scholarly journals Sympathetic and parasympathetic involvement in time constrained sequential foraging

2019 ◽  
Author(s):  
Neil M. Dundon ◽  
Neil Garrett ◽  
Viktoriya Babenko ◽  
Matt Cieslak ◽  
Nathaniel D. Daw ◽  
...  

AbstractAppraising sequential offers relative to an unknown future opportunity and a time cost requires an optimization policy that draws on a learned estimate of an environment’s richness. Converging evidence points to a learning asymmetry, whereby estimates of this richness update with a bias toward integrating positive information. We replicate this bias in a sequential foraging (prey selection) task and probe associated activation within two branches of the autonomic system, sympathetic and parasympathetic branches, using trial-by-trial measures of simultaneously recorded cardiac autonomic physiology. In general, lower value offers were accepted during periods of autonomic drive, both in the sympathetic (shorter pre-ejection period PEP) and parasympathetic (higher HF HRV) branches. In addition, we reveal a unique adaptive role for the sympathetic branch in learning. It was specifically associated with adaptation to a deteriorating environment: it correlated with both the rate of negative information integration in belief estimates and downward changes in moment-to-moment environmental richness, and was predictive of optimal performance on the task. The findings are consistent with a parallel processing framework whereby autonomic function serves both learning and executive demands of prey selection.Significance statementThe value of choices (accepting a job) depends on context (richness of the current job market). Learning contexts, therefore, is crucial for optimal decision-making. Humans demonstrate a bias when learning contexts; we learn faster about improvements vs deteriorations. New techniques allow us to cleanly measure fast acting stress responses that might fluctuate with trial-by-trial learning. Using these new methods, we observe here that increased stress – specifically sympathetic (heart contractility) – might help overcome the learning bias (making us faster at learning contextual deterioration) and thereafter guide us toward better context appropriate decisions. For the first time we show that specific building blocks of good decision-making might benefit from short bursts of specific inputs of the stress system.

2021 ◽  
pp. medethics-2020-107134
Author(s):  
Thana Cristina de Campos-Rudinsky ◽  
Eduardo Undurraga

Although empirical evidence may provide a much desired sense of certainty amidst a pandemic characterised by uncertainty, the vast gamut of available COVID-19 data, including misinformation, has instead increased confusion and distrust in authorities’ decisions. One key lesson we have been gradually learning from the COVID-19 pandemic is that the availability of empirical data and scientific evidence alone do not automatically lead to good decisions. Good decision-making in public health policy, this paper argues, does depend on the availability of reliable data and rigorous analyses, but depends above all on sound ethical reasoning that ascribes value and normative judgement to empirical facts.


Stat ◽  
2021 ◽  
Author(s):  
Hengrui Cai ◽  
Rui Song ◽  
Wenbin Lu

Philosophies ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 24
Author(s):  
Steven Umbrello ◽  
Stefan Lorenz Sorgner

Strong arguments have been formulated that the computational limits of disembodied artificial intelligence (AI) will, sooner or later, be a problem that needs to be addressed. Similarly, convincing cases for how embodied forms of AI can exceed these limits makes for worthwhile research avenues. This paper discusses how embodied cognition brings with it other forms of information integration and decision-making consequences that typically involve discussions of machine cognition and similarly, machine consciousness. N. Katherine Hayles’s novel conception of nonconscious cognition in her analysis of the human cognition-consciousness connection is discussed in relation to how nonconscious cognition can be envisioned and exacerbated in embodied AI. Similarly, this paper offers a way of understanding the concept of suffering in a way that is different than the conventional sense of attributing it to either a purely physical state or a conscious state, instead of grounding at least a type of suffering in this form of cognition.


Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2963
Author(s):  
Melinda Timea Fülöp ◽  
Miklós Gubán ◽  
György Kovács ◽  
Mihály Avornicului

Due to globalization and increased market competition, forwarding companies must focus on the optimization of their international transport activities and on cost reduction. The minimization of the amount and cost of fuel results in increased competition and profitability of the companies as well as the reduction of environmental damage. Nowadays, these aspects are particularly important. This research aims to develop a new optimization method for road freight transport costs in order to reduce the fuel costs and determine optimal fueling stations and to calculate the optimal quantity of fuel to refill. The mathematical method developed in this research has two phases. In the first phase the optimal, most cost-effective fuel station is determined based on the potential fuel stations. The specific fuel prices differ per fuel station, and the stations are located at different distances from the main transport way. The method developed in this study supports drivers’ decision-making regarding whether to refuel at a farther but cheaper fuel station or at a nearer but more expensive fuel station based on the more economical choice. Thereafter, it is necessary to determine the optimal fuel volume, i.e., the exact volume required including a safe amount to cover stochastic incidents (e.g., road closures). This aspect of the optimization method supports drivers’ optimal decision-making regarding optimal fuel stations and how much fuel to obtain in order to reduce the fuel cost. Therefore, the application of this new method instead of the recently applied ad-hoc individual decision-making of the drivers results in significant fuel cost savings. A case study confirmed the efficiency of the proposed method.


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