2009/11/01 by Armando Machado, Maria Teresa Malheiro, Wolfram Erlhagen · 2 citations
Computer Science · Neuroscience · #Music and Audio Processing #Neural dynamics and brain function #Neuroscience and Music Perception
paper · doi:10.1901/jeab.2009.92-423
crossref issued 2009/11/01 · crossref published 2009/11/01 · crossref published-print 2009/11/01 · openalex publication_date 2009/11/01 · crossref created 2010/05/31 · crossref published-online 2013/02/26 · crossref deposited 2023/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/15 · crossref indexed 2026/08/02
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 must meet.