Confidence-related decision making.
Neurons have been recorded that reflect in their firing rates the confidence in a decision. Here we show how this could arise as an emergent property in an integrate-and-fire attractor network model of decision making. The attractor network has populations of neurons that respond to each of the possible choices, each biased by the evidence for that choice, and there is competition between the attractor states until one population wins the competition and finishes with high firing that represents the decision. Noise resulting from the random spiking times of individual neurons makes the decision making probabilistic. We also show that a second attractor network can make decisions based on the confidence in the first decision. This system is supported by and accounts for neuronal responses recorded during decision making and makes predictions about the neuronal activity that will be found when a decision is made about whether to stay with a first decision or to abort the trial and start again. The research shows how monitoring can be performed in the brain and this has many implications for understanding cognitive functioning.
Insabato
Andrea
A
Theoretical and Computational Neuroscience, Center for Brain and Cognition, Universitat Pompeu Fabra, and Institució Catalana de Recerca i Estudis Avancats, Barcelona, Spain.
Pannunzi
Mario
M
Rolls
Edmund T
ET
Deco
Gustavo
G
D000465:Algorithms; D001290:Attitude; D001921:Brain / Q000502:physiology; D003198:Computer Simulation; D003657:Decision Making / Q000502:physiology*; D055724:Electrophysiological Phenomena; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D018091:Receptors, AMPA / Q000502:physiology; D018079:Receptors, GABA / Q000502:physiology; D013569:Synapses / Q000502:physiology
D018091:Receptors, AMPA; D018079:Receptors, GABA