Schmajuk Nestor A, Bushnell Philip J
Department of Psychology and Neuroscience, Duke University, Durham, NC 27708, USA.
Behav Processes. 2009 Nov;82(3):340-51. doi: 10.1016/j.beproc.2009.08.005. Epub 2009 Aug 21.
We applied a neural network model of classical conditioning proposed by Schmajuk et al. (1996) to visual signal detection and discrimination tasks designed to assess sustained attention in rats (Bushnell, 1999). We used a sign-tracking description of signal detection behavior by assuming that rats approach the location of the lever that they expect will be rewarded. We also assumed that although the visual signals contribute to set the occasion for the approach response to be generated, they do not become strongly associated with reward. The model accurately described Bushnell's (1999) results showing an increased proportion of hits with increasing signal intensity, decreased proportion of hits with increasing trial rate, and lower accuracy in a discrimination task compared to a detection task. In addition, observation of the behavior of rats performing the task confirmed assumptions and predictions of the model: (a) rats learn to approach the location of the lever they expect to be rewarded; (b) during the pre-signal interval, rats approach the location of the blank lever because it matches the intensity of the light they experience during that interval; and (c) the rats' behavior is directed to the location of the levers and not towards the light, which acts only as an "occasion setter" for the lever to be selected and pressed.
我们将施马朱克等人（1996年）提出的经典条件作用神经网络模型应用于旨在评估大鼠持续注意力的视觉信号检测和辨别任务（布什内尔，1999年）。我们通过假设大鼠接近它们预期会得到奖励的杠杆位置，使用了信号检测行为的符号追踪描述。我们还假设，尽管视觉信号有助于为产生接近反应创造条件，但它们不会与奖励紧密关联。该模型准确描述了布什内尔（1999年）的结果，即随着信号强度增加，命中比例增加；随着试验频率增加，命中比例降低；与检测任务相比，辨别任务的准确性更低。此外，对执行任务的大鼠行为的观察证实了该模型的假设和预测：（a）大鼠学会接近它们预期会得到奖励的杠杆位置；（b）在信号前间隔期间，大鼠接近空白杠杆的位置，因为它与它们在该间隔期间经历的光强度相匹配；（c）大鼠的行为指向杠杆的位置，而不是朝向光，光仅作为选择和按压杠杆的“条件设定者”。
