On diffusion processes with variable drift rates as models for decision making during learning
On diffusion processes with variable drift rates as models for decision making during learning
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DOI：
10.1088/1367-2630/10/1/015006
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发表时间：
2008-01-31
影响因子：
3.3
通讯作者：
Gold, J. I.
中科院分区：
文献类型：
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作者：
Eckhoff, P.;Holmes, P.;Gold, J. I.
我们调查Ornstein-Uhlenbeck和扩散过程与可变漂移率的模型的证据积累在视觉辨别任务。我们推导出幂律和指数漂移率模型，并表征这些模型的参数如何影响心理测量功能描述性能的准确性作为刺激强度和观看时间的函数。我们将模型拟合到学习任务的猴子的心理物理数据，以确定最佳捕获性能的参数，因为它随着训练而提高。信息量最大的参数是描述用于形成决策的感官证据的信噪比的总体漂移率，该漂移率随着训练而稳步增加。相比之下，次要参数描述的时间过程中的漂移运动观察没有表现出稳定的趋势。结果表明，相对简单的版本的扩散模型可以适应在训练过程中的行为，从而给出了一个定量的学习效果的基础决策过程。
We investigate Ornstein-Uhlenbeck and diffusion processes with variable drift rates as models of evidence accumulation in a visual discrimination task. We derive power-law and exponential drift-rate models and characterize how parameters of these models affect the psychometric function describing performance accuracy as a function of stimulus strength and viewing time. We fit the models to psychophysical data from monkeys learning the task to identify parameters that best capture performance as it improves with training. The most informative parameter was the overall drift rate describing the signal-to-noise ratio of the sensory evidence used to form the decision, which increased steadily with training. In contrast, secondary parameters describing the time course of the drift during motion viewing did not exhibit steady trends. The results indicate that relatively simple versions of the diffusion model can fit behavior over the course of training, thereby giving a quantitative account of learning effects on the underlying decision process.