{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:32PIGQDQOPQ35BPJMMTATWSPMB","short_pith_number":"pith:32PIGQDQ","schema_version":"1.0","canonical_sha256":"de9e83407073e1be85e9632609da4f605dff05bb5f434d8855a226ca86e9e74f","source":{"kind":"arxiv","id":"2206.14576","version":1},"attestation_state":"computed","paper":{"title":"Using cognitive psychology to understand GPT-3","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Eric Schulz, Marcel Binz","submitted_at":"2022-06-21T20:06:03Z","abstract_excerpt":"We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and causal reasoning abilities on a battery of canonical experiments from the literature. We find that much of GPT-3's behavior is impressive: it solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multi-armed bandit task, and shows signatures of model-based reinforcement learning. Yet we also find that small perturbations to vigne"},"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":"2206.14576","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-06-21T20:06:03Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"56eb3af5f3dc765dee9d256be6c03e1feb8bafb78ccdda2ae83e8bfefbfce299","abstract_canon_sha256":"1a3dd2aff4e429c3f7f13156e6662c6eb91f2bb7df2266664520e528b6f0121c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:43:50.942939Z","signature_b64":"5MXJVxhfsUQVu18JrUg5h3nCix8uAHfhaMDoCr8V4GymJUuldj72TrkzMIbUFewDR7rQj2M55Qt9UI1Zvx6/Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de9e83407073e1be85e9632609da4f605dff05bb5f434d8855a226ca86e9e74f","last_reissued_at":"2026-07-05T05:43:50.942545Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:43:50.942545Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Using cognitive psychology to understand GPT-3","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Eric Schulz, Marcel Binz","submitted_at":"2022-06-21T20:06:03Z","abstract_excerpt":"We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and causal reasoning abilities on a battery of canonical experiments from the literature. We find that much of GPT-3's behavior is impressive: it solves vignette-based tasks similarly or better than human subjects, is able to make decent decisions from descriptions, outperforms humans in a multi-armed bandit task, and shows signatures of model-based reinforcement learning. Yet we also find that small perturbations to vigne"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.14576","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/2206.14576/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":"2206.14576","created_at":"2026-07-05T05:43:50.942615+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.14576v1","created_at":"2026-07-05T05:43:50.942615+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.14576","created_at":"2026-07-05T05:43:50.942615+00:00"},{"alias_kind":"pith_short_12","alias_value":"32PIGQDQOPQ3","created_at":"2026-07-05T05:43:50.942615+00:00"},{"alias_kind":"pith_short_16","alias_value":"32PIGQDQOPQ35BPJ","created_at":"2026-07-05T05:43:50.942615+00:00"},{"alias_kind":"pith_short_8","alias_value":"32PIGQDQ","created_at":"2026-07-05T05:43:50.942615+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.07186","citing_title":"Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB","json":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB.json","graph_json":"https://pith.science/api/pith-number/32PIGQDQOPQ35BPJMMTATWSPMB/graph.json","events_json":"https://pith.science/api/pith-number/32PIGQDQOPQ35BPJMMTATWSPMB/events.json","paper":"https://pith.science/paper/32PIGQDQ"},"agent_actions":{"view_html":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB","download_json":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB.json","view_paper":"https://pith.science/paper/32PIGQDQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.14576&json=true","fetch_graph":"https://pith.science/api/pith-number/32PIGQDQOPQ35BPJMMTATWSPMB/graph.json","fetch_events":"https://pith.science/api/pith-number/32PIGQDQOPQ35BPJMMTATWSPMB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB/action/storage_attestation","attest_author":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB/action/author_attestation","sign_citation":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB/action/citation_signature","submit_replication":"https://pith.science/pith/32PIGQDQOPQ35BPJMMTATWSPMB/action/replication_record"}},"created_at":"2026-07-05T05:43:50.942615+00:00","updated_at":"2026-07-05T05:43:50.942615+00:00"}