Adaptive search space pruning in complex strategic problems
Fig 4
Participants’ search is influenced by their previous choices and is not explained by an internal search model or by reduced attention to the opponent.
A) Distribution of participants’ moves on the full version of board configuration II. B) Distribution of participants’ moves on the truncated version of board configuration II. C) Mean entropy of the distribution of participants’ first moves in the truncated boards and the distribution of moves in the equivalent board state in the full boards. D) Mean entropy of the distribution of participants’ first moves in the truncated boards and the distribution of moves in the equivalent board state in the full boards as predicted by the “Interaction” scoring strategies with an added shutter heuristic (shutter = 0). **, p < 0.01. E) Mean entropy of the distribution of participants’ first moves in the truncated boards and the distribution of moves in the equivalent board state in the full boards as predicted by the “Interaction” scoring strategy when ignoring opponent’s winning paths, thus focusing only on one’s own pieces. F) Mean entropy of the distribution of participants’ first moves in the truncated boards and the distribution of moves in the equivalent board state in the full boards as predicted by an internal search model, using alpha-beta pruning search with branching factor k = 7 and limited depth d = 1. All error bars are 95% confidence intervals.