To characterize how the population represented events in the present trial, we used a linear regression predicting the activity of each neuron at each time point as a function of the choice (top or bottom), second-step state (left or right), and outcome (rewarded or not) that occurred on the trial, as well as the interactions between these events. This and later analyses included only sessions for which we had sufficient coverage of all trial types (n = 3 mice, 11 sessions, 1,314 neurons, 2,671 trials), as in some imaging sessions with few blocks and trials there was no coverage of trial types that occur infrequently in those blocks. We evaluated the population coefficient of partial determination (i.e., the fraction of variance across the population uniquely explained by each predictor) as a function of time relative to trial events (Figure 3E). Representation of choice ramped up smoothly over the second preceding the choice, then decayed smoothly until approximately 500 ms after trial outcome. Representation of second-step state increased rapidly following the choice, peaked at second-step port entry, then decayed over the second following the outcome and was the strongest represented trial event.
