While most models of decision-making assume that individuals assign options absolute values, animals often assess options comparatively, violating principles of economic rationality. Such 'irrational' preferences are especially common when two rewards vary along multiple dimensions of quality and a third, 'decoy' option is available. Bumblebees are models of decision-making, yet whether they are subject to decoy effects is unknown. We addressed this question using bumblebees () choosing between flowers that varied in their nectar concentration and reward rate. We first gave bees a choice between two flower types, one higher in concentration and the other higher in reward rate. Bees were then given a choice between these flowers and either a 'concentration' or 'rate' decoy, designed to be asymmetrically dominated on each axis. The rate decoy increased bees' preference in the expected direction, while the concentration decoy did not. In a second experiment, we manipulated choices along two single reward dimensions to test whether this discrepancy was explained by differences in how concentration versus reward rate were evaluated. We found that low-concentration decoys increased bees' preference for the medium option as predicted, whereas low-rate decoys had no effect. Our results suggest that both low- and high-value flowers can influence pollinator preferences in ways previously unconsidered.
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http://dx.doi.org/10.1098/rspb.2024.0843 | DOI Listing |
Microsyst Nanoeng
December 2024
School of Mechanical and Electrical Engineering, Soochow University, No.8 Jixue Road, Suzhou City, Jiangsu, 215000, China.
Microscopic imaging is a critical tool in scientific research, biomedical studies, and engineering applications, with an urgent need for system miniaturization and rapid, precision autofocus techniques. However, traditional microscopes and autofocus methods face hardware limitations and slow software speeds in achieving this goal. In response, this paper proposes the implementation of an adaptive Liquid Lens Microscope System utilizing Deep Reinforcement Learning-based Autofocus (DRLAF).
View Article and Find Full Text PDFInt J Clin Pharm
December 2024
School of International Pharmaceutical Business, China Pharmaceutical University, No. 639 Longmian Road, Jiangning District, Nanjing, 211198, Jiangsu, China.
Background: Temporal discounting, the preference for immediate over delayed rewards, affects decision-making in domains like health and finance. Understanding the differences in how people discount health outcomes compared to monetary rewards is crucial to shaping health policy and technology assessments.
Aim: This systematic review and meta-analysis aimed to compare temporal discounting parameters between health outcomes and monetary rewards and evaluate their overall relationship.
Isr J Health Policy Res
December 2024
Department of Nursing, School of Health Sciences, Ashkelon Academic College, Yitshak Ben Zvi 12, Ashkelon, Israel.
Background: Preserving new graduate nurses in the profession is an essential step for addressing the nursing shortage and sustaining the future of the profession. This study aimed to examine the relationship between employment characteristics and job satisfaction of novice nurses and their willingness to stay in the nursing profession in the next 5 years.
Methods: Novice nurses' intention to stay in the profession was assessed, considering demographics, employment characteristics, and components of job satisfaction.
PLoS Comput Biol
December 2024
Department of Psychology, University of California, Berkeley, Berkeley, California, United States of America.
Computational modeling has revealed that human research participants use both rapid working memory (WM) and incremental reinforcement learning (RL) (RL+WM) to solve a simple instrumental learning task, relying on WM when the number of stimuli is small and supplementing with RL when the number of stimuli exceeds WM capacity. Inspired by this work, we examined which learning systems and strategies are used by adolescent and adult mice when they first acquire a conditional associative learning task. In a version of the human RL+WM task translated for rodents, mice were required to associate odor stimuli (from a set of 2 or 4 odors) with a left or right port to receive reward.
View Article and Find Full Text PDFCogn Neurodyn
December 2024
School of Systems Science, Beijing Normal University, Beijing, 100875 China.
Adaptive mechanisms of learning models play critical roles in interpreting adaptive behavior of humans and animals. Different learning models, varying from Bayesian models, deep learning or regression models to reward-based reinforcement learning models, adopt similar update rules. These update rules can be reduced to the same generalized mathematical form: the Rescorla-Wagner equation.
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