From reinforcement learning to agency: Frameworks for understanding basal cognition
Open accessAbstract
Organisms play, explore, and mimic those around them. Is there a purpose to this behavior? Are organisms just behaving, or are they trying to achieve goals? We believe this is a false dichotomy. To that end, to understand organisms, we attempt to unify two approaches for understanding complex agents, whether evolved or engineered. We argue that formalisms describing multiscale competencies and goal-directedness in biology (e.g., TAME), and reinforcement learning (RL), can be combined in a symbiotic framework. While RL has been largely focused on higher-level organisms and robots of high complexity, TAME is naturally capable of describing lower-level organisms and minimal agents as well. We propose several novel questions that come from using RL/TAME to understand biology as well as ones that come from using biology to formulate new theory in AI. We hope that the research programs proposed in this piece shape future efforts to understand biological organisms and also future efforts to build artificial agents.
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In applications where action or intelligence is needed, such as for safety or efficiency management through automation with utilities, robotics or autonomous vehicles or processes and systems, agentic AI systems use action-based datasets to learn how to recognize anomalies, mitigate threats, or identify the best actions to optimize processes.
“In applications where action or intelligence is needed, such as for safety or efficiency management through automation with utilities, robotics or autonomous vehicles or processes and systems (such as supply chains), agentic AI systems use action-based datasets, which contain information about conducted actions, so they can learn how to recognize anomalies [117], mitigate threats, or identify the”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [158]
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1 retracted
The Evolution and Ecology of Resistance in Cancer TherapyPreprint
Gatenby, R. A., & Brown, J. S. (2017) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a033415 · Cited by 130 · see citing works
RetractedReinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728 · see citing works
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956 · see citing works
Curiosity-Driven Exploration by Self-Supervised Prediction
Pathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.70 · Cited by 1,815 · see citing works
Swarm robotics: a review from the swarm engineering perspective
Brambilla, M., Ferrante, E., Birattari, M., & Dorigo, M. (2013) · Swarm Intelligence · doi:10.1007/s11721-012-0075-2 · Cited by 1,791 · see citing works
Model-based influences on humans’ choices and striatal prediction errors
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.027 · Cited by 1,779 · see citing works
Optimal control theory: An introduction
Levine, W. S. (1972) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1972.1100008 · Cited by 1,631 · see citing works
Behavior, Purpose and Teleology
Rosenblueth, A., Wiener, N., & Bigelow, J. (1943) · Philosophy of Science · doi:10.1086/286788 · Cited by 1,579 · see citing works
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450 · see citing works
Intrinsic noise in gene regulatory networks
Thattai, M., & van Oudenaarden, A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.151588598 · Cited by 1,428 · see citing works
The hippocampus as a predictive map
Stachenfeld, K. L., Botvinick, M. M., & Gershman, S. J. (2017) · Nature Neuroscience · doi:10.1038/nn.4650 · Cited by 1,000 · see citing works
Ion channels enable electrical communication in bacterial communities
Prindle, A. B., Liu, J., Asally, M., Ly, S., García‐Ojalvo, J., & Süel, G. M. (2015) · Nature · doi:10.1038/nature15709 · Cited by 794 · see citing works
A survey and critique of multiagent deep reinforcement learning
Hernández-Leal, P., Kartal, B., & Taylor, M. E. (2019) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-019-09421-1 · Cited by 747 · see citing works
Behavioral studies of Pavlovian conditioning
Rescorla, R. A. (1988) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.11.030188.001553 · Cited by 715 · see citing works
Seminars in Cell & Developmental Biology
Lee, H., & Kirkwood, A. (2011) · Cited by 692 · see citing works
Reinforcement learning, fast and slow
Botvinick, M., Ritter, S., Wang, J. X., Kurth-Nelson, Z., Blundell, C., & Hassabis, D. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.006 · Cited by 667 · see citing works
Prefrontal cortex as a meta-reinforcement learning system
Wang, J. X., Kurth-Nelson, Z., Kumaran, D., Tirumala, D., Soyer, H., Leibo, J. Z., Hassabis, D., & Botvinick, M. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0147-8 · Cited by 596 · see citing works
Inverse Problems in the Mathematical Sciences
Groetsch, C. W. (1993) · Vieweg+Teubner eBooks · doi:10.1007/978-3-322-99202-4 · Cited by 520 · see citing works
Bioelectric signaling: Reprogrammable circuits underlying embryogenesis, regeneration, and cancer
Levin, M. (2021) · Cell · doi:10.1016/j.cell.2021.02.034 · Cited by 505 · see citing works
Self-citationTrial-by-trial data analysis using computational models
Daw, N. D. (2011) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199600434.003.0001 · Cited by 472 · see citing works
A scalable pipeline for designing reconfigurable organisms
Kriegman, S., Blackiston, D. J., Levin, M. G., & Bongard, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910837117 · Cited by 467 · see citing works
Self-citationSlicing across Kingdoms: Regeneration in Plants and Animals
Birnbaum, K. D., & Alvarado, A. S. (2008) · Cell · doi:10.1016/j.cell.2008.01.040 · Cited by 424 · see citing works
The cognitive cell: bacterial behavior reconsidered
Lyon, P. (2015) · Frontiers in Microbiology · doi:10.3389/fmicb.2015.00264 · Cited by 411 · see citing works
On Having No Head: Cognition throughout Biological Systems
Baluška, F., & Levin, M. G. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00902 · Cited by 383 · see citing works
Self-citationCouzin, I. D. (2007) · Nature · doi:10.1038/445715a · Cited by 327 · see citing works
Learning by Playing - Solving Sparse Reward Tasks from ScratchPreprint
Riedmiller, M., Hafner, R., Lampe, T., Neunert, M., Degrave, J., Van de Wiele, T., Mnih, V., Heess, N., & Springenberg, J. T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.10567 · Cited by 314 · see citing works
Levin, M. G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02688 · Cited by 310 · see citing works
Self-citationReinforcement learning in artificial and biological systems
Neftci, E., & Averbeck, B. B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0025-4 · Cited by 303 · see citing works
Is Your Brain Really Necessary?
Lewin, R. A. (1980) · Science · doi:10.1126/science.7434023 · Cited by 262 · see citing works
The biogenic approach to cognition
Lyon, P. (2005) · Cognitive Processing · doi:10.1007/s10339-005-0016-8 · Cited by 259 · see citing works
A step-by-step tutorial on active inference and its application to empirical data
Smith, R., Friston, K. J., & Whyte, C. J. (2022) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2021.102632 · Cited by 246 · see citing works
Pezzulo, G., & Levin, M. G. (2016) · Journal of The Royal Society Interface · doi:10.1098/rsif.2016.0555 · Cited by 231 · see citing works
Self-citationMesolimbic dopamine release conveys causal associations
Jeong, H., Taylor, A., Floeder, J. R., Lohmann, M., Mihalaş, Ş., Wu, B., Zhou, M., Burke, D. A., & Namboodiri, V. M. K. (2022) · Science · doi:10.1126/science.abq6740 · Cited by 226 · see citing works
2013 IEEE Congress on Evolutionary Computation
(2013) · doi:10.1109/cec20747.2013 · Cited by 220 · see citing works
Oviedo, N. J., Morokuma, J., Walentek, P., Kema, I. P., Gu, M. B., Ahn, J., Hwang, J. S., Gojobori, T., & Levin, M. G. (2009) · Developmental Biology · doi:10.1016/j.ydbio.2009.12.012 · Cited by 219 · see citing works
Self-citationBacterial strategies for chemotaxis response
Celani, A., & Vergassola, M. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909673107 · Cited by 217 · see citing works
Recognition and Alleviation of Pain in Laboratory Animals
Criado, A. (2010) · Laboratory Animals · doi:10.1258/la.2010.201003 · Cited by 199 · see citing works
Levin, M. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.768201 · Cited by 195 · see citing works
Self-citationAutomatic Goal Generation for Reinforcement Learning AgentsPreprint
Florensa, C., Held, D., Geng, X., & Abbeel, P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.06366 · Cited by 193 · see citing works
The successor representation: Its computational logic and neural substrates
Gershman, S. J. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.0151-18.2018 · Cited by 191 · see citing works
The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition
Schrauwen, B., Defour, J., Verstraeten, D., & Campenhout, J. V. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74690-4_48 · Cited by 181 · see citing works
Pezzulo, G., & Levin, M. G. (2015) · Integrative Biology · doi:10.1039/c5ib00221d · Cited by 167 · see citing works
Self-citationGünel, S., Rhodin, H., Morales, D., Campagnolo, J., Ramdya, P., & Fua, P. (2019) · eLife · doi:10.7554/elife.48571 · Cited by 167 · see citing works
Blundell, C., Uria, B., Pritzel, A., Li, Y., Ruderman, A., Leibo, J. Z., Rae, J., Wierstra, D., & Hassabis, D. (2016) · arXiv · doi:10.48550/arxiv.1606.04460 · Cited by 162 · see citing works
Multigenerational memory and adaptive adhesion in early bacterial biofilm communities
Lee, C. K., de Anda, J., Baker, A. E., Bennett, R. R., Luo, Y., Lee, E. Y., Keefe, J. A., Helali, J. S., Ma, J., Zhao, K., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720071115 · Cited by 160 · see citing works
Fankhauser, G. (1945) · Journal of Experimental Zoology · doi:10.1002/jez.1401000310 · Cited by 157 · see citing works
Encoding Membrane-Potential-Based Memory within a Microbial Community
Yang, C.-Y., Bialecka-Fornal, M., Weatherwax, C., Larkin, J., Prindle, A. B., Liu, J., García‐Ojalvo, J., & Süel, G. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.04.002 · Cited by 146 · see citing works
Memory in Microbes: Quantifying History-Dependent Behavior in a Bacterium
Wolf, D. M., Fontaine, L., Bischofs, I., Price, G., Keasling, J. D., & Arkin, A. P. (2008) · PLoS ONE · doi:10.1371/journal.pone.0001700 · Cited by 143 · see citing works
Long-Term, Stochastic Editing of Regenerative Anatomy via Targeting Endogenous Bioelectric Gradients
Durant, F., Morokuma, J., Fields, C., Williams, K. B., Adams, D. S., & Levin, M. G. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2017.04.011 · Cited by 137 · see citing works
Self-citationMarkov decision processes with delays and asynchronous cost collection
Katsikopoulos, K. V., & Engelbrecht, S. E. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.809799 · Cited by 129 · see citing works
Reconsidering the evidence for learning in single cells
Gershman, S. J., Balbi, P. E., Gallistel, C. R., & Gunawardena, J. (2021) · eLife · doi:10.7554/elife.61907 · Cited by 125 · see citing works
Kinematic self-replication in reconfigurable organisms
Kriegman, S., Blackiston, D. J., Levin, M. G., & Bongard, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112672118 · Cited by 125 · see citing works
Self-citationFields, C., & Levin, M. (2022) · Entropy · doi:10.3390/e24060819 · Cited by 121 · see citing works
Self-citationIigaya, K., Fonseca, M. S., Murakami, M., Mainen, Z. F., & Dayan, P. (2018) · Nature Communications · doi:10.1038/s41467-018-04840-2 · Cited by 120 · see citing works
Planarian regeneration: achievements and future directions after 20 years of research
Saló, E., Abril, J. F., Adell, T., Cebrià, F., Eckelt, K., Fernández-Taboada, E., Handberg-Thorsager, M., Iglesias, M., Molina, M. D., & Rodríguez-Esteban, G. (2009) · The International Journal of Developmental Biology · doi:10.1387/ijdb.072414es · Cited by 118 · see citing works
A Complex Hierarchy of Avoidance Behaviors in a Single-Cell Eukaryote
Dexter, J. P., Prabakaran, S., & Gunawardena, J. (2019) · Current Biology · doi:10.1016/j.cub.2019.10.059 · Cited by 116 · see citing works
Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind
Levin, M. G. (2023) · Animal Cognition · doi:10.1007/s10071-023-01780-3 · Cited by 116 · see citing works
Self-citationLearning from Bacteria about Natural Information Processing
Ben‐Jacob, E. (2009) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05022.x · Cited by 113 · see citing works
Vandenberg, L. N., Adams, D. S., & Levin, M. G. (2012) · Developmental Dynamics · doi:10.1002/dvdy.23770 · Cited by 111 · see citing works
Self-citationThe Axolotl Model for Regeneration and Aging Research: A Mini-Review
McCusker, C., & Gardiner, D. M. (2011) · Gerontology · doi:10.1159/000323761 · Cited by 111 · see citing works
Emmons‐Bell, M., Durant, F., Hammelman, J., Bessonov, N. M., Volpert, V. A., Morokuma, J., Pinet, K., Adams, D. S., Pietak, A. M., Lobo, D., et al. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms161126065 · Cited by 109 · see citing works
Self-citationJacob, E. B., Shapira, Y., & Tauber, A. I. (2005) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2005.05.096 · Cited by 101 · see citing works
Levin, M. G. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04790-z · Cited by 99 · see citing works
Self-citationMetabolic basis of brain-like electrical signalling in bacterial communities
Martinez-Corral, R., Liu, J., Prindle, A. B., Süel, G. M., & García‐Ojalvo, J. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0382 · Cited by 98 · see citing works
Mathews, J., & Levin, M. G. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.03.008 · Cited by 97 · see citing works
Self-citationA linear-encoding model explains the variability of the target morphology in regeneration
Lobo, D., Solano, M., Bubenik, G. A., & Levin, M. G. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0918 · Cited by 91 · see citing works
Self-citationLevin, M. G., Pietak, A. M., & Bischof, J. (2018) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2018.04.003 · Cited by 91 · see citing works
Self-citationBiswas, S., Manicka, S., Hoel, E. P., & Levin, M. G. (2021) · iScience · doi:10.1016/j.isci.2021.102131 · Cited by 90 · see citing works
Self-citationBioelectrical approaches to cancer as a problem of the scaling of the cellular self
Levin, M. G. (2021) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2021.04.007 · Cited by 82 · see citing works
Self-citationSynthetic collective intelligence
Solé, R. V., Amor, D. R., Duran‐Nebreda, S., Pueyo, N. C., Carbonell-Ballestero, M., & Montañez, R. (2016) · Biosystems · doi:10.1016/j.biosystems.2016.01.002 · Cited by 78 · see citing works
Clawson, W. P., & Levin, M. G. (2022) · Biological Journal of the Linnean Society · doi:10.1093/biolinnean/blac073 · Cited by 77 · see citing works
Self-citationMemory inception and preservation in slime moulds: the quest for a common mechanism
Boussard, A., Delescluse, J., Pérez‐Escudero, A., & Dussutour, A. (2019) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2018.0368 · Cited by 71 · see citing works
What can ecosystems learn? Expanding evolutionary ecology with learning theory
Power, D. A., Watson, R. A. R., Szathmáry, E., Mills, R., Powers, S. T., Doncaster, C. P., & Czapp, B. (2015) · Biology Direct · doi:10.1186/s13062-015-0094-1 · Cited by 71 · see citing works
Pinet, K., & McLaughlin, K. A. (2019) · Developmental Biology · doi:10.1016/j.ydbio.2019.04.001 · Cited by 66 · see citing works
Synthetic morphology with agential materials
Davies, J. A., & Levin, M. G. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-022-00001-9 · Cited by 66 · see citing works
Self-citationThere’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines
Bongard, J., & Levin, M. G. (2023) · Biomimetics · doi:10.3390/biomimetics8010110 · Cited by 66 · see citing works
Self-citationOrigin of multicellular organisms as an inevitable consequence of dynamical systems
Furusawa, C., & Kaneko, K. (2002) · The Anatomical Record · doi:10.1002/ar.10164 · Cited by 65 · see citing works
Future medicine: from molecular pathways to the collective intelligence of the body
Lagasse, E., & Levin, M. G. (2023) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2023.06.007 · Cited by 62 · see citing works
Self-citationDurant, F., Lobo, D., Hammelman, J., & Levin, M. G. (2016) · Regeneration · doi:10.1002/reg2.54 · Cited by 61 · see citing works
Self-citationThe stability of memories during brain remodeling: A perspective
Blackiston, D. J., Shomrat, T., & Levin, M. G. (2015) · Communicative & Integrative Biology · doi:10.1080/19420889.2015.1073424 · Cited by 60 · see citing works
Self-citationA review of inverse reinforcement learning theory and recent advances
Shao, Z., & Joo, E. M. (2012) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2012.6256507 · Cited by 53 · see citing works
Abramson, C. I., & Levin, M. G. (2021) · Communicative & Integrative Biology · doi:10.1080/19420889.2021.2005863 · Cited by 50 · see citing works
Self-citationThe meaning and origin of goal-directedness: a dynamical systems perspective
Heylighen, F. (2022) · Biological Journal of the Linnean Society · doi:10.1093/biolinnean/blac060 · Cited by 50 · see citing works
Associative memory in gene regulation networks
Watson, R. A. R., Buckley, C. L., Mills, R., & Davies, A. (2010) · ePrints Soton (University of Southampton) · Cited by 48 · see citing works
Tsividis, P. A., Pouncy, T., Xu, J. L., Tenenbaum, J. B., & Gershman, S. J. (2017) · Other univ. web domain · Cited by 44 · see citing works
Design for an Individual: Connectionist Approaches to the Evolutionary Transitions in Individuality
Watson, R. A. R., Levin, M. G., & Buckley, C. L. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2022.823588 · Cited by 43 · see citing works
Self-citationRegulative development as a model for origin of life and artificial life studies
Fields, C., & Levin, M. G. (2023) · Biosystems · doi:10.1016/j.biosystems.2023.104927 · Cited by 42 · see citing works
Self-citationBioengineering Self-Organizing Signaling Centers to Control Embryoid Body Pattern Elaboration
Glykofrydis, F., Cachat, É., Berzanskyte, I., Dzierzak, E. A., & Davies, J. A. (2021) · ACS Synthetic Biology · doi:10.1021/acssynbio.1c00060 · Cited by 36 · see citing works
The need for a concept of shape homeostasis
Harris, A. K. (2018) · Biosystems · doi:10.1016/j.biosystems.2018.09.012 · Cited by 36 · see citing works
Emmons‐Bell, M., Durant, F., Tung, A., Pietak, A. M., Miller, K. A., Kane, A., Martyniuk, C. J., Davidian, D. C. C., Morokuma, J., & Levin, M. G. (2019) · iScience · doi:10.1016/j.isci.2019.11.014 · Cited by 35 · see citing works
Self-citationBryant, D. M., Sousounis, K., Farkas, J. E., Bryant, S., Thao, N., Guzikowski, A. R., Monaghan, J. R., Levin, M. G., & Whited, J. L. (2017) · Developmental Biology · doi:10.1016/j.ydbio.2017.02.013 · Cited by 33 · see citing works
Self-citationThe mind is flat : the remarkable shallowness of the improvising brain
Nick Chater, A. (2018) · University of Warwick · doi:10.82444/warw.33788746 · Cited by 33 · see citing works
Active inference, morphogenesis, and computational psychiatry
Pio‐Lopez, L., Kuchling, F., Tung, A., Pezzulo, G., & Levin, M. G. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.988977 · Cited by 27 · see citing works
Self-citationProbabilistic adaptation in changing microbial environments
Yarden Katz, A., & Springer, M. (2016) · PeerJ · doi:10.7717/peerj.2716 · Cited by 26 · see citing works
Is your brain really necessary?
Lorber, J. (1981) · PubMed · Cited by 25 · see citing works
Inferring learning rules from animal decision-making.
Ashwood, Z. C., Roy, N. A., Bak, J. H., & Pillow, J. W. (2020) · PubMed · Cited by 25 · see citing works
Self-organizing circuitry and emergent computation in mouse embryonic stem cells
Halley, J. D., Smith‐Miles, K., Winkler, D. A., Kalkan, T., Huang, S., & Smith, A. G. (2011) · Stem Cell Research · doi:10.1016/j.scr.2011.11.001 · Cited by 20 · see citing works
Embodying probabilistic inference in biochemical circuitsPreprint
Yarden Katz, A., Springer, M., & Fontana, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.10161 · Cited by 16 · see citing works
Vital, J. E. C., Nunes, A. D. M., York, B. S. D. A. C. N., de Sousa, B. D. A., Nascimento, M. F., Formiga, M. F., & Fernandes, A. T. N. S. F. (2021) · Complementary Therapies in Clinical Practice · doi:10.1016/j.ctcp.2021.101420 · Cited by 16 · see citing works
Collective Intelligence of Morphogenesis as a Teleonomic ProcessPreprint
Levin, M. G. (2022) · doi:10.31234/osf.io/5ywqu · Cited by 14 · see citing works
Self-citationResolving teleology’s false dilemma
Babcock, G., & McShea, D. W. (2022) · Biological Journal of the Linnean Society · doi:10.1093/biolinnean/blac058 · Cited by 14 · see citing works
Locus of Control and Biofeedback: A Review of the Literature
Zimet, G. D. (1979) · Perceptual and Motor Skills · doi:10.2466/pms.1979.49.3.871 · Cited by 14 · see citing works
Framework for solving time-delayed Markov Decision Processes
Sawaya, Y., Issa, G., & Marzen, S. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033034 · Cited by 4 · see citing works
Self-citationMarzen, S. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.06.007 · Cited by 4 · see citing works
Self-citation
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Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207 · see citing works
Biological agency: a concept without a research program
DiFrisco, J., & Gawne, R. (2024) · Journal of Evolutionary Biology · doi:10.1093/jeb/voae153 · Cited by 23 · see citing works
Cognition all the way down 2.0: neuroscience beyond neurons in the diverse intelligence era
Chis-Ciure, R., & Levin, M. G. (2025) · Synthese · doi:10.1007/s11229-025-05319-6 · Cited by 7 · see citing works
Self-citationPersuading large language models to comply with objectionable requests
Meincke, L., Shapiro, D., Duckworth, A. L., Mollick, E., Mollick, L., den Bulte, C. V., & Cialdini, R. B. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2535868123 · Cited by 5 · see citing works
Marzen, S. (2025) · Interface Focus · doi:10.1098/rsfs.2024.0062 · Cited by 2 · see citing works
Self-citationEarly transitions in the evolution of cognition
Kourki, A. N. (2025) · History & Philosophy of the Life Sciences · doi:10.1007/s40656-025-00709-y · Cited by 1 · see citing works
Isakson, B. E. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-823303-0.00011-2
Ideas on sauropod kidneys and digestion
Isakson, B. E. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-823303-0.00007-0
Latent Agency: A Proto-Cognitive Property
Rodriguez, F., & Husbands, P. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-93631-9_23
A Dharmakīrtian Model of Relevance Realization in Cognitive Agents
Amir, N., & Dunne, J. D. (2025) · Contemporary Buddhism · doi:10.1080/14639947.2025.2564476
Position Paper: Bounded Alignment: What (Not) To Expect From AGI Agents
Minai, A. A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228305
Material‐Based Intelligence: Autonomous Adaptation and Embodied Computation in Physical Substrates
Baulin, V., Füchslin, R., Giacometti, A., Hauser, H., & Werner, M. (2026) · Advanced Intelligent Systems · doi:10.1002/aisy.202501450 · Cited by 1 · see citing works
Homeostasis and causal control
Weinberger, N. (2026) · Biology & Philosophy · doi:10.1007/s10539-026-10018-8
Beyond the Engine: Active Inference and the Driver-Interface Architecture for Adaptive AGI
Macrine, S. L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-33195-3_4
From Retained Change to Learning,A reverse reconstruction of event-driven changePreprint
Hintsanen, A. D. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7434345
- Authors
- Seifert, G., Sealander, A., Marzen, S., & Levin, M.
- Affiliations
- Claremont McKenna College (US)
- Tufts University (US)
- Corresponding author
- Marzen, Sarah
- Venue
- BioSystems
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 235
- Pages
- 105107
- ISSN
- 0303-2647 (ISSN-L)
- OpenAlex
- W4389956244
- Cited by
- 16
- References
- 108
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 3
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 368 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Biosystems (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Air Force Office of Scientific Research
- John Templeton Foundation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Keywords
- basal cognition, reinforcement learning, goal-directed behavior, agency, living organisms, artificial agents, cognitive evolution, AI theory
- MeSH terms
- Cognition, Learning, Motivation, Reinforcement, Psychology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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