Extended Data Fig. 4: Hypothesis encoding in RSC is task-specific and is a function of task learning.

(a) Foraging and dot-hunting tasks are interleaved, allowing comparisons of how the same neural population represents hypotheses. (b) We predict the number of encountered landmarks either within condition (for example foraging from foraging, each time using one trial as test, fitting to all others), or across. Only the first 2 landmarks were predicted to allow use of the same classifier across both despite the higher number of landmarks in the dot-hunting task. Train and test sets were split by trial. Decoding was done with a regression tree on low-pass filtered firing rates. Performance was quantified as mean error on the N of landmarks. (c) Example dot-hunting trial, the performance from using the foraging predictor is lower. (d) Summary stats from all sessions, means and bootstrapped CIs. The prediction is significantly better when using training data from the same category than when using the neural code from the other; for example dot-hunting to predict the foraging (P= ~0 / ~ 0 within vs. across categories for predicting dot-hunting and foraging landmark state), showing that hypothesis coding is task-specific. (e) To test whether hypothesis encoding is a specific function of task learning or a general feature of RSC, we examined whether coding persisted in case when mice performed the task but were not yet performing well. We first examined the ability to predict correct vs. incorrect port choice (same as in Fig. 4) as a function of per-session task performance. We analyzed data from sessions from the entire training period where the 2 landmarks were used, with at least 5 correct and 5 incorrect choices (N = 42 sessions), due to the closely spaced recordings, neurons might be re-recorded across sessions. On average we analyzed ~15-30 port visits per session (number of trials was unaffected by behavioral performance: CI of slope = [-7.7, 2.7], p = 0.33). Predictions were made as before with a test/train split on balanced hit/miss data with a regression tree. Prediction performance was at chance level ( ~ 47%, P = 0.81 vs. chance) for low performance sessions (total correct choice ratio of 0.8 or lower), and the same as in our initial analysis (Fig. 4) for sessions with high mouse performance ( ~ 66%, P = 0.00096 vs. chance). Overall, prediction performance was significantly correlated with task performance (P = 0.0014 vs. constant model). Individual mice are indicated with colored markers. (f) We also analyzed the more general decoding of landmark encounter count (same as Fig. 1) in all of the 92 sessions with 2 landmarks, and also found a significant correlation (p = 0.0045 vs. constant model), showing that hypothesis encoding throughout the task is driven by task learning. (g) As a control experiment, we tested whether decoding the number of landmarks encountered in the interleaved dot-hunting task might also be affected by task performance, if for instance the neural encoding and performance was a function of general spatial learning, habituation to the arena, motivation, etc., and we found this correlation to be flat (P = .6, CI for slope = [-0.17, 0.29]). We conclude that the encoding of hypothesis state is task-specific and a function of the mouse performing the task.