Monday, August 08, 2016 4:00pm - 5:00pm
Monday, August 8 at 4 pm in Walter Hall 102
Richard Watson, University of Southampton
Expanding Evolutionary Theory with Learning Theory: Implications for the evolution of development and evolvability.
In order for evolution by natural selection to explain the adaptation that we observe in natural populations we must account for the availability of suitable variations that natural selection can act on. Rupert Riedl, an early pioneer of evolutionary developmental biology, suggested that this is facilitated by a specific developmental organisation that is itself a product of past selection. However, the construction of a theoretical framework to formalise such ‘evolution of evolvability’ has been continually frustrated by the indisputable fact that natural selection cannot favour structures for benefits they have not yet produced. Here we resolve this problem. Recent work shows that short-term selective pressures on gene interactions are functionally equivalent to a simple type of associative learning, well-understood in neural network research. This is important for the evolution of evolvability because this type of learning system can clearly change in a way that improves its performance on future test cases, i.e., before it has been exposed to those cases, without the need for the future to cause the past. Recognising a formal link with the conditions that enable such predictive generalisation in learning systems unlocks well-established theory to apply to understanding the evolution of evolvability. Here we use this to elucidate, and demonstrate for the first time, conditions where short-term selective pressures (producing the organisation that Riedl suggested) alter evolutionary trajectories in a manner that systematically improves long-term evolutionary outcomes. More generally, we discuss and demonstrate how learning theory can be converted to the evolutionary domain to demystify theoretical problems in evo-devo, evo-eco and the major evolutionary transitions.
Watson, R. A., & Szathmáry, E. (2016). How Can Evolution Learn?. Trends in ecology & evolution, 31(2), 147-157.
Host: Dan Weinreich