scholarly journals Comparison of fMRI Experimental Paradigm for Decoding Color Constancy

2021 ◽  
Vol 21 (9) ◽  
pp. 2272
Author(s):  
Mei Kuang ◽  
Zong-Yi Zhan ◽  
Ping Jiang ◽  
Xin-Yu Du ◽  
Shao-Bing Gao (Corresponding Author)
2016 ◽  
Vol 32 (1) ◽  
pp. 17-38 ◽  
Author(s):  
Florian Schmitz ◽  
Karsten Manske ◽  
Franzis Preckel ◽  
Oliver Wilhelm

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.


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

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

Author(s):  
Tobias Alf Kroll ◽  
A. Alexandre Trindade ◽  
Amber Asikis ◽  
Melissa Salas ◽  
Marcy Lau ◽  
...  

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.


2020 ◽  
Author(s):  
Kate Ergo ◽  
Luna De Vilder ◽  
Esther De Loof ◽  
Tom Verguts

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.


1988 ◽  
Author(s):  
Alexander H. Levis ◽  
Jeff T. Casey ◽  
Anne-Claire Louvet

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