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

Published online by Cambridge University Press:  05 January 2017

Martin Desseilles  and
Christophe Phillips
Martin Desseilles
Affiliation:
Cyclotron Research Centre, University of Liège B30, B-4000 Liège, Belgium c.phillips@ulg.ac.be http://www.cyclotron.ulg.ac.be/cms/c_15006/fr/christophe-phillips Clinique Psychiatrique des Frères Alexiens, B-4841 Henri-Chapelle, Belgium http://mentalhealthsciences.com/index_en.html Department of Psychology, University of Namur, B-5000 Namur, Belgium. martin.desseilles@unamur.be
Christophe Phillips
Affiliation:
Cyclotron Research Centre, University of Liège B30, B-4000 Liège, Belgium c.phillips@ulg.ac.be http://www.cyclotron.ulg.ac.be/cms/c_15006/fr/christophe-phillips
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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.

Information

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e238
Copyright
Copyright © Cambridge University Press 2016 

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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):200–207.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