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Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

Figure 6

Bayesian observer and actor model components.

Candidate (i) sensory and (ii) motor likelihoods, independently chosen for the sensory and motor noise components of the model. The likelihoods are Gaussians with either constant or ‘scalar’ (i.e. homogeneous linear) variability. The amount of variability for the sensory (resp. motor) component is scaled by parameter (resp. ). iii) Candidate priors for the Medium Uniform (top) and Medium Peaked (bottom) blocks. The candidate priors for the Short Uniform (resp. Long Uniform) blocks are identical to those of the Medium Uniform block, shifted by 150 ms in the negative (resp. positive) direction. See Methods for a description of the priors. iv) Candidate subjective error maps. The graphs show the error as a function of the response duration, for different discrete stimuli (drawn in different colors). From top to bottom: Skewed error ; Standard error ; and Fractional error . The scale is irrelevant, as the model is invariant to rescaling of the error map. The squared subjective error map defines the loss function (as per Eq. 1).

Figure 6

doi: https://doi.org/10.1371/journal.pcbi.1002771.g006