@article{efa004aa47554ae1b5f23d3fddff36ea,
title = "Rats use memory confidence to guide decisions",
abstract = "Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.",
keywords = "behavior, confidence, decision making, deep neural network, machine learning, memory, metamemory, rat, spatial memory",
author = "Joo, {Hannah R.} and Hexin Liang and Chung, {Jason E.} and Charlotte Geaghan-Breiner and Fan, {Jiang Lan} and Nachman, {Benjamin P.} and Adam Kepecs and Frank, {Loren M.}",
note = "Funding Information: We thank J. Kuhl for design contributions to Figures 1 A and 1B and the graphical abstract. We thank the Frank and Kepecs labs, particularly D. Liu, G. Rothschild, and T. Davidson for advice on early versions of this task; A. Comrie for discussion and assistance; T. Ott for modeling advice; and P. Masset for the idea of temporal betting and comments on the manuscript. We are grateful to J. Berke, M. Brainard, A. Nelson, and V. Sohal for advice on task design and analysis and to U. Rutishauser for comments on the manuscript. We are thankful for the catalytic advice to focus on behavior and build a model, the GeMM being a product of those conversations. This work was funded by NIMH F30MH115582 (H.R.J.), F30MH109292 (J.E.C.), and R01MH097061 (A.K.); NINDS U01 NS107667 (L.M.F.) and U01 NS094288 (L.M.F. and A.K.); NIGMS MSTP grant T32GM007618 (J.E.C. and H.R.J.); Howard Hughes Medical Institute (L.M.F.); and DOE DE-AC02-05CH11231 (B.P.N.). We thank NVIDIA for providing Volta GPUs for the neural network training. Funding Information: We thank J. Kuhl for design contributions to Figures 1A and 1B and the graphical abstract. We thank the Frank and Kepecs labs, particularly D. Liu, G. Rothschild, and T. Davidson for advice on early versions of this task; A. Comrie for discussion and assistance; T. Ott for modeling advice; and P. Masset for the idea of temporal betting and comments on the manuscript. We are grateful to J. Berke, M. Brainard, A. Nelson, and V. Sohal for advice on task design and analysis and to U. Rutishauser for comments on the manuscript. We are thankful for the catalytic advice to focus on behavior and build a model, the GeMM being a product of those conversations. This work was funded by NIMH F30MH115582 (H.R.J.), F30MH109292 (J.E.C.), and R01MH097061 (A.K.); NINDS U01 NS107667 (L.M.F.) and U01 NS094288 (L.M.F. and A.K.); NIGMS MSTP grant T32GM007618 (J.E.C. and H.R.J.); Howard Hughes Medical Institute (L.M.F.); and DOE DE-AC02-05CH11231 (B.P.N.). We thank NVIDIA for providing Volta GPUs for the neural network training. Conceptualization, H.R.J. J.E.C. A.K. and L.M.F.; methodology, H.R.J. J.L.F. J.E.C. H.L. and C.G.-B.; software, H.R.J. J.L.F. J.E.C. H.L. B.P.N. and C.G.-B.; formal analysis, H.R.J. H.L. and B.P.N.; investigation, H.R.J. H.L. and C.G.-B.; resources, L.M.F. and A.K.; data curation, H.R.J. H.L. J.L.F. and J.E.C.; writing – original draft, H.R.J. and H.L.; writing – reviewing and editing, H.R.J. H.L. L.M.F. J.L.F. A.K. and B.P.N.; visualization, H.R.J. H.L. and B.P.N.; supervision, A.K. and L.M.F.; project administration, H.R.J.; funding acquisition, L.M.F. A.K. J.E.C. and H.R.J. The authors declare no competing interests. While citing references scientifically relevant for this work, we actively worked to promote gender balance in our reference list. Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2021",
month = oct,
day = "25",
doi = "10.1016/j.cub.2021.08.013",
language = "English",
volume = "31",
pages = "4571--4583.e4",
journal = "Current Biology",
issn = "0960-9822",
number = "20",
}