What do our sampling assumptions affect: how we encode data or how we reason from it?


In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively different inferences on the basis of the same evidence. Yet relatively little is known about how and when these two sources of evidence are combined. For instance, do sampling assumptions affect how the sample contents are encoded, or is any influence exerted only at the point of retrieval when a decision is to be made? We report two experiments aimed at exploring this issue. By systematically varying both the sampling cover story and whether it is given before or after the training stimuli we are able to determine whether encoding or retrieval issues drive the impact of sampling assumptions. Across two experiments we find that the sampling cover story affects generalization when it is presented before the training stimuli, but not after, which we interpret in favor of an encoding account.

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