The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
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.”
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
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RIFT: A Fractal-Holographic Theory of Consciousness and Autopoietic Control
Bieberich, E. (2026) · Brain Sciences · doi:10.3390/brainsci16090983
Metadata
- 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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