Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
 DetailsÜbersicht
  Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning

Isbilen, E., Frost, R. L. A., Monaghan, P., & Christiansen, M. (2018). Bridging artificial and natural language learning: Comparing processing- and reflection-based measures of learning. In C. Kalish, M. Rau, J. Zhu, & T. T. Rogers (Eds.), Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018) (pp. 1856-1861). Austin, TX: Cognitive Science Society.

Item is

 

einblenden: alle ausblenden: alle

Basisdaten

einblenden: ausblenden:
Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-0001-BEC9-7 Versions-Permalink: https://hdl.handle.net/21.11116/0000-0001-C5B9-0
Genre: Konferenzbeitrag

Dateien

einblenden: Dateien
ausblenden: Dateien
:
Isbilen_etal_2018.pdf (Verlagsversion), 205KB
Öffnen   Speichern
Datei-Permalink:
https://hdl.handle.net/21.11116/0000-0001-BECB-5
Name:
Isbilen_etal_2018.pdf
Beschreibung:
-
OA-Status:
Sichtbarkeit:
Öffentlich
MIME-Typ / Prüfsumme:
application/pdf / [MD5]
Technische Metadaten:
Öffnen
Copyright Datum:
-
Copyright Info:
-
Lizenz:
-

Externe Referenzen

einblenden:
ausblenden:
externe Referenz:
http://mindmodeling.org/cogsci2018/papers/0358/0358.pdf (Ergänzendes Material)
Beschreibung:
-
OA-Status:

Urheber

einblenden:
ausblenden:
 Urheber:
Isbilen, Erin1, Autor
Frost, Rebecca Louise Ann2, Autor           
Monaghan, Padraic3, Autor           
Christiansen, Morten1, Autor
Affiliations:
1Cornell University, Ithaca, NY, USA, ou_persistent22              
2Language Development Department, MPI for Psycholinguistics, Max Planck Society, ou_2340691              
3Lancaster University, Lancaster, UK, ou_persistent22              

Inhalt

einblenden:
ausblenden:
Schlagwörter: -
 Zusammenfassung: A common assumption in the cognitive sciences is that artificial and natural language learning rely on shared mechanisms. However, attempts to bridge the two have yielded ambiguous results. We suggest that an empirical disconnect between the computations employed during learning and the methods employed at test may explain these mixed results. Further, we propose statistically-based chunking as a potential computational link between artificial and natural language learning. We compare the acquisition of non-adjacent dependencies to that of natural language structure using two types of tasks: reflection-based 2AFC measures, and processing-based recall measures, the latter being more computationally analogous to the processes used during language acquisition. Our results demonstrate that task-type significantly influences the correlations observed between artificial and natural language acquisition, with reflection-based and processing-based measures correlating within – but not across – task-type. These findings have fundamental implications for artificial-to-natural language comparisons, both methodologically and theoretically.

Details

einblenden:
ausblenden:
Sprache(n): eng - English
 Datum: 2018-07
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: -
 Art des Abschluß: -

Veranstaltung

einblenden:
ausblenden:
Titel: the 40th Annual Conference of the Cognitive Science Society (CogSci 2018)
Veranstaltungsort: Madison, WI, USA
Start-/Enddatum: 2018-07-25 - 2017-07-28

Entscheidung

einblenden:

Projektinformation

einblenden:

Quelle 1

einblenden:
ausblenden:
Titel: Proceedings of the 40th Annual Conference of the Cognitive Science Society (CogSci 2018)
Genre der Quelle: Konferenzband
 Urheber:
Kalish, Charles, Herausgeber
Rau, Martina, Herausgeber
Zhu, Jerry, Herausgeber
Rogers, Timothy T., Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Austin, TX : Cognitive Science Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1856 - 1861 Identifikator: ISBN: 978-0-9911967-8-4