Abstract
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.
Original language | English |
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Article number | 201844 |
Pages (from-to) | 1-19 |
Journal | Royal Society Open Science |
Volume | 8 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2021 |
Bibliographical note
Funding Information:Data accessibility. The datasets analysed for this study can be found in the Open Science Framework (https://osf.io/ kqjxf/). Authors’ contributions. S.C.M., L.v.M. and H.v.R. contributed to the development of ideas and the theoretical underpinnings, all authors contributed to the modelling of the data, and to the preparation of the manuscript. Competing interests. We have no competing interests. Funding. S.C.M., J.d.J. and H.v.R. were supported by the research program ‘Interval Timing in the Real World: A functional, computational and neuroscience approach’ with project no. 453-16-005, awarded to H.v.R., which is financed by The Netherlands Organization for Scientific Research (NWO).
Publisher Copyright:
© 2021 The Authors.
Keywords
- Ageing
- Bayesian observer model
- Central tendency effect
- Interval timing
- Prior distributions