We present a stochastic learning model that combines the essential elements of Hebbian and Rescorla-Wagner theories for operant conditioning. The model was used to predict the behavioral data of rats performing a vibrotactile yes/no detection task. Probabilistic nature of learning was implemented by trial-by-trial variability in the random distributions of associative strengths between the sensory and the response representations. By using measures derived from log-likelihoods (corrected Akaike and Bayesian information criteria), the proposed model and its subtypes were compared with each other, and with previous models in the literature, including reinforcement learning model with softmax rule and drift diffusion model. The main difference between these models was the level of stochasticity which was implemented as associative variation or response selection. The proposed model with subject-dependent variance coefficient (SVC) and with trial-dependent variance coefficient (TVC) resulted in better trial-by-trial fits to experimental data than the other tested models based on information criteria. Additionally, surrogate data were simulated with estimated parameters and the performance of the models were compared based on psychophysical measures (A’: non-parametric sensitivity index, hits and false alarms on receiver operating characteristics). Especially the TVC model could produce psychophysical measures closer to those of the experimental data than the alternative models. The presented approach is novel for linking psychophysical response measures with learning in a yes/no detection task, and may be used in neural engineering applications.

中文翻译：

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自由行为大鼠的心理物理检测和学习：操作性条件反射的概率动力学模型。

我们提出了一个随机学习模型，它结合了 Hebbian 和 Rescorla-Wagner 理论的基本要素，用于操作性条件反射。该模型用于预测执行振动触觉是/否检测任务的大鼠的行为数据。学习的概率性质是通过感官和反应表征之间关联强度随机分布的逐次试验变异性来实现的。通过使用从对数似然（更正的 Akaike 和贝叶斯信息标准）得出的度量，将所提出的模型及其子类型相互比较，并与文献中以前的模型进行比较，包括具有 softmax 规则的强化学习模型和漂移扩散模型。这些模型之间的主要区别在于随机性水平，它作为关联变异或响应选择实施。与基于信息标准的其他测试模型相比，所提出的具有主题相关方差系数 (SVC) 和试验相关方差系数 (TVC) 的模型比其他测试模型更适合实验数据。此外，使用估计参数模拟了替代数据，并根据心理物理学测量比较了模型的性能（A' : 非参数灵敏度指数、关于接收器操作特性的命中和误报）。特别是 TVC 模型可以产生比替代模型更接近实验数据的心理物理测量。所提出的方法是将心理物理反应测量与是/否检测任务中的学习联系起来的新颖方法，并可用于神经工程应用。