Proceedings of the Annual Meeting of the Cognitive Science Society

Current 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 stimulus could be adapted to autocorrelated stimulus. We found that Bayesian models captured most of the empirical results 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 means of the series increase in both tasks, which matched qualitative predictions of the Bayesian models.