The purpose of the present study was to determine the degree to which lesions in the septum and other anatomically related structures result in the presence and/or permanence of an overresponding symptom on a differential-reinforcement-of-low-rate (DRL 20 sec) schedule. Animals were given 15 days of training to determine the presence or absence of overresponding. Then, animals that overresponded were divided into two groups, with one receiving 15 days of cued DRL training and 15 days of regular DRL training while the other received 30 days of regular DRL training. Overresponding occurred following lesions in septum, hippocampus, medialis dorsalis, and ventral thalamus pars dorsalis. While in effect, cued DRL facilitated performance in controls and in operated animals but did not facilitate performance following its removal in septals. Although the hippocampals continued to overrespond with extended training on a regular DRL schedule, exposure to the cued DRL allowed hippocampals to reduce responding and increase the frequency of obtained reinforcements. Lesions in medialis dorsalis and ventral thalamus led to an overresponding that disappeared with prolonged regular DRL training. Finally, it was shown that the cued DRL training actually functioned as a time-out from DRL training. The variations in the permanence of the overresponding symptom according to lesion locus preclude the identification of the lesion-induced dysfunction based solely on the presence or absence of overresponding.
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PLoS One
January 2025
Institute of Visual Informatics, The National University of Malaysia (UKM), Bangi, Malaysia.
Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.
View Article and Find Full Text PDFCogn Sci
January 2025
Department of Learning, Teaching, and Literacies, University of Pennsylvania.
There has been considerable research on confusion and frustration that has treated them as two unitary constructs, distinct from each other. In this article, we argue that there is instead a constellation of different types of confusion and frustration, with different antecedents, manifestations, and impacts, and that the commonalities between many types of confusion and frustration justify thinking of them as part of the same constellation of affect, distinct from other prominent affective categories. We discuss how these types of affect have been considered historically and in key models.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Economics and Business (ICADE), Universidad Pontificia Comillas, Madrid, Spain.
Financial portfolio management investment policies computed quantitatively by modern portfolio theory techniques like the Markowitz model rely on a set of assumptions that are not supported by data in high volatility markets such as the technological sector or cryptocurrencies. Hence, quantitative researchers are looking for alternative models to tackle this problem. Concretely, portfolio management (PM) is a problem that has been successfully addressed recently by Deep Reinforcement Learning (DRL) approaches.
View Article and Find Full Text PDFJ Adolesc
January 2025
School of Education, University of California, Irvine, California, USA.
Introduction: Individuals' math value beliefs are theorized to influence who persists in STEM. However, the existing findings on gender differences in adolescents' math value beliefs are inconsistent. The goal of this study was to use three existing datasets to help clarify when gender differences emerge for high school adolescents and for whom (i.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Qualcomm Communication Technologies (Shanghai) Co., Ltd., Shanghai 201208, China.
In this article, we consider an UAV (unmanned aerial vehicle)-assisted free space optical (FSO) secure communication network. Since FSO signal is impossible to detect by eavesdroppers without proper beam alignment and security authentication, a BS employs FSO technique to transfer information to multiple authenticated sensors, to improve the transmission security and reliability with the help of an UAV relay with decode and forward (DF) mode. All the sensors need to first send information to the UAV to obtain security authentication, and then the UAV forwards corresponding information to them.
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