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A model of multisecond timing behaviour under peak-interval procedures

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Abstract

In this study, the authors developed a fundamental theory of interval timing behaviour, inspired by the learning-to-time (LeT) model and the scalar expectancy theory (SET) model, and based on quantitative analyses of such timing behaviour. Our experiments used the peak-interval procedure with rats. The proposed model of timing behaviour comprises clocks, a regulator, a mixer, a response, and memory. Using our model, we calculated the basic clock speeds indicated by the subjects’ behaviour under such peak procedures. In this model, the scalar property can be defined as a kind of transposition, which can then be measured quantitatively. The Akaike information criterion (AIC) values indicated that the current model fit the data slightly better than did the SET model. Our model may therefore provide a useful addition to SET for the analysis of timing behaviour.

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Notes

  1. Abbreviations. FI schedule, fixed interval schedule; PI procedure, peak-interval procedure.

  2. Abbreviations. SET, the scalar expectancy theory; LeT model, the learning-to-time model.

  3. Abbreviation. BeT, the behavioural theory-of-timing model.

  4. Abbreviation. PD, Poisson decomposition.

  5. Abbreviation. ITI, intertrial interval.

  6. Abbreviation. AIC, the Akaike information criterion.

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Acknowledgments

The authors wish to thank Hiromi Ohtake, Ryo Kobayashi, and Hiroshi Tango for their comments regarding the mathematics, Ken’ichiro Shimatani for his comments regarding the statistics, Simon Fraser and Anne Macaskill for their comments regarding written English, and Asako Ujita for her assistance with the experiment. The authors also thank Takayuki Sakagami and Peter Killeen for their valuable advice about behavioural sciences.

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The authors declare that they have no conflict of interest

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Correspondence to Takayuki Hasegawa.

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Action Editor: Mark van Rossum

Appendix

Appendix

The mean value of the correlation coefficients

For each correlation coefficient r, after normalization by Fisher’s z′ transformation (r-to- z′ transformation) \(z^{\prime } = \frac {\,1\,}{\,2\,}\ln \frac {\,1 + r\,}{\,1 - r\,}\), we calculated the arithmetic mean \(\overline {z^{\prime }}\) of z′. Then we converted back to get the mean \(\overline {r}\) shown in all panels in Figs. 10 and 14.

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Hasegawa, T., Sakata, S. A model of multisecond timing behaviour under peak-interval procedures. J Comput Neurosci 38, 301–313 (2015). https://doi.org/10.1007/s10827-014-0542-4

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  • DOI: https://doi.org/10.1007/s10827-014-0542-4

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