Fig. 4

Decoding behavioural decisions through normalized synergy and redundancy in GC-connected pairs. A We used normalized synergy and redundancy to classify behavioural decisions as correct (blue squares) or incorrect (black dots) perceptual discriminations. The solid orange line is the decision boundary of an SVM trained using both synergy and redundancy values as predictive features. Green and purple dashed lines are the decision boundaries of SVM trained using only redundancy or synergy, respectively, as a predictive feature. Correct decisions are characterized by clustered values in both synergy and redundancy, whilst incorrect decisions show lower redundancy values and a uniform distribution of synergy. B SVM cross-validated classification accuracy from 10,000 bootstrap samples. Colours of the distributions match the ones of decision boundaries in A. Statistical comparisons across different models were made using the Wilcoxon rank-sum test (***p < 0.001)