American Society of Naturalists

A membership society whose goal is to advance and to diffuse knowledge of organic evolution and other broad biological principles so as to enhance the conceptual unification of the biological sciences.

“The evolutionary origin of associative learning”

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Anselmo C. Pontes, Robert B. Mobley, Charles Ofria, Christoph Adami, and Fred C. Dyer (Jan 2020)

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Digital evolution uncovers the origin of associative learning and reflexive behaviors animal cognition artificial life

Though learning is crucial to most behaviors, we know very little about how it evolved. Understanding how learning first appeared could provide clues as to how it works, and have implications for many fields such as neuroscience, education, psychology, and animal behavior. It may also help us build computers that learn the same way natural organisms do.

The results from this study are the first demonstration of the evolution of associative learning in an artificial organism without a brain. Since the evolution of learning cannot be observed through fossils – and would take more than a lifetime to watch in nature – the MSU interdisciplinary team composed of biologists and computer scientists used a digital evolution program that allowed them to observe tens of thousands of generations of evolution in just a few hours, a feat unachievable with living systems.

In the study, organisms evolved to learn and use environmental signals to help them navigate the environment and find food. While the environment was simulated, the evolution was real. The programs that controlled the digital organism were subject to genetic variation from mutation, inheritance, and competitive selection. Organisms were tasked to follow a trail alongside signals that – if interpreted correctly – pointed where the path went next. In the beginning of the simulation, organisms were “blank slates,” incapable of sensing, moving, or learning. Over the generations, organisms evolved more and more complex behaviors, even learning by association and correcting mistakes.

Interestingly, MSU researchers were not just able to see how certain environments fostered the evolution of learning, but how populations evolved through the same behavioral phases that previous scientists speculated should happen but didn’t have the technology to see.


Learning is a widespread ability among animals and, like physical traits, is subject to evolution. But how did learning first arise? What selection pressures and phenotypic preconditions fostered its evolution? Neither the fossil record nor phylogenetic comparative studies provide answers to these questions. Here, we take a novel approach by studying digital organisms in environments that promote the evolution of navigation and associative learning. Starting with a non-learning, sessile ancestor, we evolve multiple populations in four different environments, each consisting of nutrient trails with various layouts. Trail nutrients cue organisms on which direction to follow, provided they evolve to acquire and use those cues. Thus, each organism is tested on how well it navigates a randomly selected trail before reproducing. We find that behavior evolves modularly and in a predictable sequence, where simpler behaviors are necessary precursors for more complex ones. Associative learning is only one of many successful behaviors to evolve, and its origin depends on the environment possessing certain information patterns that organisms can exploit. Environmental patterns that are stable across generations foster the evolution of reflexive behavior, while environmental patterns that vary across generations, but remain consistent for periods within an organism’s lifetime, foster the evolution of learning behavior. Both types of environmental patterns are necessary, since the prior evolution of simple reflexive behaviors provides the building blocks for learning to arise. Finally, we observe that an intrinsic value system evolves alongside behavior and supports associative learning by providing reinforcement for behavior conditioning.