Approaching subjective interval timing with a non-Gaussian perspective

Aquino, Tomas G.; Camargo, Raphael Y. de; Reyes, Marcelo Bussotti

doi:10.1016/j.jmp.2018.03.005

Cited by 1 publication

(1 citation statement)

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“…Moreover, empirical work has demonstrated that response functions in timing studies are better described by skewed distributions such as the Wald or inverse Gaussian, skewed-normal or lognormal distribution (see, e.g. [27][28][29], and the discussion in [9]; for a mathematical approach, see [30]). Given these considerations, a theoretically reasonable prior would consist of a mixture of skewed distributions each representing a stimulus duration.…”

Section: First Theoretical Consideration: Shape Of the Priormentioning

confidence: 99%

Conceptually plausible Bayesian inference in interval timing

et al. 2021

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In a world that is uncertain and noisy, perception makes use of optimization procedures that rely on the statistical properties of previous experiences. A well-known example of this phenomenon is the central tendency effect observed in many psychophysical modalities. For example, in interval timing tasks, previous experiences influence the current percept, pulling behavioural responses towards the mean. In Bayesian observer models, these previous experiences are typically modelled by unimodal statistical distributions, referred to as the prior. Here, we critically assess the validity of the assumptions underlying these models and propose a model that allows for more flexible, yet conceptually more plausible, modelling of empirical distributions. By representing previous experiences as a mixture of lognormal distributions, this model can be parametrized to mimic different unimodal distributions and thus extends previous instantiations of Bayesian observer models. We fit the mixture lognormal model to published interval timing data of healthy young adults and a clinical population of aged mild cognitive impairment patients and age-matched controls, and demonstrate that this model better explains behavioural data and provides new insights into the mechanisms that underlie the behaviour of a memory-affected clinical population.

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