At least 2 information-processing tasks underlie decisions from experience (DFE): information search (sampling) and information integration. Which strategies do we use to solve these tasks and how does their interplay shape the process of preference construction? Here, we model the interaction of different sampling and integration strategies within a sequential sampling framework. We simulate the implied sampling and decision processes for a set of binary choice problems. We find that the interplay of sampling and integration strategies can produce various systematic choice patterns. For instance, with a round-wise integration of outcomes, changes in the sampling strategy shift preferences for average returns to frequent returns. Such a shift causes low rates of expected value maximization and a robust underweighting of rare outcomes pattern. We also use cumulative prospect theory (CPT) to model the simulated choice data. While accounting for sampling error, we find that shifts in choice patterns due to changes in the information-processing strategies are reflected in characteristic shapes of CPT’s value and weighting function. For instance, preference for frequent returns due to a round-wise integration of outcomes and a back-and-forth sampling mechanism are linked to an S-shaped weighting function and a highly compressed value function. Our findings highlight that commonly observed choice patterns in DFE can be explained in terms of strategic responses to underlying search and integration tasks. They also underscore the potential of integrating different model classes and the potential of descriptive models such as CPT to capture characteristics of the actual information-processing mechanisms.