Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity
Jonas A Thiele, Joshua Faskowitz, Olaf Sporns, Kirsten Hilger, Stephen Fleming · 2024
Read Choosing explanation over performance: Insights from machine learning-based prediction of human intelligence from brain connectivity by Jonas A Thiele, Joshua Faskowitz, Olaf Sporns, Kirsten Hilger, Stephen Fleming (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11631348.txt.
Description
Jonas Thiele and co-authors argue in 2024 that brain-based prediction studies should prize explanation over raw score. Many studies forecast intelligence from brain wiring with real accuracy but little insight into how the brain works. In a registered study of 806 healthy adults, with a second sample for checking, they tested which connections forecast general, stored, and fluid intelligence.
Which kind of intelligence and which task during scanning both mattered a lot. Many different sets of connections could forecast well, spread across the whole brain. That breadth fits and extends older theories that stress certain regions. The authors urge future studies to map predictive features with care rather than chase top scores alone.
Catalog record
| CREATOR | Jonas A Thiele, Joshua Faskowitz, Olaf Sporns, Kirsten Hilger, Stephen Fleming |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 65 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |