Correctly selecting appropriate actions in an uncertain environment requires gathering experience about the available actions by sampling them over several trials. Recent findings suggest that the human rostral cingulate zone (RCZ) is important for the integration of extended action-outcome associations across multiple trials and in coding the subjective value of each action. During functional magnetic resonance imaging, healthy volunteers performed two versions of a probabilistic reversal learning task with high (HP) or low (LP) reward probabilities that required them to integrate action-outcome relations over lower or higher numbers of trials, respectively. In the HP session, subjects needed fewer trials to adjust their behavior in response to a reversal of response-reward contingencies. Similarly, the learning rate derived from a reinforcement learning model was higher in the HP condition. This was accompanied by a stronger response of the RCZ to negative feedback upon reversals in the HP condition. Furthermore, RCZ activity related to negative reward prediction errors varied as a function of the learning rate, which determines to what extent the prediction error is used to update action values. These data show that RCZ responses vary as a function of the information content provided by the environment. The more likely a negative event indicates the need for behavioral adaptations, the more prominent is the response of the RCZ. Thus, both the window of trials over which reinforcement information is integrated and adjustment of action values in the RCZ covary with the stochastics of the environment.
The purpose of this paper is to measure the amount of compression that can be accomplished using a heuristic coding algorithm. The scheme is based on the locality of the input symbols within a self adaptive sequential list. An IBM-PC based system has been developed for both encoding and decoding. The results using different adaptive sequential lists are presented. The test data include text files and binary files such as computer programs in its executable forms and 256 gray-levels image. Performance evaluation is discussed in terms of compression ratio, encoding time and decoding time.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.