Music of the 7Ts: Predicting and Decoding Multivoxel fMRI Responses with Acoustic, Schematic, and Categorical Music Features
- Submitting institution
-
Goldsmiths' College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2896
- Type
- D - Journal article
- DOI
-
10.3389/fpsyg.2017.01179
- Title of journal
- Frontiers in Psychology
- Article number
- 1179
- First page
- -
- Volume
- 8
- Issue
- -
- ISSN
- 1664-1078
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- URL
-
http://research.gold.ac.uk/id/eprint/24664/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
0
- Research group(s)
-
-
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The authors demonstrate parallel, distributed, and overlapping representations of musical features using machine learning models, high-field fMRI, and naturalistic music stimuli. These results support their hypothesis that music cognition is neurally represented by multivoxel pattern spaces whose geometric properties, such as distance between response vectors, underlie observed human musical behaviour. The current study extends prior work in stimulus-model-based encoding of music representational spaces by providing maps, not only of audio-based feature encoding, but also of schematic music features.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -