Fig. 8: Analysis of single unit representations.

a Example decision neuron, strongly predictive of decision output (\({R}_{decision}^{2}=0.83\)), but not opt-out response (\({R}_{opt-out}^{2}=0.004\)). b Example confidence neuron, strongly predictive of opt-out response (\({R}_{opt-out}^{2}=0.87\)), but not decision output (\({R}_{decision}^{2}=0.16\)). Kiani & Shadlen³ found that decision-making neurons in the lateral intraparietal cortex (LIP) implicitly coded for confidence. Decision neurons in our neural network model showed this same pattern. c They showed higher activity for trials on which their preferred stimulus (T_in) was chosen vs. trials on which a sure target (T_S) was chosen, whereas (d) they were less active for trials on which their non-preferred stimulus (T_opp) was chosen. Each point in c and d represents the average normalized (against the pre-stimulus baseline) activation of an individual neuron (over N ≥ 2700 trials) ± the standard error of the mean. Black dots represent neurons with statistically significant deviations from the diagonal (two-sided two-sample t-tests, p < 0.05). White dots represent neurons with non-significant deviations (two-sided two-sample t-tests, p > 0.05). Note that error bars are present on these plots, but are too small to be visible. Neural network results reflect a single example network, but all 100 trained networks displayed a qualitatively similar pattern. Source data are provided as a Source Data file.