Does jumping occur during the information accumulation process?
Levy Flights models have obtained good fits to experimental data. They differ from conventional evidence accumulation models in assuming a power-law distribution for accumulation noise, which causes some jumps during the information accumulation process. In this study, we plan to examine whether jumping during the accumulation process has real psychological meaning or only causes overfitting of the data. To this end, we have examined the effect of practice and feedback on within-trial variability, (i.e. the parameter that defines jump size) against the between trial variability parameters. Four versions of the Levy Flights model, with and without parameter variability, were fitted to behavioral data from a study by Evans and Brown (2017), and a five-layer deep inference neural network is utilized to fit the models. The results show the jump size has a systematic decreasing trend but the other variability parameters do not have any specific pattern during the experiment, speaking against the possibility of this parameter trading off with existing variability parameters. The results suggest that the Levy Flights model is not simply improving fits due to increased model complexity, and that further investigation of a potential psychological interpretation of jumps in evidence accumulation are warranted.