Figure 2.

Probabilistic policy exploration model. (a) In the naïve Reinforcement Learning (RL) phase, possibly used features were abstracted as policies, as follows: π₁, using shape information (1 dim), π₂, using colour information (1 dim), π₃, using pattern information (1 dim), π₄, using combinations of colour and shape information (2 dim), π₅, using combinations of shape and pattern information (2 dim), π₆, using combinations of colour and pattern information (2 dim), π₇, using combinations of shape, colour, and pattern information (3 dim). (b) A schematic diagram of the hidden Markov model (HMM)-based policy search model. (c) A schematic diagram of the softmax function-based policy search model. (d) Comparison of model results. blue: HMM-based model, green: Softmax function-based policy search model, paired t-test p = 0.0080, mean ± SEM. (e) Representative fitted policy probability. Each policy is represented by an individual colour.