date: 2023-09-21T12:38:38Z pdf:PDFVersion: 1.5 pdf:docinfo:title: Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer xmp:CreatorTool: Adobe InDesign CS5.5 (7.5) access_permission:can_print_degraded: true subject: Psychol Sci 2023.34:1007-1023 dc:format: application/pdf; version=1.5 pdf:docinfo:creator_tool: Adobe InDesign CS5.5 (7.5) access_permission:fill_in_form: true pdf:encrypted: false dc:title: Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer modified: 2023-09-21T12:38:38Z cp:subject: Psychol Sci 2023.34:1007-1023 pdf:docinfo:custom:CrossMarkDomains[1]: journals.sagepub.com pdf:docinfo:subject: Psychol Sci 2023.34:1007-1023 pdf:docinfo:creator: Edward A. Vessel, Laura Pasqualette, Cem Uran, Sarah Koldehoff, Giacomo Bignardi, and Martin Vinck meta:author: Edward A. Vessel, Laura Pasqualette, Cem Uran, Sarah Koldehoff, Giacomo Bignardi, and Martin Vinck trapped: False meta:creation-date: 2023-09-13T07:13:12Z pdf:docinfo:custom:CrossmarkMajorVersionDate: 2023-08-14 created: 2023-09-13T07:13:12Z access_permission:extract_for_accessibility: true Creation-Date: 2023-09-13T07:13:12Z pdf:docinfo:custom:doi: 10.1177/09567976231188107 pdf:docinfo:custom:CrossmarkDomainExclusive: true Author: Edward A. Vessel, Laura Pasqualette, Cem Uran, Sarah Koldehoff, Giacomo Bignardi, and Martin Vinck producer: Adobe PDF Library 9.9; modified using iText 4.2.0 by 1T3XT CrossmarkDomainExclusive: true pdf:docinfo:producer: Adobe PDF Library 9.9; modified using iText 4.2.0 by 1T3XT doi: 10.1177/09567976231188107 pdf:unmappedUnicodeCharsPerPage: 0 dc:description: Psychol Sci 2023.34:1007-1023 Keywords: aesthetic valuation,artwork,identity,machine learning,open data access_permission:modify_annotations: true dc:creator: Edward A. Vessel, Laura Pasqualette, Cem Uran, Sarah Koldehoff, Giacomo Bignardi, and Martin Vinck description: Psychol Sci 2023.34:1007-1023 dcterms:created: 2023-09-13T07:13:12Z Last-Modified: 2023-09-21T12:38:38Z dcterms:modified: 2023-09-21T12:38:38Z title: Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer xmpMM:DocumentID: uuid:7b8cba33-6c4b-430b-ad98-3ac39d84f07b Last-Save-Date: 2023-09-21T12:38:38Z CrossMarkDomains[1]: journals.sagepub.com pdf:docinfo:keywords: aesthetic valuation,artwork,identity,machine learning,open data pdf:docinfo:modified: 2023-09-21T12:38:38Z meta:save-date: 2023-09-21T12:38:38Z Content-Type: application/pdf X-Parsed-By: org.apache.tika.parser.DefaultParser creator: Edward A. Vessel, Laura Pasqualette, Cem Uran, Sarah Koldehoff, Giacomo Bignardi, and Martin Vinck dc:subject: aesthetic valuation,artwork,identity,machine learning,open data access_permission:assemble_document: true xmpTPg:NPages: 17 pdf:charsPerPage: 3842 access_permission:extract_content: true access_permission:can_print: true pdf:docinfo:trapped: False meta:keyword: aesthetic valuation,artwork,identity,machine learning,open data access_permission:can_modify: true pdf:docinfo:created: 2023-09-13T07:13:12Z CrossmarkMajorVersionDate: 2023-08-14