Uppal Abhinuv, Ferdinand Vanessa, Marzen Sarah
W.M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna Colleges, Claremont, CA 91711, USA.
Melbourne School of Psychological Sciences, University of Melbourne, Parkville, Victoria 3050, Australia.
Entropy (Basel). 2020 Aug 15;22(8):896. doi: 10.3390/e22080896.
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.
认知系统展现出惊人的预测能力，使它们能够从其环境中的规律中获取奖励。生物体如何预测环境输入以及预测得有多好？作为回答该问题的前提，我们首先探讨在给定一系列来自观察者的输入和预测的情况下，预测策略推断的局限性。我们研究贝叶斯观察者的特殊情况，考虑观察者在构建模型时随机忽略数据的可能性。我们证明，对于由有限阶马尔可夫模型生成的二元刺激，可以正确推断观察者的预测模型。然而，除非我们能够获取观察者的多个“克隆体”，否则不一定能推断出模型的参数值。随着刺激变得越来越复杂，正确推断需要指数级增加的数据点、计算能力和计算时间。这些因素对我们在实验或观察环境中推断观察者预测策略的能力构成了实际限制。
