Publication:

Testing learning mechanisms of rule-based judgment

Date

Date

Date
2019
Journal Article
Published version

Citations

Citation copied

Hoffmann, J. A., von Helversen, B., & Rieskamp, J. (2019). Testing learning mechanisms of rule-based judgment. Decision (Washington), 6(4), 305–334. https://doi.org/10.1037/dec0000109

Abstract

Abstract

Abstract

Weighing the importance of different pieces of information is a key determinant of making accurate judgments. In social judgment theory, these weighting processes have been successfully described with linear models. How people learn to make judgments has received less attention. Although the hitherto proposed delta learning rule can perfectly learn to solve linear problems, reanalyzing a previous experiment showed that it does not adequately describe human learning. To provide a more accurate description of learning processes we amend

Additional indexing

Creators (Authors)

  • Hoffmann, Janina A
  • von Helversen, Bettina
  • Rieskamp, Jörg

Journal/Series Title

Journal/Series Title

Journal/Series Title

Volume

Volume

Volume
6

Number

Number

Number
4

Page range/Item number

Page range/Item number

Page range/Item number
305

Page end

Page end

Page end
334

Item Type

Item Type

Item Type
Journal Article

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Language

Language

Language
English

Publication date

Publication date

Publication date
2019-10-01

Date available

Date available

Date available
2019-05-03

Publisher

Publisher

Publisher

ISSN or e-ISSN

ISSN or e-ISSN

ISSN or e-ISSN
2325-9965

Additional Information

Additional Information

Additional Information
This article may not exactly replicate the final version published in the APA journal. It is not the copy of record.

OA Status

OA Status

OA Status
Green

Citations

Citation copied

Hoffmann, J. A., von Helversen, B., & Rieskamp, J. (2019). Testing learning mechanisms of rule-based judgment. Decision (Washington), 6(4), 305–334. https://doi.org/10.1037/dec0000109

Green Open Access
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