Publicação
Semantic feature production norms for manipulable objects
| Resumo: | Feature generation tasks and feature databases are important for understanding how knowledge is organized in semantic memory, as they reflect not only the kinds of information that individuals hold about objects but also how objects are conceptually represented. Traditionally, semantic norms focus on a variety of object categories and, as a result, have a small number of concepts per semantic category. Here, our main goal is to create a more fine-grained feature database exclusively for one category of objects-manipulable objects. This database contributes to the understanding of within-category, content-specific processing. To achieve this, we asked 130 participants to freely generate features for 80 manipulable objects and another group of 32 participants to generate action features for the same objects. We then compared our databases with other published semantic norms and found high similarity between them. In our databases, we calculated the similarity between objects in terms of visual, functional, encyclopaedic, and action feature types using Spearman correlation, Baker's gamma index, and cophenetic correlation. We discovered that objects were grouped in a distinctive and meaningful way according to feature type. Finally, we tested the validity of our databases by asking three groups of participants to perform a feature verification experiment while manipulating production frequency. Our results demonstrate that participants can recognize and associate the features of our databases with specific manipulable objects. Participants were faster to verify high-frequency features than low-frequency features. Overall, our data provide important insights into how we process manipulable objects and can be used to further inform cognitive and neural theories of object processing and identification. |
|---|---|
| Autores principais: | Valério, Daniela |
| Outros Autores: | Hussain, Akbar; Almeida, Jorge |
| Assunto: | Semantic norms; features; manipulable objects; semantic similarity; within-category |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Coimbra |
| Idioma: | inglês |
| Origem: | Estudo Geral - Universidade de Coimbra |
| _version_ | 1877781207353131008 |
|---|---|
| author | Valério, Daniela |
| author2 | Hussain, Akbar Almeida, Jorge |
| author2_role | author author |
| author_facet | Valério, Daniela Hussain, Akbar Almeida, Jorge |
| author_role | author |
| contributor_name_str_mv | Estudo Geral |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Valério, Daniela\"},{\"Person.name\":\"Hussain, Akbar\"},{\"Person.name\":\"Almeida, Jorge\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Estudo Geral |
| datacite.creators.creator.creatorName.fl_str_mv | Valério, Daniela Hussain, Akbar Almeida, Jorge |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Semantic norms; features; manipulable objects; semantic similarity; within-category |
| datacite.titles.title.fl_str_mv | Semantic feature production norms for manipulable objects |
| dc.contributor.none.fl_str_mv | Estudo Geral |
| dc.creator.none.fl_str_mv | Valério, Daniela Hussain, Akbar Almeida, Jorge |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/10316/118845 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Semantic norms; features; manipulable objects; semantic similarity; within-category |
| dc.title.fl_str_mv | Semantic feature production norms for manipulable objects |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Feature generation tasks and feature databases are important for understanding how knowledge is organized in semantic memory, as they reflect not only the kinds of information that individuals hold about objects but also how objects are conceptually represented. Traditionally, semantic norms focus on a variety of object categories and, as a result, have a small number of concepts per semantic category. Here, our main goal is to create a more fine-grained feature database exclusively for one category of objects-manipulable objects. This database contributes to the understanding of within-category, content-specific processing. To achieve this, we asked 130 participants to freely generate features for 80 manipulable objects and another group of 32 participants to generate action features for the same objects. We then compared our databases with other published semantic norms and found high similarity between them. In our databases, we calculated the similarity between objects in terms of visual, functional, encyclopaedic, and action feature types using Spearman correlation, Baker's gamma index, and cophenetic correlation. We discovered that objects were grouped in a distinctive and meaningful way according to feature type. Finally, we tested the validity of our databases by asking three groups of participants to perform a feature verification experiment while manipulating production frequency. Our results demonstrate that participants can recognize and associate the features of our databases with specific manipulable objects. Participants were faster to verify high-frequency features than low-frequency features. Overall, our data provide important insights into how we process manipulable objects and can be used to further inform cognitive and neural theories of object processing and identification. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | estudogl_4feba7c07679f4558118c2630a23a0a3 |
| identifier.url.fl_str_mv | https://hdl.handle.net/10316/118845 |
| inst_facet_str | urn:organizationAcronym:uc{{{_:::_}}}Universidade de Coimbra |
| instacron_str | uc |
| institution | Universidade de Coimbra |
| instname_str | Universidade de Coimbra |
| language | eng |
| network_acronym_str | estudogl |
| network_name_str | Estudo Geral - Universidade de Coimbra |
| oai_identifier_str | oai:estudogeral.uc.pt:10316/118845 |
| organization_str_mv | urn:organizationAcronym:uc |
| person_str_mv | Valério, Daniela Hussain, Akbar Almeida, Jorge |
| publishDate | 2023 |
| repo_facet_str | urn:repositoryAcronym:estudogl{{{_:::_}}}Estudo Geral - Universidade de Coimbra |
| reponame_str | Estudo Geral - Universidade de Coimbra |
| repository_id_str | urn:repositoryAcronym:estudogl |
| service_str_mv | urn:repositoryAcronym:estudogl |
| spelling | engengFeature generation tasks and feature databases are important for understanding how knowledge is organized in semantic memory, as they reflect not only the kinds of information that individuals hold about objects but also how objects are conceptually represented. Traditionally, semantic norms focus on a variety of object categories and, as a result, have a small number of concepts per semantic category. Here, our main goal is to create a more fine-grained feature database exclusively for one category of objects-manipulable objects. This database contributes to the understanding of within-category, content-specific processing. To achieve this, we asked 130 participants to freely generate features for 80 manipulable objects and another group of 32 participants to generate action features for the same objects. We then compared our databases with other published semantic norms and found high similarity between them. In our databases, we calculated the similarity between objects in terms of visual, functional, encyclopaedic, and action feature types using Spearman correlation, Baker's gamma index, and cophenetic correlation. We discovered that objects were grouped in a distinctive and meaningful way according to feature type. Finally, we tested the validity of our databases by asking three groups of participants to perform a feature verification experiment while manipulating production frequency. Our results demonstrate that participants can recognize and associate the features of our databases with specific manipulable objects. Participants were faster to verify high-frequency features than low-frequency features. Overall, our data provide important insights into how we process manipulable objects and can be used to further inform cognitive and neural theories of object processing and identification.application/pdfporSemantic feature production norms for manipulable objectsValério, DanielaHussain, AkbarAlmeida, JorgeHostingInstitutionOrganizationalEstudo Gerale-mailmailto:inf@sib.uc.ptinf@sib.uc.ptISSNIsPartOf0264-3294ISSNIsPartOf1464-0627DOIIsPartOf10.1080/02643294.2023.227918520232023-01-01T00:00:00Z2023-01-01T00:00:00ZHandlehttps://hdl.handle.net/10316/118845http://purl.org/coar/access_right/c_abf2open accessSemantic norms; features; manipulable objects; semantic similarity; within-category3245627 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articleapplication/pdfhttps://estudogeral.uc.pt/bitstream/10316/118845/1/Semantic%20feature%20production%20norms%20for%20manipulable%20objects%20%281%29.pdf |
| spellingShingle | Semantic feature production norms for manipulable objects Valério, Daniela Semantic norms; features; manipulable objects; semantic similarity; within-category |
| status | SINGLETON |
| subject.fl_str_mv | Semantic norms; features; manipulable objects; semantic similarity; within-category |
| title | Semantic feature production norms for manipulable objects |
| title_full | Semantic feature production norms for manipulable objects |
| title_fullStr | Semantic feature production norms for manipulable objects |
| title_full_unstemmed | Semantic feature production norms for manipulable objects |
| title_short | Semantic feature production norms for manipulable objects |
| title_sort | Semantic feature production norms for manipulable objects |
| topic | Semantic norms; features; manipulable objects; semantic similarity; within-category |
| topic_facet | Semantic norms; features; manipulable objects; semantic similarity; within-category |
| url | https://hdl.handle.net/10316/118845 |
| visible | 1 |
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