Conceptually plausible Bayesian inference in interval timing

Sarah C. Maaß*, Joost de Jong, Leendert van Maanen, Hedderik van Rijn

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

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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.

Originele taal-2English
Artikelnummer201844
Aantal pagina's19
TijdschriftRoyal Society Open Science
Volume8
Nummer van het tijdschrift8
DOI's
StatusPublished - 18-aug-2021

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