Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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自相关贝叶斯采样器：概率判断、估计、置信区间、选择、置信度判断和响应时间的理性过程。


将嘈杂（感官）信息最佳地转化为分类决策的决策规范模型在质量上与人类行为不匹配。事实上，领先的计算模型只有通过添加偏离规范原则的特定任务假设才能获得高度的经验证实。作为回应，我们提供了一种贝叶斯方法，该方法隐式地产生可能答案（假设）的后验分布以响应感觉信息。但我们假设大脑无法直接访问这个后验，而只能根据它们的后验概率对假设进行采样。因此，我们认为决策中规范性关注的主要问题是整合随机假设，而不是随机感官信息，以做出分类决策。这意味着人类反应的变异性主要来自后验采样，而不是感觉噪声。由于人类假设生成是序列相关的，因此假设样本将是自相关的。在这个新问题表述的指导下，我们开发了一个新过程，即自相关贝叶斯采样器 （ABS），它在复杂的采样算法中为自相关假设的生成奠定了基础。ABS 提供了一种单一机制，定性地解释了概率判断、估计、置信区间、选择、置信度判断、响应时间及其关系的许多经验效应。我们的分析证明了在探索规范模型时视角转变的统一力量。它还举例说明了“贝叶斯大脑”使用样本而不是概率进行操作，并且人类行为的可变性可能主要反映计算噪声而不是感官噪声。 （PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。