Neuman Yair, Cohen Yochai, Tamir Boaz
Department of Cognitive and Brain Sciences and the Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Gilasio Coding, Tel-Aviv, Israel.
R Soc Open Sci. 2021 Jan 20;8(1):201011. doi: 10.1098/rsos.201011. eCollection 2021 Jan.
Prediction in natural environments is a challenging task, and there is a lack of clarity around how a myopic organism can make short-term predictions given limited data availability and cognitive resources. In this context, we may ask what kind of resources are available to the organism to help it address the challenge of short-term prediction within its own cognitive limits. We point to one potentially important resource: , which are extensively used in physics but not in the study of cognitive processes. We explain the potential importance of ordinal patterns for short-term prediction, and how natural constraints imposed through (i) ordinal pattern types, (ii) their transition probabilities and (iii) their irreversibility signature may support short-term prediction. Having tested these ideas on a massive dataset of Bitcoin prices representing a highly fluctuating environment, we provide preliminary empirical support showing how organisms characterized by bounded rationality may generate short-term predictions by relying on ordinal patterns.
在自然环境中进行预测是一项具有挑战性的任务，而且对于近视生物如何在数据可用性和认知资源有限的情况下做出短期预测，目前还缺乏清晰的认识。在这种背景下，我们可能会问，生物可以利用哪些资源来帮助它在自身认知范围内应对短期预测的挑战。我们指出一种潜在的重要资源： ，它在物理学中被广泛使用，但在认知过程研究中却未被使用。我们解释了序数模式对于短期预测的潜在重要性，以及通过（i）序数模式类型、（ii）它们的转移概率和（iii）它们的不可逆特征所施加的自然约束如何支持短期预测。在一个代表高度波动环境的比特币价格海量数据集上对这些想法进行测试后，我们提供了初步的实证支持，表明具有有限理性的生物如何通过依赖序数模式来生成短期预测。
