Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach.
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| Title: | Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach. |
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| Authors: | Lozano, José H.1 (AUTHOR) joseh.lozano@uam.es, Revuelta, Javier1 (AUTHOR) |
| Source: | Intelligence. Sep2020, Vol. 82, pN.PAG-N.PAG. 1p. |
| Abstract: | The present study aimed to investigate practice effects associated with the abstract rules involved in the Raven's Advanced Progressive Matrices (RAPM) under standard administration conditions. To that end, a linear logistic test modeling approach was used in combination with Carpenter, Just, and Shell's (1990) taxonomy of rules. Several operation-specific learning models were used in order to test different contingent and non-contingent learning hypotheses. The models were fitted to a sample of responses from 293 participants to Sets I and II of the RAPM. A Bayesian framework was adopted for model estimation and evaluation. The perceptual variables involved in the items were included in the analyses in order to control their influence on performance on the RAPM. The results did not provide evidence of rule learning during the RAPM. Instead, they suggested the existence of fatigue effects associated with each of the rules. Interestingly, the results revealed the existence of learning effects associated with the items' perceptual properties. • Linear logistic test models were used to investigate practice effects in the RAPM. • No evidence was found of rule learning during the test. • The results suggested fatigue effects associated with the abstract rules. • There were found learning effects associated with the items' perceptual properties. [ABSTRACT FROM AUTHOR] |
| Copyright of Intelligence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 145995843 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lozano%2C+José+H%2E%22">Lozano, José H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> joseh.lozano@uam.es</i><br /><searchLink fieldCode="AR" term="%22Revuelta%2C+Javier%22">Revuelta, Javier</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligence%22">Intelligence</searchLink>. Sep2020, Vol. 82, pN.PAG-N.PAG. 1p. – Name: Abstract Label: Abstract Group: Ab Data: The present study aimed to investigate practice effects associated with the abstract rules involved in the Raven's Advanced Progressive Matrices (RAPM) under standard administration conditions. To that end, a linear logistic test modeling approach was used in combination with Carpenter, Just, and Shell's (1990) taxonomy of rules. Several operation-specific learning models were used in order to test different contingent and non-contingent learning hypotheses. The models were fitted to a sample of responses from 293 participants to Sets I and II of the RAPM. A Bayesian framework was adopted for model estimation and evaluation. The perceptual variables involved in the items were included in the analyses in order to control their influence on performance on the RAPM. The results did not provide evidence of rule learning during the RAPM. Instead, they suggested the existence of fatigue effects associated with each of the rules. Interestingly, the results revealed the existence of learning effects associated with the items' perceptual properties. • Linear logistic test models were used to investigate practice effects in the RAPM. • No evidence was found of rule learning during the test. • The results suggested fatigue effects associated with the abstract rules. • There were found learning effects associated with the items' perceptual properties. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Intelligence is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.intell.2020.101468 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Titles: – TitleFull: Investigating operation-specific learning effects in the Raven's Advanced Progressive Matrices: A linear logistic test modeling approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lozano, José H. – PersonEntity: Name: NameFull: Revuelta, Javier IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 01602896 Numbering: – Type: volume Value: 82 Titles: – TitleFull: Intelligence Type: main |
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