English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  A theory of the detection and learning of structured representations of similarity and relative magnitude

Doumas, L. A. A., Hamer, A., Puebla, G., & Martin, A. E. (2017). A theory of the detection and learning of structured representations of similarity and relative magnitude. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 1955-1960). Austin, TX: Cognitive Science Society.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002E-12AF-1 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-C06F-9
Genre: Conference Paper

Files

show Files
hide Files
:
Doumas_etal_2017.pdf (Publisher version), 533KB
View   Save
File Permalink:
https://hdl.handle.net/11858/00-001M-0000-002E-12B1-A
Name:
Doumas_etal_2017.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Doumas, Leonidas A. A. 1, Author
Hamer, Aaron1, Author
Puebla, Guillermo1, Author
Martin, Andrea E.1, 2, Author           
Affiliations:
1Department of Psychology, University of Edinburgh, Edinburgh, UK, ou_persistent22              
2Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792545              

Content

show
hide
Free keywords: -
 Abstract: Responding to similarity, difference, and relative magnitude (SDM) is ubiquitous in the animal kingdom. However, humans seem unique in the ability to represent relative magnitude (‘more’/‘less’) and similarity (‘same’/‘different’) as abstract relations that take arguments (e.g., greater-than (x,y)). While many models use structured relational representations of magnitude and similarity, little progress has been made on how these representations arise. Models that developuse these representations assume access to computations of similarity and magnitude a priori, either encoded as features or as output of evaluation operators. We detail a mechanism for producing invariant responses to “same”, “different”, “more”, and “less” which can be exploited to compute similarity and magnitude as an evaluation operator. Using DORA (Doumas, Hummel, & Sandhofer, 2008), these invariant responses can serve be used to learn structured relational representations of relative magnitude and similarity from pixel images of simple shapes

Details

show
hide
Language(s): eng - English
 Dates: 2017-05-132017
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: -
 Degree: -

Event

show
hide
Title: the 39th Annual Conference of the Cognitive Science Society (CogSci 2017)
Place of Event: London, UK
Start-/End Date: 2017-07-26 - 2017-07-29

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017)
Source Genre: Proceedings
 Creator(s):
Gunzelmann, Glenn, Editor
Howes, Andrew, Editor
Tenbrink, Thora, Editor
Davelaar, Eddy, Editor
Affiliations:
-
Publ. Info: Austin, TX : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1955 - 1960 Identifier: -