Evolution of integrated causal structures in animats exposed to environments of increasing complexity
Open accessAbstract
Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment's causal structure. We investigated the evolution of small, adaptive logic-gate networks ("animats") in task environments where falling blocks of different sizes have to be caught or avoided in a 'Tetris-like' game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks ("brains") with many concepts, leading to an increase in their internal complexity.
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Albantakis et al. (2014) analyzed networks with 3-6 nodes.
“Albantakis et al. (2014) analyzed networks with 3-6 nodes.”
From Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis (2025) · cites this paper as Albantakis et al., 2014
The Methods section (“Animats”; page unavailable) says the animat brains consisted of 8 binary elements: 2 sensors, 4 hidden elements, and 2 motors. The Results section describes a limit of 4 hidden elements, not networks of 3–6 nodes. (LLM, conf=0.88)
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Entropy balance and information processing in bipartite and nonbipartite composite systems
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Of Evolution, Systems and Complexity
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Artificial intelligence, consciousness and psychiatry
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Dissociating Artificial Intelligence from Artificial Consciousness
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“…consciousness may split under some concurrent tasks [29], why specific contents of consciousness such as the visual field, temporal flow or mental objects may feel the way they do [40, 41, 91–93], and why consciousness may have evolved due to selective pressure for information integration [94–96].”
Self-citationIntrinsic units: identifying a system’s causal grain
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Non-Separability of Physical Systems as a Foundation of Consciousness
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Is the maximum entropy production just a heuristic principle? Metaphysics on natural determination
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How cognitive and environmental constraints influence the reliability of simulated animats in groups
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We need to explain subjective experience, but its explanation may not be mechanistic
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Building and Understanding the Minimal Self
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How Morphological Computation Shapes Integrated Information in Embodied Agents
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Upper bounds for integrated information
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The role of conditional independence in the evolution of intelligent systems
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Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
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Fitness and neural complexity of animats exposed to environmental change
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The Evolution of Neuroplasticity and the Effect on Integrated Information
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Formal Approaches to a Definition of AgentsPreprint
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MKER: multi-modal knowledge extraction and reasoning for future event prediction
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To be or to know? Information in the pristine present
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Account of consciousness by Christof Koch: Review and questions
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Outsourcing Control Requires Control Complexity
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- Authors
- Albantakis, L., Hintze, A., Koch, C., Adami, C., & Tononi, G.
- Affiliations
- University of Wisconsin–Madison (US)
- Michigan State University (US)
- Corresponding author
- Albantakis, Larissa
- Tononi, Giulio
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-12-18
- Volume
- 10
- Issue
- 12
- Pages
- e1003966
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4270440
- OpenAlex
- W2070484080
- Cited by
- 101
- References
- 61
- Citations by year
- 2026: 8 · 2025: 7 · 2024: 10 · 2023: 5 · 2022: 8 · 2021: 12 · 2020: 10 · 2019: 12 · 2018: 10 · 2017: 8 · 2016: 6 · 2015: 5
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Paul G. Allen Family Foundation
- Templeton World Charity Foundation
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Embodied and Extended Cognition
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- animats, causal structure, information integration, conceptual information, environmental complexity, sequential memory, sensor integration, cognitive evolution, internal complexity
- MeSH terms
- Adaptation, Physiological, Algorithms, Computer Simulation, Biological Evolution, Models, Neurological, Selection, Genetic, Statistics, Nonparametric, Computational Biology, Feedback, Physiological, Genetic Fitness
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