Learning-Induced Shifts in Mice Navigational Strategies Are Unveiled by a Minimal Behavioral Model of Spatial Exploration

Animals are progressively more likely to take shorter paths to the goal. Distribution of Relative trial length (RTL) for different trial groups and sessions. For each session, trials from different animals were grouped in four categories: first trial only, from second to 11th trial, from 12th to 21st trial, and from 22nd to 31st trial. RTL = 1 corresponds to perfect trial. Last column, Probability of RTL < 1.5 (optimal trial) over time. Last row, RTL computed after randomly shuffling the Goal Locations across trials. The absence of learning-induced changes indicates their specificity for goal-directed behavior. Error bars show STD computed by 50 bootstraps.

Quantification of animal trajectories departure from optimal path. Distance from optimal path (DFOP) over time for all trial groups and sessions. Last column panels, Comparison of the first trial for the three sessions. The effects of learning can be seen in the progressive reduction of the distance within each session. Additionally, DFOP decreases on the first trial of every successive session. Once a new Goal Location is introduced, convergence to asymptotic performance is faster than during initial learning. Finally, the insertion of barriers has only limited effects on behavior.

Learning sharpens animal performance by progressively reducing trajectory Distance from optimal path (DFOP). Results of the parametric fit of the curves in Figure 3. Top row, Example of the obtained match between experimental curves and the parametric fit. Bottom, value of the fit parameters over time for all conditions and sessions. Error bars STD from bootstrapping. This fit allows us to quantify: (1) the amount of “stray,” that is, the average maximum distance from the optimal path; (2) the average length of the trials; and (3) how fast the animal will go back to the correct path after straying away in the beginning. Maximum distance and descending length show a decreasing modulation over time: within one session, across sessions, and during Goal Location shift, consistently with learning effects. Descending length shows instead no significant improvement, in line with a persistent influence of a random component on behavior.

A minimal mathematical model reproduces the main properties of animal behavior. Model fitting to experimental data. Left column, Simulated-experimental Kolmogorov-Smirnov (KS) distance for a range of tested foresight values. Red triangles indicate location of best-fit F value for different sets of trials. Right column, Comparison of cumulative distributions for different trial groups and corresponding simulation results with best F value. All shown data are from build-up phase.

Further behavioral features matched by model fits. Top row, Evolution of the maximal DFOP and the fraction of time spent in the maze outer ring for the build-up phase. Rows 2–5, Same data as Figure 5. Using the same parameters obtained from fitting RTL distributions, the model also reproduces other aspects of animal behavior such as the distribution of maximal distance from the optimal path (left) and the distribution of relative time spent in the outer or inner ring of the maze on each trial (right). Real and model-based distributions are non-significantly different for every experimental phase and trial group (KS, all p > 0.1).