Journal article

Inferring an Observer's Prediction Strategy in Sequence Learning Experiments

Abhinuv Uppal, Vanessa Ferdinand, Sarah Marzen

Entropy: international and interdisciplinary journal of entropy and information studies | MDPI | Published : 2020

Abstract

Cognitive systems exhibit astounding prediction capabilities that allow them to reap rewards from regularities in their environment. How do organisms predict environmental input and how well do they do it? As a prerequisite to answering that question, we first address the limits on prediction strategy inference, given a series of inputs and predictions from an observer. We study the special case of Bayesian observers, allowing for a probability that the observer randomly ignores data when building her model. We demonstrate that an observer’s prediction model can be correctly inferred for binary stimuli generated from a finite-order Markov model. However, we can not necessarily infer the mode..

View full abstract

Grants

Awarded by Air Force Office of Scientific Research


Funding Acknowledgements

This research was funded by Air Force Office of Scientific Research under award number FA9550-19-1-0411.