Figure 3.

(A) 2D feature space for a toy perceptual decision problem involving classifying two possible stimuli (e.g. left- and right-tilted Gabors). Each Gaussian indicates the likelihood of observing a pair of features (e.g. orientation) given each stimulus class (where $\mu =[3.5\hspace{0.17em}7],\hspace{0.17em}[7\hspace{0.17em}3.5]$ or $[3.5\hspace{0.17em}3.5]$ and Σ is the identity matrix). The right-tilted stimuli occupy the right-hand side of the grid; left-tilted stimuli occupy the left-hand side of the grid. The absence of stimulation is represented by a distribution in which activation of each feature is low towards the origin. In 2D SDT, there are three stimulus classes organized in a flat (non-hierarchical) structure. (B) The same 2D feature space from (A), modified to make explicit the hierarchical aspect of the HOSS model. A higher-order awareness state (a₁) nests perceptual states w₁ and w₂.