Poster No:
1616
Submission Type:
Abstract Submission
Authors:
Chuanji Gao1, Svetlana Shinkareva2, Rutvik Desai2
Institutions:
1Nanjing Normal University, Nanjing, China, 2University of South Carolina, Columbia, SC
First Author:
Co-Author(s):
Introduction:
Both representational similarity analysis (RSA) and regression approaches are widely used in psychology and neuroscience research for model selection. RSA offers several advantages, including the flexibility to relate models, brain regions, subjects, species, and behavior, making it a valuable analytical framework. However, the reliability and power of RSA, as compared to traditional regression approaches, has not been examined.
Methods:
In this study, we compared the finite sampling behavior of RSA and linear regression. We generated data using a linear regression model with known population parameters and repeatedly drew samples of size N from the population. The sampling distribution for each population parameter was estimated by aggregating the parameter estimates across all samples.
For RSA, the parameter of interest was the correlation coefficient between the dissimilarities of Y and X. For linear regression, the parameter of interest was the adjusted R². We calculated the percentage of correct model selections across 1,000 replications.
Results:
Our findings indicate that regression yielded a higher percentage of correct model selections than RSA when the number of trials was small (but larger than the number of features). The percentage of correct model selections increased with the sample size (number of stimuli) for both regression and RSA; however, regression consistently outperformed RSA until both methods reached a performance ceiling.
Similar patterns were observed for simulated fMRI datasets.
Conclusions:
These results suggest that while RSA is a powerful and versatile analysis technique, researchers should carefully consider the choices of RSA and regression approaches in contexts where both are applicable. Model selection based solely on RSA can be less reliable, providing potentially misleading results.
Modeling and Analysis Methods:
Multivariate Approaches 1
Univariate Modeling 2
Keywords:
Data analysis
FUNCTIONAL MRI
Multivariate
Statistical Methods
Univariate
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
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Are you Internal Review Board (IRB) certified?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
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Simulation (of behavioral and fMRI data)
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