Comparing serial reproduction and serial prediction of random walk

Existing studies of the serial reproduction paradigm focused on stimuli that were statistically independent of each other. We explored serial reproductions of random walk series and examined whether Bayesian models previously built for independent stimuli could be adapted to autocorrelated stimuli. We found that Bayesian models captured most of the empirical result qualitatively, but could be further improved by incorporating recency effects. Besides, given that the optimal strategy of iterative prediction of random walk was to reproduce the current stimuli, we also compared serial prediction of random walk to serial reproduction. We found that serially reproduced and predicted series both decorrelate as a function of chain position and that the mean of the series significantly in both tasks, which matched qualitative predictions of the Bayesian models.