Testing the Mate Preference Priority Model with the Profile‐Based Experimental Paradigm: A Replication and Extension

2021 ◽  
Author(s):  
Jose C. Yong ◽  
Yi Wen Tan ◽  
Norman P. Li ◽  
Andrea L. Meltzer
Keyword(s):  
Mate Preference ◽  
Experimental Paradigm ◽  
Priority Model

scholarly journals Mate preferences in the US and Singapore: A cross-cultural test of the mate preference priority model

2011 ◽  
Vol 50 (2) ◽  
pp. 291-294 ◽  
Author(s):  
Norman P. Li ◽  
Katherine A. Valentine ◽  
Lily Patel
Keyword(s):  
Mate Preferences ◽  
Cross Cultural ◽  
Mate Preference ◽  
The Us ◽  
Priority Model

scholarly journals Mate preference priorities in the East and West: A cross‐cultural test of the mate preference priority model

2019 ◽  
Vol 88 (3) ◽  
pp. 606-620 ◽  
Author(s):  
Andrew G. Thomas ◽  
Peter K. Jonason ◽  
Jesse D. Blackburn ◽  
Leif Edward Ottesen Kennair ◽  
Rob Lowe ◽  
...  
Keyword(s):  
Cross Cultural ◽  
Mate Preference ◽  
East And West ◽  
Priority Model

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.

Effect type but not attribute framing alters nocebo headaches in an experimental paradigm.

2017 ◽  
Vol 4 (3) ◽  
pp. 259-273 ◽  
Author(s):  
Fawn C. Caplandies ◽  
Ben Colagiuri ◽  
Suzanne G. Helfer ◽  
Andrew L. Geers
Keyword(s):  
Experimental Paradigm

Discovering Optimal Training Policies: A New Experimental Paradigm

2012 ◽  
Author(s):  
Robert V. Lindsey ◽  
Michael C. Mozer ◽  
Harold Pashler
Keyword(s):  
Experimental Paradigm

Effect of Physical Height on Mate Preference and Selection

2006 ◽  
Author(s):  
Tanya C. Farman ◽  
Sally D. Stabb
Keyword(s):  
Mate Preference ◽  
Physical Height

The DUT task: A novel experimental paradigm to investigate the variability of eye movements in whole-text reading for meaning

2018 ◽  
Vol 4 (2) ◽  
pp. 124-143
Author(s):  
Tobias Alf Kroll ◽  
A. Alexandre Trindade ◽  
Amber Asikis ◽  
Melissa Salas ◽  
Marcy Lau ◽  
...  
Keyword(s):  
Eye Movements ◽  
Experimental Paradigm ◽  
Text Reading

Reward Prediction Errors Drive Declarative Learning Irrespective of Agency

2020 ◽  
Author(s):  
Kate Ergo ◽  
Luna De Vilder ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Learning Theory ◽  
Learning Effect ◽  
Steady Increase ◽  
Prediction Errors ◽  
Experimental Paradigm ◽  
Reward Prediction ◽  
Declarative Learning

Recent years have witnessed a steady increase in the number of studies investigating the role of reward prediction errors (RPEs) in declarative learning. Specifically, in several experimental paradigms RPEs drive declarative learning; with larger and more positive RPEs enhancing declarative learning. However, it is unknown whether this RPE must derive from the participant’s own response, or whether instead any RPE is sufficient to obtain the learning effect. To test this, we generated RPEs in the same experimental paradigm where we combined an agency and a non-agency condition. We observed no interaction between RPE and agency, suggesting that any RPE (irrespective of its source) can drive declarative learning. This result holds implications for declarative learning theory.

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