Memory strength drives hybrid search efficiency
Abstract
In hybrid search tasks, observers search for any of multiple targets held in memory (e.g., finding all of the ingredients for a recipe in the supermarket). Performance declines with the log of the number of memorized targets, a cost often attributed to a serial scan through memory. In standard models of recognition memory, however, holding more items in memory weakens the match strength between each memory representation and the corresponding visual item – independent of any serial search process. Here we propose an account of hybrid search based on memory strength, testing how memory strength affects hybrid search separately from memory set size. We found that strengthening memories through repetition and deeper encoding improved hybrid search at matched set sizes (Exp. 1). Taking advantage of NFL and NHL fans' pre-existing memory strength for sports logos (Exps. 2–3), we next found search was better for a larger set of familiar items (16 logos) than for a smaller set of unfamiliar items (12 logos), a reversal of the usual memory set-size effect. A standard global matching model (REM), with encoding parameters fit only to memory performance, not search data, reproduced these results and the canonical, approximately log-linear set-size cost in discriminability. Memory strength is thus a potentially strong determinant of hybrid search efficiency, with set-size effects not necessarily reflecting any serial memory search. Instead, models of recognition memory may provide a productive framework for the memory component of hybrid search.
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BibTeX
@misc{simpson2026memory,
title = {{Memory strength drives hybrid search efficiency}},
author = {Dyllan Simpson and Lauren Williams and Haven Vander Bie and Viola S. Störmer and Timothy Brady},
howpublished = {PsyArXiv},
year = {2026},
month = nov,
doi = {10.31234/osf.io/4gcdb_v1},
url = {https://osf.io/preprints/psyarxiv/4gcdb_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Memory strength drives hybrid search efficiency
AU - Dyllan Simpson
AU - Lauren Williams
AU - Haven Vander Bie
AU - Viola S. Störmer
AU - Timothy Brady
PY - 2026
DA - 2026/11/06
PB - PsyArXiv
DO - 10.31234/osf.io/4gcdb_v1
UR - https://osf.io/preprints/psyarxiv/4gcdb_v1/
AB - In hybrid search tasks, observers search for any of multiple targets held in memory (e.g., finding all of the ingredients for a recipe in the supermarket). Performance declines with the log of the number of memorized targets, a cost often attributed to a serial scan through memory. In standard models of recognition memory, however, holding more items in memory weakens the match strength between each memory representation and the corresponding visual item – independent of any serial search process. Here we propose an account of hybrid search based on memory strength, testing how memory strength affects hybrid search separately from memory set size. We found that strengthening memories through repetition and deeper encoding improved hybrid search at matched set sizes (Exp. 1). Taking advantage of NFL and NHL fans' pre-existing memory strength for sports logos (Exps. 2–3), we next found search was better for a larger set of familiar items (16 logos) than for a smaller set of unfamiliar items (12 logos), a reversal of the usual memory set-size effect. A standard global matching model (REM), with encoding parameters fit only to memory performance, not search data, reproduced these results and the canonical, approximately log-linear set-size cost in discriminability. Memory strength is thus a potentially strong determinant of hybrid search efficiency, with set-size effects not necessarily reflecting any serial memory search. Instead, models of recognition memory may provide a productive framework for the memory component of hybrid search.
ER -