Free-lunch learning: modeling spontaneous recovery of memory

Falling towards Forgetfulness: Synaptic Decay Prevents Spontaneous Recovery of Memory

Long after a new language has been learned and forgotten, relearning a few words seems to trigger the recall of other words. This “free-lunch learning” (FLL) effect has been demonstrated both in humans and in neural network models. Specifically, previous work proved that linear networks that learn a set of associations, then partially forget them all, and finally relearn some of the associations, show improved performance on the remaining (i.e., nonrelearned) associations. Here, we prove that relearning forgotten associations decreases performance on nonrelearned associations; an effect we call negative free-lunch learning. The difference between free-lunch learning and the negative free-lunch learning presented here is due to the particular method used to induce forgetting. Specifically, if forgetting is induced by isotropic drifting of weight vectors (i.e., by adding isotropic noise), then free-lunch learning is observed. However, as proved here, if forgetting is induced by weight values that simply decay or fall towards zero, then negative free-lunch learning is observed. From a biological perspective, and assuming that nervous systems are analogous to the networks used here, this suggests that evolution may have selected physiological mechanisms that involve forgetting using a form of synaptic drift rather than synaptic decay, because synaptic drift, but not synaptic decay, yields free-lunch learning.

If you learn a skill, then partially forget it, does relearning part of that skill induce recovery of other parts of the skill? More generally, if you learn a set of associations, then partially forget them, does relearning a subset induce recovery of the remaining associations? In previous work, in which participants learned the layout of a scrambled computer keyboard, the answer to this question appeared to be “yes.” More recently, we modeled this “free-lunch learning” effect using artificial neural networks, in which the synaptic strength between each pair of model neurons is a connection weight. We proved that if forgetting is induced by allowing each weight value to drift randomly, then free-lunch learning is almost inevitable. However, if, after learning a set of associations, forgetting is induced by allowing each connection weight to decay or fall toward zero, then relearning a subset of associations decreases performance on the remaining associations. This suggests that evolution may have selected physiological mechanisms that involve forgetting using a form of synaptic drift rather than synaptic decay, because synaptic drift yields free-lunch learning, whereas decay does not.

Distributed representations accelerate evolution of adaptive behaviours

Animals with rudimentary innate abilities require substantial learning to transform those abilities into useful skills, where a skill can be considered as a set of sensory–motor associations. Using linear neural network models, it is proved that if skills are stored as distributed representations, then within-lifetime learning of part of a skill can induce automatic learning of the remaining parts of that skill. More importantly, it is shown that this “free-lunch” learning (FLL) is responsible for accelerated evolution of skills, when compared with networks which either 1) cannot benefit from FLL or 2) cannot learn. Specifically, it is shown that FLL accelerates the appearance of adaptive behaviour, both in its innate form and as FLL-induced behaviour, and that FLL can accelerate the rate at which learned behaviours become innate.

Some behaviours are purely innate (e.g., blinking), whereas other, “apparently innate,” behaviours require a degree of learning to refine them into a useful skill (e.g., nest building). In terms of biological fitness, it matters how quickly such learning occurs, because time spent learning is time spent not eating, or time spent being eaten, both of which reduce fitness. Using artificial neural networks as model organisms, it is proven that it is possible for an organism to be born with a set of “primed” connections which guarantee that learning part of a skill induces automatic learning of other skill components, an effect known as free-lunch learning (FLL). Critically, this effect depends on the assumption that associations are stored as distributed representations. Using a genetic algorithm, it is shown that primed organisms can evolve within 30 generations. This has three important consequences. First, primed organisms learn quickly, which increases their fitness. Second, the presence of FLL effectively accelerates the rate of evolution, for both learned and innate skill components. Third, FLL can accelerate the rate at which learned behaviours become innate. These findings suggest that species may depend on the presence of distributed representations to ensure rapid evolution of adaptive behaviours.