How does the brain combine information?

This signal integration happens at many levels, from combining images across the left and right eyes to give us binocular vision, to blending smell, taste and touch to experience eating some delicious food. Information is also integrated across time, for example in adaptation effects that change our perception of subsequent stimuli.

Understanding signal combination is particularly important in clinical conditions where normal processes break down. A good example is amblyopia - a disorder of binocular vision where one eye contributes less to visual perception, and most patients lack stereo depth perception. These problems were long believed to be impossible to treat in adulthood because the orthodox position was that amblyopes lacked binocular neurons. However work published by Dr Baker in 2007 demonstrated that binocular combination is possible in amblyopia, paving the way for the development of novel treatments and therapies that are currently being evaluated around the world.

The lab uses a combination of techniques, mostly in human participants, including psychophysics, electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) and computational modelling. The overarching goal is to develop computational algorithms that can account for signal combination within and between sensory modalities, and across different measurement paradigms. This work is currently funded by a grant from the BBSRC.

Example publications

Lygo, F., Richard, B., Wade, A.R., Morland, A.B. & Baker, D.H. (2021). Neural markers of suppression in impaired binocular vision. Neuroimage, 230: 117780, http://dx.doi.org/10.1016/j.neuroimage.2021.117780

Baker, D.H., Lygo, F.A., Meese, T.S. & Georgeson, M.A. (2018). Binocular summation revisited: beyond √2. Psychological Bulletin, 144(11): 1186-1199, http://dx.doi.org/10.1037/bul0000163

Baker, D.H. & Wade, A.R. (2017). Evidence for an optimal algorithm underlying signal combination in human visual cortex. Cerebral Cortex, 27: 254-264, http://dx.doi.org/10.1093/cercor/bhw395