REFRESH: A New Approach to Modeling Dimensional Biases in Perceptual Similarity and Categorization.
Saved in:
| Title: | REFRESH: A New Approach to Modeling Dimensional Biases in Perceptual Similarity and Categorization. |
|---|---|
| Authors: | Sanborn, Adam N.1 a.n.sanborn@warwick.ac.uk, Heller, Katherine2, Austerweil, Joseph L.3, Chater, Nick4 |
| Source: | Psychological Review. Nov2021, Vol. 128 Issue 6, p1145-1186. 42p. |
| Subject Terms: | *Prior learning, Selectivity (Psychology), Stimulus & response (Psychology), Hierarchical clustering (Cluster analysis) |
| Abstract: | Much categorization behavior can be explained by family resemblance: New items are classified by comparison with previously learned exemplars. However, categorization behavior also shows a variety of dimensional biases, where the underlying space has so-called "separable" dimensions: Ease of learning categories depends on how the stimuli align with the separable dimensions of the space. For example, if a set of objects of various sizes and colors can be accurately categorized using a single separable dimension (e.g., size), then category learning will be fast, while if the category is determined by both dimensions, learning will be slow. To capture these dimensional biases, almost all models of categorization supplement family resemblance with either rule-based systems or selective attention to separable dimensions. But these models do not explain how separable dimensions initially arise; they are presumed to be unexplained psychological primitives. We develop, instead, a pure family resemblance version of the Rational Model of Categorization (RMC), which we term the Rational Exclusively Family RESemblance Hierarchy (REFRESH), which does not presuppose any separable dimensions in the space of stimuli. REFRESH infers how the stimuli are clustered and uses a hierarchical prior to learn expectations about the variability of clusters across categories. We first demonstrate the dimensional alignment of natural-category features and then show how through a lifetime of categorization experience REFRESH will learn prior expectations that clusters of stimuli will align with separable dimensions. REFRESH captures the key dimensional biases and also explains their stimulus-dependence and how they are learned and develop. [ABSTRACT FROM AUTHOR] |
| Copyright of Psychological Review is the property of American Psychological Association 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 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 161756992 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: REFRESH: A New Approach to Modeling Dimensional Biases in Perceptual Similarity and Categorization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sanborn%2C+Adam+N%2E%22">Sanborn, Adam N.</searchLink><relatesTo>1</relatesTo><i> a.n.sanborn@warwick.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Heller%2C+Katherine%22">Heller, Katherine</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Austerweil%2C+Joseph+L%2E%22">Austerweil, Joseph L.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Chater%2C+Nick%22">Chater, Nick</searchLink><relatesTo>4</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Psychological+Review%22">Psychological Review</searchLink>. Nov2021, Vol. 128 Issue 6, p1145-1186. 42p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink><br /><searchLink fieldCode="DE" term="%22Selectivity+%28Psychology%29%22">Selectivity (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Stimulus+%26+response+%28Psychology%29%22">Stimulus & response (Psychology)</searchLink><br /><searchLink fieldCode="DE" term="%22Hierarchical+clustering+%28Cluster+analysis%29%22">Hierarchical clustering (Cluster analysis)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Much categorization behavior can be explained by family resemblance: New items are classified by comparison with previously learned exemplars. However, categorization behavior also shows a variety of dimensional biases, where the underlying space has so-called "separable" dimensions: Ease of learning categories depends on how the stimuli align with the separable dimensions of the space. For example, if a set of objects of various sizes and colors can be accurately categorized using a single separable dimension (e.g., size), then category learning will be fast, while if the category is determined by both dimensions, learning will be slow. To capture these dimensional biases, almost all models of categorization supplement family resemblance with either rule-based systems or selective attention to separable dimensions. But these models do not explain how separable dimensions initially arise; they are presumed to be unexplained psychological primitives. We develop, instead, a pure family resemblance version of the Rational Model of Categorization (RMC), which we term the Rational Exclusively Family RESemblance Hierarchy (REFRESH), which does not presuppose any separable dimensions in the space of stimuli. REFRESH infers how the stimuli are clustered and uses a hierarchical prior to learn expectations about the variability of clusters across categories. We first demonstrate the dimensional alignment of natural-category features and then show how through a lifetime of categorization experience REFRESH will learn prior expectations that clusters of stimuli will align with separable dimensions. REFRESH captures the key dimensional biases and also explains their stimulus-dependence and how they are learned and develop. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Psychological Review is the property of American Psychological Association 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=161756992 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1037/rev0000310 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 42 StartPage: 1145 Subjects: – SubjectFull: Prior learning Type: general – SubjectFull: Selectivity (Psychology) Type: general – SubjectFull: Stimulus & response (Psychology) Type: general – SubjectFull: Hierarchical clustering (Cluster analysis) Type: general Titles: – TitleFull: REFRESH: A New Approach to Modeling Dimensional Biases in Perceptual Similarity and Categorization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sanborn, Adam N. – PersonEntity: Name: NameFull: Heller, Katherine – PersonEntity: Name: NameFull: Austerweil, Joseph L. – PersonEntity: Name: NameFull: Chater, Nick IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 0033295X Numbering: – Type: volume Value: 128 – Type: issue Value: 6 Titles: – TitleFull: Psychological Review Type: main |
| ResultId | 1 |
