Normalization also explains neural activity underlying higher-order processes such as multisensory integration ( 8) and visual attention ( 9). Originally proposed to explain nonlinear responses in primary visual cortex ( 1), divisive normalization has been widely observed in sensory systems and characterizes responses including contrast gain control in the retina and thalamus ( 2, 3), surround suppression in the middle temporal area ( 4, 5), ventral stream responses to multiple objects ( 6), and gain control in auditory cortex ( 7). Neural coding is governed by a small number of canonical computations implemented in diverse circuits and mechanisms, a prominent example being divisive normalization, in which the initial input-driven activity of a neuron is divided by the summed activity of a large pool of neighboring neurons. These findings suggest that the neural mechanism of value coding critically influences stochastic choice behavior and provide a generalizable quantitative framework for examining context effects in decision making.Ī fundamental question in neuroscience is how the brain represents behaviorally relevant variables. In value-guided choice experiments, both monkey and human choosers show novel context-dependent behavior consistent with normalization. Here, we show that choice models using normalization generate significant (and classically irrational) choice phenomena driven by either the value or number of alternative options. Recent electrophysiological studies have extended these insights beyond the sensory domain, demonstrating an analogous algorithm for the value signals that guide decision making, but the effects of normalization on choice behavior are unknown. In sensory systems, divisive normalization seems to be a canonical neural computation, observed in areas ranging from retina to cortex and mediating processes including contrast adaptation, surround suppression, visual attention, and multisensory integration.
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Understanding the neural code is critical to linking brain and behavior.
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