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Venditto, S. J. C.

Publications and source records attributed to Venditto, S. J. C..

2 recordsLinked to original sources

Coordinated cross-brain activity during accumulation of sensory evidence and decision commitment

Decision-making is thought to involve two phases: first, evidence accumulation, then, decision commitment. Accumulation is represented broadly in the brain, but whether this best matches single versus multiple accumulator models is unknown. Here, analysis of simultaneous, bilateral recordings across sensory, association, and motor regions, in both cortex and subcortical structures, strongly supports a single accumulator, and suggests that accumulated noise (diffusion) originates frontally. Decision commitment can occur internally, without an overt report. Long-standing competing models of covert commitment disagree as to whether it is abrupt or continuous. Temporally aligning data on a single-trial estimator of internal, covert commitment ("nTc") revealed a rapid ([≤]50 ms) drop in the accumulators sensitivity to incoming sensory evidence, favoring abrupt transition models, and indicating a discrete cross-brain state change at nTc that was first detectable in motor regions. These data discriminate between decades-old models of accumulation, and between decades-old models of the transition to commitment.

neuroscience↗

Dynamic reinforcement learning reveals time-dependent shifts in strategy during reward learning.

Different brain systems have been hypothesized to subserve multiple "experts" that compete to generate behavior. In reinforcement learning, two general processes, one model-free (MF) and one model-based (MB), are often modeled as a mixture of agents (MoA) and hypothesized to capture differences between automaticity vs. deliberation. However, shifts in strategy cannot be captured by a static MoA. To investigate such dynamics, we present the mixture-of-agents hidden Markov model (MoA-HMM), which simultaneously learns inferred action values from a set of agents and the temporal dynamics of underlying "hidden" states that capture shifts in agent contributions over time. Applying this model to a multi-step, reward-guided task in rats reveals a progression of within-session strategies: a shift from initial MB exploration to MB exploitation, and finally to reduced engagement. The inferred states predict changes in both response time and OFC neural encoding during the task, suggesting that these states are capturing real shifts in dynamics.

neuroscience↗