Topic: sensory noise

Our brains predict future sensory input based on their current beliefs about the world around us, but interpreting prediction errors can be challenging in a volatile environment because they can be caused by stochastic sensory noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to improve precision, but the two should be segregated when environmental changes render prior beliefs irrelevant. Bayesian inference provides a statistically optimal solution to deal with such problems of causal uncertainty [1]. However, the method quickly becomes memory intensive an

This study explores the decision-making processes involved in trajectory extrapolation tasks. Vertical paths were disturbed in the horizontal direction by noise generated by Random Walks (RDW) or Independent and Identically Distributed (IID) noise. In the Experiment, participants (n = 28) extrapolated if a moving point would end right or left of the screen’s center. We modeled path extrapolation using a three-parameter Random Sampling Model (RSM), and a Multi-Layer Perceptron (MLP). Statistically, for RDW paths, the optimal extrapolation strategy is to use the last visible position, whereas fo