Encoding models for developmental cognitive computational neuroscience: Promise, challenges, and potential
View abstract on PubMed
Summary
This summary is machine-generated.Encoding models can explain brain activity using artificial neural networks. This approach, applied to developmental cognitive neuroscience, offers insights into changing brain representations with learning and development.
Area Of Science
- Cognitive computational neuroscience
- Neuroscience
- Artificial Intelligence
Background
- Encoding models explain brain activity using latent features, including artificial neural networks.
- Most studies focus on adults, limiting understanding of developmental changes in brain representations.
Purpose Of The Study
- To advocate for the use of encoding models in developmental cognitive neuroscience.
- To explore how encoding models can capture changes in brain representations during development.
Main Methods
- Overview of encoding models used in adult functional magnetic resonance imaging (fMRI) research.
- Discussion of challenges in applying encoding models to developmental neuroimaging.
Main Results
- Adult fMRI studies using encoding models often involve few subjects but many samples.
- Encoding models typically require task-based neuroimaging data.
Conclusions
- Developmental studies can overcome challenges by employing functional alignment techniques and naturalistic paradigms.
- Encoding model analysis in developmental neuroimaging can yield significant theoretical advancements.
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