Tickle Hannah, Tsetsos Konstantinos, Speekenbrink Maarten, Summerfield Christopher
Department of Experimental Psychology.
Department of Neurophysiology and Pathophysiology.
Psychol Rev. 2023 Jan;130(1):1-22. doi: 10.1037/rev0000315. Epub 2021 Sep 27.
When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
在做决策时，动物必须在收集信息的益处与长时间思考的机会成本之间进行权衡。在自然环境中，决定何时停止积累信息并做出选择具有挑战性，因为与决策相关的信息的可靠性本身可能会随时间发生不可预测的变化（可变方差或“异方差性”）。我们要求人类执行一项分类任务，在该任务中，离散的、连续取值的样本（定向光栅）依次出现，直到观察者做出选择。人类行为最好用一个模型来描述，该模型通过感官信号的逆预测误差对其进行自适应加权，并将结果量与线性紧急信号整合到决策阈值。这个模型近似于一个贝叶斯模型的输出，该贝叶斯模型计算正确反应的完整后验概率，并成功预测了神经信号中决策信息的自适应加权。决策信息的自适应加权可能已经进化，以促进在异方差自然环境中的最优停止。（PsycInfo数据库记录（c）2023美国心理学会，保留所有权利）
