Neurons in the Frontal Lobe Encode the Value of Multiple Decision Variables

• Published in: Journal of cognitive neuroscience Vol. 21; no. 6; pp. 1162 - 1178
• Main Authors: Kennerley, Steven W., Dahmubed, Aspandiar F., Lara, Antonio H., Wallis, Jonathan D.
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.06.2009; MIT Press Journals, The
• Subjects: Action Potentials; Analysis of Variance; Animals; Brain; Brain Mapping; Decision making; Decision Making - physiology; Eye Movements - physiology; Frontal Lobe - cytology; Functional Laterality - physiology; Macaca mulatta; Male; Neural Pathways - physiology; Neurons; Neurons - physiology; Neuropsychological Tests; Photic Stimulation; Probability; Reaction Time - physiology; Reward; Time Factors
• ISSN: 0898-929X; 1530-8898
• DOI: 10.1162/jocn.2009.21100

A central question in behavioral science is how we select among choice alternatives to obtain consistently the most beneficial outcomes. Three variables are particularly important when making a decision: the potential payoff, the probability of success, and the cost in terms of time and effort. A key brain region in decision making is the frontal cortex as damage here impairs the ability to make optimal choices across a range of decision types. We simultaneously recorded the activity of multiple single neurons in the frontal cortex while subjects made choices involving the three aforementioned decision variables. This enabled us to contrast the relative contribution of the anterior cingulate cortex (ACC), the orbito-frontal cortex, and the lateral prefrontal cortex to the decision-making process. Neurons in all three areas encoded value relating to choices involving probability, payoff, or cost manipulations. However, the most significant signals were in the ACC, where neurons encoded multiplexed representations of the three different decision variables. This supports the notion that the ACC is an important component of the neural circuitry underlying optimal decision making.