scholarly journals A direct and conceptual replication of post-loss speeding when gambling

2020 ◽  
Author(s):  
Charlotte Eben ◽  
Zhang Chen ◽  
Luc Vermeylen ◽  
Joel Billieux ◽  
Frederick Verbruggen
Keyword(s):  
Cognitive Control ◽  
Sample Size ◽  
Lower Probability ◽  
Large Sample Size ◽  
Impulsive Action ◽  
Large Sample ◽  
Optimal Outcomes ◽  
Response Choice ◽  
Correct Location

To investigate the response to sub-optimal outcomes, Verbruggen et al. (2017) conducted a study in which participants chose between a gamble and a non-gamble option. The non-gamble option was a guaranteed amount of points, whereas the gamble option was associated with a higher amount but a lower probability of winning. The authors observed that participants initiated the next trial faster after a loss compared to wins or non-gambles. In the present study, we directly replicated these findings in the lab and online. We also designed another task controlling for the number of trials per outcome. In this task, participants guessed where a reward was hidden. They won points if they selected the correct location, but lost points if they selected the incorrect location. We included neutral trials as a baseline. Again, participants sped up after a loss relative to wins and neutral trials (but only with a response choice in neutral trials and a large sample size). These findings appear inconsistent with cognitive-control frameworks, which assume that sub-optimal outcomes typically lead to slower responses; instead, they suggest that sub-optimal outcomes can invigorate behaviour consistent with accounts of frustrative non-reward and impulsive action.

scholarly journals A direct and conceptual replication of post-loss speeding when gambling

2020 ◽  
Vol 7 (5) ◽  
pp. 200090
Author(s):  
Charlotte Eben ◽  
Zhang Chen ◽  
Luc Vermeylen ◽  
Joël Billieux ◽  
Frederick Verbruggen
Keyword(s):  
Cognitive Control ◽  
Sample Size ◽  
Lower Probability ◽  
Large Sample Size ◽  
Impulsive Action ◽  
Large Sample ◽  
Response Choice ◽  
Link Type ◽  
Correct Location

To investigate the response to suboptimal outcomes, Verbuggen et al. (Verbruggen F, Chambers CD, Lawrence NS, McLaren IPL. 2017 Winning and losing: effects on impulsive action. J. Exp. Psychol.: Hum. Percept. Perform. 43 , 147. ( doi:10.1037/xhp0000284 )) conducted a study in which participants chose between a gamble and a non-gamble option. The non-gamble option was a guaranteed amount of points, whereas the gamble option was associated with a higher amount but a lower probability of winning. The authors observed that participants initiated the next trial faster after a loss compared to wins or non-gambles. In the present study, we directly replicated these findings in the laboratory and online. We also designed another task controlling for the number of trials per outcome. In this task, participants guessed where a reward was hidden. They won points if they selected the correct location, but lost points if they selected the incorrect location. We included neutral trials as a baseline. Again, participants sped up after a loss relative to wins and neutral trials (but only with a response choice in neutral trials and a large sample size). These findings appear inconsistent with cognitive-control frameworks, which assume that suboptimal outcomes typically lead to slower responses; instead, they suggest that suboptimal outcomes can invigorate behaviour, consistent with accounts of frustrative non-reward and impulsive action.

Heart Rate Response to Exercise

2018 ◽  
pp. 437-445
Author(s):  
Gregory S. Thomas
Keyword(s):  
Heart Rate ◽  
Physical Fitness ◽  
Sample Size ◽  
Older Persons ◽  
Heart Rate Response ◽  
Large Sample Size ◽  
Maximum Heart Rate ◽  
Large Sample ◽  
Response To Exercise

The chapter Heart Rate Response to Exercise reviews the studies performed to estimate a patient’s maximum predicted heart rate. While the commonly used formula (220 – age), developed in 1971, is easy to remember, it underestimates the actual maximum heart rate in older persons. Studies of large sample size have found the maximum heart rate to be relatively independent of sex and physical fitness but to incrementally decline with age. The decrease with age is less than 1 beat per minute per year, however. A more accurate and recommended formula is [(208) – (0.7)(age)] as developed by Tanaka and colleagues.

Extended criterion for comparison of empirical distributions

1970 ◽  
Vol 7 (01) ◽  
pp. 1-20 ◽  
Author(s):  
Ora Engleberg Percus ◽  
Jerome K. Percus
Keyword(s):  
Sample Size ◽  
Generating Function ◽  
Relative Number ◽  
Function Technique ◽  
Large Sample Size ◽  
Large Sample ◽  
Empirical Distributions ◽  
Asymptotic Problem ◽  
Explicit Expressions

A generating function technique is used to determine the probability that the deviation between two empirical distributions drawn from the same population lies within a given band a specified number of times. We also treat the asymptotic problem of very large sample size, and obtain explicit expressions when the relative number of failures is very small or very large.

scholarly journals A Mass-Ratiometry-Based CD45 Barcoding Method for Mass Cytometry Detection

2019 ◽  
Vol 24 (4) ◽  
pp. 408-419
Author(s):  
Hongu Meng ◽  
Antony Warden ◽  
Lulu Zhang ◽  
Ting Zhang ◽  
Yiyang Li ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Sample Size ◽  
Single Cell ◽  
Large Sample Size ◽  
Single Cell Level ◽  
Mass Cytometry ◽  
Cell Level ◽  
Large Sample ◽  
Antibody Staining ◽  
Instrument Sensitivity

Mass cytometry (CyTOF) is a critical cell profiling tool in acquiring multiparameter proteome data at the single-cell level. A major challenge in CyTOF analysis is sample-to-sample variance arising from the pipetting process, staining variation, and instrument sensitivity. To reduce such variations, cell barcoding strategies that enable the combination of individual samples prior to antibody staining and data acquisition on CyTOF are often utilized. The most prevalent barcoding strategy is based on a binary scheme that cross-examines the existence or nonexistence of certain mass signals; however, it is limited by low barcoding efficiency and high cost, especially for large sample size. Herein, we present a novel barcoding method for CyTOF application based on mass ratiometry. Different mass tags with specific fixed ratios are used to label CD45 antibody to achieve sample barcoding. The presented method exponentially increases the number of possible barcoded samples with the same amount of mass tags compared with conventional methods. It also reduces the overall time for the labeling process to 40 min and avoids the need for expensive commercial barcoding buffer reagents. Moreover, unlike the conventional barcoding process, this strategy does not pre-permeabilize cells before the barcoding procedure, which offers additional benefits in preserving surface biomarker signals.

MoFe‐Codoped Ni 3 S 2 /Ni(OH) 2 Nanosheets with Large Sample Size toward High‐Performance Oxygen Evolution

2019 ◽  
Vol 7 (9) ◽  
pp. 1801053
Author(s):  
Liu Xie ◽  
Rui Tong ◽  
Wen Zhang ◽  
Dejian Wang ◽  
Tao Liu ◽  
...  
Keyword(s):  
Sample Size ◽  
Oxygen Evolution ◽  
High Performance ◽  
Large Sample Size ◽  
Large Sample

Application of Big data scientific research analysis platform in clinical medical research

2021 ◽  
pp. 1-12
Author(s):  
Jing Wang ◽  
Jie Wei ◽  
Long Li ◽  
Lijian Zhang
Keyword(s):  
Big Data ◽  
Drug Discovery ◽  
Data Quality ◽  
Sample Size ◽  
Real World ◽  
Medical Data ◽  
Large Sample Size ◽  
Research Implementation ◽  
Large Sample ◽  
Application Systems

With the rapid development of evidence-based medicine, translational medicine, and pharmacoeconomics in China, as well as the country’s strong commitment to clinical research, the demand for physicians’ research continues to increase. In recent years, real-world studies are attracting more and more attention in the field of health care, as a method of post-marketing re-evaluation of drugs, RWS can better reflect the effects of drugs in real clinical settings. In the past, it was difficult to ensure data quality and efficiency of research implementation because of the large sample size required and the large amount of medical data involved. However, due to the large sample size required and the large amount of medical data involved, it is not only time-consuming and labor-intensive, but also prone to human error, making it difficult to ensure data quality and efficiency of research implementation. This paper analyzes and summarizes the existing application systems of big data analytics platforms, and concludes that big data research analytics platforms using natural language processing, machine learning and other artificial intelligence technologies can help RWS to quickly complete the collection, integration, processing, statistics and analysis of large amounts of medical data, and deeply mine the intrinsic value of the data, real-world research in new drug development, drug discovery, drug discovery, drug discovery, and drug discovery. It has a broad application prospect for multi-level and multi-angle needs such as economics, medical insurance cost control, indications/contraindications evaluation, and clinical guidance.

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