Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and nonsensory response bias. In the framework of signal detection theory, these aspects of discrimination performance are quantified through separate measures, sensitivity (d') for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single‐trial level as a consequence of reinforcement history. We subjected rats to a two‐stimulus two‐response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three signal‐detection‐theory‐based criterion learning models with respect to their ability to fit experimentally observed fluctuations of response bias on a trial‐by‐trial level. These models shift the criterion by a fixed step (1) after each reinforced response or (2) after each nonreinforced response or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady‐state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial‐based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies.

中文翻译：

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强化及其省略对知觉决策中反应偏差的逐次试验变化的影响

众所周知，感知选择任务中的辨别表现既反映了感官辨别能力，也反映了非感官反应偏差。在信号检测理论的框架中，辨别性能的这些方面通过单独的测量、灵敏度（d'）用于感官辨别力和决策标准（C) 的响应偏差。然而，尚不清楚单次试验水平上的反应偏差（即标准）如何因强化历史而变化。我们让大鼠接受听觉刺激的双刺激双反应条件辨别任务，并通过两种反应的不等强化概率诱导反应偏差。我们比较了三种基于信号检测理论的标准学习模型，比较了它们在逐次试验水平上拟合实验观察到的反应偏差波动的能力。这些模型将标准移动固定步长（1）在每个强化响应之后或（2）在每个非强化响应之后或（3）在两者之后。我们发现所有三个模型都未能捕获数据的基本方面。观察到稳态标准值非常符合基于广义匹配律的信号检测行为模型，我们构建了该模型的基于试验的版本，并发现它可以更好地解释变化条件下的响应偏差波动加固突发事件。