Normative evidence accumulation in unpredictable environments
Normative evidence accumulation in unpredictable environments
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DOI：
10.7554/elife.08825
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发表时间：
2015-08-31
期刊：
影响因子：
7.7
通讯作者：
Gold, Joshua I.
中科院分区：
文献类型：
--
作者：
Glaze, Christopher M.;Kable, Joseph W.;Gold, Joshua I.
在我们的动态世界中，关于噪声刺激的决定可能需要时间上的证据积累来识别稳定的信号，需要区分来检测这些信号中不可预测的变化，或者两者兼而有之。标准模型可以解释在这些环境中的学习，但尚未应用于更快的决策过程。我们提出了一种新的、标准化的自适应学习模型，该模型通过充当具有非吸收边界的泄漏累加器来形成决策。对于离散和连续的情况，这些动态都取决于证据和平衡信号识别和变化检测的统计数据的预期变化率。我们发现，对于两个不同的任务，人类受试者学习了这些期望，尽管不完美，然后根据规范模型使用它们做出决定。这些结果代表了对不可预测环境中决策制定的统一、经验支持的描述，为潜在神经信号的预期驱动的动态提供了新的见解。
In our dynamic world, decisions about noisy stimuli can require temporal accumulation of evidence to identify steady signals, differentiation to detect unpredictable changes in those signals, or both. Normative models can account for learning in these environments but have not yet been applied to faster decision processes. We present a novel, normative formulation of adaptive learning models that forms decisions by acting as a leaky accumulator with non-absorbing bounds. These dynamics, derived for both discrete and continuous cases, depend on the expected rate of change of the statistics of the evidence and balance signal identification and change detection. We found that, for two different tasks, human subjects learned these expectations, albeit imperfectly, then used them to make decisions in accordance with the normative model. The results represent a unified, empirically supported account of decision-making in unpredictable environments that provides new insights into the expectation-driven dynamics of the underlying neural signals.