Topic: decision variable

Drift-diffusion models have been widely used to study the neural mechanisms of decisions guided by noisy stimuli. In this framework, momentary evidence is integrated through a scalar decision variable, which has been found to correlate with spike rates in many brain areas. However, the relationship between the decision variable (i.e., accumulated evidence) and spike rate is widely assumed to be fixed over the course of a single trial, even though sensory and motor weights on spiking activity vary over time. Allowing the weight of the decision variable to be temporally inhomogeneous may capture

Classical models of decision-making, including drift-diffusion and other accumulate-to-bound models, assume that the decision rule for committing to a decision based on accumulated evidence is determined prior to observing the evidence. These predefined rules are typically modeled as thresholds, or bounds, on the accumulated evidence (“decision variable”) that are either fixed [1-2] or collapsing over time [3], in both cases serving to balance decision speed (emphasized when bounds are low and reached quickly) and accuracy (emphasized when bounds are high and allow for more evidence to be accu

Cognitive flexibility plays a key role in ensuring an individual's survival. The prefrontal cortex and striatum are both essential to cognitive flexibility but competing theories debate which structure among these two leads the role in detecting and representing new circumstances for a change, giving largely opposing predictions on neural activities in the prefrontal cortex and striatum during flexible behavior. We trained head-restrained mice to perform an action-outcome based dynamic foraging task and simultaneously recorded single-neuron activities in the medial prefrontal cortex (mPFC)