The influence of single option or forced-exposure (FE) trials was studied in the suboptimal choice task. Pigeons chose between an optimal alternative that led to food half of the time and a suboptimal alternative that led to food 20% of the time. Choice of the suboptimal alternative was compared across groups of subjects that received different numbers of FE trials during training. In Experiment 1, subjects received 100% FE trials, 67% FE trials, or only choice trials. Pigeons in the two groups that had FE trials developed extreme preference for the signaled suboptimal alternative over the unsignaled optimal alternative, while pigeons that had no FE trials showed pronounced individual differences. Experiment 2 compared 10% and 90% FE trials. When neither alternative signaled trial outcomes, both groups of subjects strongly preferred the optimal alternative. When the suboptimal alternative provided differential signals, the subjects in the 90% FE group developed strong preference for the suboptimal alternative and subjects in the 10% FE group maintained preference for the optimal alternative. The results of both experiments demonstrate that FE trials can have substantial effects on the development of preference in the suboptimal choice task.
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http://dx.doi.org/10.3758/s13423-022-02092-2 | DOI Listing |
EJNMMI Radiopharm Chem
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Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
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View Article and Find Full Text PDFJ Pediatr Urol
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Department of Pediatric Surgery and Urology, Medical University of Silesia, ul. Medyków 16, 40-752, Katowice, Poland.
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View Article and Find Full Text PDFNeural Netw
January 2025
School of Mathematical Sciences, Harbin Engineering University, Harbin 150001, China.
Multi-view clustering has garnered significant attention due to its capacity to utilize information from multiple perspectives. The concept of anchor graph-based techniques was introduced to manage large-scale data better. However, current methods rely on K-means or uniform sampling to select anchors in the original space.
View Article and Find Full Text PDFBiometrics
January 2025
MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0SR, United Kingdom.
Dynamic treatment regimes (DTRs) formalize medical decision-making as a sequence of rules for different stages, mapping patient-level information to recommended treatments. In practice, estimating an optimal DTR using observational data from electronic medical record (EMR) databases can be complicated by nonignorable missing covariates resulting from informative monitoring of patients. Since complete case analysis can provide consistent estimation of outcome model parameters under the assumption of outcome-independent missingness, Q-learning is a natural approach to accommodating nonignorable missing covariates.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Mobile Systems Engineering, Dankook University, Yongin 16890, Republic of Korea.
As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models and characterizes the performance of heterogeneous random access-based wireless mutual broadcast (RA-WMB) with distinct transmit (Tx) power levels, leveraging a marked Poisson point process to account for nodes' various Tx power. In particular, this study enables the performance of RA-WMB with heterogeneous Tx power to be represented in terms of the performance of RA-WMB with a common Tx power by deriving an equivalent Tx power based on the probability distribution of heterogeneous Tx power and the path loss exponent.
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