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    Area of Science:

    • Cognitive Neuroscience
    • Neuroimaging
    • Computational Neuroscience

    Background:

    • Neural representations are key to understanding cognitive experience.
    • Classic representational similarity analysis (cRSA) assesses representation quality using similarity matrices but cannot model trial-level variance.
    • Limitations of cRSA hinder the assessment of subject, stimulus, and trial effects on neural representations.

    Purpose of the Study:

    • Introduce trial-level representational similarity analysis (tRSA), a novel framework for analyzing neural representations.
    • Evaluate the performance and advantages of tRSA compared to cRSA.
    • Demonstrate the application and benefits of tRSA using simulated and real neuroimaging data.

    Main Methods:

    • Formal introduction of the trial-level representational similarity analysis (tRSA) framework.
    • Utilizing multi-level models to estimate neural representation strength at the individual trial level.
    • Verification and comparison of tRSA against cRSA using simulated data and real fMRI datasets.

    Main Results:

    • tRSA demonstrates strong correspondence with cRSA in quantifying overall representation strength.
    • The multi-level approach of tRSA is more theoretically sound and sensitive to effects than cRSA.
    • tRSA shows greater robustness to issues encountered with cRSA in real fMRI data.
    • Novel findings regarding neural representations were identified exclusively through tRSA.

    Conclusions:

    • tRSA is a versatile and robust analytical framework for cognitive neuroscience.
    • tRSA overcomes the limitations of cRSA by enabling trial-level analysis.
    • The tRSA approach facilitates a more nuanced understanding of neural representations and their underlying variances.