A dynamic Bayesian model of homeostatic control
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Higher levels of control enable prospective control with inference on current bodily state and prediction of its future evolution.
“By contrast, assuming that the brain maintains a model of bodily states and the external environment, higher levels enable prospective control, with two essential components: inference (on current bodily state) and prediction (of its future evolution, on its own and in response to chosen actions) (Sterling, 2012; Penny and Stephan, 2014; Pezzulo et al., 2015; Seth, 2015a).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Penny and Stephan, 2014
First attempts to account for homeostasis and allostasis in formal models of decision-making have only surfaced relatively recently.
“Current models of decision-making do not incorporate all of these aspects, and first attempts of accounting for homeostasis and allostasis in formal models of decision-making have only surfaced relatively recently (e.g., Keramati and Gutkin, 2014; Penny and Stephan, 2014; Pezzulo et al., 2015).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Penny and Stephan, 2014
Current models of decision-making do not incorporate all of the four aspects of homeostatic utility.
“Current models of decision-making do not incorporate all of these aspects, and first attempts of accounting for homeostasis and allostasis in formal models of decision-making have only surfaced relatively recently (e.g., Keramati and Gutkin, 2014; Penny and Stephan, 2014; Pezzulo et al., 2015).”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Penny and Stephan, 2014
Cites
(1978) · UA Campus Repository (The University of Arizona) · Cited by 8,010 · see citing works
Neural Information Processing Systems
(2018) · The MIT Press eBooks · doi:10.7551/mitpress/11474.003.0014 · Cited by 3,775 · see citing works
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,782 · see citing works
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533 · see citing works
Organization for physiological homeostasis
Cannon, W. B. (1929) · Physiological Reviews · doi:10.1152/physrev.1929.9.3.399 · Cited by 1,844 · see citing works
A free energy principle for the brain
Friston, K., Kilner, J., & Harrison, L. (2006) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2006.10.001 · Cited by 1,369 · see citing works
Hippocampal place-cell sequences depict future paths to remembered goals
Pfeiffer, B. E., & Foster, D. J. (2013) · Nature · doi:10.1038/nature12112 · Cited by 1,216 · see citing works
Allostasis: A model of predictive regulation
Sterling, P. (2012) · Physiology & Behavior · doi:10.1016/j.physbeh.2011.06.004 · Cited by 1,073 · see citing works
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 636 · see citing works
The construction system of the brain
Hassabis, D., & Maguire, E. A. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0296 · Cited by 483 · see citing works
Efficient computation of optimal actions
Todorov, E. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0710743106 · Cited by 417 · see citing works
Foster, D. J., & Wilson, M. A. (2007) · Hippocampus · doi:10.1002/hipo.20345 · Cited by 381 · see citing works
Neuronal computations underlying the firing of place cells and their role in navigation
Burgess, N., & O'Keefe, J. M. (1996) · Hippocampus · doi:10.1002/(sici)1098-1063(1996)6:6 · Cited by 240 · see citing works
Planning by Probabilistic Inference.
Attias, H. T. (2003) · Cited by 204 · see citing works
Johnson, A. C., & Redish, A. D. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.08.009 · Cited by 91 · see citing works
Forward and Backward Inference in Spatial Cognition
Penny, W. D., Zeidman, P., & Burgess, N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003383 · Cited by 67 · see citing works
Self-citationA Reinforcement Learning Theory for Homeostatic Regulation
Keramati, M., & Gutkin, B. S. (2011) · City Research Online (City University London) · Cited by 39 · see citing works
Simultaneous Localisation and Planning
Penny, W. D. (2014) · International Workshop on Cognitive Information Processing (CIP) · doi:10.1109/cip.2014.6844499 · Cited by 6 · see citing works
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Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression
Stephan, K. E., Manjaly, Z. M., Mathys, C. D., Weber, L. A. E., Paliwal, S., Gard, T., Tittgemeyer, M., Fleming, S. M., Haker, H., Seth, A. K., et al. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00550 · Cited by 437 · see citing works
“Current models of decision-making do not incorporate all of these aspects, and first attempts of accounting for homeostasis and allostasis in formal models of decisionmaking have only surfaced relatively recently (e.g., Keramati and Gutkin, 2014; Penny and Stephan, 2014; Pezzulo et al., 2015).”
BackgroundInfluentialSelf-citationComputational Models of Interoception and Body Regulation
Petzschner, F. H., Garfinkel, S. N., Paulus, M. P., Koch, C., & Khalsa, S. S. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2020.09.012 · Cited by 227 · see citing works
Petzschner, F. H., Weber, L. A. E., Gard, T., & Stephan, K. E. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.05.012 · Cited by 181 · see citing works
Self-citationCorcoran, A. W., Pezzulo, G., & Hohwy, J. (2020) · Biology & Philosophy · doi:10.1007/s10539-020-09746-2 · Cited by 161 · see citing works
Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice?
Haker, H., Schneebeli, M., & Stephan, K. E. (2016) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2016.00107 · Cited by 130 · see citing works
Self-citationInteroception as modeling, allostasis as control
Sennesh, E., Theriault, J., Brooks, D., van de Meent, J.-W., Barrett, L. F., & Quigley, K. S. (2022) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108242 · Cited by 107 · see citing works
The first prior: From co-embodiment to co-homeostasis in early life
Ciaunica, A., Constant, A., Preissl, H., & Fotopoulou, K. (2021) · Consciousness and Cognition · doi:10.1016/j.concog.2021.103117 · Cited by 105 · see citing works
Valuing what happens: a biogenic approach to valence and (potentially) affect
Lyon, P., & Kuchling, F. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0752 · Cited by 56 · see citing works
A computationally informed distinction of interoception and exteroception
Toussaint, B., Heinzle, J., & Stephan, K. E. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2024.105608 · Cited by 32 · see citing works
Self-citationEmergence of integrated behaviors through direct optimization for homeostasis
Yoshida, N., Daikoku, T., Nagai, Y., & Kuniyoshi, Y. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106379 · Cited by 15 · see citing works
- Authors
- Penny, W., & Stephan, K.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University of Zurich (CH)
- Corresponding author
- Penny, Will
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 60-69
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1737819696
- Cited by
- 10
- References
- 18
- Citations by year
- 2024: 2 · 2021: 3 · 2020: 2 · 2017: 1 · 2016: 2
- FWCI
- 1.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- homeostatic regulation, dynamic Bayesian networks, hidden states, message passing, value function, cognitive maps, hippocampus, goal-directed navigation
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