Object-Label-Order Effect When Learning from an Inconsistent Source
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| Title: | Object-Label-Order Effect When Learning from an Inconsistent Source |
|---|---|
| Language: | English |
| Authors: | Ma, Timmy, Komarova, Natalia L. |
| Source: | Cognitive Science. Aug 2019 43(8). |
| Availability: | Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA |
| Peer Reviewed: | Y |
| Page Count: | 25 |
| Publication Date: | 2019 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Reliability, Associative Learning, Symbolic Learning, Sequential Learning, Ambiguity (Context), Models |
| DOI: | 10.1111/cogs.12737 |
| ISSN: | 1551-6709 |
| Abstract: | Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, where the source is inconsistent by design: It uses both "correct" and "incorrect" object-label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object-Label (OL) and the Label-Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image-utterance associations, despite the ambiguous learning task. |
| Abstractor: | As Provided |
| Entry Date: | 2019 |
| Accession Number: | EJ1225843 |
| Database: | ERIC |
| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwH6M7pqLYV5yI6tWKNCg8QYAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDDJorORCkpDDMcR72wIBEICBmzjOwwiEkgllx3rBWZsWMGHxCBM2Dt8iPsjXF3dWc1kwEXG1imTwNmYf-JrbvFm1l1EIuwOJamnXjB4oqNCKsPmslzWRz3cn9LDo5spCJepUQYNvDMbRocaw7VOwokjDN8pkknXwB07XCC8MlwldOehFdmUj__zalpOD-h0p8w4TjBjfzDgtaJFmQmgF82bC6ZNwvHhvtZjOX25K Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Object-Label-Order Effect When Learning from an Inconsistent Source – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Timmy%22">Ma, Timmy</searchLink><br /><searchLink fieldCode="AR" term="%22Komarova%2C+Natalia+L%2E%22">Komarova, Natalia L.</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Cognitive+Science%22"><i>Cognitive Science</i></searchLink>. Aug 2019 43(8). – Name: Avail Label: Availability Group: Avail Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 25 – Name: DatePubCY Label: Publication Date Group: Date Data: 2019 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Reliability%22">Reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Associative+Learning%22">Associative Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Symbolic+Learning%22">Symbolic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+Learning%22">Sequential Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ambiguity+%28Context%29%22">Ambiguity (Context)</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1111/cogs.12737 – Name: ISSN Label: ISSN Group: ISSN Data: 1551-6709 – Name: Abstract Label: Abstract Group: Ab Data: Learning in natural environments is often characterized by a degree of inconsistency from an input. These inconsistencies occur, for example, when learning from more than one source, or when the presence of environmental noise distorts incoming information; as a result, the task faced by the learner becomes ambiguous. In this study, we investigate how learners handle such situations. We focus on the setting where a learner receives and processes a sequence of utterances to master associations between objects and their labels, where the source is inconsistent by design: It uses both "correct" and "incorrect" object-label pairings. We hypothesize that depending on the order of presentation, the result of the learning may be different. To this end, we consider two types of symbolic learning procedures: the Object-Label (OL) and the Label-Object (LO) process. In the OL process, the learner is first exposed to the object, and then the label. In the LO process, this order is reversed. We perform experiments with human subjects, and also construct a computational model that is based on a nonlinear stochastic reinforcement learning algorithm. It is observed experimentally that OL learners are generally better at processing inconsistent input compared to LO learners. We show that the patterns observed in the learning experiments can be reproduced in the simulations if the model includes (a) an ability to regularize the input (and also to do the opposite, i.e., undermatch) and (b) an ability to take account of implicit negative evidence (i.e., interactions among different objects/labels). The model suggests that while both types of learners utilize implicit negative evidence in a similar way, there is a difference in regularization patterns: OL learners regularize the input, whereas LO learners undermatch. As a result, OL learners are able to form a more consistent system of image-utterance associations, despite the ambiguous learning task. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2019 – Name: AN Label: Accession Number Group: ID Data: EJ1225843 |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1225843 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/cogs.12737 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 25 Subjects: – SubjectFull: Reliability Type: general – SubjectFull: Associative Learning Type: general – SubjectFull: Symbolic Learning Type: general – SubjectFull: Sequential Learning Type: general – SubjectFull: Ambiguity (Context) Type: general – SubjectFull: Models Type: general Titles: – TitleFull: Object-Label-Order Effect When Learning from an Inconsistent Source Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Timmy – PersonEntity: Name: NameFull: Komarova, Natalia L. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Type: published Y: 2019 Identifiers: – Type: issn-electronic Value: 1551-6709 Numbering: – Type: volume Value: 43 – Type: issue Value: 8 Titles: – TitleFull: Cognitive Science Type: main |
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