{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CF2YOBYXRJN7TS5WMX5HJW4WGI","short_pith_number":"pith:CF2YOBYX","schema_version":"1.0","canonical_sha256":"11758707178a5bf9cbb665fa74db9632324e2f3858b9bf2052b755a869ef1c5e","source":{"kind":"arxiv","id":"2508.04148","version":1},"attestation_state":"computed","paper":{"title":"STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Alexander Tuzhilin, Michel Wedel, Moshe Unger","submitted_at":"2025-08-06T07:20:31Z","abstract_excerpt":"The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decision environment, for which currently no foundational models are available. The architecture, called STARE (Spatio-Temporal Attention Representation for Eye Tracking), uses a new tokenization strategy, which involves mapping the x- and y- pixel coordinates of eye-movement time series on predefined, contiguous Regions of Interest. That tokenization makes the spatio-temporal eye-movement data available to the Chronos, a "},"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":"2508.04148","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2025-08-06T07:20:31Z","cross_cats_sorted":[],"title_canon_sha256":"0672e6e995b69bd5e51fc65e1b247bae15d3bea0e1160f54721ecf27991083da","abstract_canon_sha256":"c66b17354b9c87a27f44d194b307f62fb64696e85b44c8249ce5de38ca5be38e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:21.513026Z","signature_b64":"doJ774XxBvaU4hfeitVbECtpoo+wSfh9BG2MtBuw4ckaFp5gA7Gc8PZ0v3yo5D0u5F0/pOqos9aEd4gT5bYeBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11758707178a5bf9cbb665fa74db9632324e2f3858b9bf2052b755a869ef1c5e","last_reissued_at":"2026-07-05T11:49:21.512563Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:21.512563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Alexander Tuzhilin, Michel Wedel, Moshe Unger","submitted_at":"2025-08-06T07:20:31Z","abstract_excerpt":"The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decision environment, for which currently no foundational models are available. The architecture, called STARE (Spatio-Temporal Attention Representation for Eye Tracking), uses a new tokenization strategy, which involves mapping the x- and y- pixel coordinates of eye-movement time series on predefined, contiguous Regions of Interest. That tokenization makes the spatio-temporal eye-movement data available to the Chronos, a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.04148","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/2508.04148/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":"2508.04148","created_at":"2026-07-05T11:49:21.512619+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.04148v1","created_at":"2026-07-05T11:49:21.512619+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.04148","created_at":"2026-07-05T11:49:21.512619+00:00"},{"alias_kind":"pith_short_12","alias_value":"CF2YOBYXRJN7","created_at":"2026-07-05T11:49:21.512619+00:00"},{"alias_kind":"pith_short_16","alias_value":"CF2YOBYXRJN7TS5W","created_at":"2026-07-05T11:49:21.512619+00:00"},{"alias_kind":"pith_short_8","alias_value":"CF2YOBYX","created_at":"2026-07-05T11:49:21.512619+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/CF2YOBYXRJN7TS5WMX5HJW4WGI","json":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI.json","graph_json":"https://pith.science/api/pith-number/CF2YOBYXRJN7TS5WMX5HJW4WGI/graph.json","events_json":"https://pith.science/api/pith-number/CF2YOBYXRJN7TS5WMX5HJW4WGI/events.json","paper":"https://pith.science/paper/CF2YOBYX"},"agent_actions":{"view_html":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI","download_json":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI.json","view_paper":"https://pith.science/paper/CF2YOBYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.04148&json=true","fetch_graph":"https://pith.science/api/pith-number/CF2YOBYXRJN7TS5WMX5HJW4WGI/graph.json","fetch_events":"https://pith.science/api/pith-number/CF2YOBYXRJN7TS5WMX5HJW4WGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI/action/storage_attestation","attest_author":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI/action/author_attestation","sign_citation":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI/action/citation_signature","submit_replication":"https://pith.science/pith/CF2YOBYXRJN7TS5WMX5HJW4WGI/action/replication_record"}},"created_at":"2026-07-05T11:49:21.512619+00:00","updated_at":"2026-07-05T11:49:21.512619+00:00"}