Inferring an Observer's Prediction Strategy in Sequence Learning Experiments

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Uppal, A; Ferdinand, V; Marzen, SDate
2020-08-01Source Title
Entropy: international and interdisciplinary journal of entropy and information studiesPublisher
MDPIUniversity of Melbourne Author/s
Ferdinand, VanessaAffiliation
Melbourne School of Psychological SciencesMetadata
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Journal ArticleCitations
Uppal, A., Ferdinand, V. & Marzen, S. (2020). Inferring an Observer's Prediction Strategy in Sequence Learning Experiments. ENTROPY, 22 (8), https://doi.org/10.3390/e22080896.Access Status
Open AccessAbstract
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.
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