Predictive models avoid excessive reductionism in cognitive neuroimaging

Gaël Varoquaux (1) , Russell Poldrack (2)

1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
2 Department of Psychology [Stanford]

Understanding the organization of complex behavior as it relates to the brain requires modeling the behavior, the relevant mental processes, and the corresponding neural activity. Experiments in cognitive neuroscience typically study a psychological process via controlled manipulations, reducing behavior to one of its component. Such reductionism can easily lead to paradigm-bound theories. Predictive models can generalize brain-mind associations to arbitrary new tasks and stimuli. We argue that they are needed to broaden theories beyond specific paradigms. Predicting behavior from neural activity can support robust reverse inference, isolating brain structures that support particular mental processes. The converse prediction enables modeling brain responses as a function of a complete description of the task, rather than building on oppositions.

Mots clés: machine learning; brain imaging; cognitive models; psychology

Domaines: Psychologie; Neurosciences; Intelligence artificielle [cs.AI]; Applications [stat.AP]; Machine Learning [stat.ML]; Méthodes et statistiques

Gaël Varoquaux, Russell Poldrack. Predictive models avoid excessive reductionism in cognitive neuroimaging. Current Opinion in Neurobiology, In press. ⟨hal-01856412v2⟩