Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, previous work has suggested that they covary in their prevalence and that their proposed neural substrates overlap. Here we demonstrate that during decision-making, history biases and apparent lapses can both arise from a common cognitive process that is optimal under mistaken beliefs that the world is changing i.e. nonstationary. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model’s predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct decision-making datasets of male rats, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.

试验历史偏倚和失误是在感知决策过程中观察到的两种最常见的次优性。这些次优性通常被认为是由不同的过程引起的。然而，以前的研究表明它们的患病率是相互变异的，并且他们提出的神经基质重叠。在这里，我们证明了在决策过程中，历史偏差和明显的失误都可能源于一个共同的认知过程，在错误地认为世界正在变化的情况下，即非平稳性，这种认知过程是最佳的。这对应于 accumulation-to-bound 模型，该模型对 Acculator 的初始状态进行了与历史记录相关的更新。我们测试了我们的模型对历史偏差和失误的相对普遍性的预测，并表明它们在雄性大鼠的两个不同的决策数据集中得到了有力的证实，包括来自一种新的反应时间任务的数据。我们的模型通过假设代理可以生成准随机选择的过程，提高了精确预测试验内和试验之间决策动态的能力。