Fig. 7 ∣. Neural context discrimination is well explained by associative-learning models.

a, Schematic of the computational model. The input layer contains neurons that form a basis for representing the stimulus using radial basis functions. Activations are linearly combined via the matrix W, and thresholding is applied to the output layer via a KWTA mechanism to achieve the output activation. W is updated after each stimulus presentation using Hebbian learning. Model training is performed by drawing trials randomly from either the rare morph prior or the frequent morph prior. b, Left: trial-by-trial similarity matrix, as in Fig. 3a, for an example model trained under the rare morph condition. Right: same as the left, but for an example model trained under the frequent morph condition. c, Average trial-by-trial similarity matrices across model instantiations (n = 50) for rare morph (left) and frequent morph (right) trained models. d, Left: the average rare morph similarity matrix minus the average frequent morph similarity matrix. Right: same as the left, but the similarity matrix for each model is z-scored before averaging. e, Q estimates for accumulated rare morph trained models (left) and frequent morph models (right). f, Smoothed histogram of ΔD_KL values for rare morph (maroon) and frequent morph (blue) trained models. Red line indicates zero. g, Reconstructed priors for rare morph (top, black, mean ±s.e.m.) and frequent morph (bottom, black, mean ±s.e.m.) trained models. Note that this is the unsupervised recovery of the priors. Ideal rare morph (maroon) and frequent morph (blue) priors are shown for reference. ΔD_KL is for the averaged reconstructed prior.