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. 2012 Nov 29;8(11):e1002771. doi: 10.1371/journal.pcbi.1002771

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© 2012 Acerbi et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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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).

Inline graphic — 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).