We can all feel exhausted after a day of work, even if we have spent it sitting at a desk. The intuitive concept of mental effort pervades virtually all domains of human information processing and has become an indispensable ingredient for general theories of cognition (Anderson, 2007; Shenhav et al., 2017; Lieder & Griffiths, 2015). However, inconsistent use of the term across cognitive sciences, including cognitive psychology, education, human-factors engineering and artificial intelligence, makes it one of the least well-defined theoretical constructs across fields. A number of recent approaches lay the foundation for a consensus by offering formal accounts of mental effort. Yet, reaching a multifield-wide consensus on the operationalization of mental effort will require cross-talk between different empirical and computational approaches, including symbolic architectures, non-parametric Bayesian statistics and neural networks. The purpose of this full-day workshop is to review and integrate these emerging perspectives.
%0 Conference Paper
%1 musslick2020mental
%A Musslick, Sebastian
%A Wirzberger, Maria
%A Grahek, Ivan
%A Bustamante, Laura
%A Shenhav, Amitai
%A Cohen, Johnathan D.
%B Proceedings of the 42nd Annual Conference of the Cognitive Science Society
%D 2020
%E Denison, Stephanie
%E Mack, Michael
%E Xu, Yang
%E Armstrong, Blair C.
%I Cognitive Science Society
%K
%P 1-2
%T Mental effort: One construct, many faces?
%U https://cognitivesciencesociety.org/cogsci20/papers/0001/0001.pdf
%X We can all feel exhausted after a day of work, even if we have spent it sitting at a desk. The intuitive concept of mental effort pervades virtually all domains of human information processing and has become an indispensable ingredient for general theories of cognition (Anderson, 2007; Shenhav et al., 2017; Lieder & Griffiths, 2015). However, inconsistent use of the term across cognitive sciences, including cognitive psychology, education, human-factors engineering and artificial intelligence, makes it one of the least well-defined theoretical constructs across fields. A number of recent approaches lay the foundation for a consensus by offering formal accounts of mental effort. Yet, reaching a multifield-wide consensus on the operationalization of mental effort will require cross-talk between different empirical and computational approaches, including symbolic architectures, non-parametric Bayesian statistics and neural networks. The purpose of this full-day workshop is to review and integrate these emerging perspectives.
@inproceedings{musslick2020mental,
abstract = {We can all feel exhausted after a day of work, even if we have spent it sitting at a desk. The intuitive concept of mental effort pervades virtually all domains of human information processing and has become an indispensable ingredient for general theories of cognition (Anderson, 2007; Shenhav et al., 2017; Lieder & Griffiths, 2015). However, inconsistent use of the term across cognitive sciences, including cognitive psychology, education, human-factors engineering and artificial intelligence, makes it one of the least well-defined theoretical constructs across fields. A number of recent approaches lay the foundation for a consensus by offering formal accounts of mental effort. Yet, reaching a multifield-wide consensus on the operationalization of mental effort will require cross-talk between different empirical and computational approaches, including symbolic architectures, non-parametric Bayesian statistics and neural networks. The purpose of this full-day workshop is to review and integrate these emerging perspectives.},
added-at = {2023-09-23T09:33:58.000+0200},
author = {Musslick, Sebastian and Wirzberger, Maria and Grahek, Ivan and Bustamante, Laura and Shenhav, Amitai and Cohen, Johnathan D.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22ce5ad32ad7833994d19cb437d723614/llis},
booktitle = {Proceedings of the 42nd Annual Conference of the Cognitive Science Society},
editor = {Denison, Stephanie and Mack, Michael and Xu, Yang and Armstrong, Blair C.},
interhash = {e9712d1a773253a50b60b6f1436dfef4},
intrahash = {2ce5ad32ad7833994d19cb437d723614},
keywords = {},
pages = {1-2},
publisher = {Cognitive Science Society},
timestamp = {2023-09-23T09:33:58.000+0200},
title = {Mental effort: One construct, many faces?},
url = {https://cognitivesciencesociety.org/cogsci20/papers/0001/0001.pdf},
year = 2020
}