scholarly journals Use of Bayesian Inference for Parameter Recovery in DC and AC Voltammetry

2017 ◽  
Vol 5 (6) ◽  
pp. 917-935 ◽  
Author(s):  
David J. Gavaghan ◽  
Jonathan Cooper ◽  
Aidan C. Daly ◽  
Christopher Gill ◽  
Kathryn Gillow ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Parameter Recovery ◽  
Ac Voltammetry

The Multiple Faces of Risk-Taking

2016 ◽  
Vol 32 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Florian Schmitz ◽  
Karsten Manske ◽  
Franzis Preckel ◽  
Oliver Wilhelm
Keyword(s):  
Risk Taking ◽  
Basic Research ◽  
Formal Models ◽  
Experimental Paradigm ◽  
Nomological Network ◽  
Balloon Analogue Risk Task ◽  
Parameter Recovery ◽  
Task Specificity ◽  
Available Information ◽  
Criterion Variables

Abstract. The Balloon-Analogue Risk Task (BART; Lejuez et al., 2002 ) is one of the most popular behavioral tasks suggested to assess risk-taking in the laboratory. Previous research has shown that the conventionally computed score is predictive, but neglects available information in the data. We suggest a number of alternative scores that are motivated by theories of risk-taking and that exploit more of the available data. These scores can be grouped around (1) risk-taking, (2) task performance, (3) impulsive decision making, and (4) reinforcement sequence modulation. Their theoretical rationale is detailed and their validity is tested within the nomological network of risk-taking, deviance, and scholastic achievement. Two multivariate studies were conducted with youths (n = 435) and with adolescents/young adults (n = 316). Additionally, we tested formal models suggested for the BART that decompose observed behavior into a set of meaningful parameters. A simulation study with parameter recovery was conducted, and the data from the two studies were reanalyzed using the models. Most scores were reliable and differentially predictive of criterion variables and may be used in basic research. However, task specificity and the generally moderate validity do not warrant use of the experimental paradigm for diagnostic purposes.

Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

How do academics assess the results of multiple experiments?

2018 ◽  
Author(s):  
Olmo Van den Akker ◽  
Linda Dominguez Alvarez ◽  
Marjan Bakker ◽  
Jelte M. Wicherts ◽  
Marcel A. L. M. van Assen
Keyword(s):  
Bayesian Inference ◽  
Secondary Analysis ◽  
Individual Participant Data ◽  
Additional Experiment ◽  
Underlying Theory ◽  
Individual Participant ◽  
Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

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