Across a wide range of cognitive tasks, recent experience influences behavior. For example, when individuals repeatedly perform a simple two-alternative forced-choice task (2AFC), response latencies vary dramatically based on the immediately preceding trial sequence. These sequential effects have been interpreted as adaptation to the statistical structure of an uncertain, changing environment (e.g., Jones and Sieck, 2003; Mozer, Kinoshita, and Shettel, 2007; Yu and Cohen, 2008). The Dynamic Belief Model (DBM) (Yu and Cohen, 2008) explains sequential effects in 2AFC tasks as a rational consequence of a dynamic internal representation that tracks second-order statistics of the trial sequence (repetition rates) and predicts whether the upcoming trial will be a repetition or an alternation of the previous trial. Experimental results suggest that first-order statistics (base rates) also influence sequential effects. We propose a model that learns both first- and second-order sequence properties, each according to the basic principles of the DBM but under a unified inferential framework. This model, the Dynamic Belief Mixture Model (DBM2), obtains precise, parsimonious fits to data. Furthermore, the model predicts dissociations in behavioral (Maloney, Martello, Sahm, and Spillmann, 2005) and electrophysiological studies (Jentzsch and Som-mer, 2002), supporting the psychological and neurobiological reality of its two components.
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机译：在广泛的认知任务中，最近的经历会影响行为。例如，当个人重复执行简单的两选强制选择任务（2AFC）时，响应延迟会根据紧接的先前试用序列而有很大差异。这些顺序效应被解释为适应不确定，变化的环境的统计结构（例如，Jones和Sieck，2003年; Mozer，Kinoshita和Shettel，2007年; Yu和Cohen，2008年）。动态信念模型（DBM）（Yu and Cohen，2008）将2AFC任务中的顺序效应解释为动态内部表示的合理结果，该动态内部表示跟踪试验序列的二阶统计（重复率）并预测即将进行的试验是否会是先前试验的重复或替代。实验结果表明，一阶统计量（基本速率）也影响顺序效应。我们提出一个学习一阶和二阶序列属性的模型，每个模型都根据DBM的基本原理但在统一的推理框架下进行。该模型称为动态信念混合模型（DBM2），可精确精确地拟合数据。此外，该模型还预测了行为学（Maloney，Martello，Sahm和Spillmann，2005年）和电生理学研究（Jentzsch和Som-mer，2002年）的分离，支持了这两个成分的心理和神经生物学现实。
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