Context, emotion, and the strategic pursuit of goals: interactions among multiple brain systems controlling motivated behavior

Aaron J. Gruber, Robert J. McDonald

Motivated behavior exhibits properties that change with experience and partially dissociate among a number of brain structures. Here, we review evidence from rodent experiments demonstrating that multiple brain systems acquire information in parallel and either cooperate or compete for behavioral control. We propose a conceptual model of systems interaction wherein a ventral emotional memory network involving ventral striatum (VS), amygdala, ventral hippocampus, and ventromedial prefrontal cortex triages behavioral responding to stimuli according to their associated affective outcomes. This system engages autonomic and postural responding (avoiding, ignoring, approaching) in accordance with associated stimulus valence (negative, neutral, positive), but does not engage particular operant responses. Rather, this emotional system suppresses or invigorates actions that are selected through competition between goal-directed control involving dorsomedial striatum (DMS) and habitual control involving dorsolateral striatum (DLS). The hippocampus provides contextual specificity to the emotional system, and provides an information rich input to the goal-directed system for navigation and discriminations involving ambiguous contexts, complex sensory configurations, or temporal ordering. The rapid acquisition and high capacity for episodic associations in the emotional system may unburden the more complex goal-directed system and reduce interference in the habit system from processing contingencies of neutral stimuli. Interactions among these systems likely involve inhibitory mechanisms and neuromodulation in the striatum to form a dominant response strategy. Innate traits, training methods, and task demands contribute to the nature of these interactions, which can include incidental learning in non-dominant systems. Addition of these features to reinforcement learning models of decision-making may better align theoretical predictions with behavioral and neural correlates in animals.