MENTAL EFFORT:
ONE CONSTRUCT, MANY FACES?
2020 Talks
1st Workshop on Mental Effort
1st Workshop on Mental Effort
July 29, 2020
Virtual Event at 42nd Annual Meeting of the Cognitive Science Society
Pre-recorded Talks of the 2020 Workshop on Mental Effort
Pre-recorded Talks of the 2020 Workshop on Mental Effort
- Sebastian Musslick: Introductory Remarks.
- Wouter Kool: Neural and computational signatures of cost-benefit decision making.
- Eliana Vassena: Meta-control as the neurobiological solution to the effort allocation problem.
- Tom Verguts: Cognitive effort via neural synchronisation.
- Amitai Shenhav: Empirical and theoretical applications of the Expected Value of Control model.
- Jonathan D. Cohen: On the rational boundedness of cognitive control.
- Thomas L. Griffiths: Modeling the rational use of mental effort.
- Matthew M. Botvinick: Why is mental effort costly? Some ideas inspired by recent AI research.
- Lena Rosendahl: Using quantum mechanical potential wells to model task sets and its implications for mental effort.
- Ivan Grahek: Individual differences in the allocation of mental effort.
- Nele Russwinkel & Kai Preuss: Workload-over-time modeling in a cognitive architecture.
- Maria Wirzberger: Cognitive load in instructional design.
Introductory Remarks.
Introductory Remarks.
Sebastian Musslick (Princeton University)
Neural and computational signatures of cost-benefit decision making.
Neural and computational signatures of cost-benefit decision making.
Wouter Kool (Washington University in St. Louis)
Recommended References
Kool, W., & Botvinick, M. M. (2018). Mental labour. Nature Human Behaviour, 2, 899-908.
Kool, W., Gershman, S. J.*, & Cushman, F. A.* (2018). Planning complexity registers as a cost in metacontrol. Journal of Cognitive Neuroscience, 30, 1391-1404.
Kool, W., Gershman, S. J.*, & Cushman, F. A.* (2017). Cost-benefit arbitration between multiple reinforcement learning systems. Psychological Science, 28, 1321-1333.
Meta-control as the neurobiological solution to the effort allocation problem.
Meta-control as the neurobiological solution to the effort allocation problem.
Eliana Vassena (Radboud University)
Recommended References
Vassena, E., Deraeve, J., & Alexander, W. H. (2019). Task-specific prioritization of reward and effort information: Novel insights from behavior and computational modeling. Cognitive, Affective, & Behavioral Neuroscience, 19(3), 619-636.
Silvetti, M., Vassena, E., Abrahamse, E., & Verguts, T. (2018). Dorsal anterior cingulate-brainstem ensemble as a reinforcement meta-learner. PLoS computational biology, 14(8), e1006370.
Vassena, E., Holroyd, C. B., & Alexander, W. H. (2017). Computational models of anterior cingulate cortex: At the crossroads between prediction and effort. Frontiers in neuroscience, 11, 316.
Cognitive effort via neural synchronisation.
Cognitive effort via neural synchronisation.
Tom Verguts (Ghent University)
Recommended References
Aben, B., Calderon, C. B., Van den Bussche, E., & Verguts, T. (2020). Cognitive effort modulates connectivity between dorsal anterior cingulate cortex and task-relevant cortical areas. Journal of Neuroscience, 40, 3838–3848.
Verbeke, P., & Verguts, T. (2019). Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma. PLoS Computational Biology.
Verbeke, P., Ergo, K., De Loof, E., & Verguts, T. (2020). Learning to synchronize: Midfrontal theta dynamics during reversal learning. BioRxiv.
Empirical and theoretical applications of the Expected Value of Control model.
Empirical and theoretical applications of the Expected Value of Control model.
Amitai Shenhav (Brown University)
Recommended References
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T.L., Cohen, J.D., & Botvinick, M.M. (2017). Toward a rational and mechanistic account of mental effort. Annual Reviews of Neuroscience 40: 99-124.
Frömer, R, Lin, H., Dean Wolf, C.K., Inzlicht, M. & Shenhav, A. When effort matters: Expectations of reward and efficacy guide cognitive control allocation. BioRxiv 095935.
Leng, X., Ritz, H., Yee, D., & Shenhav, A. (2020). Dissociable influences of reward and punishment on adaptive cognitive control. In Proceedings of the 42nd Annual Meeting of the Cognitive Science Society. Toronto, CA.
On the rational boundedness of cognitive control.
On the rational boundedness of cognitive control.
Jonathan D. Cohen (Princeton University, Princeton Neuroscience Institute)
Recommended References
MMusslick, S., & Cohen, J. D. (2021). Rationalizing constraints on the capacity for cognitive control. Trends in Cognitive Sciences. 25(9), 757–775. doi: https://doi.org/10.1016/j.tics.2021.06.001
Musslick, S., Jang, J. S., Shvartsman, M., Shenhav, A., & Cohen, J. D. (2018). Constraints associated with cognitive control and the stability-flexibility dilemma. In Pro- ceedings of the 40th Annual Meeting of the Cognitive Science Society (pp. 806–811). Madi- son, WI.
Musslick, S., Saxe, A., Özcimder, K., Dey, B., Henselman, G., & Cohen, J. D. (2017). Multitasking capability versus learning efficiency in neural network architectures. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society (pp. 829–834). London, UK.
Modeling the rational use of mental effort.
Modeling the rational use of mental effort.
Thomas L. Griffiths (Princeton University)
Recommended References
Lieder, F., & Griffiths, T. L. (2020). Resource-rational analysis: understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences, 43.
Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., & Griffiths, T. (2018). A resource-rational analysis of human planning. In CogSci.
Lieder, F., Krueger, P. M., & Griffiths, T. (2017). An automatic method for discovering rational heuristics for risky choice. In CogSci.
Why is mental effort costly? Some ideas inspired by recent AI research.
Why is mental effort costly? Some ideas inspired by recent AI research.
Matthew M. Botvinick (Google Deepmind, University College London)
Recommended References
Kool, Wouter, and Matthew Botvinick. "Mental labour." Nature human behaviour 2, no. 12 (2018): 899-908.
Whye Teh, Yee, Victor Bapst, Wojciech Marian Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu. "Distral: Robust multitask reinforcement learning." arXiv (2017): arXiv-1707.
Goyal, Anirudh, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, and Sergey Levine. "Infobot: Transfer and exploration via the information bottleneck." arXiv preprint arXiv:1901.10902 (2019).
Using quantum mechanical potential wells to model task sets and its implications for mental effort.
Using quantum mechanical potential wells to model task sets and its implications for mental effort.
Lena Rosendhal (Princeton University)
Individual differences in the allocation of mental effort.
Individual differences in the allocation of mental effort.
Ivan Grahek (Brown University)
Recommended References
Grahek, I., Shenhav, A., Musslick, S., Krebs, R. M., & Koster, E. H. (2019). Motivation and cognitive control in depression. Neuroscience & Biobehavioral Reviews, 102, 371-381.
Grahek, I., Musslick, S., & Shenhav, A. (2020). A computational perspective on the roles of affect in cognitive control. International Journal of Psychophysiology, 151, 25-34.
Grahek, I., Froemer, R., & Shenhav, A. (2020). Learning when effort matters: Neural dynamics underlying updating and adaptation to changes in performance efficacy. bioRxiv.
Workload-over-time modeling in a cognitive architecture.
Workload-over-time modeling in a cognitive architecture.
Nele Russwinkel and Kai Preuss (Technische Universität Berlin)
Recommended References
Jo, S., Myung, R., & Yoon, D. (2012). Quantitative prediction of mental workload with the ACT-R cognitive architecture. International Journal of Industrial Ergonomics, 42(4), 359–370.
Klaproth, O.W., Halbrügge, M., Krol, L.R., Vernaleken, C., Zander, T.O., & Russwinkel, N. (2020), A Neuroadaptive Cognitive Model for Dealing With Uncertainty in Tracing Pilots' Cognitive State. Topics in Cognitive Science, 12(3), 1012-1029.
Preuss, K., Hilton, C., Gramann, K., & Russwinkel, N. (2020). Cognitive Processing Stages During Mental Folding Are Reflected in Eye Movements. In Symposium on Eye Tracking Research and Applications (ETRA’20 Adjunct), Stuttgart, Germany. New York, USA: ACM.
Cognitive load in instructional design.
Cognitive load in instructional design.
Maria Wirzberger (University of Stuttgart)
Recommended References
Borst, J. P., & Anderson, J. R. (2017). A step-by-step tutorial on using the cognitive architecture ACT-R in combination with fMRI data. Journal of Mathematical Psychology, 76, 94–103.
Whelan, R. R. (2007). Neuroimaging of cognitive load in instructional multimedia. Educational Research Review, 2, 1-12.
Wirzberger, M., Borst, J. P., Krems, J. F., & Rey, G. D. (2020). Memory-related cognitive load effects in an interrupted learning task: A model-based explanation. Trends in Neuroscience and Education, 20, 100139.