{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:NR2FFG2GKZ3WZJQ46IKIZ6FELF","short_pith_number":"pith:NR2FFG2G","schema_version":"1.0","canonical_sha256":"6c74529b4656776ca61cf2148cf8a4597cd9434a647743e7d93049be9a6e8220","source":{"kind":"arxiv","id":"2403.01116","version":1},"attestation_state":"computed","paper":{"title":"MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Chong Li, Shaonan Wang, Xiaohan Zhang, Yunhao Zhang","submitted_at":"2024-03-02T07:49:57Z","abstract_excerpt":"Pre-trained computational language models have recently made remarkable progress in harnessing the language abilities which were considered unique to humans. Their success has raised interest in whether these models represent and process language like humans. To answer this question, this paper proposes MulCogBench, a multi-modal cognitive benchmark dataset collected from native Chinese and English participants. It encompasses a variety of cognitive data, including subjective semantic ratings, eye-tracking, functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). To asse"},"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":"2403.01116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-02T07:49:57Z","cross_cats_sorted":[],"title_canon_sha256":"734f32545710c304bb03c2aba32d42eaf0828ea02afb9a5ab54fbb5b993864df","abstract_canon_sha256":"6d5b3bd6e4aea437131fc2300bb146650455f337f84445f760e73f838a47121f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:51:16.720881Z","signature_b64":"LNjyW+U5ICvFNYqmKRSGwduoMCBY6bE0ptTynms9oNflQpxqNmEIn1YWYiu4uga5Wyw8u7UlIYrzynWVBkx5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c74529b4656776ca61cf2148cf8a4597cd9434a647743e7d93049be9a6e8220","last_reissued_at":"2026-07-05T07:51:16.720494Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:51:16.720494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MulCogBench: A Multi-modal Cognitive Benchmark Dataset for Evaluating Chinese and English Computational Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chengqing Zong, Chong Li, Shaonan Wang, Xiaohan Zhang, Yunhao Zhang","submitted_at":"2024-03-02T07:49:57Z","abstract_excerpt":"Pre-trained computational language models have recently made remarkable progress in harnessing the language abilities which were considered unique to humans. Their success has raised interest in whether these models represent and process language like humans. To answer this question, this paper proposes MulCogBench, a multi-modal cognitive benchmark dataset collected from native Chinese and English participants. It encompasses a variety of cognitive data, including subjective semantic ratings, eye-tracking, functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG). To asse"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.01116","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.01116/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2403.01116","created_at":"2026-07-05T07:51:16.720549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.01116v1","created_at":"2026-07-05T07:51:16.720549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.01116","created_at":"2026-07-05T07:51:16.720549+00:00"},{"alias_kind":"pith_short_12","alias_value":"NR2FFG2GKZ3W","created_at":"2026-07-05T07:51:16.720549+00:00"},{"alias_kind":"pith_short_16","alias_value":"NR2FFG2GKZ3WZJQ4","created_at":"2026-07-05T07:51:16.720549+00:00"},{"alias_kind":"pith_short_8","alias_value":"NR2FFG2G","created_at":"2026-07-05T07:51:16.720549+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.08309","citing_title":"Improving MLLM's Document Image Machine Translation via Synchronously Self-reviewing Its OCR Proficiency","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF","json":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF.json","graph_json":"https://pith.science/api/pith-number/NR2FFG2GKZ3WZJQ46IKIZ6FELF/graph.json","events_json":"https://pith.science/api/pith-number/NR2FFG2GKZ3WZJQ46IKIZ6FELF/events.json","paper":"https://pith.science/paper/NR2FFG2G"},"agent_actions":{"view_html":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF","download_json":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF.json","view_paper":"https://pith.science/paper/NR2FFG2G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.01116&json=true","fetch_graph":"https://pith.science/api/pith-number/NR2FFG2GKZ3WZJQ46IKIZ6FELF/graph.json","fetch_events":"https://pith.science/api/pith-number/NR2FFG2GKZ3WZJQ46IKIZ6FELF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF/action/storage_attestation","attest_author":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF/action/author_attestation","sign_citation":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF/action/citation_signature","submit_replication":"https://pith.science/pith/NR2FFG2GKZ3WZJQ46IKIZ6FELF/action/replication_record"}},"created_at":"2026-07-05T07:51:16.720549+00:00","updated_at":"2026-07-05T07:51:16.720549+00:00"}