Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/11628

TitleLearning to time : a perspective
Author(s)Machado, Armando
Malheiro, Teresa
Erlhagen, Wolfram
KeywordsScalar-expectancy-theory
Fixed-interval schedules
Temporal discrimination task
Behavioraltheory
Theory set
Bisection task
Pacemaker rate
Reinforcement-omission
Stimulus duration
Learning-to-Time (LeT) model
Scalar Expectancy Theory (SET)
mathematical models
temporal discrimination
timing
Issue dateNov-2009
PublisherSociety for the Experimental Analysis of Behavior (SEAB)
JournalJournal of the Experimental Analysis of Behavior
Citation"Journal of the Experimental Analysis of Behavior". ISSN 0022-5002. 92:3 (Nov. 2009) 423-458.
Abstract(s)In the last decades, researchers have proposed a large number of theoretical models of timing. These models make different assumptions concerning how animals learn to time events and how such learning is represented in memory. However, few studies have examined these different assumptions either empirically or conceptually. For knowledge to accumulate, variation in theoretical models must be accompanied by selection of models and model ideas. To that end, we review two timing models, Scalar Expectancy Theory (SET), the dominant model in the Field, and the Learning-to-Time (LeT) model, one of the few models dealing explicitly with learning. In the first part of this article, we describe how each model works in prototypical concurrent and retrospective timing tasks, identify their structural similarities, and classify their differences concerning temporal learning and memory. In the Second part, we review a series of studies that examined these differences and conclude that both the memory structure postulated by SET and the state dynamics postulated by LeT are probably incorrect. In the third part, we propose a hybrid model that may improve on its parents. The hybrid model accounts for the typical findings in fixed-interval schedules, the peak procedure, mixed fixed interval schedules, simple and double temporal bisection, and temporal generalization tasks. In the fourth and last part, we identify seven challenges that any timing model trust meet.
TypeArticle
URIhttp://hdl.handle.net/1822/11628
DOI10.1901/jeab.2009.92-423
ISSN0022-5002
Publisher versionhttp://seab.envmed.rochester.edu/jeab/articles/2009/jeab-92-03-0423.pdf
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

Files in This Item:
File Description SizeFormat 
Learning_resumo.pdf
  Restricted access
Dados recolhidos66,58 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID