ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2012
Coevolution of learning and data-acquisition mechanisms: a model for cognitive evolution.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 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
- Social demonstration of colour preference improves the learning of associated demonstrated actions.Animal cognition · 2024Article
- House sparrows use learned information selectively based on whether reward is hidden or visible.Animal cognition · 2022Article
- A reaction norm framework for the evolution of learning: how cumulative experience shapes phenotypic plasticity.Biological reviews of the Cambridge Philosophical Society · 2022Article
- Stress axis programming generates long-term effects on cognitive abilities in a cooperative breeder.Proceedings. Biological sciences · 2022Article
- Long-term repeatability of cognitive performance.Royal Society open science · 2022Article
- 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
- How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning.Neuroscience and biobehavioral reviews · 2020Review
- Reciprocity: Different behavioural strategies, cognitive mechanisms and psychological processes.Learning & behavior · 2019Review
- Cultural transmission in an ever-changing world: trial-and-error copying may be more robust than precise imitation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2018Review
- Cognitive performance is linked to group size and affects fitness in Australian magpies.Nature · 2018Article
- Constructive anthropomorphism: a functional evolutionary approach to the study of human-like cognitive mechanisms in animals.Proceedings. Biological sciences · 2017Article
- A social insect perspective on the evolution of social learning mechanisms.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- The evolution of cognitive mechanisms in response to cultural innovations.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Trial-and-error copying of demonstrated actions reveals how fledglings learn to 'imitate' their mothers.Proceedings. Biological sciences · 2017Article
- Evolution of protolinguistic abilities as a by-product of learning to forage in structured environments.Proceedings. Biological sciences · 2015Article
- The evolution of continuous learning of the structure of the environment.Journal of the Royal Society, Interface · 2014Article
- Meaning attribution in the West African green monkey: influence of call type and context.Animal cognition · 2014Article
- Reconsidering the evolution of brain, cognition, and behavior in birds and mammals.Frontiers in psychology · 2013Article
- Animal minds: from computation to evolution.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2012Article
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2 authors.
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Abstract
A fundamental and frequently overlooked aspect of animal learning is its reliance on compatibility between the learning rules used and the attentional and motivational mechanisms directing them to process the relevant data (called here data-acquisition mechanisms). We propose that this coordinated action, which may first appear fragile and error prone, is in fact extremely powerful, and critical for understanding cognitive evolution. Using basic examples from imprinting and associative learning, we argue that by coevolving to handle the natural distribution of data in the animal's environment, learning and data-acquisition mechanisms are tuned jointly so as to facilitate effective learning using relatively little memory and computation. We then suggest that this coevolutionary process offers a feasible path for the incremental evolution of complex cognitive systems, because it can greatly simplify learning. This is illustrated by considering how animals and humans can use these simple mechanisms to learn complex patterns and represent them in the brain. We conclude with some predictions and suggested directions for experimental and theoretical work.
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