Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-19T08:21:04.039Z Has data issue: false hasContentIssue false

How cognition affects perception: Brain activity modelling to unravel top-down dynamics

Published online by Cambridge University Press:  05 January 2017

Martin Desseilles
Affiliation:
Cyclotron Research Centre, University of Liège B30, B-4000 Liège, [email protected]://www.cyclotron.ulg.ac.be/cms/c_15006/fr/christophe-phillips Clinique Psychiatrique des Frères Alexiens, B-4841 Henri-Chapelle, Belgiumhttp://mentalhealthsciences.com/index_en.html Department of Psychology, University of Namur, B-5000 Namur, Belgium. [email protected]
Christophe Phillips
Affiliation:
Cyclotron Research Centre, University of Liège B30, B-4000 Liège, [email protected]://www.cyclotron.ulg.ac.be/cms/c_15006/fr/christophe-phillips

Abstract

In this commentary on Firestone & Scholl's (F&S's) article, we argue that researchers should use brain-activity modelling to investigate top-down mechanisms. Using functional brain imaging and a specific cognitive paradigm, modelling the BOLD signal provided new insight into the dynamic causalities involved in the influence of cognitions on perceptions.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Desseilles, M., Balteau, E., Sterpenich, V., Dang-Vu, T. T., Darsaud, A., Vandewalle, G., Albouy, G., Salmon, E., Peters, F., Schmidt, C., Schabus, M., Gais, S., Degueldre, C., Phillips, C., Luxen, A., Ansseau, M., Maquet, P. & Schwartz, S. (2009) Abnormal neural filtering of irrelevant visual information in depression. Journal of Neuroscience 29(5):1395–403.Google Scholar
Desseilles, M., Schwartz, S., Dang-Vu, T. T., Sterpenich, V., Ansseau, M., Maquet, P. & Phillips, C. (2011) Depression alters “top-down” visual attention: A dynamic causal modeling comparison between depressed and healthy subjects. NeuroImage 54(2):1662–68.CrossRefGoogle ScholarPubMed
Friston, K. J., Ashburner, J., Kiebel, S. J., Nichols, T. E. & Penny, W. D., eds. (2007) Statistical parametric mapping: The analysis of functional brain images. Academic Press.Google Scholar
Friston, K. J. & Buchel, C. (2000) Attentional modulation of effective connectivity from V2 to V5/MT in humans. Proceedings of the National Academy of Sciences USA 97(13):7591–96.Google Scholar
Friston, K. J., Harrison, L. & Penny, W. (2003) Dynamic causal modelling. NeuroImage 19(4):1273–302.Google Scholar
Gitelman, D. R., Penny, W. D., Ashburner, J. & Friston, K. J. (2003) Modeling regional and psychophysiologic interactions in fMRI: The importance of hemodynamic deconvolution. NeuroImage 19(1):200207.Google Scholar
Penny, W. D., Stephan, K. E., Daunizeau, J., Rosa, M. J., Friston, K. J., Schofield, T. M. & Leff, A. P. (2010) Comparing families of dynamic causal models. PLoS Computational Biology 6(3):e1000709.Google Scholar
Schwartz, S., Vuilleumier, P., Hutton, C., Maravita, A., Dolan, R. J. & Driver, J. (2005) Attentional load and sensory competition in human vision: Modulation of fMRI responses by load at fixation during task-irrelevant stimulation in the peripheral visual field. Cerebral Cortex 15:770–86.Google Scholar