Preference exploration and learning: the role of intensiveness and extensiveness of experience

Abstract

Abstract In this article, the authors partition the construct of experience into intensiveness (i.e., amount) and extensiveness (i.e., breadth) and examine the impact of the two specific types of experience on preference learning. In the first three studies, the authors' theory that experience can be partitioned into intensiveness (i.e., amount) and extensiveness (i.e., breadth) of experience and that extensiveness has a greater impact on preference learning is supported in environments where prior experience is measured. Further, in study 4 they demonstrate that extensiveness or breadth of experience exerts a larger influence on preference learning in an experiment where each unique type of experience is manipulated as well as measured.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 30 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.