A computational model reveals classical conditioning mechanisms underlying visual signal detection in rats.

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.