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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.


Categorization maps for (a) the experimental data from Desain and Honing (2003, used with permission) and (b) the resonance model. The transparent areas in (a) indicate areas where there was a large amount of disagreement between the participants.
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Figure 3: Categorization maps for (a) the experimental data from Desain and Honing (2003, used with permission) and (b) the resonance model. The transparent areas in (a) indicate areas where there was a large amount of disagreement between the participants.

Mentions: In a first experiment, 29 highly trained musicians categorized the rhythm sequences and the result was that even though the rhythms occurred on a more or less continuous time scale, the participants tended to stick to a limited number of categories, with 1-1-1 being the most common. Twelve categories, all categories considered, stood out as being the most common and the location in performance space of these categories are shown in Figure 3a. One participant was presented with all 66 rhythm sequences at six different occasions and, as a measure of consistency, the entropy was calculated of her responses for each rhythm. These entropy values were mapped on to the performance space and the resulting entropy map is shown in Figure 2a.


A prototype-based resonance model of rhythm categorization.

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

Categorization maps for (a) the experimental data from Desain and Honing (2003, used with permission) and (b) the resonance model. The transparent areas in (a) indicate areas where there was a large amount of disagreement between the participants.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Categorization maps for (a) the experimental data from Desain and Honing (2003, used with permission) and (b) the resonance model. The transparent areas in (a) indicate areas where there was a large amount of disagreement between the participants.
Mentions: In a first experiment, 29 highly trained musicians categorized the rhythm sequences and the result was that even though the rhythms occurred on a more or less continuous time scale, the participants tended to stick to a limited number of categories, with 1-1-1 being the most common. Twelve categories, all categories considered, stood out as being the most common and the location in performance space of these categories are shown in Figure 3a. One participant was presented with all 66 rhythm sequences at six different occasions and, as a measure of consistency, the entropy was calculated of her responses for each rhythm. These entropy values were mapped on to the performance space and the resulting entropy map is shown in Figure 2a.

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.