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The efficacy of linear integration for reward gathering in a nonstationary foraging task


, : The efficacy of linear integration for reward gathering in a nonstationary foraging task. Society for Neuroscience Abstract Viewer & Itinerary Planner : Abstract No 385 2

A number of recent studies have examined decision-making and the attribution of value in the ethologically important context of foraging behavior. An important aspect of many tasks used in such studies is nonstationarity in the relative rates of reward associated with available foraging targets. Reward gathering in a nonstationary environment presents a challenging problem: animals must sample the environment to estimate the relative value of foraging patches, while concurrently capitalizing on their discoveries by harvesting rewards as efficiently as possible. A concurrent poster from this laboratory suggests that animals employ a simple strategy of linear integration over reward history to estimate the relative value of foraging targets, and then match their relative response rates to these estimates. Using numerical simulation we evaluated the efficacy of this family of strategies both in terms of reward maximization and as an implementation of classically described matching behavior. We find that this simple strategy can gather over 90% of the rewards available to an optimal Bayesian reward harvester. As we expect, the best performing integration time constants vary with, but are generally shorter than, the time constants of the underlying nonstationarities in the world. Interestingly, the integration time constants that maximize reward rate differ somewhat from those that best capture matching behavior. Ultimately, the integration time constants estimated from human and monkey behavioral data more closely resemble those of the best reward harvester than those of the best matcher.

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