{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZHWNX5D6UEHY6AFJ7FOX52GJCT","short_pith_number":"pith:ZHWNX5D6","schema_version":"1.0","canonical_sha256":"c9ecdbf47ea10f8f00a9f95d7ee8c914c1f42586389809882d93ad9d42086761","source":{"kind":"arxiv","id":"2502.03560","version":1},"attestation_state":"computed","paper":{"title":"Simulating Errors in Touchscreen Typing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Antti Oulasvirta, Danqing Shi, Francisco Erivaldo Fernandes Junior, Shumin Zhai, Yujun Zhu","submitted_at":"2025-02-05T19:17:33Z","abstract_excerpt":"Empirical evidence shows that typing on touchscreen devices is prone to errors and that correcting them poses a major detriment to users' performance. Design of text entry systems that better serve users, across their broad capability range, necessitates understanding the cognitive mechanisms that underpin these errors. However, prior models of typing cover only motor slips. The paper reports on extending the scope of computational modeling of typing to cover the cognitive mechanisms behind the three main types of error: slips (inaccurate execution), lapses (forgetting), and mistakes (incorrec"},"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":"2502.03560","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2025-02-05T19:17:33Z","cross_cats_sorted":[],"title_canon_sha256":"2c5076cd0f29d6b60aff9dc8743fe4fc2eef01f839d6a541c9272bce0c907edc","abstract_canon_sha256":"904e4930d87b45400287d08dcc25aa5eaf6a43d7084039dbbcd9d199fc52674b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:12.160300Z","signature_b64":"1AeZrlXWV5lYojMQ2nj0ztcMmo12lFFO5LL/u3gBQW7+hXvDzXoLhfTHMvXzpntTcrF08U8d+EO+VhVBKdUCAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9ecdbf47ea10f8f00a9f95d7ee8c914c1f42586389809882d93ad9d42086761","last_reissued_at":"2026-07-05T10:10:12.159801Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:12.159801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simulating Errors in Touchscreen Typing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Antti Oulasvirta, Danqing Shi, Francisco Erivaldo Fernandes Junior, Shumin Zhai, Yujun Zhu","submitted_at":"2025-02-05T19:17:33Z","abstract_excerpt":"Empirical evidence shows that typing on touchscreen devices is prone to errors and that correcting them poses a major detriment to users' performance. Design of text entry systems that better serve users, across their broad capability range, necessitates understanding the cognitive mechanisms that underpin these errors. However, prior models of typing cover only motor slips. The paper reports on extending the scope of computational modeling of typing to cover the cognitive mechanisms behind the three main types of error: slips (inaccurate execution), lapses (forgetting), and mistakes (incorrec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03560","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/2502.03560/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":"2502.03560","created_at":"2026-07-05T10:10:12.159864+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.03560v1","created_at":"2026-07-05T10:10:12.159864+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03560","created_at":"2026-07-05T10:10:12.159864+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZHWNX5D6UEHY","created_at":"2026-07-05T10:10:12.159864+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZHWNX5D6UEHY6AFJ","created_at":"2026-07-05T10:10:12.159864+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZHWNX5D6","created_at":"2026-07-05T10:10:12.159864+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.18488","citing_title":"Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications","ref_index":25,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT","json":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT.json","graph_json":"https://pith.science/api/pith-number/ZHWNX5D6UEHY6AFJ7FOX52GJCT/graph.json","events_json":"https://pith.science/api/pith-number/ZHWNX5D6UEHY6AFJ7FOX52GJCT/events.json","paper":"https://pith.science/paper/ZHWNX5D6"},"agent_actions":{"view_html":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT","download_json":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT.json","view_paper":"https://pith.science/paper/ZHWNX5D6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.03560&json=true","fetch_graph":"https://pith.science/api/pith-number/ZHWNX5D6UEHY6AFJ7FOX52GJCT/graph.json","fetch_events":"https://pith.science/api/pith-number/ZHWNX5D6UEHY6AFJ7FOX52GJCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT/action/storage_attestation","attest_author":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT/action/author_attestation","sign_citation":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT/action/citation_signature","submit_replication":"https://pith.science/pith/ZHWNX5D6UEHY6AFJ7FOX52GJCT/action/replication_record"}},"created_at":"2026-07-05T10:10:12.159864+00:00","updated_at":"2026-07-05T10:10:12.159864+00:00"}