TY - GEN
T1 - A neural implementation of MINERVA 2
AU - Reichle, Erik D.
AU - Veldre, Aaron
AU - Yu, Lili
AU - Andrews, Sally
N1 - Copyright the Author(s) 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2022/7
Y1 - 2022/7
N2 - The MINERVA 2 (Hintzman, 1984) model of human memory has been used to simulate a variety of cognitive phenomena. These simulations, however, describe cognitive phenomena at Marr’s (1982) representation/algorithm level, with little effort to link the core assumptions of the model to an underlying neural implementation (however, see Kelly et al., 2017). This article describes a possible neural implementation of MINERVA 2—one that is simple and arguably biologically plausible. This implementation suggests a novel method for generating response latencies and provides a concrete example to support Marr’s claim that the representations and algorithms that mediate human performance in a variety of different cognitive tasks (e.g., decision making; Dougherty, Gettys, & Ogden, 1999) can be investigated and simulated without reference to their underlying neural implementation.
AB - The MINERVA 2 (Hintzman, 1984) model of human memory has been used to simulate a variety of cognitive phenomena. These simulations, however, describe cognitive phenomena at Marr’s (1982) representation/algorithm level, with little effort to link the core assumptions of the model to an underlying neural implementation (however, see Kelly et al., 2017). This article describes a possible neural implementation of MINERVA 2—one that is simple and arguably biologically plausible. This implementation suggests a novel method for generating response latencies and provides a concrete example to support Marr’s claim that the representations and algorithms that mediate human performance in a variety of different cognitive tasks (e.g., decision making; Dougherty, Gettys, & Ogden, 1999) can be investigated and simulated without reference to their underlying neural implementation.
KW - connectionist network
KW - MINERVA 2
KW - neural network
UR - http://www.scopus.com/inward/record.url?scp=85146429784&partnerID=8YFLogxK
M3 - Conference proceeding contribution
AN - SCOPUS:85146429784
T3 - Proceedings of the Annual Meeting of the Cognitive Science Society
SP - 2278
EP - 2284
BT - CogSci2022
A2 - Culbertson, Jennifer
A2 - Perfors, Andrew
A2 - Rabagliati, Hugh
A2 - Ramenzoni, Veronica
PB - Cognitive Science Society
CY - Austin, Texas
T2 - Annual Meeting of the Cognitive Science Society (44th : 2022)
Y2 - 27 July 2022 through 30 July 2022
ER -