Neural Correlates of Optimal Multisensory Decision Making under Time-Varying Reliabilities with an Invariant Linear Probabilistic Population Code

Perceptual decisions are often based on multiple sensory inputs whose reliabilities rapidly vary over time, yet little is known about how our brain integrates these inputs to optimize behavior. Here we show multisensory evidence with time-varying reliability can be accumulated near optimally, in a Bayesian sense, by simply taking time-invariant linear combinations of neural activity across time and modalities, as long as the neural code for the sensory inputs is close to an invariant linear probabilistic population code (ilPPC). Recordings in the lateral intraparietal area (LIP) while macaques optimally performed a vestibular-visual multisensory decision-making task revealed that LIP population activity reflects an integration process consistent with the ilPPC theory. Moreover, LIP accumulates momentary evidence proportional to vestibular acceleration and visual velocity which are encoded in sensory areas with a close approximation to ilPPCs. Together, these results provide a remarkably simple and biologically plausible solution to optimal multisensory decision making.