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Adaptive evidence accumulation across multi-trial timescales

Banff International Research Station for Mathematical Innovation and Discovery

Natural environments can have dynamics spanning multiple timescales, and the evidence animals use to make decisions is often relevant to future decisions. To understand decision-making under these conditions we analyze how a model ideal observer accumulates evidence to freely make choices across a sequence of correlated trials. We use principles of probabilistic inference to show that an ideal observer incorporates information obtained on one trial as an initial bias on the next. This bias decreases the time, but not the accuracy of the next decision. Furthermore, in finite sequences of trials the rate of reward is maximized when the observer deliberates longer for early decisions, but responds more quickly towards the end of the sequence. Our model also explains experimentally observed patterns in decision times and choices, providing a mathematically principled foundation for evidence-accumulation models of sequential decisions.

Mathematics; Mechanics of particles and systems; Biology and other natural sciences

video/mp4

Author affiliation: University of Colorado

2019-11-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/72290

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/