ArticleJournal of the Royal Society, Interface2014
The evolution of continuous learning of the structure of the environment.
Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Evolution of diverse (and advanced) cognitive abilities through adaptive fine-tuning of learning and chunking mechanisms.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Sequence representation as an early step in the evolution of language.PLoS computational biology · 2023Article
- Modelling how cleaner fish approach an ephemeral reward task demonstrates a role for ecologically tuned chunking in the evolution of advanced cognition.PLoS biology · 2022Article
- Implementing the DEcision-Aid for Lupus (IDEAL): study protocol of a multi-site implementation trial with observational, case study design : Implementing the DEcision-Aid for Lupus.Implementation science communications · 2021Article
- The evolution of cognitive mechanisms in response to cultural innovations.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Evolution of protolinguistic abilities as a by-product of learning to forage in structured environments.Proceedings. Biological sciences · 2015Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Continuous, 'always on', learning of structure from a stream of data is studied mainly in the fields of machine learning or language acquisition, but its evolutionary roots may go back to the first organisms that were internally motivated to learn and represent their environment. Here, we study under what conditions such continuous learning (CL) may be more adaptive than simple reinforcement learning and examine how it could have evolved from the same basic associative elements. We use agent-based computer simulations to compare three learning strategies: simple reinforcement learning; reinforcement learning with chaining (RL-chain) and CL that applies the same associative mechanisms used by the other strategies, but also seeks statistical regularities in the relations among all items in the environment, regardless of the initial association with food. We show that a sufficiently structured environment favours the evolution of both RL-chain and CL and that CL outperforms the other strategies when food is relatively rare and the time for learning is limited. This advantage of internally motivated CL stems from its ability to capture statistical patterns in the environment even before they are associated with food, at which point they immediately become useful for planning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.