Becoming famous without being recognized: Unconscious influences of memory produced by dividing attention.
Abstract
The familiarity of names produced by their prior presentation can be misinterpreted as fame. We used this false fame effect to separately study the effects of divided attention on familiarity versus conscious recollection. In a first experiment, famous and nonfamous names were presented to be read under conditions of full vs. divided attention. Divided attention greatly reduced later recognition memory performance but had no effect on gains in familiarity as measured by fame judgments. In later experiments, we placed recognition memory and familiarity in opposition by presenting only nonfamous names to be read in the first phase. Recognizing a name as earlier read on the later fame test allowed Ss to be certain that it was nonfamous. Divided attention at study or during the fame test reduced list recognition performance but had no effect on familiarity. We conclude that conscious recollection is an attention-demanding act that is separate from assessing familiarity. Folk wisdom suggests that we benefit from experience by consciously remembering those experiences and applying the knowledge gained from them to the current situation. In contrast, research shows that many effects of prior experience
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Becoming famous without being recognized: Unconscious influences of memory produced by dividing attention. https://app.cochat.ai/papers/10.1037//0096-3445.118.2.115
- The paper
- Jacoby, L. L., Woloshyn, V., & Kelley, C. (1989). Becoming famous without being recognized: Unconscious influences of memory produced by dividing attention. Journal of Experimental Psychology: General. https://doi.org/10.1037//0096-3445.118.2.115
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.