Markov chain modeling of within-trial strategy evolution
a) Markov chain model incorporating 4 strategies (random, serial clockwise, serial counterclockwise and spatial). To generate vestibule sequences, the model iteratively draws a strategy and then a vestibule using the stochastic process associated with the selected strategy, repeating this operation until the goal (vestibule 0) is reached. At the beginning of the trials (i = 1), the strategy is drawn according to a set of probabilities determining the initial strategy. Subsequently (i > 1), the model transitions between strategies based on another set of probabilities. Numbers indicate the probabilities obtained from the model best fit.
b) Experimental and model (best fit) distributions for segment size and vestibule visits, implemented on the level of individual segments for the first 10 segments of the trials. The data from days 6 to15 were used for this analysis.
c) Overlays of experimental (blue) and model (red) distributions for both trial length and serial bout length, using the same fit instance as in (b).
d) Evolution of the mean square error (m.s.e) across generations of the genetic algorithm, for the 10 repetitions of the genetic algorithm.
e-f) Blue circles, set of probabilities determining the initial strategy (e) and strategy transitions (f), generated by 10 repetitions of the genetic algorithm. Red circles, the probability set that produced the smallest m.s.e.