Possible explanations for sequence effects include: Gambler's fallacy: Once annotators have developed an idea of the distribution of scores/labels, they can come to expect even small sequences to follow the distribution.
in binary annotation tasks, if they expect that True (1) and False (0) items are equally likely, then they believe the sequence 00000 (100% False and 0% True) is less likely than the sequence 01010 (50% False and 50% True).