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Research Finds Learning Strategies are Associated With Distinct Neural Signatures
Although exactly how the brain orchestrates this process remains unclear, a new study by researchers at the California Institute of Technology (Caltech) suggests that a combination of two distinct learning strategies guides our behavior.
A paper about the work will appear in the May 27 issue of the journal Neuron.
One accepted learning strategy, called model-free learning, relies on trial-and-error comparisons between the reward we expect in a given situation and the reward we actually get. The result of this comparison is the generation of a "reward prediction error," which corresponds to that difference. For example, a reward prediction error might correspond to the difference between the projected monetary return on a financial investment and our real earnings.
In the second mechanism, called model-based learning, the brain generates a cognitive map of the environment that describes the relationship between different situations. "Model-based learning is associated with the generation of a 'state prediction error,' which represents the brain's level of surprise in a new situation given its current estimate of the environment," says Jan Gläscher, a postdoctoral scholar at Caltech and the lead author of the study.
"Think about a situation in which you always take the same route when driving home after work, but on a particular day the usual way is blocked due to construction work," Gläscher says. "A model-free learning system would be helplessly lost; it is only concerned with taking actions that in the past were rewarding, so if those actions are no longer available it wouldn't be able to decide where to go next. But a model-based system would be able to query its cognitive map and figure out an efficient detour using an alternative route."
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