Using State Effects to Build Convergent Models
Choices depend on one's internal state. Long-recognized have been the effects of emotion and mood states; e.g., anger can lead to impulsive and overly optimistic choices, while fear and sadness can lead to considered, analytic, but pessimistic choices (Lerner and Keltner, 2001). Sleep deprivation leads to attentional lapses and to impairments in memory, reducing the quality of subsequent decisions and increasing risk-seeking behavior (Killgore et al., 2006), with concomitant effects on brain function (Venkatraman et al., 2007). And, state manipulations of key neurotransmitter systems can have effects similar to those of chronic drug abuse and of brain damage (Rogers et al., 1999). Given the wide variety of phenomena investigated, the study of state effects provides some of the clearest applications for decision neuroscience research.
The standard approach, so far, has been the characterization (cataloging) of each state effect separately. That is, researchers adopt a paradigm used in prior decision neuroscience research and then measure how a single manipulation of state alters the functioning of targeted brain regions. The result has been a collection of independent observations – each valuable in itself, but difficult to combine. A challenge for subsequent research, therefore, will be to create mechanistic models that allow generalization across a range of states. For one potential direction, consider the growing evidence that emotion interacts with cognitive control in a complex, and not necessarily antagonistic, manner (Gray et al., 2002; Pessoa, 2008). A general model of control would need to predict the effects of individual difference variables across several states; e.g., how trait impulsiveness influences choices under anger, following sleep deprivation, and after consumption of alcohol. Even more problematic will be creating models that account for combinations of states, such as interactions between drug abuse and depression. A key milestone for the maturity of decision neuroscience, as a discipline, will be the development of biologically plausible models that can predict behavior across a range of states.