Modeling perceptual learning: difficulties and how they can be overcome
Modeling perceptual learning: difficulties and how they can be overcome
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DOI：
10.1007/s004220050418
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发表时间：
1998-02-01
影响因子：
1.9
通讯作者：
Fahle, M
中科院分区：
文献类型：
--
作者：
Herzog, MH;Fahle, M
我们以游标辨别任务为例，考察了反馈和注意在训练游标辨别任务中的作用。人类的学习，即使是简单的刺激，如游标，也依赖于比之前预期的更复杂的机制-排除了简单的神经网络模型。这些发现不仅是经验上的奇怪之处，也证明了目前的模型未能反映学习过程的一些重要特征。我们将列出神经网络的一些问题，并开发一个新的模型，通过结合自上而下的机制来解决这些问题。与神经网络相反，在我们的模型中，学习不仅仅是由刺激集驱动的。对绩效的内部估计和对任务的了解也被纳入其中。我们的模型表明，在某些条件下，只有部分刺激的可检测性被增强，而整体性能的改善归因于决策标准的改变。一项实验证实了这一预测。
We investigated the roles of feedback and attention in training a vernier discrimination task as an example of perceptual learning. Human learning even of simple stimuli, such as verniers, relies on more complex mechanisms than previously expected-ruling out simple neural network models. These findings are not just an empirical oddity but are evidence that present models fail to reflect some important characteristics of the learning process. We will list some of the problems of neural networks and develop a new model that solves them by incorporating top-down mechanisms. Contrary to neural networks, in our model learning is not driven by the set of stimuli only. Internal estimations of performance and knowledge about the task are also incorporated. Our model implies that under certain conditions the detectability of only some of the stimuli is enhanced while the overall improvement of performance is attributed to a change of decision criteria. An experiment confirms this prediction.