18T12:29:39.896362+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ","json":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ.json","graph_json":"https://pith.science/api/pith-number/R4OYYRVECHGNLFPGLN5J3N4PIZ/graph.json","events_json":"https://pith.science/api/pith-number/R4OYYRVECHGNLFPGLN5J3N4PIZ/events.json","paper":"https://pith.science/paper/R4OYYRVE"},"agent_actions":{"view_html":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ","download_json":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ.json","view_paper":"https://pith.science/paper/R4OYYRVE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1512.00899&json=true","fetch_graph":"https://pith.science/api/pith-number/R4OYYRVECHGNLFPGLN5J3N4PIZ/graph.json","fetch_events":"https://pith.science/api/pith-number/R4OYYRVECHGNLFPGLN5J3N4PIZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ/action/storage_attestation","attest_author":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ/action/author_attestation","sign_citation":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ/action/citation_signature","submit_replication":"https://pith.science/pith/R4OYYRVECHGNLFPGLN5J3N4PIZ/action/replication_record"}},"created_at":"2026-05-18T01:25:23.882480+00:00","updated_at":"2026-05-18T01:25:23.882480+00:00"}