Influence of Plasticity and Learning on Evolution under Directional Selection
By Ingo Paenke, Bernhard Sendhoff, Tadeusz J. Kawecki. 2007
127 citations recorded by the source corpus.
Subfields: Ecology, Ecology, Evolution, Behavior and Systematics, Genetics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Tadeusz J. Kawecki
- Bernhard Sendhoff
- Ingo Paenke
- Sebastian Risi
- Kenneth O. Stanley
- Andrea Soltoggio
- R. Lande
- Robert D. Holt
In Ecology, Evolution, Behavior and Systematics
- Tadeusz J. Kawecki
- Bernhard Sendhoff
- Ingo Paenke
- Sebastian Risi
- Kenneth O. Stanley
- Andrea Soltoggio
- R. Lande
- Robert D. Holt
In Genetics
- Tadeusz J. Kawecki
- Bernhard Sendhoff
- Ingo Paenke
- Sebastian Risi
- Kenneth O. Stanley
- Andrea Soltoggio
- R. Lande
Citations in the corpus
- Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Paenke et al. (2007) investigated the influence of plasticity and learning on evolution under directional selection; Mery and Burns (2010) demonstrate that behavioral plasticity affects evolution.”
- Evolutionary ecology of learning: insights from fruit flies (2009, unknown): “Paenke et al. (2007) derived more general conditions for the Baldwin effect: learning will accelerate the response to directional selection if the proportional increase in fitness supplied by learning is greater in individuals that are genetically fitter.”
- Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation (2009, background): “Phenotypic adaptation to environmental fluctuations must frequently occur by preexisting plasticity, and recent models of the Baldwin effect usually assume that phenotypic plasticity itself does not evolve (e.g. Price et al., 2003; Price, 2006; Paenke et al., 2007).”
- The Influence of Learning on Evolution: A Mathematical Framework (2009, methodology): “In order to derive the gain function in [29], we represented a population as a probability distribution and had to limit the analysis to symmetric distribution functions.”