Adaptive behavior in optimal sequential search

Abstract

Sequential decision making - making a decision where available options are encountered successively - is a hallmark of everyday life. Such decisions require deciding to accept or reject an alternative without knowing potential future options. Prior work focused on understanding choice behavior by developing decision models that capture human choices in such tasks. We investigated people’s adaptive behavior in changing environments in light of their cognitive strategies. We present two studies in which we modified (1) outcome variance and (2) the time horizon and provide empirical evidence that people adapt to both context manipulations. Furthermore, we apply a recently developed threshold model of optimal stopping to our data to disentangle different cognitive processes involved in optimal stopping behavior. The results from Study 1 show that participants adaptively scaled the values of the sampling distribution to its variance, suggesting that the value of an option is perceived in relative rather than absolute terms. The results from Study 2 suggest that increasing the time horizon decreases the initial acceptance level, but less strongly than would be optimal. Furthermore, for longer sequences, participants more weakly adjusted this acceptance threshold over time than for shorter sequences. Further correlations between individual estimates in each condition indicate that individual differences between the participants’ thresholds remain fairly stable between the conditions, pointing toward an additive effect of our manipulations.