Decision making: from neuroscience to psychiatry

Abstract

Adaptive behaviors increase the likelihood of survival and reproduction and improve the quality of life. However, it is often difficult to identify optimal behaviors in real life due to the complexity of the decision maker's environment and social dynamics. As a result, although many different brain areas and circuits are involved in decision making, evolutionary and learning solutions adopted by individual decision makers sometimes produce suboptimal outcomes. Although these problems are exacerbated in numerous neurological and psychiatric disorders, their underlying neurobiological causes remain incompletely understood. In this review, theoretical frameworks in economics and machine learning and their applications in recent behavioral and neurobiological studies are summarized. Examples of such applications in clinical domains are also discussed for substance abuse, Parkinson's disease, attention-deficit/hyperactivity disorder, schizophrenia, mood disorders, and autism. Findings from these studies have begun to lay the foundations necessary to improve diagnostics and treatment for various neurological and psychiatric disorders.

Figure 1

Models of decision making. A. Utility functions with different types of risk preference. B. Value functions in prospect theory. Solid (dotted) line shows the value function with (without) loss aversion. C. Exponential vs. hyperbolic temporal discount functions. D. Weights assigned to the previous outcomes at different time lags according to two different learning rates in a model-free reinforcement learning algorithm.

Figure 2

Brain areas encoding reward signals during a matching pennies task that was identified with a multi-voxel pattern analysis (Vickery et al., 2011).

Figure 3

Hypothetical outcome signals in the orbitofrontal cortex. A. Visual stimuli displayed during the choice and feedback epochs of a rock-paper-scissors task used in Abe and Lee (2011). Different colors for feedback stimuli were associated with different amounts of juice reward. B. Payoff matrix (left) and changes in choice probabilities (right) during the same task (R, rock; P, paper; S, scissors). Dotted lines correspond to the Nash-equilibrium strategy (0.5 for rock and 0.25 for paper and scissors, respectively). C. Activity of a neuron in the orbitofrontal cortex that encoded the hypothetical outcomes from unchosen actions. Spike density functions are plotted separately according to the position (columns) and payoffs (line colors) of the winning target and the position of the target chosen by the animal (rows).

Figure 4

Functions and dysfunctions of the default network. A–C. Cortical areas activated by the recall of autobiographical memory (A), episodic future thinking (B), and mental simulation of other people’s perspective (C). Reproduced from Buckner et al. (2008). D. Deactivation in the default network (blue, top) is absent in the brains of autistic individuals (black outlines, bottom; Kennedy et al., 2006).