Cortical circuit dynamics underlying multisensory decision making

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Project Summary To navigate and guide locomotion in a complex 3D environment, humans and animals must make countless judgments of their direction of self-motion, or heading. Each of these is a multisensory perceptual decision, able to achieve greater accuracy and precision by combining signals from the visual, vestibular, and kinesthetic senses. At the same time, the brain must decide when to commit to a course of action (e.g., to quickly change direction to avoid an obstacle), and make predictions of the likelihood of success in that action. These features of a decision—choice accuracy, response time (RT), and confidence—have been studied by psychologists for over a century, but primarily for only a single modality, instead of the more natural case of integrating multiple sources of sensory evidence. Moreover, the neural basis of multisensory integration is largely studied at the level of individual cells or brain regions, whereas nearly all perceptual and cognitive functions depend on population-level computations and communication between areas. To address these gaps, we trained monkeys to perform a visual-vestibular heading discrimination task which measures choice, RT, and confidence via a post-decision wager (PDW). During performance of the task, we will record ensemble activity simultaneously from two key nodes in the sensory cortical network representing visual and vestibular self-motion cues (MST and PIVC, respectively), as well as one node (lateral intraparietal area, LIP) in the downstream decision network that converts sensory evidence into a motor plan. These regions have individually been linked to heading perception, but little is known about how their coordinated activity patterns, observable only though population recordings, support multisensory decision making. In Aim 1 of the proposal, we will quantify the coordinated activity across sensory neural populations and test whether the perceptual improvement from multisensory integration depend on the strength of coupling between them, beyond what can be explained by their activity considered independently. When visual and vestibular cues are artificially placed in conflict, we will ask whether and how the relative precision of heading estimates decoded from these sensory populations predicts choice and confidence, guided by predictions of a multisensory evidence accumulation model. In Aim 2, we will extend our investigation of inter-areal interactions to the decision stage, quantifying the strength and timing of functional coupling between LIP and each of the two sensory areas. The relative timing of this coordinated activity can indicate feedforward versus feedback processes, revealing how perceptual decisions evolve via recurrent loops between sensation and degree of belief in a proposition (or commitment to a plan of action). The results will yield new insights into the representation and readout of sensory evidence and its associated degree of (un)certainty, and will advance a population- and circuit-level understanding of decision computations in a multisensory task.