Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories
Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at vario...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-09-01
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| Series: | Frontiers in Communication |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fcomm.2017.00012/full |
| _version_ | 1811341194674831360 |
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| author | Takahiro Soshi Norio Fujimaki Atsushi Matsumoto Aya S. Ihara |
| author_facet | Takahiro Soshi Norio Fujimaki Atsushi Matsumoto Aya S. Ihara |
| author_sort | Takahiro Soshi |
| collection | DOAJ |
| description | Accumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish), along with 195 verbal features. Healthy adults participated in memory-based feature-animal matching verification tests. Analyses included a hierarchical clustering analysis, support vector machine, and independent component analysis to specify features effective for classifications. Quantitative and qualitative comparisons for significant features were conducted between super-ordinate and sub-ordinate levels. The number of significant features was larger for super-ordinate than sub-ordinate levels. Qualitatively, the proportion of biological features was larger than cultural/affective features in both the levels, while the proportion of affective features increased at the sub-ordinate level. To summarize, the two types of features differentially function to establish category representations. |
| first_indexed | 2024-04-13T18:54:03Z |
| format | Article |
| id | doaj.art-db7e3486a6e24feaa0769dcb4eb2b325 |
| institution | Directory Open Access Journal |
| issn | 2297-900X |
| language | English |
| last_indexed | 2024-04-13T18:54:03Z |
| publishDate | 2017-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Communication |
| spelling | doaj.art-db7e3486a6e24feaa0769dcb4eb2b3252022-12-22T02:34:20ZengFrontiers Media S.A.Frontiers in Communication2297-900X2017-09-01210.3389/fcomm.2017.00012257527Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate CategoriesTakahiro Soshi0Norio Fujimaki1Atsushi Matsumoto2Aya S. Ihara3Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka University, Kobe, JapanCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka University, Kobe, JapanCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka University, Kobe, JapanCenter for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, Osaka University, Kobe, JapanAccumulating evidence suggests that category representations are based on features. Distinguishing features are considered to define categories, because of all-or-none responses for objects in different categories; however, it is unclear how distinguishing features actually classify objects at various category levels. The present study included 75 animals within three classes (mammal, bird, and fish), along with 195 verbal features. Healthy adults participated in memory-based feature-animal matching verification tests. Analyses included a hierarchical clustering analysis, support vector machine, and independent component analysis to specify features effective for classifications. Quantitative and qualitative comparisons for significant features were conducted between super-ordinate and sub-ordinate levels. The number of significant features was larger for super-ordinate than sub-ordinate levels. Qualitatively, the proportion of biological features was larger than cultural/affective features in both the levels, while the proportion of affective features increased at the sub-ordinate level. To summarize, the two types of features differentially function to establish category representations.http://journal.frontiersin.org/article/10.3389/fcomm.2017.00012/fullcategory representationdistinguishing featurelong-term memoryclassification analysissupport vector machine |
| spellingShingle | Takahiro Soshi Norio Fujimaki Atsushi Matsumoto Aya S. Ihara Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories Frontiers in Communication category representation distinguishing feature long-term memory classification analysis support vector machine |
| title | Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories |
| title_full | Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories |
| title_fullStr | Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories |
| title_full_unstemmed | Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories |
| title_short | Memory-Based Specification of Verbal Features for Classifying Animals into Super-Ordinate and Sub-Ordinate Categories |
| title_sort | memory based specification of verbal features for classifying animals into super ordinate and sub ordinate categories |
| topic | category representation distinguishing feature long-term memory classification analysis support vector machine |
| url | http://journal.frontiersin.org/article/10.3389/fcomm.2017.00012/full |
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