COGNITIVE DECISION PROCESSES OF THE BASAL GANGLIA REWARD SYSTEM 303 * M. Shatner, G. Havazelet-Heimer, A. Raz, and Hagai Bergman, Department of Physiology and the Center for Neural Computations, The Hebrew University - Hadassah Medical School, Jerusalem, Israel 303 COGNITIVE DECISION PROCESSES AND FUNCTIONAL CHARACTERISTICS OF THE BASAL GANGLIA REWARD SYSTEM Moti Shatner, Gali Havazelet-Heimer, Aeyal Raz and Hagai Bergman* 1. INTRODUCTION Cognitive behavior of individuals is generally described in terms of reactions to rewards or to predictions concerning future rewards. Physiological systems that are involved in reward mechanism might thus be correlated with various cognitive effects. Such basal gan- glia systems include the midbrain dopaminergic system (Schultz 1998), and the striatal toni- cally active neurons - TANs (Aosaki et al. 1995, Raz et al. 1996). In this chapter we propose that both midbrain dopaminergic and striatal cholinergic interneurons (TANs) continuously emit a complex tri-phasic neural message (neural signature of reward) which is modulated by the fitness of the environment to the animal predictions. A major field of cognitive psychology research is Decision Theory, which describes decision processes and anomalies. A key finding in Decision Theory (Kahneman and Tversky, 1979) is that the behavior of an individual is shifting from risk-aversion (when possible gains are predicted) to risk seeking (when possible losses are predicted). The second section of the current chapter presents an analysis of this effect from the basal ganglia point of view, and offers insights for the origin of the behavioral asymmetry. It was found (e.g., Schultz, 1998), that dopamine neurons tend not to respond to stimuli which predict future aversive rewards. The third section of this chapter proposes an evolu- tionary explanation to the asymmetrical nature of the basal ganglia reward system. We propose that responses to aversive stimuli are not handled by the basal ganglia system, since this system is devoted for the more complex control of sequential behavior. Other more primitive systems, based on pattern detection algorithms, are called into action following aversive stimuli.