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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.


Maps over categorization consistency. (a) shows the relative entropy of the categorical choices for the single participant given the same rhythm sequences multiple times from Desain and Honing (2003, used with permission). The relative entropy is calculated as the Shannon entropy divided by the theoretical maximum attainable entropy. (b) shows the signal-to-noise measure calculated from the activation patterns generated by the resonance model.
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Figure 2: Maps over categorization consistency. (a) shows the relative entropy of the categorical choices for the single participant given the same rhythm sequences multiple times from Desain and Honing (2003, used with permission). The relative entropy is calculated as the Shannon entropy divided by the theoretical maximum attainable entropy. (b) shows the signal-to-noise measure calculated from the activation patterns generated by the resonance model.

Mentions: Desain and Honing (2003) employed a novel paradigm where musically educated participants were asked to categorize 66 different rhythm sequences by transcribing them into common music notation. The sequences all lasted for one second and consisted of four tone onsets and were therefore uniquely determined by the three interonset intervals (IOI) between the tones. Two such possible sequences are shown in Figure 1a and 1b where a possible categorization of 1b could be ♪♪ ♩ (or 1-1-2 when written as an integer ratio). Any possible 1 s, four-tone rhythm sequence can be thought of as a point in a three dimensional triangular performance space that determines the lengths of the three IOIs as shown in Figure 1. The 66 rhythm sequences in Desain and Honing's experiment were constructed so that they evenly covered the area in the performance space with the constraint that no IOI would be shorter than 153 ms. The location of these sequences in the performance space can be seen in Figure 2b where each circle marks the position of one of the 66 sequences.


A prototype-based resonance model of rhythm categorization.

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

Maps over categorization consistency. (a) shows the relative entropy of the categorical choices for the single participant given the same rhythm sequences multiple times from Desain and Honing (2003, used with permission). The relative entropy is calculated as the Shannon entropy divided by the theoretical maximum attainable entropy. (b) shows the signal-to-noise measure calculated from the activation patterns generated by the resonance model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Maps over categorization consistency. (a) shows the relative entropy of the categorical choices for the single participant given the same rhythm sequences multiple times from Desain and Honing (2003, used with permission). The relative entropy is calculated as the Shannon entropy divided by the theoretical maximum attainable entropy. (b) shows the signal-to-noise measure calculated from the activation patterns generated by the resonance model.
Mentions: Desain and Honing (2003) employed a novel paradigm where musically educated participants were asked to categorize 66 different rhythm sequences by transcribing them into common music notation. The sequences all lasted for one second and consisted of four tone onsets and were therefore uniquely determined by the three interonset intervals (IOI) between the tones. Two such possible sequences are shown in Figure 1a and 1b where a possible categorization of 1b could be ♪♪ ♩ (or 1-1-2 when written as an integer ratio). Any possible 1 s, four-tone rhythm sequence can be thought of as a point in a three dimensional triangular performance space that determines the lengths of the three IOIs as shown in Figure 1. The 66 rhythm sequences in Desain and Honing's experiment were constructed so that they evenly covered the area in the performance space with the constraint that no IOI would be shorter than 153 ms. The location of these sequences in the performance space can be seen in Figure 2b where each circle marks the position of one of the 66 sequences.

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.