Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale—for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation.

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