Abstract
Mathematical modeling is a useful tool for understanding the neurodynamical and computational mechanisms of cognitive abilities like time perception, and for linking neurophysiology to psychology. In this chapter, we discuss several biophysical models of time perception and how they can be tested against experimental evidence. After a brief overview on the history of computational timing models, we list a number of central psychological and physiological findings that such a model should be able to account for, with a focus on the scaling of the variability of duration estimates with the length of the interval that needs to be estimated. The functional form of this scaling turns out to be predictive of the underlying computational mechanism for time perception. We then present four basic classes of timing models (ramping activity, sequential activation of neuron populations, state space trajectories and neural oscillators) and discuss two specific examples in more detail. Finally, we review to what extent existing theories of time perception adhere to the experimental constraints.
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Notes
- 1.
- 2.
More precisely, the scalar property requires that the entire distribution of a time estimate scales with the physical duration, i.e. it also includes the linear psychophysical law.
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Furthermore, it is required that the temporal correlations of the process decay to zero for sufficiently long times.
- 4.
It should be noted, however, that there is a computational study which considered such failures as a possible basis for the scalar property [88].
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Acknowledgments
This work was funded by grants from the German ministry for education and research (BMBF, 01GQ1003B) and the Deutsche Forschungsgemeinschaft to D.D. (DFG, Du 354/6-1 & 7-2).
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Hass, J., Durstewitz, D. (2014). Neurocomputational Models of Time Perception. In: Merchant, H., de Lafuente, V. (eds) Neurobiology of Interval Timing. Advances in Experimental Medicine and Biology, vol 829. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1782-2_4
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