Towards a model-based cognitive neuroscience of stopping – a neuroimaging perspective. (July 2018)

• Record Type: Journal Article
• Authors: Sebastian, Alexandra; Forstmann, Birte U.; Matzke, Dora
• Is Part Of: Neuroscience and biobehavioral reviews. Volume 90(2018)
• Page Start: 130
• Page End: 136
• Publication Date: 2018-07
• Subjects: Decision-making components -- Diffusion decision model -- Independent horse-race model -- Individual differences -- Stop-signal task -- Trigger failures; Psychophysiology -- Periodicals; Human behavior -- Periodicals; Animal behavior -- Periodicals; Neurology -- Periodicals; Behavior -- Periodicals; Ethology -- Periodicals; Neurology -- Periodicals; Psychophysiologie -- Périodiques; Comportement humain -- Périodiques; Animaux -- Mœurs et comportement -- Périodiques; Neurologie -- Périodiques; Animal behavior; Human behavior; Neurology; Psychophysiology; Periodicals; Electronic journals; 573.8
• DOI: 10.1016/j.neubiorev.2018.04.011
• Languages: English
• ISSNs: 0149-7634

Abstract

Highlights: The complete horse-race model allows estimating the entire distribution of SSRTs. Estimating trigger failures corrects for bias in SSRT estimates. Model parameters allow decomposing inhibitory deficits in clinical samples. Model-based approaches improve the identification of neural pathways of stopping. Abstract: Our understanding of the neural correlates of response inhibition has greatly advanced over the last decade. Nevertheless the specific function of regions within this stopping network remains controversial. The traditional neuroimaging approach cannot capture many processes affecting stopping performance. Despite the shortcomings of the traditional neuroimaging approach and a great progress in mathematical and computational models of stopping, model-based cognitive neuroscience approaches in human neuroimaging studies are largely lacking. To foster model-based approaches to ultimately gain a deeper understanding of the neural signature of stopping, we outline the most prominent models of response inhibition and recent advances in the field. We highlight how a model-based approach in clinical samples has improved our understanding of altered cognitive functions in these disorders. Moreover, we show how linking evidence-accumulation models and neuroimaging data improves the identification of neural pathways involved in the stopping process and helps to delineate these from neural networks of related but distinct functions. In conclusion, adopting a model-based approach is indispensable to identifying the actual neural processes underlying stopping.