The Neural Computations In The Caudate Nucleus For Reward-Biased Perceptual Decision-Making

Decision-making is a complex process in which our brain has to combine different sources of information, such as noisy sensory evidence and expected reward, in appropriate ways to obtain the outcome that satisfies the decision-maker. Despite various studies on perceptual decision-making and value-based decision making, it is still unclear how the brain combines sensory and reward information to make a complex decision. A prime candidate for mediating this process is the basal ganglia pathway. This pathway is known to make separate contributions to perceptual decisions based on the interpretation of uncertain sensory evidence and value-based decisions that select among outcome options. To begin to investigate what computations are performed by the brain, particularly in the basal ganglia, we trained monkeys to perform a reward-biased visual motion direction discrimination task and performed single-unit extracellular recordings in the caudate nucleus, the input station in the basal ganglia. Fitting the monkeys’ behaviors to a drift-diffusion model, we found that the monkeys used a rational heuristic to combine sensory and reward information. This heuristic is suboptimal but leads to good-enough outcomes. We also found that the monkeys’ reward biases were sensitive to the changes in the reward functions from session to session. This adaptive adjustment could be a possible reason underlying the individual variability in their decision strategies. By recording in the caudate nucleus, we found that it is involved in both the decision-formation and evaluation: before the monkey started accumulating sensory evidence, the caudate neurons represented the reward context that could be used to form a reward bias; during decision-formation, some caudate neurons jointly represented sensory evidence and reward information, which could facilitate the combining of sensory and reward information appropriately. After a decision is made, caudate nucleus represented both decision confidence and reward expectation, two evaluation-related quantities that influence the monkeys’ subsequent decision behaviors

Detailed Inventory Record Inaccuracy Analysis

This dissertation performs a methodical analysis to understand the behavior of inventory record inaccuracy (IRI) when it is influenced by demand, supply and lead time uncertainty in both online and offline retail environment separately. Additionally, this study identifies the susceptibility of the inventory systems towards IRI due to conventional perfect data visibility assumptions. Two different alternatives for such methods are presented and analyzed; the IRI resistance and the error control methods. The discussed methods effectively countered various aspects of IRI; the IRI resistance method performs better on stock-out and lost sales, whereas error control method keeps lower inventory. Furthermore, this research also investigates the value of using a secondary source of information (automated data capturing) along with traditional inventory record keeping methods to control the effects of IRI. To understand the combined behavior of the pooled data sources an infinite horizon discounted Markov decision process (MDP) is generated and optimized. Moreover, the traditional cost based reward structure is abandoned to put more emphasis on the effects of IRI. Instead a new measure is developed as inventory performance by combining four key performance metrics; lost sales, amount of correction, fill rate and amount of inventory counted. These key metrics are united under a unitless platform using fuzzy logic and combined through additive methods. The inventory model is then analyzed to understand the optimal policy structure, which is proven to be of a control limit type