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Article

Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments

1
W.M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna Colleges, Claremont, CA 91711, USA
2
Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria 3050, Australia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(8), 896; https://doi.org/10.3390/e22080896
Received: 1 July 2020 / Revised: 10 August 2020 / Accepted: 12 August 2020 / Published: 15 August 2020
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
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 model’s parameter values unless we have access to several “clones” of the observer. As stimuli become increasingly complicated, correct inference requires exponentially more data points, computational power, and computational time. These factors place a practical limit on how well we are able to infer an observer’s prediction strategy in an experimental or observational setting. View Full-Text
Keywords: stochastic processes; prediction; Bayesian models; sequence learning stochastic processes; prediction; Bayesian models; sequence learning
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MDPI and ACS Style

Uppal, A.; Ferdinand, V.; Marzen, S. Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments. Entropy 2020, 22, 896. https://doi.org/10.3390/e22080896

AMA Style

Uppal A, Ferdinand V, Marzen S. Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments. Entropy. 2020; 22(8):896. https://doi.org/10.3390/e22080896

Chicago/Turabian Style

Uppal, Abhinuv, Vanessa Ferdinand, and Sarah Marzen. 2020. "Inferring an Observer’s Prediction Strategy in Sequence Learning Experiments" Entropy 22, no. 8: 896. https://doi.org/10.3390/e22080896

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