Predicting decisions by taking a glimpse into the brain

Recent studies have indicated that the brain makes decisions on visual and auditory stimuli using accumulated sensory evidence, and that this process is orchestrated by a network of neurons from the front (the prefrontal area) and the back (the parietal area) of the brain. Ksander de Winkel and his colleagues from the Department of Prof. Bülthoff at the Max Planck Institute for Biological Cybernetics investigated whether these findings also apply to decisions on self-motion stimuli (passive motion of one's own body). The results showed that the scientists could predict how well a participant was able to tell the different motions apart, which is an indication that an accumulation of sensory evidence of self-motion was measured. These findings provide support for the idea that the network of prefrontal and parietal neurons is 'modality-independent', meaning that the neurons in this network are dedicated to collect evidence and to make decisions using any type of sensory information and are not dependent on visual and vestibular (concerning the equilibrium) cues.

Our scientist Ksander de Winkel (incl. Alessandro Nesti, Hasan Ayaz and Heinrich H. Bülthoff) published a new study in Science Direct.

1. Why were you interested in this topic?
In our research group, we investigate how our brain constructs perceptions of self-motion and spatial orientation. In general, we can only make inferences on how the brain does this based on what people are able to tell us about their perceptions. This is so because equipment that can be used to peer into the brain is usually not suitable to use with moving participants. For instance, an MRI scanner confines people to a very small space, and they have to lie perfectly still for the device to generate useful data; and EEG-equipment is very susceptible to noise in the measurements due to electrical equipment and movement. There is, however, an alternative technique called functional Near-Infrared Spectroscopy (fNIRS). fNIRS makes use of the fact that basically all tissues are transparent to near-infrared light, except blood -which reflects it. If we thus shine such light into the brain and measure how much of it is reflected back, we can tell which parts of the brain are most active. In theory, fNIRS equipment is not susceptible to noise due to electrical signals or movements, and the equipment is small and mobile enough to use with moving participants. In this study, we therefore investigated whether it can actually be used to perform neuroimaging in moving participants, and whether the recordings can be used as physiological evidence to build our theories on perception of self-motion and spatial orientation. 

2. What should the average person take away from your study?
For researchers it is valuable to know that it is possible to perform neuroimaging in moving participants using fNIRS, which we hope will inspire future research that requires neuroimaging in physically active people. From a more general Neuroscientific perspective, the take-home message is that we provide supporting evidence for the notion that there is a dedicated decision making network in the brain that is made up of neurons in the prefrontal and parietal areas. 

3. What is the added value of your study/paper for society?

Knowledge on the function of cortical areas already has practical medical applications. In the present case, one could use the knowledge that patients who are having difficulties in making decisions might have damages to these specific areas of the brain, and vice versa: a person with physical trauma in these areas might experience difficulties in making decisions. Moreover, this research could encourage other researchers to adopt the fNIRS technique to investigate how the brain functions in physically active people -and who knows what they may find out? 

4. Are there any major caveats? What questions still need to be addressed?
Although we did perform quite an elaborate set of analyses, there are more advanced signal-analysis techniques that may prove to distill even more information out of the neuroimaging data. Consequently, we can ask the question how much (more) information these recordings hold, and what other properties of cortical processing can be learned from the recordings. 

5. Is there anything else you would like to add? 
Well, I'm excited to explore what else we may learn about the brain using this technique! 
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