Neurons in the dorsolateral prefrontal cortex (DLPFC) encode a diverse array of sensory and mnemonic signals, but little is known about how this information is dynamically routed during decision making. We analyzed the neuronal activity in the DLPFC of monkeys performing a probabilistic reversal task where information about the probability and magnitude of reward was provided by the target color and numerical cues, respectively. The location of the target of a given color was randomized across trials, and therefore was not relevant for subsequent choices. DLPFC neurons encoded signals related to both task-relevant and irrelevant features, and task-relevant mnemonic signals were encoded congruently with choice signals. Furthermore, only the task-relevant signals related to previous events were more robustly encoded following rewarded outcomes. Thus, multiple types of neural signals are flexibly routed in the DLPFC so as to favor actions that maximize reward.

We describe a new error reconciliation protocol Winnow based on the exchange of parity and Hamming's "syndrome" for N −bit subunits of a large data set. Winnow was developed in the context of quantum key distribution and offers significant advantages and net higher efficiency compared to other widely used protocols within the quantum cryptography community. A detailed mathematical analysis of Winnow is presented in the context of practical implementations of quantum key distribution; in particular, the information overhead required for secure implementation is one of the most important criteria in the evaluation of a particular error reconciliation protocol. The increase in efficiency for Winnow is due largely to the reduction in authenticated public communication required for its implementation.

A fundamental but rarely contested assumption in economics and neuroeconomics is that decisionmakers compute subjective values of risky options by multiplying functions of reward probability and magnitude. In contrast, an additive strategy for valuation allows flexible combination of reward information required in uncertain or changing environments. We hypothesized that the level of uncertainty in the reward environment should determine the strategy used for valuation and choice. To test this hypothesis, we examined choice between risky options in humans and monkeys across three tasks with different levels of uncertainty. We found that whereas humans and monkeys adopted a multiplicative strategy under risk when probabilities are known, both species spontaneously adopted an additive strategy under uncertainty when probabilities must be learned. Additionally, the level of volatility influenced relative weighting of certain and uncertain reward information and this was reflected in the encoding of reward magnitude by neurons in the dorsolateral prefrontal cortex. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: