Encoding modeling

Understanding neural representation
Updated: 2024-09-08

Linear modeling with nonlinear transformation of the external information has been widely used to understand how the human brain processes real-world environment (Kim et al., 2024; Kim et al., 2023; Kim, 2022; Leahy et al., 2021) .

LEA
Fig 1. Overview of Linearized Encoding Analysis (LEA)

Methodological issues

Reverse double-dipping

RDD
Fig 2. Reserve double-dipping: data dips you, twice!

Time series prediction

SmoothCorr
Fig 3. Spurious correlation between smooth time series

Resources

  • Kim, 2024-09-07, Linearized Encoding Modeling: a Predictive Analysis Methodology for Music Perception, Korean Society for Music Perception and Cognition (KSMPC) Summer School 24, Session 3 lecture. [slides] [code] [repo]

References

2024

  1. Linguistic modulation of the neural encoding of phonemes
    Seung-Goo Kim, Federico De Martino , and Tobias Overath
    2024

2023

  1. Emotion-relevant Representations of Music Extracted by Convolutional Neural Networks Are Encoded in Medial Prefrontal Cortex
    Seung-Goo KimTobias Overath, and Daniela Sammler
    Proceedings – The Joint Conference of the 17th International Conference on Music Perception and Cognition (ICMPC) and the 7th Conference of the Asia-Pacific Society for the Cognitive Sciences of Music (APSCOM), 2023

2022

  1. On the encoding of natural music in computational models and human brains
    Seung-Goo Kim
    Frontiers in Neuroscience, 2022

2021

  1. An Analytical Framework of Tonal and Rhythmic Hierarchy in Natural Music Using the Multivariate Temporal Response Function
    J. Leahy , Seung-Goo Kim, J. Wan , and 1 more author
    Frontiers in Neuroscience, 2021