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A prototype-based resonance model of rhythm categorization.

Bååth R, Lagerstedt E, Gärdenfors P - Iperception (2014)

Bottom Line: This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003).The model accurately replicates the experimental data.Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.

View Article: PubMed Central - PubMed

Affiliation: Lund University Cognitive Science, Lund University, LUX, Lund, Sweden; e-mail: rasmus.baath@lucs.lu.se.

ABSTRACT
Categorization of rhythmic patterns is prevalent in musical practice, an example of this being the transcription of (possibly not strictly metrical) music into musical notation. In this article we implement a dynamical systems' model of rhythm categorization based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model accurately replicates the experimental data. Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.

No MeSH data available.


The oscillator activation and corresponding categorization over time for an oscillator network given a rhythm pattern that scored low entropy in Desain and Honing (2003).
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Figure 6: The oscillator activation and corresponding categorization over time for an oscillator network given a rhythm pattern that scored low entropy in Desain and Honing (2003).

Mentions: Figure 6 shows the oscillator activation over time and the corresponding dynamic categorization given a rhythm sequence that was assigned low entropy in Desain and Honing's data. Compare this with Figure 7 that shows the oscillator activation and dynamic categorization for a rhythm that was assigned high entropy in Desain and Honing's data. For the low entropy rhythm the signal-to-noise ratio is high and the categorization is more stable. For the high entropy rhythm, however, the signal-to-noise ratio is low and the categorization in never stable, that is, there never emerges one clear winner.


A prototype-based resonance model of rhythm categorization.

Bååth R, Lagerstedt E, Gärdenfors P - Iperception (2014)

The oscillator activation and corresponding categorization over time for an oscillator network given a rhythm pattern that scored low entropy in Desain and Honing (2003).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4441029&req=5

Figure 6: The oscillator activation and corresponding categorization over time for an oscillator network given a rhythm pattern that scored low entropy in Desain and Honing (2003).
Mentions: Figure 6 shows the oscillator activation over time and the corresponding dynamic categorization given a rhythm sequence that was assigned low entropy in Desain and Honing's data. Compare this with Figure 7 that shows the oscillator activation and dynamic categorization for a rhythm that was assigned high entropy in Desain and Honing's data. For the low entropy rhythm the signal-to-noise ratio is high and the categorization is more stable. For the high entropy rhythm, however, the signal-to-noise ratio is low and the categorization in never stable, that is, there never emerges one clear winner.

Bottom Line: This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003).The model accurately replicates the experimental data.Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.

View Article: PubMed Central - PubMed

Affiliation: Lund University Cognitive Science, Lund University, LUX, Lund, Sweden; e-mail: rasmus.baath@lucs.lu.se.

ABSTRACT
Categorization of rhythmic patterns is prevalent in musical practice, an example of this being the transcription of (possibly not strictly metrical) music into musical notation. In this article we implement a dynamical systems' model of rhythm categorization based on the resonance theory of rhythm perception developed by Large (2010). This model is used to simulate the categorical choices of participants in two experiments of Desain and Honing (2003). The model accurately replicates the experimental data. Our results support resonance theory as a viable model of rhythm perception and show that by viewing rhythm perception as a dynamical system it is possible to model central properties of rhythm categorization.

No MeSH data available.