Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts
Table 4
Additional models were constructed with substitution or addition of the alternative features that might be expected to interact with effects of state-independent action hysteresis. Each alternative was fixed within a new subset of eight models building up to constant bias and exponential state-independent hysteresis (“-CE1”). Variations on substitution of state-dependent hysteresis in particular were also tested up to two parameters. Listed for each participant group are the best-fitting models (per AICc score) among each subset of eight models as well as the full set of 44 models. Although there appears to be some quantitative evidence suggesting state-dependent hysteresis in addition to state-independent hysteresis, the lack of qualitative validation with falsification leaves this quantitative result inconclusive. Hence the 2CE1 model remains preferred for a final model. “df” stands for degrees of freedom. See also Figs S-W and Tables Q-U in S1 Text.