Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but not in Operant Learning

There is a long history of experiments, in which participantsare instructed to generate a long sequence of binary random numbers.The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper,we usedgeneralized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, weused logistic regression analysis in order to characterize the temporal sequence of participants’ choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seem irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect area monotonousdecreasing function of the delay, yet these individual sequential effectsare largely averaged outin a population analysis because of heterogeneity.The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation.Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in therandom sequence generation task, different participants adoptdifferent cognitive strategiesto suppress sequential dependencies when generating the “random” sequences.