Biology subjects

Reijniers, J.

Publications and source records attributed to Reijniers, J..

3 recordsLinked to original sources

bioRxiv2024

Insights into dynamic sound localisation: A direction-dependent comparison between human listeners and a Bayesian model.

Self-motion is an essential but often overlooked component of sound localisation. While the directional information of a source is implicitly contained in head-centred acoustic cues, that acoustic input needs to be continuously combined with sensorimotor information about the head orientation in order to decode these cues to a world-centred frame of reference. On top of that, the use of head movement significantly reduces ambiguities in the directional information provided by the incoming sound. In this work, we evaluate a Bayesian model that predicts dynamic sound localisation, by comparing its predictions to human performance measured in a behavioural sound-localisation experiment. Model parameters were set a-priori, based on results from various psychoacoustic and sensorimotor studies, i.e., without any post-hoc parameter fitting to behavioral results. In a spatial analysis, we evaluated the models capability to predict spatial localisation responses. Further, we investigated specific effects of the stimulus duration, the spatial prior and sizes of various model uncertainties on the predictions. The spatial analysis revealed general agreement between the predictions and the actual behaviour. The altering of the model uncertainties and stimulus duration revealed a number of interesting effects providing new insights on modelling the human integration of acoustic and sensorimotor information in a localisation task. Author summaryIn everyday life, sound localisation requires both interaural and monaural acoustic information. In addition to this, sensorimotor information about the position of the head is required to create a stable and accurate representation of our acoustic environment. Bayesian inference is an effective mathematical framework to model how humans combine information from different sources and form beliefs about the world. Here, we compare the predictions from a Bayesian model for dynamic sound localisation with data from a localisation experiment. We show that we can derive the model parameter values from previous psychoacoustic and sensorimotor experiments and that the model without any post-hoc fitting, can predict general dynamic localisation performance. Finally, the discrepancies between the modelled data and behavioural data are analysed by testing the effects of adjusting the model parameters.

neuroscience↗

bioRxiv2017

Comparing arenavirus infection patterns in experimentally and naturally infected rodent hosts

Infectious diseases of wildlife are typically studied using data on antibody and pathogen presence/level. In order to interpret these data, it is necessary to know the course of antibodies and pathogen presence/levels after infection. Such data are typically collected using experimental infection studies in which host individuals are inoculated in the laboratory and sampled over an extended period, but because laboratory conditions are controlled and much less variable than natural conditions, the immune response and pathogen dynamics may differ. Here, we compared Morogoro arenavirus infection patterns between naturally and experimentally infected multimammate mice (Mastomys natalensis). Longitudinal samples were collected during three months of bi-weekly trapping in Morogoro, Tanzania, and antibody titer and viral RNA presence were determined to assess whether the natural temporal patterns are similar to those previously observed in the laboratory. A good match with laboratory data was found for 52% of naturally infected individuals, while most of the mismatches can be explained by the presence of chronically infected individuals (35%), maternal antibodies (10%) and an antibody detection limit (25%). These results suggest that while laboratory data are useful for interpreting field samples, there can still be differences due to conditions that were not tested in the laboratory.\n\nImportant noticeThis is a pre-print version of the manuscript, made available through bioRxiv.org. Note that this manuscript has not yet been peer-reviewed, and has been submitted to a peer-reviewed journal.

immunology↗

bioRxiv2017

The shape of the contact-density function matters when modelling disease transmission in fluctuating populations

Models of disease transmission in a population with changing densities must assume a relation between infectious contacts and density. Typically, a choice is made between a constant (frequency-dependence) and a linear (density-dependence) contact-density function, but it is becoming increasingly clear that intermediate, nonlinear functions intermediate are more realistic. It is currently not clear however what the exact consequences would be of different contact-density functions in fluctuating populations. By combining field data on rodent host (Mastomys natalensis) demography, experimentally-derived contact-density data, and laboratory and field data Morogoro virus infection dynamics, we explored the effects of different contact-density function shapes on transmission dynamics and invasion/persistence. While invasion and persistence were clearly affected by the shape of the function, the effects on outbreak characteristics such as infection prevalence and seroprevalence were less obvious. This means that it may be difficult to distinguish between the different shapes based on how well models fit to real data. The shape of the transmission-density function should therefore be chosen with care, and is ideally based on existing information such as a previously quantified contact- or transmission-density relationship or the underlying biology of the host species in relation to the infectious agent.

epidemiology↗