Acetylcholine, Norepinephrine, and Spatial Attention

Abstract

A rich body of experimental data indicates that the neuromodulatory systems acetylcholine (ACh) and norepinephrine (NE) are crucially involved in a variety of cognitive tasks. However, there is little consensus on their independent and joint computational functions. We present a theory in which cortical ACh and NE report different aspects of uncertainty: ACh reports expected uncertainty, coming, for instance, from known variability or ignorance about the parameters of a task, and NE signals unexpected uncertainty, as when significant aspects of the task are unpredictably changed by the experimenter (Yu & Dayan, 2002). These different sorts of uncertainty should, according to statistical learning theories, interact in a specific way to control the integration and acquisition of top-down and bottom-up information. Here, we apply these ideas to a new spatial attention task (Bentley, personal communication), which is an extension of the classical Posner Task to contextual cueing. Unlike the original Posner Task, this new spatial attention task allows the experimenter to separately manipulate expected and unexpected variations in the top-down information. In our model of the task, ACh and NE interact in a precisely specified and partly opponent, partly synergistic manner. Simulation results from the model closely replicate existent data (Phillips , 2000). Additional properties of ACh and NE interactions are predicted from our simulation of pharmacological manipulations: in some circumstances, for instance, the detrimental effects of lesioning one system can be partially alleviated by lesioning the other. The model also makes specific, experimentally tractable predictions regarding trial-to-trial responses of the ACh and NE neurons, as well as certain psychophysical measures such as reaction time. Experiments based on these predictions could yield insights into the workings of these neuromodulatory systems, as well as a new perspective on spatial attention.

Keywords: Computational Neuro Science, Conferences/0306001