title: Decision-making, errors, and confidence in the brain.
abstract: To provide a fundamental basis for understanding decision-making and decision confidence, we analyze a neuronal spiking attractor-based model of decision-making. The model predicts probabilistic decision-making with larger neuronal responses and larger functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent (BOLD) responses on correct than on error trials because the spiking noise-influenced decision attractor state of the network is consistent with the external evidence. Moreover, the model predicts that the neuronal activity and the BOLD response will become larger on correct trials as the discriminability ΔI increases and confidence increases and will become smaller as confidence decreases on error trials as ΔI increases. Confidence is thus an emergent property of the model. In an fMRI study of an olfactory decision-making task, we confirm these predictions for cortical areas including medial prefrontal cortex and the cingulate cortex implicated in choice decision-making, showing a linear increase in the BOLD signal with ΔI on correct trials, and a linear decrease on error trials. These effects were not found in a control area, the orbitofrontal cortex, where reward value useful for the choice is represented on a continuous scale but that is not implicated in the choice itself. This provides a unifying approach to decision-making and decision confidence and to how spiking-related noise affects choice, confidence, synaptic and neuronal activity, and fMRI signals.
authors.affiliation: Oxford Centre for Computational Neuroscience, Oxford, UK. Edmund.Rolls@oxcns.org
authors.forename: Edmund T
authors.initials: ET
authors.lastname: Rolls
authors.forename: Fabian
authors.initials: F
authors.lastname: Grabenhorst
authors.forename: Gustavo
authors.initials: G
authors.lastname: Deco
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D003657:Decision Making / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D012201:Reward